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    (sizeof(type) == sizeof(Py_ssize_t) &&\
          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
                               v == (type)PY_SSIZE_T_MAX)))  )
#if defined (__cplusplus) && __cplusplus >= 201103L
    #include <cstdlib>
    #define __Pyx_sst_abs(value) std::abs(value)
#elif SIZEOF_INT >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) abs(value)
#elif SIZEOF_LONG >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) labs(value)
#elif defined (_MSC_VER) && defined (_M_X64)
    #define __Pyx_sst_abs(value) _abs64(value)
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define __Pyx_sst_abs(value) llabs(value)
#elif defined (__GNUC__)
    #define __Pyx_sst_abs(value) __builtin_llabs(value)
#else
    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
#endif
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
#define __Pyx_PyBytes_FromString        PyBytes_FromString
#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
#if PY_MAJOR_VERSION < 3
    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#else
    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
#endif
#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
#if PY_MAJOR_VERSION < 3
static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
{
    const Py_UNICODE *u_end = u;
    while (*u_end++) ;
    return (size_t)(u_end - u - 1);
}
#else
#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
#endif
#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode
#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_ASSUME_SAFE_MACROS
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
#else
#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
#endif
#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
    PyObject* sys;
    PyObject* default_encoding = NULL;
    PyObject* ascii_chars_u = NULL;
    PyObject* ascii_chars_b = NULL;
    const char* default_encoding_c;
    sys = PyImport_ImportModule("sys");
    if (!sys) goto bad;
    default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
    Py_DECREF(sys);
    if (!default_encoding) goto bad;
    default_encoding_c = PyBytes_AsString(default_encoding);
    if (!default_encoding_c) goto bad;
    if (strcmp(default_encoding_c, "ascii") == 0) {
        __Pyx_sys_getdefaultencoding_not_ascii = 0;
    } else {
        char ascii_chars[128];
        int c;
        for (c = 0; c < 128; c++) {
            ascii_chars[c] = c;
        }
        __Pyx_sys_getdefaultencoding_not_ascii = 1;
        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
        if (!ascii_chars_u) goto bad;
        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
            PyErr_Format(
                PyExc_ValueError,
                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
                default_encoding_c);
            goto bad;
        }
        Py_DECREF(ascii_chars_u);
        Py_DECREF(ascii_chars_b);
    }
    Py_DECREF(default_encoding);
    return 0;
bad:
    Py_XDECREF(default_encoding);
    Py_XDECREF(ascii_chars_u);
    Py_XDECREF(ascii_chars_b);
    return -1;
}
#endif
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
#else
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
static char* __PYX_DEFAULT_STRING_ENCODING;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
    PyObject* sys;
    PyObject* default_encoding = NULL;
    char* default_encoding_c;
    sys = PyImport_ImportModule("sys");
    if (!sys) goto bad;
    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
    Py_DECREF(sys);
    if (!default_encoding) goto bad;
    default_encoding_c = PyBytes_AsString(default_encoding);
    if (!default_encoding_c) goto bad;
    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
    Py_DECREF(default_encoding);
    return 0;
bad:
    Py_XDECREF(default_encoding);
    return -1;
}
#endif
#endif


/* Test for GCC > 2.95 */
#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
  #define likely(x)   __builtin_expect(!!(x), 1)
  #define unlikely(x) __builtin_expect(!!(x), 0)
#else /* !__GNUC__ or GCC < 2.95 */
  #define likely(x)   (x)
  #define unlikely(x) (x)
#endif /* __GNUC__ */
static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }

static PyObject *__pyx_m;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_cython_runtime;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static PyObject *__pyx_empty_unicode;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;

/* Header.proto */
#if !defined(CYTHON_CCOMPLEX)
  #if defined(__cplusplus)
    #define CYTHON_CCOMPLEX 1
  #elif defined(_Complex_I)
    #define CYTHON_CCOMPLEX 1
  #else
    #define CYTHON_CCOMPLEX 0
  #endif
#endif
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    #include <complex>
  #else
    #include <complex.h>
  #endif
#endif
#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)
  #undef _Complex_I
  #define _Complex_I 1.0fj
#endif


static const char *__pyx_f[] = {
  "sklearn/tree/_splitter.pyx",
  "stringsource",
  "sklearn/tree/_splitter.pxd",
  "__init__.pxd",
  "type.pxd",
  "sklearn/tree/_criterion.pxd",
  "sklearn/tree/_tree.pxd",
  "sklearn/neighbors/quad_tree.pxd",
  "sklearn/tree/_utils.pxd",
};
/* BufferFormatStructs.proto */
#define IS_UNSIGNED(type) (((type) -1) > 0)
struct __Pyx_StructField_;
#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)
typedef struct {
  const char* name;
  struct __Pyx_StructField_* fields;
  size_t size;
  size_t arraysize[8];
  int ndim;
  char typegroup;
  char is_unsigned;
  int flags;
} __Pyx_TypeInfo;
typedef struct __Pyx_StructField_ {
  __Pyx_TypeInfo* type;
  const char* name;
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} __Pyx_StructField;
typedef struct {
  __Pyx_StructField* field;
  size_t parent_offset;
} __Pyx_BufFmt_StackElem;
typedef struct {
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  size_t fmt_offset;
  size_t new_count, enc_count;
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  int is_complex;
  char enc_type;
  char new_packmode;
  char enc_packmode;
  char is_valid_array;
} __Pyx_BufFmt_Context;


/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":725
 * # in Cython to enable them only on the right systems.
 * 
 * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t
 */
typedef npy_int8 __pyx_t_5numpy_int8_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":726
 * 
 * ctypedef npy_int8       int8_t
 * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int32      int32_t
 * ctypedef npy_int64      int64_t
 */
typedef npy_int16 __pyx_t_5numpy_int16_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":727
 * ctypedef npy_int8       int8_t
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_int64      int64_t
 * #ctypedef npy_int96      int96_t
 */
typedef npy_int32 __pyx_t_5numpy_int32_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":728
 * ctypedef npy_int16      int16_t
 * ctypedef npy_int32      int32_t
 * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_int96      int96_t
 * #ctypedef npy_int128     int128_t
 */
typedef npy_int64 __pyx_t_5numpy_int64_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":732
 * #ctypedef npy_int128     int128_t
 * 
 * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t
 */
typedef npy_uint8 __pyx_t_5numpy_uint8_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":733
 * 
 * ctypedef npy_uint8      uint8_t
 * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint32     uint32_t
 * ctypedef npy_uint64     uint64_t
 */
typedef npy_uint16 __pyx_t_5numpy_uint16_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":734
 * ctypedef npy_uint8      uint8_t
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uint64     uint64_t
 * #ctypedef npy_uint96     uint96_t
 */
typedef npy_uint32 __pyx_t_5numpy_uint32_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":735
 * ctypedef npy_uint16     uint16_t
 * ctypedef npy_uint32     uint32_t
 * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_uint96     uint96_t
 * #ctypedef npy_uint128    uint128_t
 */
typedef npy_uint64 __pyx_t_5numpy_uint64_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":739
 * #ctypedef npy_uint128    uint128_t
 * 
 * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<
 * ctypedef npy_float64    float64_t
 * #ctypedef npy_float80    float80_t
 */
typedef npy_float32 __pyx_t_5numpy_float32_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":740
 * 
 * ctypedef npy_float32    float32_t
 * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<
 * #ctypedef npy_float80    float80_t
 * #ctypedef npy_float128   float128_t
 */
typedef npy_float64 __pyx_t_5numpy_float64_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":749
 * # The int types are mapped a bit surprising --
 * # numpy.int corresponds to 'l' and numpy.long to 'q'
 * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longlong   long_t
 * ctypedef npy_longlong   longlong_t
 */
typedef npy_long __pyx_t_5numpy_int_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":750
 * # numpy.int corresponds to 'l' and numpy.long to 'q'
 * ctypedef npy_long       int_t
 * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longlong   longlong_t
 * 
 */
typedef npy_longlong __pyx_t_5numpy_long_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":751
 * ctypedef npy_long       int_t
 * ctypedef npy_longlong   long_t
 * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_ulong      uint_t
 */
typedef npy_longlong __pyx_t_5numpy_longlong_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":753
 * ctypedef npy_longlong   longlong_t
 * 
 * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<
 * ctypedef npy_ulonglong  ulong_t
 * ctypedef npy_ulonglong  ulonglong_t
 */
typedef npy_ulong __pyx_t_5numpy_uint_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":754
 * 
 * ctypedef npy_ulong      uint_t
 * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 */
typedef npy_ulonglong __pyx_t_5numpy_ulong_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":755
 * ctypedef npy_ulong      uint_t
 * ctypedef npy_ulonglong  ulong_t
 * ctypedef npy_ulonglong  ulonglong_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_intp       intp_t
 */
typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":757
 * ctypedef npy_ulonglong  ulonglong_t
 * 
 * ctypedef npy_intp       intp_t             # <<<<<<<<<<<<<<
 * ctypedef npy_uintp      uintp_t
 * 
 */
typedef npy_intp __pyx_t_5numpy_intp_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":758
 * 
 * ctypedef npy_intp       intp_t
 * ctypedef npy_uintp      uintp_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_double     float_t
 */
typedef npy_uintp __pyx_t_5numpy_uintp_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":760
 * ctypedef npy_uintp      uintp_t
 * 
 * ctypedef npy_double     float_t             # <<<<<<<<<<<<<<
 * ctypedef npy_double     double_t
 * ctypedef npy_longdouble longdouble_t
 */
typedef npy_double __pyx_t_5numpy_float_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":761
 * 
 * ctypedef npy_double     float_t
 * ctypedef npy_double     double_t             # <<<<<<<<<<<<<<
 * ctypedef npy_longdouble longdouble_t
 * 
 */
typedef npy_double __pyx_t_5numpy_double_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":762
 * ctypedef npy_double     float_t
 * ctypedef npy_double     double_t
 * ctypedef npy_longdouble longdouble_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_cfloat      cfloat_t
 */
typedef npy_longdouble __pyx_t_5numpy_longdouble_t;

/* "_criterion.pxd":15
 * cimport numpy as np
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X             # <<<<<<<<<<<<<<
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 */
typedef npy_float32 __pyx_t_7sklearn_4tree_10_criterion_DTYPE_t;

/* "_criterion.pxd":16
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight             # <<<<<<<<<<<<<<
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 */
typedef npy_float64 __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t;

/* "_criterion.pxd":17
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters             # <<<<<<<<<<<<<<
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 */
typedef npy_intp __pyx_t_7sklearn_4tree_10_criterion_SIZE_t;

/* "_criterion.pxd":18
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer             # <<<<<<<<<<<<<<
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 */
typedef npy_int32 __pyx_t_7sklearn_4tree_10_criterion_INT32_t;

/* "_criterion.pxd":19
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer             # <<<<<<<<<<<<<<
 * 
 * cdef class Criterion:
 */
typedef npy_uint32 __pyx_t_7sklearn_4tree_10_criterion_UINT32_t;

/* "_tree.pxd":16
 * cimport numpy as np
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X             # <<<<<<<<<<<<<<
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 */
typedef npy_float32 __pyx_t_7sklearn_4tree_5_tree_DTYPE_t;

/* "_tree.pxd":17
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight             # <<<<<<<<<<<<<<
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 */
typedef npy_float64 __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t;

/* "_tree.pxd":18
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters             # <<<<<<<<<<<<<<
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 */
typedef npy_intp __pyx_t_7sklearn_4tree_5_tree_SIZE_t;

/* "_tree.pxd":19
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer             # <<<<<<<<<<<<<<
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 */
typedef npy_int32 __pyx_t_7sklearn_4tree_5_tree_INT32_t;

/* "_tree.pxd":20
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer             # <<<<<<<<<<<<<<
 * 
 * from ._splitter cimport Splitter
 */
typedef npy_uint32 __pyx_t_7sklearn_4tree_5_tree_UINT32_t;

/* "sklearn/neighbors/quad_tree.pxd":12
 * cimport numpy as np
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X             # <<<<<<<<<<<<<<
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 */
typedef npy_float32 __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t;

/* "sklearn/neighbors/quad_tree.pxd":13
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters             # <<<<<<<<<<<<<<
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 */
typedef npy_intp __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t;

/* "sklearn/neighbors/quad_tree.pxd":14
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer             # <<<<<<<<<<<<<<
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 */
typedef npy_int32 __pyx_t_7sklearn_9neighbors_9quad_tree_INT32_t;

/* "sklearn/neighbors/quad_tree.pxd":15
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer             # <<<<<<<<<<<<<<
 * 
 * # This is effectively an ifdef statement in Cython
 */
typedef npy_uint32 __pyx_t_7sklearn_9neighbors_9quad_tree_UINT32_t;

/* "_utils.pxd":16
 * from sklearn.neighbors.quad_tree cimport Cell
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X             # <<<<<<<<<<<<<<
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 */
typedef npy_float32 __pyx_t_7sklearn_4tree_6_utils_DTYPE_t;

/* "_utils.pxd":17
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight             # <<<<<<<<<<<<<<
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 */
typedef npy_float64 __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t;

/* "_utils.pxd":18
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters             # <<<<<<<<<<<<<<
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 */
typedef npy_intp __pyx_t_7sklearn_4tree_6_utils_SIZE_t;

/* "_utils.pxd":19
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer             # <<<<<<<<<<<<<<
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 */
typedef npy_int32 __pyx_t_7sklearn_4tree_6_utils_INT32_t;

/* "_utils.pxd":20
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer             # <<<<<<<<<<<<<<
 * 
 * cdef enum:
 */
typedef npy_uint32 __pyx_t_7sklearn_4tree_6_utils_UINT32_t;

/* "sklearn/tree/_splitter.pxd":17
 * from ._criterion cimport Criterion
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X             # <<<<<<<<<<<<<<
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 */
typedef npy_float32 __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t;

/* "sklearn/tree/_splitter.pxd":18
 * 
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight             # <<<<<<<<<<<<<<
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 */
typedef npy_float64 __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t;

/* "sklearn/tree/_splitter.pxd":19
 * ctypedef np.npy_float32 DTYPE_t          # Type of X
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters             # <<<<<<<<<<<<<<
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 */
typedef npy_intp __pyx_t_7sklearn_4tree_9_splitter_SIZE_t;

/* "sklearn/tree/_splitter.pxd":20
 * ctypedef np.npy_float64 DOUBLE_t         # Type of y, sample_weight
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer             # <<<<<<<<<<<<<<
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 */
typedef npy_int32 __pyx_t_7sklearn_4tree_9_splitter_INT32_t;

/* "sklearn/tree/_splitter.pxd":21
 * ctypedef np.npy_intp SIZE_t              # Type for indices and counters
 * ctypedef np.npy_int32 INT32_t            # Signed 32 bit integer
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer             # <<<<<<<<<<<<<<
 * 
 * cdef struct SplitRecord:
 */
typedef npy_uint32 __pyx_t_7sklearn_4tree_9_splitter_UINT32_t;
/* Declarations.proto */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    typedef ::std::complex< float > __pyx_t_float_complex;
  #else
    typedef float _Complex __pyx_t_float_complex;
  #endif
#else
    typedef struct { float real, imag; } __pyx_t_float_complex;
#endif
static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float);

/* Declarations.proto */
#if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    typedef ::std::complex< double > __pyx_t_double_complex;
  #else
    typedef double _Complex __pyx_t_double_complex;
  #endif
#else
    typedef struct { double real, imag; } __pyx_t_double_complex;
#endif
static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double);


/*--- Type declarations ---*/
struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion;
struct __pyx_obj_7sklearn_4tree_5_tree_Tree;
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder;
struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree;
struct __pyx_obj_7sklearn_4tree_6_utils_Stack;
struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap;
struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue;
struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator;
struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter;
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":764
 * ctypedef npy_longdouble longdouble_t
 * 
 * ctypedef npy_cfloat      cfloat_t             # <<<<<<<<<<<<<<
 * ctypedef npy_cdouble     cdouble_t
 * ctypedef npy_clongdouble clongdouble_t
 */
typedef npy_cfloat __pyx_t_5numpy_cfloat_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":765
 * 
 * ctypedef npy_cfloat      cfloat_t
 * ctypedef npy_cdouble     cdouble_t             # <<<<<<<<<<<<<<
 * ctypedef npy_clongdouble clongdouble_t
 * 
 */
typedef npy_cdouble __pyx_t_5numpy_cdouble_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":766
 * ctypedef npy_cfloat      cfloat_t
 * ctypedef npy_cdouble     cdouble_t
 * ctypedef npy_clongdouble clongdouble_t             # <<<<<<<<<<<<<<
 * 
 * ctypedef npy_cdouble     complex_t
 */
typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;

/* "../../../../../usr/lib/python3/dist-packages/Cython/Includes/numpy/__init__.pxd":768
 * ctypedef npy_clongdouble clongdouble_t
 * 
 * ctypedef npy_cdouble     complex_t             # <<<<<<<<<<<<<<
 * 
 * cdef inline object PyArray_MultiIterNew1(a):
 */
typedef npy_cdouble __pyx_t_5numpy_complex_t;
struct __pyx_t_7sklearn_4tree_5_tree_Node;
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c;
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances;
struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build;

/* "_tree.pxd":25
 * from ._splitter cimport SplitRecord
 * 
 * cdef struct Node:             # <<<<<<<<<<<<<<
 *     # Base storage structure for the nodes in a Tree object
 * 
 */
struct __pyx_t_7sklearn_4tree_5_tree_Node {
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t left_child;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t right_child;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t feature;
  __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t threshold;
  __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t impurity;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t n_node_samples;
  __pyx_t_7sklearn_4tree_5_tree_DOUBLE_t weighted_n_node_samples;
};

/* "_tree.pxd":63
 *                           double weighted_n_samples) nogil except -1
 *     cdef int _resize(self, SIZE_t capacity) nogil except -1
 *     cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1             # <<<<<<<<<<<<<<
 * 
 *     cdef np.ndarray _get_value_ndarray(self)
 */
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c {
  int __pyx_n;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t capacity;
};

/* "_tree.pxd":78
 *     cdef object _decision_path_sparse_csr(self, object X)
 * 
 *     cpdef compute_feature_importances(self, normalize=*)             # <<<<<<<<<<<<<<
 * 
 * 
 */
struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances {
  int __pyx_n;
  PyObject *normalize;
};

/* "_tree.pxd":102
 *     cdef double min_impurity_decrease   # Impurity threshold for early stopping
 * 
 *     cpdef build(self, Tree tree, object X, np.ndarray y,             # <<<<<<<<<<<<<<
 *                 np.ndarray sample_weight=*,
 *                 np.ndarray X_idx_sorted=*)
 */
struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build {
  int __pyx_n;
  PyArrayObject *sample_weight;
  PyArrayObject *X_idx_sorted;
};
struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell;
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_insert_point;
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__insert_point_in_new_child;
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_summarize;
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__resize_c;
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__get_cell;

/* "sklearn/neighbors/quad_tree.pxd":20
 * # It allows us to write printf debugging lines
 * # and remove them at compile time
 * cdef enum:             # <<<<<<<<<<<<<<
 *     DEBUGFLAG = 0
 * 
 */
enum  {
  __pyx_e_7sklearn_9neighbors_9quad_tree_DEBUGFLAG = 0
};

/* "sklearn/neighbors/quad_tree.pxd":28
 * # have is_leaf and max_width consecutive as it permits to avoid padding by
 * # the compiler and keep the size coherent for both C and numpy data structures.
 * cdef struct Cell:             # <<<<<<<<<<<<<<
 *     # Base storage stucture for cells in a QuadTree object
 * 
 */
struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell {
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t parent;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t children[8];
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t cell_id;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t point_index;
  int is_leaf;
  __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t squared_max_width;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t depth;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t cumulative_size;
  __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t center[3];
  __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t barycenter[3];
  __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t min_bounds[3];
  __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t max_bounds[3];
};

/* "sklearn/neighbors/quad_tree.pxd":74
 * 
 *     # Point insertion methods
 *     cdef int insert_point(self, DTYPE_t[3] point, SIZE_t point_index,             # <<<<<<<<<<<<<<
 *                           SIZE_t cell_id=*) nogil except -1
 *     cdef SIZE_t _insert_point_in_new_child(self, DTYPE_t[3] point, Cell* cell,
 */
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_insert_point {
  int __pyx_n;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t cell_id;
};

/* "sklearn/neighbors/quad_tree.pxd":76
 *     cdef int insert_point(self, DTYPE_t[3] point, SIZE_t point_index,
 *                           SIZE_t cell_id=*) nogil except -1
 *     cdef SIZE_t _insert_point_in_new_child(self, DTYPE_t[3] point, Cell* cell,             # <<<<<<<<<<<<<<
 *                                            SIZE_t point_index, SIZE_t size=*
 *                                            ) nogil
 */
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__insert_point_in_new_child {
  int __pyx_n;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t size;
};

/* "sklearn/neighbors/quad_tree.pxd":83
 * 
 *     # Create a summary of the Tree compare to a query point
 *     cdef long summarize(self, DTYPE_t[3] point, DTYPE_t* results,             # <<<<<<<<<<<<<<
 *                         float squared_theta=*, int cell_id=*, long idx=*
 *                         ) nogil
 */
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_summarize {
  int __pyx_n;
  float squared_theta;
  int cell_id;
  long idx;
};

/* "sklearn/neighbors/quad_tree.pxd":98
 *     # Private array manipulation to manage the ``cells`` array
 *     cdef int _resize(self, SIZE_t capacity) nogil except -1
 *     cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1             # <<<<<<<<<<<<<<
 *     cdef int _get_cell(self, DTYPE_t[3] point, SIZE_t cell_id=*) nogil except -1
 *     cdef np.ndarray _get_cell_ndarray(self)
 */
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__resize_c {
  int __pyx_n;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t capacity;
};

/* "sklearn/neighbors/quad_tree.pxd":99
 *     cdef int _resize(self, SIZE_t capacity) nogil except -1
 *     cdef int _resize_c(self, SIZE_t capacity=*) nogil except -1
 *     cdef int _get_cell(self, DTYPE_t[3] point, SIZE_t cell_id=*) nogil except -1             # <<<<<<<<<<<<<<
 *     cdef np.ndarray _get_cell_ndarray(self)
 */
struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__get_cell {
  int __pyx_n;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t cell_id;
};
struct __pyx_t_7sklearn_4tree_6_utils_StackRecord;
struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord;
struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord;

/* "_utils.pxd":22
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 * cdef enum:             # <<<<<<<<<<<<<<
 *     # Max value for our rand_r replacement (near the bottom).
 *     # We don't use RAND_MAX because it's different across platforms and
 */
enum  {
  __pyx_e_7sklearn_4tree_6_utils_RAND_R_MAX = 0x7FFFFFFF
};

/* "_utils.pxd":69
 * 
 * # A record on the stack for depth-first tree growing
 * cdef struct StackRecord:             # <<<<<<<<<<<<<<
 *     SIZE_t start
 *     SIZE_t end
 */
struct __pyx_t_7sklearn_4tree_6_utils_StackRecord {
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t start;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t end;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t depth;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t parent;
  int is_left;
  double impurity;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t n_constant_features;
};

/* "_utils.pxd":95
 * 
 * # A record on the frontier for best-first tree growing
 * cdef struct PriorityHeapRecord:             # <<<<<<<<<<<<<<
 *     SIZE_t node_id
 *     SIZE_t start
 */
struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord {
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t node_id;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t start;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t end;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t pos;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t depth;
  int is_leaf;
  double impurity;
  double impurity_left;
  double impurity_right;
  double improvement;
};

/* "_utils.pxd":126
 * 
 * # A record stored in the WeightedPQueue
 * cdef struct WeightedPQueueRecord:             # <<<<<<<<<<<<<<
 *     DOUBLE_t data
 *     DOUBLE_t weight
 */
struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord {
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t data;
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t weight;
};
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init;
struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init;

/* "sklearn/tree/_splitter.pxd":23
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 * cdef struct SplitRecord:             # <<<<<<<<<<<<<<
 *     # Data to track sample split
 *     SIZE_t feature         # Which feature to split on.
 */
struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord {
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t feature;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t pos;
  double threshold;
  double improvement;
  double impurity_left;
  double impurity_right;
};

/* "sklearn/tree/_splitter.pxd":84
 * 
 *     # Methods
 *     cdef int init(self, object X, np.ndarray y,             # <<<<<<<<<<<<<<
 *                   DOUBLE_t* sample_weight,
 *                   np.ndarray X_idx_sorted=*) except -1
 */
struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init {
  int __pyx_n;
  PyArrayObject *X_idx_sorted;
};

/* "sklearn/tree/_splitter.pyx":270
 *             free(self.sample_mask)
 * 
 *     cdef int init(self,             # <<<<<<<<<<<<<<
 *                   object X,
 *                   np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
 */
struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init {
  int __pyx_n;
  PyArrayObject *X_idx_sorted;
};

/* "sklearn/tree/_splitter.pyx":896
 *         free(self.sorted_samples)
 * 
 *     cdef int init(self,             # <<<<<<<<<<<<<<
 *                   object X,
 *                   np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
 */
struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init {
  int __pyx_n;
  PyArrayObject *X_idx_sorted;
};

/* "_criterion.pxd":21
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 * cdef class Criterion:             # <<<<<<<<<<<<<<
 *     # The criterion computes the impurity of a node and the reduction of
 *     # impurity of a split on that node. It also computes the output statistics
 */
struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *__pyx_vtab;
  __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *y;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t y_stride;
  __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *sample_weight;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t *samples;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t start;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t pos;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t end;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t n_outputs;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t n_samples;
  __pyx_t_7sklearn_4tree_10_criterion_SIZE_t n_node_samples;
  double weighted_n_samples;
  double weighted_n_node_samples;
  double weighted_n_left;
  double weighted_n_right;
  double *sum_total;
  double *sum_left;
  double *sum_right;
};


/* "_tree.pxd":37
 * 
 * 
 * cdef class Tree:             # <<<<<<<<<<<<<<
 *     # The Tree object is a binary tree structure constructed by the
 *     # TreeBuilder. The tree structure is used for predictions and
 */
struct __pyx_obj_7sklearn_4tree_5_tree_Tree {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *__pyx_vtab;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t n_features;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t *n_classes;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t n_outputs;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t max_n_classes;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t max_depth;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t node_count;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t capacity;
  struct __pyx_t_7sklearn_4tree_5_tree_Node *nodes;
  double *value;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t value_stride;
};


/* "_tree.pxd":85
 * # =============================================================================
 * 
 * cdef class TreeBuilder:             # <<<<<<<<<<<<<<
 *     # The TreeBuilder recursively builds a Tree object from training samples,
 *     # using a Splitter object for splitting internal nodes and assigning
 */
struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder *__pyx_vtab;
  struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *splitter;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t min_samples_split;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t min_samples_leaf;
  double min_weight_leaf;
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t max_depth;
  double min_impurity_split;
  double min_impurity_decrease;
};


/* "sklearn/neighbors/quad_tree.pxd":54
 * 
 * 
 * cdef class _QuadTree:             # <<<<<<<<<<<<<<
 *     # The QuadTree object is a quad tree structure constructed by inserting
 *     # recursively points in the tree and splitting cells in 4 so that each
 */
struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_9neighbors_9quad_tree__QuadTree *__pyx_vtab;
  int n_dimensions;
  int verbose;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t n_cells_per_cell;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t max_depth;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t cell_count;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t capacity;
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t n_points;
  struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *cells;
};


/* "_utils.pxd":78
 *     SIZE_t n_constant_features
 * 
 * cdef class Stack:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t top
 */
struct __pyx_obj_7sklearn_4tree_6_utils_Stack {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *__pyx_vtab;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t capacity;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t top;
  struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *stack_;
};


/* "_utils.pxd":107
 *     double improvement
 * 
 * cdef class PriorityHeap:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t heap_ptr
 */
struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *__pyx_vtab;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t capacity;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t heap_ptr;
  struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *heap_;
};


/* "_utils.pxd":130
 *     DOUBLE_t weight
 * 
 * cdef class WeightedPQueue:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t array_ptr
 */
struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedPQueue *__pyx_vtab;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t capacity;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t array_ptr;
  struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord *array_;
};


/* "_utils.pxd":150
 * # =============================================================================
 * 
 * cdef class WeightedMedianCalculator:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t initial_capacity
 *     cdef WeightedPQueue samples
 */
struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedMedianCalculator *__pyx_vtab;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t initial_capacity;
  struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *samples;
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t total_weight;
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t k;
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t sum_w_0_k;
};


/* "sklearn/tree/_splitter.pxd":34
 *     double impurity_right  # Impurity of the right split.
 * 
 * cdef class Splitter:             # <<<<<<<<<<<<<<
 *     # The splitter searches in the input space for a feature and a threshold
 *     # to split the samples samples[start:end].
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter {
  PyObject_HEAD
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter *__pyx_vtab;
  struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *criterion;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t max_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t min_samples_leaf;
  double min_weight_leaf;
  PyObject *random_state;
  __pyx_t_7sklearn_4tree_9_splitter_UINT32_t rand_r_state;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_samples;
  double weighted_n_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *constant_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_features;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *feature_values;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t start;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t end;
  int presort;
  __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *y;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t y_stride;
  __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *sample_weight;
};


/* "sklearn/tree/_splitter.pyx":242
 * 
 * 
 * cdef class BaseDenseSplitter(Splitter):             # <<<<<<<<<<<<<<
 *     cdef DTYPE_t* X
 *     cdef SIZE_t X_sample_stride
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter __pyx_base;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *X;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_sample_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_feature_stride;
  PyArrayObject *X_idx_sorted;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_idx_sorted_ptr;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t X_idx_sorted_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_total_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *sample_mask;
};


/* "sklearn/tree/_splitter.pyx":304
 * 
 * 
 * cdef class BestSplitter(BaseDenseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best split."""
 *     def __reduce__(self):
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};


/* "sklearn/tree/_splitter.pyx":647
 * 
 * 
 * cdef class RandomSplitter(BaseDenseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best random split."""
 *     def __reduce__(self):
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};


/* "sklearn/tree/_splitter.pyx":866
 * 
 * 
 * cdef class BaseSparseSplitter(Splitter):             # <<<<<<<<<<<<<<
 *     # The sparse splitter works only with csc sparse matrix format
 *     cdef DTYPE_t* X_data
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter __pyx_base;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *X_data;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_indices;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *X_indptr;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t n_total_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *index_to_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *sorted_samples;
};


/* "sklearn/tree/_splitter.pyx":1190
 * 
 * 
 * cdef class BestSparseSplitter(BaseSparseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best split, using the sparse data."""
 * 
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};


/* "sklearn/tree/_splitter.pyx":1420
 * 
 * 
 * cdef class RandomSparseSplitter(BaseSparseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding a random split, using the sparse data."""
 * 
 */
struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};



/* "_criterion.pxd":21
 * ctypedef np.npy_uint32 UINT32_t          # Unsigned 32 bit integer
 * 
 * cdef class Criterion:             # <<<<<<<<<<<<<<
 *     # The criterion computes the impurity of a node and the reduction of
 *     # impurity of a split on that node. It also computes the output statistics
 */

struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion {
  int (*init)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t, __pyx_t_7sklearn_4tree_10_criterion_DOUBLE_t *, double, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t);
  int (*reset)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
  int (*reverse_reset)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
  int (*update)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, __pyx_t_7sklearn_4tree_10_criterion_SIZE_t);
  double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
  void (*children_impurity)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double *, double *);
  void (*node_value)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double *);
  double (*impurity_improvement)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *, double);
  double (*proxy_impurity_improvement)(struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *__pyx_vtabptr_7sklearn_4tree_10_criterion_Criterion;


/* "_tree.pxd":37
 * 
 * 
 * cdef class Tree:             # <<<<<<<<<<<<<<
 *     # The Tree object is a binary tree structure constructed by the
 *     # TreeBuilder. The tree structure is used for predictions and
 */

struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree {
  __pyx_t_7sklearn_4tree_5_tree_SIZE_t (*_add_node)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, int, int, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double, double, __pyx_t_7sklearn_4tree_5_tree_SIZE_t, double);
  int (*_resize)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, __pyx_t_7sklearn_4tree_5_tree_SIZE_t);
  int (*_resize_c)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree__resize_c *__pyx_optional_args);
  PyArrayObject *(*_get_value_ndarray)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *);
  PyArrayObject *(*_get_node_ndarray)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *);
  PyArrayObject *(*predict)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch);
  PyArrayObject *(*apply)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch);
  PyArrayObject *(*_apply_dense)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
  PyArrayObject *(*_apply_sparse_csr)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
  PyObject *(*decision_path)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, int __pyx_skip_dispatch);
  PyObject *(*_decision_path_dense)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
  PyObject *(*_decision_path_sparse_csr)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *);
  PyObject *(*compute_feature_importances)(struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_4Tree_compute_feature_importances *__pyx_optional_args);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_Tree *__pyx_vtabptr_7sklearn_4tree_5_tree_Tree;


/* "_tree.pxd":85
 * # =============================================================================
 * 
 * cdef class TreeBuilder:             # <<<<<<<<<<<<<<
 *     # The TreeBuilder recursively builds a Tree object from training samples,
 *     # using a Splitter object for splitting internal nodes and assigning
 */

struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder {
  PyObject *(*build)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, struct __pyx_obj_7sklearn_4tree_5_tree_Tree *, PyObject *, PyArrayObject *, int __pyx_skip_dispatch, struct __pyx_opt_args_7sklearn_4tree_5_tree_11TreeBuilder_build *__pyx_optional_args);
  PyObject *(*_check_input)(struct __pyx_obj_7sklearn_4tree_5_tree_TreeBuilder *, PyObject *, PyArrayObject *, PyArrayObject *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_5_tree_TreeBuilder *__pyx_vtabptr_7sklearn_4tree_5_tree_TreeBuilder;


/* "sklearn/neighbors/quad_tree.pxd":54
 * 
 * 
 * cdef class _QuadTree:             # <<<<<<<<<<<<<<
 *     # The QuadTree object is a quad tree structure constructed by inserting
 *     # recursively points in the tree and splitting cells in 4 so that each
 */

struct __pyx_vtabstruct_7sklearn_9neighbors_9quad_tree__QuadTree {
  int (*insert_point)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t, struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_insert_point *__pyx_optional_args);
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t (*_insert_point_in_new_child)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *, __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t, struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__insert_point_in_new_child *__pyx_optional_args);
  __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t (*_select_child)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *);
  int (*_is_duplicate)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *);
  long (*summarize)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree_summarize *__pyx_optional_args);
  void (*_init_cell)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *, __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t, __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t);
  void (*_init_root)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *);
  int (*_check_point_in_cell)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *);
  int (*_resize)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_SIZE_t);
  int (*_resize_c)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__resize_c *__pyx_optional_args);
  int (*_get_cell)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *, __pyx_t_7sklearn_9neighbors_9quad_tree_DTYPE_t *, struct __pyx_opt_args_7sklearn_9neighbors_9quad_tree_9_QuadTree__get_cell *__pyx_optional_args);
  PyArrayObject *(*_get_cell_ndarray)(struct __pyx_obj_7sklearn_9neighbors_9quad_tree__QuadTree *);
};
static struct __pyx_vtabstruct_7sklearn_9neighbors_9quad_tree__QuadTree *__pyx_vtabptr_7sklearn_9neighbors_9quad_tree__QuadTree;


/* "_utils.pxd":78
 *     SIZE_t n_constant_features
 * 
 * cdef class Stack:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t top
 */

struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack {
  int (*is_empty)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *);
  int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, int, double, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
  int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_Stack *, struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_Stack *__pyx_vtabptr_7sklearn_4tree_6_utils_Stack;


/* "_utils.pxd":107
 *     double improvement
 * 
 * cdef class PriorityHeap:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t heap_ptr
 */

struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap {
  int (*is_empty)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *);
  void (*heapify_up)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
  void (*heapify_down)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
  int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, int, double, double, double, double);
  int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_PriorityHeap *, struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_PriorityHeap *__pyx_vtabptr_7sklearn_4tree_6_utils_PriorityHeap;


/* "_utils.pxd":130
 *     DOUBLE_t weight
 * 
 * cdef class WeightedPQueue:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t capacity
 *     cdef SIZE_t array_ptr
 */

struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedPQueue {
  int (*is_empty)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *);
  int (*reset)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *);
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t (*size)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *);
  int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  int (*remove)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *);
  int (*peek)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *);
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t (*get_weight_from_index)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t (*get_value_from_index)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedPQueue *, __pyx_t_7sklearn_4tree_6_utils_SIZE_t);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedPQueue *__pyx_vtabptr_7sklearn_4tree_6_utils_WeightedPQueue;


/* "_utils.pxd":150
 * # =============================================================================
 * 
 * cdef class WeightedMedianCalculator:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t initial_capacity
 *     cdef WeightedPQueue samples
 */

struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedMedianCalculator {
  __pyx_t_7sklearn_4tree_6_utils_SIZE_t (*size)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *);
  int (*push)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  int (*reset)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *);
  int (*update_median_parameters_post_push)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  int (*remove)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  int (*pop)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *);
  int (*update_median_parameters_post_remove)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t, __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t);
  __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t (*get_median)(struct __pyx_obj_7sklearn_4tree_6_utils_WeightedMedianCalculator *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_6_utils_WeightedMedianCalculator *__pyx_vtabptr_7sklearn_4tree_6_utils_WeightedMedianCalculator;


/* "sklearn/tree/_splitter.pyx":54
 *     self.improvement = -INFINITY
 * 
 * cdef class Splitter:             # <<<<<<<<<<<<<<
 *     """Abstract splitter class.
 * 
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter {
  int (*init)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, PyObject *, PyArrayObject *, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *, struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init *__pyx_optional_args);
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  int (*node_split)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, double, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *);
  void (*node_value)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *, double *);
  double (*node_impurity)(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_Splitter;


/* "sklearn/tree/_splitter.pyx":242
 * 
 * 
 * cdef class BaseDenseSplitter(Splitter):             # <<<<<<<<<<<<<<
 *     cdef DTYPE_t* X
 *     cdef SIZE_t X_sample_stride
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter {
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseDenseSplitter;


/* "sklearn/tree/_splitter.pyx":304
 * 
 * 
 * cdef class BestSplitter(BaseDenseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best split."""
 *     def __reduce__(self):
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter {
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSplitter;


/* "sklearn/tree/_splitter.pyx":647
 * 
 * 
 * cdef class RandomSplitter(BaseDenseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best random split."""
 *     def __reduce__(self):
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter {
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseDenseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSplitter;


/* "sklearn/tree/_splitter.pyx":866
 * 
 * 
 * cdef class BaseSparseSplitter(Splitter):             # <<<<<<<<<<<<<<
 *     # The sparse splitter works only with csc sparse matrix format
 *     cdef DTYPE_t* X_data
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter {
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  void (*extract_nnz)(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *);
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseSparseSplitter;
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, double, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t);
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *);


/* "sklearn/tree/_splitter.pyx":1190
 * 
 * 
 * cdef class BestSparseSplitter(BaseSparseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding the best split, using the sparse data."""
 * 
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter {
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSparseSplitter;


/* "sklearn/tree/_splitter.pyx":1420
 * 
 * 
 * cdef class RandomSparseSplitter(BaseSparseSplitter):             # <<<<<<<<<<<<<<
 *     """Splitter for finding a random split, using the sparse data."""
 * 
 */

struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter {
  struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_base;
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSparseSplitter;

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static int __pyx_f_7sklearn_4tree_9_splitter_8Splitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_8Splitter_init *__pyx_optional_args); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_reset(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, double *__pyx_v_weighted_n_node_samples); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_split(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, CYTHON_UNUSED double __pyx_v_impurity, CYTHON_UNUSED struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_value(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, double *__pyx_v_dest); /* proto*/
static double __pyx_f_7sklearn_4tree_9_splitter_8Splitter_node_impurity(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_17BaseDenseSplitter_init *__pyx_optional_args); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_12BestSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_14RandomSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, PyObject *__pyx_v_X, PyArrayObject *__pyx_v_y, __pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t *__pyx_v_sample_weight, struct __pyx_opt_args_7sklearn_4tree_9_splitter_18BaseSparseSplitter_init *__pyx_optional_args); /* proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, double __pyx_v_threshold, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_zero_pos); /* proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive, int *__pyx_v_is_samples_sorted); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_18BestSparseSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_20RandomSparseSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features); /* proto*/

/* Module declarations from 'cpython.buffer' */

/* Module declarations from 'libc.string' */

/* Module declarations from 'libc.stdio' */

/* Module declarations from '__builtin__' */

/* Module declarations from 'cpython.type' */
static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;

/* Module declarations from 'cpython' */

/* Module declarations from 'cpython.object' */

/* Module declarations from 'cpython.ref' */

/* Module declarations from 'libc.stdlib' */

/* Module declarations from 'numpy' */

/* Module declarations from 'numpy' */
static PyTypeObject *__pyx_ptype_5numpy_dtype = 0;
static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;
static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;
static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;
static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;
static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/
static CYTHON_INLINE int __pyx_f_5numpy_import_array(void); /*proto*/

/* Module declarations from 'sklearn.tree._criterion' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_10_criterion_Criterion = 0;

/* Module declarations from 'sklearn.tree._tree' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_Tree = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_5_tree_TreeBuilder = 0;

/* Module declarations from 'sklearn.neighbors.quad_tree' */
static PyTypeObject *__pyx_ptype_7sklearn_9neighbors_9quad_tree__QuadTree = 0;
static float *__pyx_vp_7sklearn_9neighbors_9quad_tree_EPSILON = 0;
#define __pyx_v_7sklearn_9neighbors_9quad_tree_EPSILON (*__pyx_vp_7sklearn_9neighbors_9quad_tree_EPSILON)

/* Module declarations from 'sklearn.tree._utils' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_Stack = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_PriorityHeap = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_WeightedPQueue = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_6_utils_WeightedMedianCalculator = 0;
static __pyx_t_7sklearn_4tree_6_utils_SIZE_t (*__pyx_f_7sklearn_4tree_6_utils_rand_int)(__pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_SIZE_t, __pyx_t_7sklearn_4tree_6_utils_UINT32_t *); /*proto*/
static double (*__pyx_f_7sklearn_4tree_6_utils_rand_uniform)(double, double, __pyx_t_7sklearn_4tree_6_utils_UINT32_t *); /*proto*/
static double (*__pyx_f_7sklearn_4tree_6_utils_log)(double); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_DTYPE_t *(*__pyx_fuse_0__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_DTYPE_t **, size_t); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_SIZE_t *(*__pyx_fuse_1__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_SIZE_t **, size_t); /*proto*/
static unsigned char *(*__pyx_fuse_2__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(unsigned char **, size_t); /*proto*/
static struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord *(*__pyx_fuse_3__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_4tree_6_utils_WeightedPQueueRecord **, size_t); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t *(*__pyx_fuse_4__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_DOUBLE_t **, size_t); /*proto*/
static __pyx_t_7sklearn_4tree_6_utils_DOUBLE_t **(*__pyx_fuse_5__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(__pyx_t_7sklearn_4tree_6_utils_DOUBLE_t ***, size_t); /*proto*/
static struct __pyx_t_7sklearn_4tree_5_tree_Node *(*__pyx_fuse_6__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_4tree_5_tree_Node **, size_t); /*proto*/
static struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell *(*__pyx_fuse_7__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_9neighbors_9quad_tree_Cell **, size_t); /*proto*/
static struct __pyx_t_7sklearn_4tree_5_tree_Node **(*__pyx_fuse_8__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_4tree_5_tree_Node ***, size_t); /*proto*/
static struct __pyx_t_7sklearn_4tree_6_utils_StackRecord *(*__pyx_fuse_9__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_4tree_6_utils_StackRecord **, size_t); /*proto*/
static struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord *(*__pyx_fuse_10__pyx_f_7sklearn_4tree_6_utils_safe_realloc)(struct __pyx_t_7sklearn_4tree_6_utils_PriorityHeapRecord **, size_t); /*proto*/

/* Module declarations from 'sklearn.tree._splitter' */
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_Splitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BaseDenseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BestSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_RandomSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BaseSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_BestSparseSplitter = 0;
static PyTypeObject *__pyx_ptype_7sklearn_4tree_9_splitter_RandomSparseSplitter = 0;
static double __pyx_v_7sklearn_4tree_9_splitter_INFINITY;
static __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD;
static __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_7sklearn_4tree_9_splitter_EXTRACT_NNZ_SWITCH;
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter__init_split(struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, int); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sift_down(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static void __pyx_f_7sklearn_4tree_9_splitter_heapsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static int __pyx_f_7sklearn_4tree_9_splitter_compare_SIZE_t(void const *, void const *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_INT32_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, int *); /*proto*/
static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t); /*proto*/
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t = { "DOUBLE_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_DOUBLE_t), { 0 }, 0, 'R', 0, 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_9_splitter_INT32_t = { "INT32_t", NULL, sizeof(__pyx_t_7sklearn_4tree_9_splitter_INT32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_7sklearn_4tree_9_splitter_INT32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_7sklearn_4tree_9_splitter_INT32_t), 0 };
#define __Pyx_MODULE_NAME "sklearn.tree._splitter"
int __pyx_module_is_main_sklearn__tree___splitter = 0;

/* Implementation of 'sklearn.tree._splitter' */
static PyObject *__pyx_builtin_range;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_RuntimeError;
static PyObject *__pyx_builtin_ImportError;
static const char __pyx_k_np[] = "np";
static const char __pyx_k_inf[] = "inf";
static const char __pyx_k_data[] = "data";
static const char __pyx_k_main[] = "__main__";
static const char __pyx_k_name[] = "__name__";
static const char __pyx_k_test[] = "__test__";
static const char __pyx_k_numpy[] = "numpy";
static const char __pyx_k_range[] = "range";
static const char __pyx_k_shape[] = "shape";
static const char __pyx_k_import[] = "__import__";
static const char __pyx_k_indptr[] = "indptr";
static const char __pyx_k_reduce[] = "__reduce__";
static const char __pyx_k_indices[] = "indices";
static const char __pyx_k_presort[] = "presort";
static const char __pyx_k_randint[] = "randint";
static const char __pyx_k_strides[] = "strides";
static const char __pyx_k_getstate[] = "__getstate__";
static const char __pyx_k_itemsize[] = "itemsize";
static const char __pyx_k_setstate[] = "__setstate__";
static const char __pyx_k_TypeError[] = "TypeError";
static const char __pyx_k_criterion[] = "criterion";
static const char __pyx_k_reduce_ex[] = "__reduce_ex__";
static const char __pyx_k_ValueError[] = "ValueError";
static const char __pyx_k_csc_matrix[] = "csc_matrix";
static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
static const char __pyx_k_ImportError[] = "ImportError";
static const char __pyx_k_RuntimeError[] = "RuntimeError";
static const char __pyx_k_max_features[] = "max_features";
static const char __pyx_k_random_state[] = "random_state";
static const char __pyx_k_scipy_sparse[] = "scipy.sparse";
static const char __pyx_k_reduce_cython[] = "__reduce_cython__";
static const char __pyx_k_min_weight_leaf[] = "min_weight_leaf";
static const char __pyx_k_setstate_cython[] = "__setstate_cython__";
static const char __pyx_k_min_samples_leaf[] = "min_samples_leaf";
static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
static const char __pyx_k_X_should_be_in_csc_format[] = "X should be in csc format";
static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import";
static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd";
static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported";
static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous";
static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__";
static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import";
static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short.";
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor;
static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;
static PyObject *__pyx_n_s_ImportError;
static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;
static PyObject *__pyx_n_s_RuntimeError;
static PyObject *__pyx_n_s_TypeError;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_kp_s_X_should_be_in_csc_format;
static PyObject *__pyx_n_s_cline_in_traceback;
static PyObject *__pyx_n_s_criterion;
static PyObject *__pyx_n_s_csc_matrix;
static PyObject *__pyx_n_s_data;
static PyObject *__pyx_n_s_getstate;
static PyObject *__pyx_n_s_import;
static PyObject *__pyx_n_s_indices;
static PyObject *__pyx_n_s_indptr;
static PyObject *__pyx_n_s_inf;
static PyObject *__pyx_n_s_itemsize;
static PyObject *__pyx_n_s_main;
static PyObject *__pyx_n_s_max_features;
static PyObject *__pyx_n_s_min_samples_leaf;
static PyObject *__pyx_n_s_min_weight_leaf;
static PyObject *__pyx_n_s_name;
static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
static PyObject *__pyx_kp_s_no_default___reduce___due_to_non;
static PyObject *__pyx_n_s_np;
static PyObject *__pyx_n_s_numpy;
static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;
static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;
static PyObject *__pyx_n_s_presort;
static PyObject *__pyx_n_s_pyx_vtable;
static PyObject *__pyx_n_s_randint;
static PyObject *__pyx_n_s_random_state;
static PyObject *__pyx_n_s_range;
static PyObject *__pyx_n_s_reduce;
static PyObject *__pyx_n_s_reduce_cython;
static PyObject *__pyx_n_s_reduce_ex;
static PyObject *__pyx_n_s_scipy_sparse;
static PyObject *__pyx_n_s_setstate;
static PyObject *__pyx_n_s_setstate_cython;
static PyObject *__pyx_n_s_shape;
static PyObject *__pyx_n_s_strides;
static PyObject *__pyx_n_s_test;
static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, double __pyx_v_min_weight_leaf, PyObject *__pyx_v_random_state, int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_4__getstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_6__setstate__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v_d); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_9criterion_4__del__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_12max_features___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_12max_features_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_16min_samples_leaf___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_16min_samples_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_15min_weight_leaf___get__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_8Splitter_15min_weight_leaf_2__set__(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, PyObject *__pyx_v_value); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_8__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_8Splitter_10__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state, int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter_4__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_17BaseDenseSplitter_6__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_BaseDenseSplitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_12BestSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_14RandomSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_v_self); /* proto */
static int __pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter___cinit__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features, CYTHON_UNUSED __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf, CYTHON_UNUSED double __pyx_v_min_weight_leaf, CYTHON_UNUSED PyObject *__pyx_v_random_state, CYTHON_UNUSED int __pyx_v_presort); /* proto */
static void __pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter_2__dealloc__(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter_4__reduce_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_18BaseSparseSplitter_6__setstate_cython__(CYTHON_UNUSED struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_18BestSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_7sklearn_4tree_9_splitter_20RandomSparseSplitter___reduce__(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *__pyx_v_self); /* proto */
static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_Splitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static PyObject *__pyx_int_0;
static PyObject *__pyx_tuple_;
static PyObject *__pyx_tuple__2;
static PyObject *__pyx_tuple__3;
static PyObject *__pyx_tuple__4;
static PyObject *__pyx_tuple__5;
static PyObject *__pyx_tuple__6;
static PyObject *__pyx_tuple__7;
static PyObject *__pyx_tuple__8;
static PyObject *__pyx_tuple__9;
static PyObject *__pyx_tuple__10;
static PyObject *__pyx_tuple__11;
static PyObject *__pyx_tuple__12;
static PyObject *__pyx_tuple__13;
static PyObject *__pyx_tuple__14;
static PyObject *__pyx_tuple__15;
static PyObject *__pyx_tuple__16;

/* "sklearn/tree/_splitter.pyx":46
 * cdef DTYPE_t EXTRACT_NNZ_SWITCH = 0.1
 * 
 * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:             # <<<<<<<<<<<<<<
 *     self.impurity_left = INFINITY
 *     self.impurity_right = INFINITY
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter__init_split(struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_self, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_pos) {

  /* "sklearn/tree/_splitter.pyx":47
 * 
 * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:
 *     self.impurity_left = INFINITY             # <<<<<<<<<<<<<<
 *     self.impurity_right = INFINITY
 *     self.pos = start_pos
 */
  __pyx_v_self->impurity_left = __pyx_v_7sklearn_4tree_9_splitter_INFINITY;

  /* "sklearn/tree/_splitter.pyx":48
 * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:
 *     self.impurity_left = INFINITY
 *     self.impurity_right = INFINITY             # <<<<<<<<<<<<<<
 *     self.pos = start_pos
 *     self.feature = 0
 */
  __pyx_v_self->impurity_right = __pyx_v_7sklearn_4tree_9_splitter_INFINITY;

  /* "sklearn/tree/_splitter.pyx":49
 *     self.impurity_left = INFINITY
 *     self.impurity_right = INFINITY
 *     self.pos = start_pos             # <<<<<<<<<<<<<<
 *     self.feature = 0
 *     self.threshold = 0.
 */
  __pyx_v_self->pos = __pyx_v_start_pos;

  /* "sklearn/tree/_splitter.pyx":50
 *     self.impurity_right = INFINITY
 *     self.pos = start_pos
 *     self.feature = 0             # <<<<<<<<<<<<<<
 *     self.threshold = 0.
 *     self.improvement = -INFINITY
 */
  __pyx_v_self->feature = 0;

  /* "sklearn/tree/_splitter.pyx":51
 *     self.pos = start_pos
 *     self.feature = 0
 *     self.threshold = 0.             # <<<<<<<<<<<<<<
 *     self.improvement = -INFINITY
 * 
 */
  __pyx_v_self->threshold = 0.;

  /* "sklearn/tree/_splitter.pyx":52
 *     self.feature = 0
 *     self.threshold = 0.
 *     self.improvement = -INFINITY             # <<<<<<<<<<<<<<
 * 
 * cdef class Splitter:
 */
  __pyx_v_self->improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":46
 * cdef DTYPE_t EXTRACT_NNZ_SWITCH = 0.1
 * 
 * cdef inline void _init_split(SplitRecord* self, SIZE_t start_pos) nogil:             # <<<<<<<<<<<<<<
 *     self.impurity_left = INFINITY
 *     self.impurity_right = INFINITY
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":61
 *     """
 * 
 *     def __cinit__(self, Criterion criterion, SIZE_t max_features,             # <<<<<<<<<<<<<<
 *                   SIZE_t min_samples_leaf, double min_weight_leaf,
 *                   object random_state, bint presort):
 */

/* Python wrapper */
static int __pyx_pw_7sklearn_4tree_9_splitter_8Splitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static int __pyx_pw_7sklearn_4tree_9_splitter_8Splitter_1__cinit__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
  struct __pyx_obj_7sklearn_4tree_10_criterion_Criterion *__pyx_v_criterion = 0;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
  double __pyx_v_min_weight_leaf;
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 * cdef class BestSplitter(BaseDenseSplitter):
 *     """Splitter for finding the best split."""
 *     def __reduce__(self):             # <<<<<<<<<<<<<<
 *         return (BestSplitter, (self.criterion,
 *                                self.max_features,
 */

  /* function exit code */
  __pyx_L1_error:;
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  __Pyx_XDECREF(__pyx_t_2);
  __Pyx_XDECREF(__pyx_t_3);
  __Pyx_XDECREF(__pyx_t_4);
  __Pyx_XDECREF(__pyx_t_5);
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}

/* "sklearn/tree/_splitter.pyx":314
 *                                self.presort), self.__getstate__())
 * 
 *     cdef int node_split(self, double impurity, SplitRecord* split,             # <<<<<<<<<<<<<<
 *                         SIZE_t* n_constant_features) nogil except -1:
 *         """Find the best split on node samples[start:end]
 */

static int __pyx_f_7sklearn_4tree_9_splitter_12BestSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features) {
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_constant_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_features;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_sample_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_feature_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
  double __pyx_v_min_weight_leaf;
  __pyx_t_7sklearn_4tree_9_splitter_UINT32_t *__pyx_v_random_state;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_idx_sorted;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_sample_mask;
  struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_best;
  struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord __pyx_v_current;
  double __pyx_v_current_proxy_improvement;
  double __pyx_v_best_proxy_improvement;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_i;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_j;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_tmp;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature_idx_offset;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_feature_offset;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_j;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_visited_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_found_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_drawn_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_known_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_total_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_partition_end;
  int __pyx_r;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_t_1;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_2;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_3;
  double __pyx_t_4;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_t_5;
  int __pyx_t_6;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_7;
  int __pyx_t_8;
  int __pyx_t_9;

  /* "sklearn/tree/_splitter.pyx":322
 *         """
 *         # Find the best split
 *         cdef SIZE_t* samples = self.samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
  __pyx_v_samples = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":323
 *         # Find the best split
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start             # <<<<<<<<<<<<<<
 *         cdef SIZE_t end = self.end
 * 
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
  __pyx_v_start = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":324
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t* features = self.features
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
  __pyx_v_end = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":326
 *         cdef SIZE_t end = self.end
 * 
 *         cdef SIZE_t* features = self.features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
  __pyx_v_features = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":327
 * 
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_features = self.n_features
 * 
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
  __pyx_v_constant_features = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":328
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features             # <<<<<<<<<<<<<<
 * 
 *         cdef DTYPE_t* X = self.X
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
  __pyx_v_n_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":330
 *         cdef SIZE_t n_features = self.n_features
 * 
 *         cdef DTYPE_t* X = self.X             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 */
  __pyx_t_3 = __pyx_v_self->__pyx_base.X;
  __pyx_v_X = __pyx_t_3;

  /* "sklearn/tree/_splitter.pyx":331
 * 
 *         cdef DTYPE_t* X = self.X
 *         cdef DTYPE_t* Xf = self.feature_values             # <<<<<<<<<<<<<<
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 */
  __pyx_t_3 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
  __pyx_v_Xf = __pyx_t_3;

  /* "sklearn/tree/_splitter.pyx":332
 *         cdef DTYPE_t* X = self.X
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride             # <<<<<<<<<<<<<<
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.X_sample_stride;
  __pyx_v_X_sample_stride = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":333
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride             # <<<<<<<<<<<<<<
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.X_feature_stride;
  __pyx_v_X_feature_stride = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":334
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
  __pyx_v_max_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":335
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf             # <<<<<<<<<<<<<<
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
  __pyx_v_min_samples_leaf = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":336
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf             # <<<<<<<<<<<<<<
 *         cdef UINT32_t* random_state = &self.rand_r_state
 * 
 */
  __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
  __pyx_v_min_weight_leaf = __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":337
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state             # <<<<<<<<<<<<<<
 * 
 *         cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr
 */
  __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);

  /* "sklearn/tree/_splitter.pyx":339
 *         cdef UINT32_t* random_state = &self.rand_r_state
 * 
 *         cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* sample_mask = self.sample_mask
 * 
 */
  __pyx_t_5 = __pyx_v_self->__pyx_base.X_idx_sorted_ptr;
  __pyx_v_X_idx_sorted = __pyx_t_5;

  /* "sklearn/tree/_splitter.pyx":340
 * 
 *         cdef INT32_t* X_idx_sorted = self.X_idx_sorted_ptr
 *         cdef SIZE_t* sample_mask = self.sample_mask             # <<<<<<<<<<<<<<
 * 
 *         cdef SplitRecord best, current
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.sample_mask;
  __pyx_v_sample_mask = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":343
 * 
 *         cdef SplitRecord best, current
 *         cdef double current_proxy_improvement = -INFINITY             # <<<<<<<<<<<<<<
 *         cdef double best_proxy_improvement = -INFINITY
 * 
 */
  __pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":344
 *         cdef SplitRecord best, current
 *         cdef double current_proxy_improvement = -INFINITY
 *         cdef double best_proxy_improvement = -INFINITY             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t f_i = n_features
 */
  __pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":346
 *         cdef double best_proxy_improvement = -INFINITY
 * 
 *         cdef SIZE_t f_i = n_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t f_j
 *         cdef SIZE_t tmp
 */
  __pyx_v_f_i = __pyx_v_n_features;

  /* "sklearn/tree/_splitter.pyx":355
 *         cdef SIZE_t j
 * 
 *         cdef SIZE_t n_visited_features = 0             # <<<<<<<<<<<<<<
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0
 */
  __pyx_v_n_visited_features = 0;

  /* "sklearn/tree/_splitter.pyx":357
 *         cdef SIZE_t n_visited_features = 0
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0             # <<<<<<<<<<<<<<
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 */
  __pyx_v_n_found_constants = 0;

  /* "sklearn/tree/_splitter.pyx":359
 *         cdef SIZE_t n_found_constants = 0
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 */
  __pyx_v_n_drawn_constants = 0;

  /* "sklearn/tree/_splitter.pyx":360
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 *         cdef SIZE_t n_known_constants = n_constant_features[0]             # <<<<<<<<<<<<<<
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants
 */
  __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);

  /* "sklearn/tree/_splitter.pyx":362
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t current_feature_value
 *         cdef SIZE_t partition_end
 */
  __pyx_v_n_total_constants = __pyx_v_n_known_constants;

  /* "sklearn/tree/_splitter.pyx":366
 *         cdef SIZE_t partition_end
 * 
 *         _init_split(&best, end)             # <<<<<<<<<<<<<<
 * 
 *         if self.presort == 1:
 */
  __pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);

  /* "sklearn/tree/_splitter.pyx":368
 *         _init_split(&best, end)
 * 
 *         if self.presort == 1:             # <<<<<<<<<<<<<<
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 1
 */
  __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
  if (__pyx_t_6) {

    /* "sklearn/tree/_splitter.pyx":369
 * 
 *         if self.presort == 1:
 *             for p in range(start, end):             # <<<<<<<<<<<<<<
 *                 sample_mask[samples[p]] = 1
 * 
 */
    __pyx_t_2 = __pyx_v_end;
    for (__pyx_t_7 = __pyx_v_start; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
      __pyx_v_p = __pyx_t_7;

      /* "sklearn/tree/_splitter.pyx":370
 *         if self.presort == 1:
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 1             # <<<<<<<<<<<<<<
 * 
 *         # Sample up to max_features without replacement using a
 */
      (__pyx_v_sample_mask[(__pyx_v_samples[__pyx_v_p])]) = 1;
    }

    /* "sklearn/tree/_splitter.pyx":368
 *         _init_split(&best, end)
 * 
 *         if self.presort == 1:             # <<<<<<<<<<<<<<
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 1
 */
  }

  /* "sklearn/tree/_splitter.pyx":381
 *         # newly discovered constant features to spare computation on descendant
 *         # nodes.
 *         while (f_i > n_total_constants and  # Stop early if remaining features             # <<<<<<<<<<<<<<
 *                                             # are constant
 *                 (n_visited_features < max_features or
 */
  while (1) {
    __pyx_t_8 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
    if (__pyx_t_8) {
    } else {
      __pyx_t_6 = __pyx_t_8;
      goto __pyx_L8_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":383
 *         while (f_i > n_total_constants and  # Stop early if remaining features
 *                                             # are constant
 *                 (n_visited_features < max_features or             # <<<<<<<<<<<<<<
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 */
    __pyx_t_8 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
    if (!__pyx_t_8) {
    } else {
      __pyx_t_6 = __pyx_t_8;
      goto __pyx_L8_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":385
 *                 (n_visited_features < max_features or
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):             # <<<<<<<<<<<<<<
 * 
 *             n_visited_features += 1
 */
    __pyx_t_8 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
    __pyx_t_6 = __pyx_t_8;
    __pyx_L8_bool_binop_done:;
    if (!__pyx_t_6) break;

    /* "sklearn/tree/_splitter.pyx":387
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 * 
 *             n_visited_features += 1             # <<<<<<<<<<<<<<
 * 
 *             # Loop invariant: elements of features in
 */
    __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);

    /* "sklearn/tree/_splitter.pyx":401
 * 
 *             # Draw a feature at random
 *             f_j = rand_int(n_drawn_constants, f_i - n_found_constants,             # <<<<<<<<<<<<<<
 *                            random_state)
 * 
 */
    __pyx_v_f_j = __pyx_f_7sklearn_4tree_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);

    /* "sklearn/tree/_splitter.pyx":404
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
    __pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
    if (__pyx_t_6) {

      /* "sklearn/tree/_splitter.pyx":406
 *             if f_j < n_known_constants:
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]             # <<<<<<<<<<<<<<
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp
 */
      __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":407
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]             # <<<<<<<<<<<<<<
 *                 features[n_drawn_constants] = tmp
 * 
 */
      (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);

      /* "sklearn/tree/_splitter.pyx":408
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp             # <<<<<<<<<<<<<<
 * 
 *                 n_drawn_constants += 1
 */
      (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;

      /* "sklearn/tree/_splitter.pyx":410
 *                 features[n_drawn_constants] = tmp
 * 
 *                 n_drawn_constants += 1             # <<<<<<<<<<<<<<
 * 
 *             else:
 */
      __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);

      /* "sklearn/tree/_splitter.pyx":404
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
      goto __pyx_L11;
    }

    /* "sklearn/tree/_splitter.pyx":414
 *             else:
 *                 # f_j in the interval [n_known_constants, f_i - n_found_constants[
 *                 f_j += n_found_constants             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_total_constants, f_i[
 *                 current.feature = features[f_j]
 */
    /*else*/ {
      __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);

      /* "sklearn/tree/_splitter.pyx":416
 *                 f_j += n_found_constants
 *                 # f_j in the interval [n_total_constants, f_i[
 *                 current.feature = features[f_j]             # <<<<<<<<<<<<<<
 *                 feature_offset = self.X_feature_stride * current.feature
 * 
 */
      __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":417
 *                 # f_j in the interval [n_total_constants, f_i[
 *                 current.feature = features[f_j]
 *                 feature_offset = self.X_feature_stride * current.feature             # <<<<<<<<<<<<<<
 * 
 *                 # Sort samples along that feature; either by utilizing
 */
      __pyx_v_feature_offset = (__pyx_v_self->__pyx_base.X_feature_stride * __pyx_v_current.feature);

      /* "sklearn/tree/_splitter.pyx":423
 *                 # sorting the array in a manner which utilizes the cache more
 *                 # effectively.
 *                 if self.presort == 1:             # <<<<<<<<<<<<<<
 *                     p = start
 *                     feature_idx_offset = self.X_idx_sorted_stride * current.feature
 */
      __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":424
 *                 # effectively.
 *                 if self.presort == 1:
 *                     p = start             # <<<<<<<<<<<<<<
 *                     feature_idx_offset = self.X_idx_sorted_stride * current.feature
 * 
 */
        __pyx_v_p = __pyx_v_start;

        /* "sklearn/tree/_splitter.pyx":425
 *                 if self.presort == 1:
 *                     p = start
 *                     feature_idx_offset = self.X_idx_sorted_stride * current.feature             # <<<<<<<<<<<<<<
 * 
 *                     for i in range(self.n_total_samples):
 */
        __pyx_v_feature_idx_offset = (__pyx_v_self->__pyx_base.X_idx_sorted_stride * __pyx_v_current.feature);

        /* "sklearn/tree/_splitter.pyx":427
 *                     feature_idx_offset = self.X_idx_sorted_stride * current.feature
 * 
 *                     for i in range(self.n_total_samples):             # <<<<<<<<<<<<<<
 *                         j = X_idx_sorted[i + feature_idx_offset]
 *                         if sample_mask[j] == 1:
 */
        __pyx_t_2 = __pyx_v_self->__pyx_base.n_total_samples;
        for (__pyx_t_7 = 0; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
          __pyx_v_i = __pyx_t_7;

          /* "sklearn/tree/_splitter.pyx":428
 * 
 *                     for i in range(self.n_total_samples):
 *                         j = X_idx_sorted[i + feature_idx_offset]             # <<<<<<<<<<<<<<
 *                         if sample_mask[j] == 1:
 *                             samples[p] = j
 */
          __pyx_v_j = (__pyx_v_X_idx_sorted[(__pyx_v_i + __pyx_v_feature_idx_offset)]);

          /* "sklearn/tree/_splitter.pyx":429
 *                     for i in range(self.n_total_samples):
 *                         j = X_idx_sorted[i + feature_idx_offset]
 *                         if sample_mask[j] == 1:             # <<<<<<<<<<<<<<
 *                             samples[p] = j
 *                             Xf[p] = X[self.X_sample_stride * j + feature_offset]
 */
          __pyx_t_6 = (((__pyx_v_sample_mask[__pyx_v_j]) == 1) != 0);
          if (__pyx_t_6) {

            /* "sklearn/tree/_splitter.pyx":430
 *                         j = X_idx_sorted[i + feature_idx_offset]
 *                         if sample_mask[j] == 1:
 *                             samples[p] = j             # <<<<<<<<<<<<<<
 *                             Xf[p] = X[self.X_sample_stride * j + feature_offset]
 *                             p += 1
 */
            (__pyx_v_samples[__pyx_v_p]) = __pyx_v_j;

            /* "sklearn/tree/_splitter.pyx":431
 *                         if sample_mask[j] == 1:
 *                             samples[p] = j
 *                             Xf[p] = X[self.X_sample_stride * j + feature_offset]             # <<<<<<<<<<<<<<
 *                             p += 1
 *                 else:
 */
            (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_X[((__pyx_v_self->__pyx_base.X_sample_stride * __pyx_v_j) + __pyx_v_feature_offset)]);

            /* "sklearn/tree/_splitter.pyx":432
 *                             samples[p] = j
 *                             Xf[p] = X[self.X_sample_stride * j + feature_offset]
 *                             p += 1             # <<<<<<<<<<<<<<
 *                 else:
 *                     for i in range(start, end):
 */
            __pyx_v_p = (__pyx_v_p + 1);

            /* "sklearn/tree/_splitter.pyx":429
 *                     for i in range(self.n_total_samples):
 *                         j = X_idx_sorted[i + feature_idx_offset]
 *                         if sample_mask[j] == 1:             # <<<<<<<<<<<<<<
 *                             samples[p] = j
 *                             Xf[p] = X[self.X_sample_stride * j + feature_offset]
 */
          }
        }

        /* "sklearn/tree/_splitter.pyx":423
 *                 # sorting the array in a manner which utilizes the cache more
 *                 # effectively.
 *                 if self.presort == 1:             # <<<<<<<<<<<<<<
 *                     p = start
 *                     feature_idx_offset = self.X_idx_sorted_stride * current.feature
 */
        goto __pyx_L12;
      }

      /* "sklearn/tree/_splitter.pyx":434
 *                             p += 1
 *                 else:
 *                     for i in range(start, end):             # <<<<<<<<<<<<<<
 *                         Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]
 * 
 */
      /*else*/ {
        __pyx_t_2 = __pyx_v_end;
        for (__pyx_t_7 = __pyx_v_start; __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
          __pyx_v_i = __pyx_t_7;

          /* "sklearn/tree/_splitter.pyx":435
 *                 else:
 *                     for i in range(start, end):
 *                         Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]             # <<<<<<<<<<<<<<
 * 
 *                     sort(Xf + start, samples + start, end - start)
 */
          (__pyx_v_Xf[__pyx_v_i]) = (__pyx_v_X[((__pyx_v_self->__pyx_base.X_sample_stride * (__pyx_v_samples[__pyx_v_i])) + __pyx_v_feature_offset)]);
        }

        /* "sklearn/tree/_splitter.pyx":437
 *                         Xf[i] = X[self.X_sample_stride * samples[i] + feature_offset]
 * 
 *                     sort(Xf + start, samples + start, end - start)             # <<<<<<<<<<<<<<
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 */
        __pyx_f_7sklearn_4tree_9_splitter_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end - __pyx_v_start));
      }
      __pyx_L12:;

      /* "sklearn/tree/_splitter.pyx":439
 *                     sort(Xf + start, samples + start, end - start)
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
      __pyx_t_6 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":440
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]             # <<<<<<<<<<<<<<
 *                     features[n_total_constants] = current.feature
 * 
 */
        (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);

        /* "sklearn/tree/_splitter.pyx":441
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature             # <<<<<<<<<<<<<<
 * 
 *                     n_found_constants += 1
 */
        __pyx_t_2 = __pyx_v_current.feature;
        (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;

        /* "sklearn/tree/_splitter.pyx":443
 *                     features[n_total_constants] = current.feature
 * 
 *                     n_found_constants += 1             # <<<<<<<<<<<<<<
 *                     n_total_constants += 1
 * 
 */
        __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);

        /* "sklearn/tree/_splitter.pyx":444
 * 
 *                     n_found_constants += 1
 *                     n_total_constants += 1             # <<<<<<<<<<<<<<
 * 
 *                 else:
 */
        __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);

        /* "sklearn/tree/_splitter.pyx":439
 *                     sort(Xf + start, samples + start, end - start)
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
        goto __pyx_L18;
      }

      /* "sklearn/tree/_splitter.pyx":447
 * 
 *                 else:
 *                     f_i -= 1             # <<<<<<<<<<<<<<
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]
 * 
 */
      /*else*/ {
        __pyx_v_f_i = (__pyx_v_f_i - 1);

        /* "sklearn/tree/_splitter.pyx":448
 *                 else:
 *                     f_i -= 1
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]             # <<<<<<<<<<<<<<
 * 
 *                     # Evaluate all splits
 */
        __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
        __pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]);
        (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
        (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7;

        /* "sklearn/tree/_splitter.pyx":451
 * 
 *                     # Evaluate all splits
 *                     self.criterion.reset()             # <<<<<<<<<<<<<<
 *                     p = start
 * 
 */
        __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 451, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":452
 *                     # Evaluate all splits
 *                     self.criterion.reset()
 *                     p = start             # <<<<<<<<<<<<<<
 * 
 *                     while p < end:
 */
        __pyx_v_p = __pyx_v_start;

        /* "sklearn/tree/_splitter.pyx":454
 *                     p = start
 * 
 *                     while p < end:             # <<<<<<<<<<<<<<
 *                         while (p + 1 < end and
 *                                Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
 */
        while (1) {
          __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
          if (!__pyx_t_6) break;

          /* "sklearn/tree/_splitter.pyx":455
 * 
 *                     while p < end:
 *                         while (p + 1 < end and             # <<<<<<<<<<<<<<
 *                                Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p += 1
 */
          while (1) {
            __pyx_t_8 = (((__pyx_v_p + 1) < __pyx_v_end) != 0);
            if (__pyx_t_8) {
            } else {
              __pyx_t_6 = __pyx_t_8;
              goto __pyx_L23_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":456
 *                     while p < end:
 *                         while (p + 1 < end and
 *                                Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):             # <<<<<<<<<<<<<<
 *                             p += 1
 * 
 */
            __pyx_t_8 = (((__pyx_v_Xf[(__pyx_v_p + 1)]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
            __pyx_t_6 = __pyx_t_8;
            __pyx_L23_bool_binop_done:;
            if (!__pyx_t_6) break;

            /* "sklearn/tree/_splitter.pyx":457
 *                         while (p + 1 < end and
 *                                Xf[p + 1] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p += 1             # <<<<<<<<<<<<<<
 * 
 *                         # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
 */
            __pyx_v_p = (__pyx_v_p + 1);
          }

          /* "sklearn/tree/_splitter.pyx":461
 *                         # (p + 1 >= end) or (X[samples[p + 1], current.feature] >
 *                         #                    X[samples[p], current.feature])
 *                         p += 1             # <<<<<<<<<<<<<<
 *                         # (p >= end) or (X[samples[p], current.feature] >
 *                         #                X[samples[p - 1], current.feature])
 */
          __pyx_v_p = (__pyx_v_p + 1);

          /* "sklearn/tree/_splitter.pyx":465
 *                         #                X[samples[p - 1], current.feature])
 * 
 *                         if p < end:             # <<<<<<<<<<<<<<
 *                             current.pos = p
 * 
 */
          __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
          if (__pyx_t_6) {

            /* "sklearn/tree/_splitter.pyx":466
 * 
 *                         if p < end:
 *                             current.pos = p             # <<<<<<<<<<<<<<
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 */
            __pyx_v_current.pos = __pyx_v_p;

            /* "sklearn/tree/_splitter.pyx":469
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            __pyx_t_8 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
            if (!__pyx_t_8) {
            } else {
              __pyx_t_6 = __pyx_t_8;
              goto __pyx_L27_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":470
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or
 *                                     ((end - current.pos) < min_samples_leaf)):             # <<<<<<<<<<<<<<
 *                                 continue
 * 
 */
            __pyx_t_8 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
            __pyx_t_6 = __pyx_t_8;
            __pyx_L27_bool_binop_done:;

            /* "sklearn/tree/_splitter.pyx":469
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":471
 *                             if (((current.pos - start) < min_samples_leaf) or
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue             # <<<<<<<<<<<<<<
 * 
 *                             self.criterion.update(current.pos)
 */
              goto __pyx_L19_continue;

              /* "sklearn/tree/_splitter.pyx":469
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            }

            /* "sklearn/tree/_splitter.pyx":473
 *                                 continue
 * 
 *                             self.criterion.update(current.pos)             # <<<<<<<<<<<<<<
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 */
            __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 473, __pyx_L1_error)

            /* "sklearn/tree/_splitter.pyx":476
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            __pyx_t_8 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
            if (!__pyx_t_8) {
            } else {
              __pyx_t_6 = __pyx_t_8;
              goto __pyx_L30_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":477
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):             # <<<<<<<<<<<<<<
 *                                 continue
 * 
 */
            __pyx_t_8 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
            __pyx_t_6 = __pyx_t_8;
            __pyx_L30_bool_binop_done:;

            /* "sklearn/tree/_splitter.pyx":476
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":478
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue             # <<<<<<<<<<<<<<
 * 
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 */
              goto __pyx_L19_continue;

              /* "sklearn/tree/_splitter.pyx":476
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            }

            /* "sklearn/tree/_splitter.pyx":480
 *                                 continue
 * 
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()             # <<<<<<<<<<<<<<
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:
 */
            __pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);

            /* "sklearn/tree/_splitter.pyx":482
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                                 best_proxy_improvement = current_proxy_improvement
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
 */
            __pyx_t_6 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":483
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:
 *                                 best_proxy_improvement = current_proxy_improvement             # <<<<<<<<<<<<<<
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
 * 
 */
              __pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;

              /* "sklearn/tree/_splitter.pyx":484
 *                             if current_proxy_improvement > best_proxy_improvement:
 *                                 best_proxy_improvement = current_proxy_improvement
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0             # <<<<<<<<<<<<<<
 * 
 *                                 if current.threshold == Xf[p]:
 */
              __pyx_v_current.threshold = (((__pyx_v_Xf[(__pyx_v_p - 1)]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0);

              /* "sklearn/tree/_splitter.pyx":486
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
 * 
 *                                 if current.threshold == Xf[p]:             # <<<<<<<<<<<<<<
 *                                     current.threshold = Xf[p - 1]
 * 
 */
              __pyx_t_6 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0);
              if (__pyx_t_6) {

                /* "sklearn/tree/_splitter.pyx":487
 * 
 *                                 if current.threshold == Xf[p]:
 *                                     current.threshold = Xf[p - 1]             # <<<<<<<<<<<<<<
 * 
 *                                 best = current  # copy
 */
                __pyx_v_current.threshold = (__pyx_v_Xf[(__pyx_v_p - 1)]);

                /* "sklearn/tree/_splitter.pyx":486
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
 * 
 *                                 if current.threshold == Xf[p]:             # <<<<<<<<<<<<<<
 *                                     current.threshold = Xf[p - 1]
 * 
 */
              }

              /* "sklearn/tree/_splitter.pyx":489
 *                                     current.threshold = Xf[p - 1]
 * 
 *                                 best = current  # copy             # <<<<<<<<<<<<<<
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 */
              __pyx_v_best = __pyx_v_current;

              /* "sklearn/tree/_splitter.pyx":482
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                                 best_proxy_improvement = current_proxy_improvement
 *                                 current.threshold = (Xf[p - 1] + Xf[p]) / 2.0
 */
            }

            /* "sklearn/tree/_splitter.pyx":465
 *                         #                X[samples[p - 1], current.feature])
 * 
 *                         if p < end:             # <<<<<<<<<<<<<<
 *                             current.pos = p
 * 
 */
          }
          __pyx_L19_continue:;
        }
      }
      __pyx_L18:;
    }
    __pyx_L11:;
  }

  /* "sklearn/tree/_splitter.pyx":492
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:             # <<<<<<<<<<<<<<
 *             feature_offset = X_feature_stride * best.feature
 *             partition_end = end
 */
  __pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
  if (__pyx_t_6) {

    /* "sklearn/tree/_splitter.pyx":493
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:
 *             feature_offset = X_feature_stride * best.feature             # <<<<<<<<<<<<<<
 *             partition_end = end
 *             p = start
 */
    __pyx_v_feature_offset = (__pyx_v_X_feature_stride * __pyx_v_best.feature);

    /* "sklearn/tree/_splitter.pyx":494
 *         if best.pos < end:
 *             feature_offset = X_feature_stride * best.feature
 *             partition_end = end             # <<<<<<<<<<<<<<
 *             p = start
 * 
 */
    __pyx_v_partition_end = __pyx_v_end;

    /* "sklearn/tree/_splitter.pyx":495
 *             feature_offset = X_feature_stride * best.feature
 *             partition_end = end
 *             p = start             # <<<<<<<<<<<<<<
 * 
 *             while p < partition_end:
 */
    __pyx_v_p = __pyx_v_start;

    /* "sklearn/tree/_splitter.pyx":497
 *             p = start
 * 
 *             while p < partition_end:             # <<<<<<<<<<<<<<
 *                 if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:
 *                     p += 1
 */
    while (1) {
      __pyx_t_6 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
      if (!__pyx_t_6) break;

      /* "sklearn/tree/_splitter.pyx":498
 * 
 *             while p < partition_end:
 *                 if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:             # <<<<<<<<<<<<<<
 *                     p += 1
 * 
 */
      __pyx_t_6 = (((__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + __pyx_v_feature_offset)]) <= __pyx_v_best.threshold) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":499
 *             while p < partition_end:
 *                 if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:
 *                     p += 1             # <<<<<<<<<<<<<<
 * 
 *                 else:
 */
        __pyx_v_p = (__pyx_v_p + 1);

        /* "sklearn/tree/_splitter.pyx":498
 * 
 *             while p < partition_end:
 *                 if X[X_sample_stride * samples[p] + feature_offset] <= best.threshold:             # <<<<<<<<<<<<<<
 *                     p += 1
 * 
 */
        goto __pyx_L37;
      }

      /* "sklearn/tree/_splitter.pyx":502
 * 
 *                 else:
 *                     partition_end -= 1             # <<<<<<<<<<<<<<
 * 
 *                     tmp = samples[partition_end]
 */
      /*else*/ {
        __pyx_v_partition_end = (__pyx_v_partition_end - 1);

        /* "sklearn/tree/_splitter.pyx":504
 *                     partition_end -= 1
 * 
 *                     tmp = samples[partition_end]             # <<<<<<<<<<<<<<
 *                     samples[partition_end] = samples[p]
 *                     samples[p] = tmp
 */
        __pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]);

        /* "sklearn/tree/_splitter.pyx":505
 * 
 *                     tmp = samples[partition_end]
 *                     samples[partition_end] = samples[p]             # <<<<<<<<<<<<<<
 *                     samples[p] = tmp
 * 
 */
        (__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]);

        /* "sklearn/tree/_splitter.pyx":506
 *                     tmp = samples[partition_end]
 *                     samples[partition_end] = samples[p]
 *                     samples[p] = tmp             # <<<<<<<<<<<<<<
 * 
 *             self.criterion.reset()
 */
        (__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
      }
      __pyx_L37:;
    }

    /* "sklearn/tree/_splitter.pyx":508
 *                     samples[p] = tmp
 * 
 *             self.criterion.reset()             # <<<<<<<<<<<<<<
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 508, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":509
 * 
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)             # <<<<<<<<<<<<<<
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 509, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":510
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)             # <<<<<<<<<<<<<<
 *             self.criterion.children_impurity(&best.impurity_left,
 *                                              &best.impurity_right)
 */
    __pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);

    /* "sklearn/tree/_splitter.pyx":511
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,             # <<<<<<<<<<<<<<
 *                                              &best.impurity_right)
 * 
 */
    ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_best.impurity_left), (&__pyx_v_best.impurity_right));

    /* "sklearn/tree/_splitter.pyx":492
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:             # <<<<<<<<<<<<<<
 *             feature_offset = X_feature_stride * best.feature
 *             partition_end = end
 */
  }

  /* "sklearn/tree/_splitter.pyx":515
 * 
 *         # Reset sample mask
 *         if self.presort == 1:             # <<<<<<<<<<<<<<
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 0
 */
  __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.presort == 1) != 0);
  if (__pyx_t_6) {

    /* "sklearn/tree/_splitter.pyx":516
 *         # Reset sample mask
 *         if self.presort == 1:
 *             for p in range(start, end):             # <<<<<<<<<<<<<<
 *                 sample_mask[samples[p]] = 0
 * 
 */
    __pyx_t_7 = __pyx_v_end;
    for (__pyx_t_2 = __pyx_v_start; __pyx_t_2 < __pyx_t_7; __pyx_t_2+=1) {
      __pyx_v_p = __pyx_t_2;

      /* "sklearn/tree/_splitter.pyx":517
 *         if self.presort == 1:
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 0             # <<<<<<<<<<<<<<
 * 
 *         # Respect invariant for constant features: the original order of
 */
      (__pyx_v_sample_mask[(__pyx_v_samples[__pyx_v_p])]) = 0;
    }

    /* "sklearn/tree/_splitter.pyx":515
 * 
 *         # Reset sample mask
 *         if self.presort == 1:             # <<<<<<<<<<<<<<
 *             for p in range(start, end):
 *                 sample_mask[samples[p]] = 0
 */
  }

  /* "sklearn/tree/_splitter.pyx":522
 *         # element in features[:n_known_constants] must be preserved for sibling
 *         # and child nodes
 *         memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)             # <<<<<<<<<<<<<<
 * 
 *         # Copy newly found constant features
 */
  memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)) * __pyx_v_n_known_constants));

  /* "sklearn/tree/_splitter.pyx":525
 * 
 *         # Copy newly found constant features
 *         memcpy(constant_features + n_known_constants,             # <<<<<<<<<<<<<<
 *                features + n_known_constants,
 *                sizeof(SIZE_t) * n_found_constants)
 */
  memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)) * __pyx_v_n_found_constants));

  /* "sklearn/tree/_splitter.pyx":530
 * 
 *         # Return values
 *         split[0] = best             # <<<<<<<<<<<<<<
 *         n_constant_features[0] = n_total_constants
 *         return 0
 */
  (__pyx_v_split[0]) = __pyx_v_best;

  /* "sklearn/tree/_splitter.pyx":531
 *         # Return values
 *         split[0] = best
 *         n_constant_features[0] = n_total_constants             # <<<<<<<<<<<<<<
 *         return 0
 * 
 */
  (__pyx_v_n_constant_features[0]) = __pyx_v_n_total_constants;

  /* "sklearn/tree/_splitter.pyx":532
 *         split[0] = best
 *         n_constant_features[0] = n_total_constants
 *         return 0             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_r = 0;
  goto __pyx_L0;

  /* "sklearn/tree/_splitter.pyx":314
 *                                self.presort), self.__getstate__())
 * 
 *     cdef int node_split(self, double impurity, SplitRecord* split,             # <<<<<<<<<<<<<<
 *                         SIZE_t* n_constant_features) nogil except -1:
 *         """Find the best split on node samples[start:end]
 */

  /* function exit code */
  __pyx_L1_error:;
  {
    #ifdef WITH_THREAD
    PyGILState_STATE __pyx_gilstate_save = __Pyx_PyGILState_Ensure();
    #endif
    __Pyx_AddTraceback("sklearn.tree._splitter.BestSplitter.node_split", __pyx_clineno, __pyx_lineno, __pyx_filename);
    #ifdef WITH_THREAD
    __Pyx_PyGILState_Release(__pyx_gilstate_save);
    #endif
  }
  __pyx_r = -1;
  __pyx_L0:;
  return __pyx_r;
}

/* "sklearn/tree/_splitter.pyx":537
 * # Sort n-element arrays pointed to by Xf and samples, simultaneously,
 * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
 * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     cdef int maxd = 2 * <int>log(n)
 *     introsort(Xf, samples, n, maxd)
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n) {
  int __pyx_v_maxd;

  /* "sklearn/tree/_splitter.pyx":538
 * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
 * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
 *     cdef int maxd = 2 * <int>log(n)             # <<<<<<<<<<<<<<
 *     introsort(Xf, samples, n, maxd)
 * 
 */
  __pyx_v_maxd = (2 * ((int)__pyx_f_7sklearn_4tree_6_utils_log(__pyx_v_n)));

  /* "sklearn/tree/_splitter.pyx":539
 * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:
 *     cdef int maxd = 2 * <int>log(n)
 *     introsort(Xf, samples, n, maxd)             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n, __pyx_v_maxd);

  /* "sklearn/tree/_splitter.pyx":537
 * # Sort n-element arrays pointed to by Xf and samples, simultaneously,
 * # by the values in Xf. Algorithm: Introsort (Musser, SP&E, 1997).
 * cdef inline void sort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     cdef int maxd = 2 * <int>log(n)
 *     introsort(Xf, samples, n, maxd)
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":542
 * 
 * 
 * cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples,             # <<<<<<<<<<<<<<
 *         SIZE_t i, SIZE_t j) nogil:
 *     # Helper for sort
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_j) {
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_t_1;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_t_2;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_3;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":545
 *         SIZE_t i, SIZE_t j) nogil:
 *     # Helper for sort
 *     Xf[i], Xf[j] = Xf[j], Xf[i]             # <<<<<<<<<<<<<<
 *     samples[i], samples[j] = samples[j], samples[i]
 * 
 */
  __pyx_t_1 = (__pyx_v_Xf[__pyx_v_j]);
  __pyx_t_2 = (__pyx_v_Xf[__pyx_v_i]);
  (__pyx_v_Xf[__pyx_v_i]) = __pyx_t_1;
  (__pyx_v_Xf[__pyx_v_j]) = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":546
 *     # Helper for sort
 *     Xf[i], Xf[j] = Xf[j], Xf[i]
 *     samples[i], samples[j] = samples[j], samples[i]             # <<<<<<<<<<<<<<
 * 
 * 
 */
  __pyx_t_3 = (__pyx_v_samples[__pyx_v_j]);
  __pyx_t_4 = (__pyx_v_samples[__pyx_v_i]);
  (__pyx_v_samples[__pyx_v_i]) = __pyx_t_3;
  (__pyx_v_samples[__pyx_v_j]) = __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":542
 * 
 * 
 * cdef inline void swap(DTYPE_t* Xf, SIZE_t* samples,             # <<<<<<<<<<<<<<
 *         SIZE_t i, SIZE_t j) nogil:
 *     # Helper for sort
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":549
 * 
 * 
 * cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     # Median of three pivot selection, after Bentley and McIlroy (1993).
 *     # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
 */

static CYTHON_INLINE __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n) {
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_a;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_b;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_c;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_r;
  int __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":552
 *     # Median of three pivot selection, after Bentley and McIlroy (1993).
 *     # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
 *     cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]             # <<<<<<<<<<<<<<
 *     if a < b:
 *         if b < c:
 */
  __pyx_v_a = (__pyx_v_Xf[0]);
  __pyx_v_b = (__pyx_v_Xf[(__pyx_v_n / 2)]);
  __pyx_v_c = (__pyx_v_Xf[(__pyx_v_n - 1)]);

  /* "sklearn/tree/_splitter.pyx":553
 *     # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
 *     cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
 *     if a < b:             # <<<<<<<<<<<<<<
 *         if b < c:
 *             return b
 */
  __pyx_t_1 = ((__pyx_v_a < __pyx_v_b) != 0);
  if (__pyx_t_1) {

    /* "sklearn/tree/_splitter.pyx":554
 *     cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
 *     if a < b:
 *         if b < c:             # <<<<<<<<<<<<<<
 *             return b
 *         elif a < c:
 */
    __pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":555
 *     if a < b:
 *         if b < c:
 *             return b             # <<<<<<<<<<<<<<
 *         elif a < c:
 *             return c
 */
      __pyx_r = __pyx_v_b;
      goto __pyx_L0;

      /* "sklearn/tree/_splitter.pyx":554
 *     cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
 *     if a < b:
 *         if b < c:             # <<<<<<<<<<<<<<
 *             return b
 *         elif a < c:
 */
    }

    /* "sklearn/tree/_splitter.pyx":556
 *         if b < c:
 *             return b
 *         elif a < c:             # <<<<<<<<<<<<<<
 *             return c
 *         else:
 */
    __pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":557
 *             return b
 *         elif a < c:
 *             return c             # <<<<<<<<<<<<<<
 *         else:
 *             return a
 */
      __pyx_r = __pyx_v_c;
      goto __pyx_L0;

      /* "sklearn/tree/_splitter.pyx":556
 *         if b < c:
 *             return b
 *         elif a < c:             # <<<<<<<<<<<<<<
 *             return c
 *         else:
 */
    }

    /* "sklearn/tree/_splitter.pyx":559
 *             return c
 *         else:
 *             return a             # <<<<<<<<<<<<<<
 *     elif b < c:
 *         if a < c:
 */
    /*else*/ {
      __pyx_r = __pyx_v_a;
      goto __pyx_L0;
    }

    /* "sklearn/tree/_splitter.pyx":553
 *     # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
 *     cdef DTYPE_t a = Xf[0], b = Xf[n / 2], c = Xf[n - 1]
 *     if a < b:             # <<<<<<<<<<<<<<
 *         if b < c:
 *             return b
 */
  }

  /* "sklearn/tree/_splitter.pyx":560
 *         else:
 *             return a
 *     elif b < c:             # <<<<<<<<<<<<<<
 *         if a < c:
 *             return a
 */
  __pyx_t_1 = ((__pyx_v_b < __pyx_v_c) != 0);
  if (__pyx_t_1) {

    /* "sklearn/tree/_splitter.pyx":561
 *             return a
 *     elif b < c:
 *         if a < c:             # <<<<<<<<<<<<<<
 *             return a
 *         else:
 */
    __pyx_t_1 = ((__pyx_v_a < __pyx_v_c) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":562
 *     elif b < c:
 *         if a < c:
 *             return a             # <<<<<<<<<<<<<<
 *         else:
 *             return c
 */
      __pyx_r = __pyx_v_a;
      goto __pyx_L0;

      /* "sklearn/tree/_splitter.pyx":561
 *             return a
 *     elif b < c:
 *         if a < c:             # <<<<<<<<<<<<<<
 *             return a
 *         else:
 */
    }

    /* "sklearn/tree/_splitter.pyx":564
 *             return a
 *         else:
 *             return c             # <<<<<<<<<<<<<<
 *     else:
 *         return b
 */
    /*else*/ {
      __pyx_r = __pyx_v_c;
      goto __pyx_L0;
    }

    /* "sklearn/tree/_splitter.pyx":560
 *         else:
 *             return a
 *     elif b < c:             # <<<<<<<<<<<<<<
 *         if a < c:
 *             return a
 */
  }

  /* "sklearn/tree/_splitter.pyx":566
 *             return c
 *     else:
 *         return b             # <<<<<<<<<<<<<<
 * 
 * 
 */
  /*else*/ {
    __pyx_r = __pyx_v_b;
    goto __pyx_L0;
  }

  /* "sklearn/tree/_splitter.pyx":549
 * 
 * 
 * cdef inline DTYPE_t median3(DTYPE_t* Xf, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     # Median of three pivot selection, after Bentley and McIlroy (1993).
 *     # Engineering a sort function. SP&E. Requires 8/3 comparisons on average.
 */

  /* function exit code */
  __pyx_L0:;
  return __pyx_r;
}

/* "sklearn/tree/_splitter.pyx":571
 * # Introsort with median of 3 pivot selection and 3-way partition function
 * # (robust to repeated elements, e.g. lots of zero features).
 * cdef void introsort(DTYPE_t* Xf, SIZE_t *samples,             # <<<<<<<<<<<<<<
 *                     SIZE_t n, int maxd) nogil:
 *     cdef DTYPE_t pivot
 */

static void __pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n, int __pyx_v_maxd) {
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t __pyx_v_pivot;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_i;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_l;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_r;
  int __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":576
 *     cdef SIZE_t i, l, r
 * 
 *     while n > 1:             # <<<<<<<<<<<<<<
 *         if maxd <= 0:   # max depth limit exceeded ("gone quadratic")
 *             heapsort(Xf, samples, n)
 */
  while (1) {
    __pyx_t_1 = ((__pyx_v_n > 1) != 0);
    if (!__pyx_t_1) break;

    /* "sklearn/tree/_splitter.pyx":577
 * 
 *     while n > 1:
 *         if maxd <= 0:   # max depth limit exceeded ("gone quadratic")             # <<<<<<<<<<<<<<
 *             heapsort(Xf, samples, n)
 *             return
 */
    __pyx_t_1 = ((__pyx_v_maxd <= 0) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":578
 *     while n > 1:
 *         if maxd <= 0:   # max depth limit exceeded ("gone quadratic")
 *             heapsort(Xf, samples, n)             # <<<<<<<<<<<<<<
 *             return
 *         maxd -= 1
 */
      __pyx_f_7sklearn_4tree_9_splitter_heapsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_n);

      /* "sklearn/tree/_splitter.pyx":579
 *         if maxd <= 0:   # max depth limit exceeded ("gone quadratic")
 *             heapsort(Xf, samples, n)
 *             return             # <<<<<<<<<<<<<<
 *         maxd -= 1
 * 
 */
      goto __pyx_L0;

      /* "sklearn/tree/_splitter.pyx":577
 * 
 *     while n > 1:
 *         if maxd <= 0:   # max depth limit exceeded ("gone quadratic")             # <<<<<<<<<<<<<<
 *             heapsort(Xf, samples, n)
 *             return
 */
    }

    /* "sklearn/tree/_splitter.pyx":580
 *             heapsort(Xf, samples, n)
 *             return
 *         maxd -= 1             # <<<<<<<<<<<<<<
 * 
 *         pivot = median3(Xf, n)
 */
    __pyx_v_maxd = (__pyx_v_maxd - 1);

    /* "sklearn/tree/_splitter.pyx":582
 *         maxd -= 1
 * 
 *         pivot = median3(Xf, n)             # <<<<<<<<<<<<<<
 * 
 *         # Three-way partition.
 */
    __pyx_v_pivot = __pyx_f_7sklearn_4tree_9_splitter_median3(__pyx_v_Xf, __pyx_v_n);

    /* "sklearn/tree/_splitter.pyx":585
 * 
 *         # Three-way partition.
 *         i = l = 0             # <<<<<<<<<<<<<<
 *         r = n
 *         while i < r:
 */
    __pyx_v_i = 0;
    __pyx_v_l = 0;

    /* "sklearn/tree/_splitter.pyx":586
 *         # Three-way partition.
 *         i = l = 0
 *         r = n             # <<<<<<<<<<<<<<
 *         while i < r:
 *             if Xf[i] < pivot:
 */
    __pyx_v_r = __pyx_v_n;

    /* "sklearn/tree/_splitter.pyx":587
 *         i = l = 0
 *         r = n
 *         while i < r:             # <<<<<<<<<<<<<<
 *             if Xf[i] < pivot:
 *                 swap(Xf, samples, i, l)
 */
    while (1) {
      __pyx_t_1 = ((__pyx_v_i < __pyx_v_r) != 0);
      if (!__pyx_t_1) break;

      /* "sklearn/tree/_splitter.pyx":588
 *         r = n
 *         while i < r:
 *             if Xf[i] < pivot:             # <<<<<<<<<<<<<<
 *                 swap(Xf, samples, i, l)
 *                 i += 1
 */
      __pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) < __pyx_v_pivot) != 0);
      if (__pyx_t_1) {

        /* "sklearn/tree/_splitter.pyx":589
 *         while i < r:
 *             if Xf[i] < pivot:
 *                 swap(Xf, samples, i, l)             # <<<<<<<<<<<<<<
 *                 i += 1
 *                 l += 1
 */
        __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_l);

        /* "sklearn/tree/_splitter.pyx":590
 *             if Xf[i] < pivot:
 *                 swap(Xf, samples, i, l)
 *                 i += 1             # <<<<<<<<<<<<<<
 *                 l += 1
 *             elif Xf[i] > pivot:
 */
        __pyx_v_i = (__pyx_v_i + 1);

        /* "sklearn/tree/_splitter.pyx":591
 *                 swap(Xf, samples, i, l)
 *                 i += 1
 *                 l += 1             # <<<<<<<<<<<<<<
 *             elif Xf[i] > pivot:
 *                 r -= 1
 */
        __pyx_v_l = (__pyx_v_l + 1);

        /* "sklearn/tree/_splitter.pyx":588
 *         r = n
 *         while i < r:
 *             if Xf[i] < pivot:             # <<<<<<<<<<<<<<
 *                 swap(Xf, samples, i, l)
 *                 i += 1
 */
        goto __pyx_L8;
      }

      /* "sklearn/tree/_splitter.pyx":592
 *                 i += 1
 *                 l += 1
 *             elif Xf[i] > pivot:             # <<<<<<<<<<<<<<
 *                 r -= 1
 *                 swap(Xf, samples, i, r)
 */
      __pyx_t_1 = (((__pyx_v_Xf[__pyx_v_i]) > __pyx_v_pivot) != 0);
      if (__pyx_t_1) {

        /* "sklearn/tree/_splitter.pyx":593
 *                 l += 1
 *             elif Xf[i] > pivot:
 *                 r -= 1             # <<<<<<<<<<<<<<
 *                 swap(Xf, samples, i, r)
 *             else:
 */
        __pyx_v_r = (__pyx_v_r - 1);

        /* "sklearn/tree/_splitter.pyx":594
 *             elif Xf[i] > pivot:
 *                 r -= 1
 *                 swap(Xf, samples, i, r)             # <<<<<<<<<<<<<<
 *             else:
 *                 i += 1
 */
        __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_v_Xf, __pyx_v_samples, __pyx_v_i, __pyx_v_r);

        /* "sklearn/tree/_splitter.pyx":592
 *                 i += 1
 *                 l += 1
 *             elif Xf[i] > pivot:             # <<<<<<<<<<<<<<
 *                 r -= 1
 *                 swap(Xf, samples, i, r)
 */
        goto __pyx_L8;
      }

      /* "sklearn/tree/_splitter.pyx":596
 *                 swap(Xf, samples, i, r)
 *             else:
 *                 i += 1             # <<<<<<<<<<<<<<
 * 
 *         introsort(Xf, samples, l, maxd)
 */
      /*else*/ {
        __pyx_v_i = (__pyx_v_i + 1);
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      __pyx_L8:;
    }

    /* "sklearn/tree/_splitter.pyx":598
 *                 i += 1
 * 
 *         introsort(Xf, samples, l, maxd)             # <<<<<<<<<<<<<<
 *         Xf += r
 *         samples += r
 */
    __pyx_f_7sklearn_4tree_9_splitter_introsort(__pyx_v_Xf, __pyx_v_samples, __pyx_v_l, __pyx_v_maxd);

    /* "sklearn/tree/_splitter.pyx":599
 * 
 *         introsort(Xf, samples, l, maxd)
 *         Xf += r             # <<<<<<<<<<<<<<
 *         samples += r
 *         n -= r
 */
    __pyx_v_Xf = (__pyx_v_Xf + __pyx_v_r);

    /* "sklearn/tree/_splitter.pyx":600
 *         introsort(Xf, samples, l, maxd)
 *         Xf += r
 *         samples += r             # <<<<<<<<<<<<<<
 *         n -= r
 * 
 */
    __pyx_v_samples = (__pyx_v_samples + __pyx_v_r);

    /* "sklearn/tree/_splitter.pyx":601
 *         Xf += r
 *         samples += r
 *         n -= r             # <<<<<<<<<<<<<<
 * 
 * 
 */
    __pyx_v_n = (__pyx_v_n - __pyx_v_r);
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  /* "sklearn/tree/_splitter.pyx":571
 * # Introsort with median of 3 pivot selection and 3-way partition function
 * # (robust to repeated elements, e.g. lots of zero features).
 * cdef void introsort(DTYPE_t* Xf, SIZE_t *samples,             # <<<<<<<<<<<<<<
 *                     SIZE_t n, int maxd) nogil:
 *     cdef DTYPE_t pivot
 */

  /* function exit code */
  __pyx_L0:;
}

/* "sklearn/tree/_splitter.pyx":604
 * 
 * 
 * cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples,             # <<<<<<<<<<<<<<
 *                            SIZE_t start, SIZE_t end) nogil:
 *     # Restore heap order in Xf[start:end] by moving the max element to start.
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_sift_down(__pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end) {
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_root;
  int __pyx_t_1;
  int __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":609
 *     cdef SIZE_t child, maxind, root
 * 
 *     root = start             # <<<<<<<<<<<<<<
 *     while True:
 *         child = root * 2 + 1
 */
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  /* "sklearn/tree/_splitter.pyx":610
 * 
 *     root = start
 *     while True:             # <<<<<<<<<<<<<<
 *         child = root * 2 + 1
 * 
 */
  while (1) {

    /* "sklearn/tree/_splitter.pyx":611
 *     root = start
 *     while True:
 *         child = root * 2 + 1             # <<<<<<<<<<<<<<
 * 
 *         # find max of root, left child, right child
 */
    __pyx_v_child = ((__pyx_v_root * 2) + 1);

    /* "sklearn/tree/_splitter.pyx":614
 * 
 *         # find max of root, left child, right child
 *         maxind = root             # <<<<<<<<<<<<<<
 *         if child < end and Xf[maxind] < Xf[child]:
 *             maxind = child
 */
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    /* "sklearn/tree/_splitter.pyx":615
 *         # find max of root, left child, right child
 *         maxind = root
 *         if child < end and Xf[maxind] < Xf[child]:             # <<<<<<<<<<<<<<
 *             maxind = child
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:
 */
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    } else {
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    __pyx_t_1 = __pyx_t_2;
    __pyx_L6_bool_binop_done:;
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      /* "sklearn/tree/_splitter.pyx":616
 *         maxind = root
 *         if child < end and Xf[maxind] < Xf[child]:
 *             maxind = child             # <<<<<<<<<<<<<<
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:
 *             maxind = child + 1
 */
      __pyx_v_maxind = __pyx_v_child;

      /* "sklearn/tree/_splitter.pyx":615
 *         # find max of root, left child, right child
 *         maxind = root
 *         if child < end and Xf[maxind] < Xf[child]:             # <<<<<<<<<<<<<<
 *             maxind = child
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:
 */
    }

    /* "sklearn/tree/_splitter.pyx":617
 *         if child < end and Xf[maxind] < Xf[child]:
 *             maxind = child
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:             # <<<<<<<<<<<<<<
 *             maxind = child + 1
 * 
 */
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      /* "sklearn/tree/_splitter.pyx":618
 *             maxind = child
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:
 *             maxind = child + 1             # <<<<<<<<<<<<<<
 * 
 *         if maxind == root:
 */
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      /* "sklearn/tree/_splitter.pyx":617
 *         if child < end and Xf[maxind] < Xf[child]:
 *             maxind = child
 *         if child + 1 < end and Xf[maxind] < Xf[child + 1]:             # <<<<<<<<<<<<<<
 *             maxind = child + 1
 * 
 */
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    /* "sklearn/tree/_splitter.pyx":620
 *             maxind = child + 1
 * 
 *         if maxind == root:             # <<<<<<<<<<<<<<
 *             break
 *         else:
 */
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      /* "sklearn/tree/_splitter.pyx":621
 * 
 *         if maxind == root:
 *             break             # <<<<<<<<<<<<<<
 *         else:
 *             swap(Xf, samples, root, maxind)
 */
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      /* "sklearn/tree/_splitter.pyx":620
 *             maxind = child + 1
 * 
 *         if maxind == root:             # <<<<<<<<<<<<<<
 *             break
 *         else:
 */
    }

    /* "sklearn/tree/_splitter.pyx":623
 *             break
 *         else:
 *             swap(Xf, samples, root, maxind)             # <<<<<<<<<<<<<<
 *             root = maxind
 * 
 */
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      /* "sklearn/tree/_splitter.pyx":624
 *         else:
 *             swap(Xf, samples, root, maxind)
 *             root = maxind             # <<<<<<<<<<<<<<
 * 
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":604
 * 
 * 
 * cdef inline void sift_down(DTYPE_t* Xf, SIZE_t* samples,             # <<<<<<<<<<<<<<
 *                            SIZE_t start, SIZE_t end) nogil:
 *     # Restore heap order in Xf[start:end] by moving the max element to start.
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":627
 * 
 * 
 * cdef void heapsort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t start, end
 * 
 */

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 * 
 *     # heapify
 *     start = (n - 2) / 2             # <<<<<<<<<<<<<<
 *     end = n
 *     while True:
 */
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  /* "sklearn/tree/_splitter.pyx":632
 *     # heapify
 *     start = (n - 2) / 2
 *     end = n             # <<<<<<<<<<<<<<
 *     while True:
 *         sift_down(Xf, samples, start, end)
 */
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  /* "sklearn/tree/_splitter.pyx":633
 *     start = (n - 2) / 2
 *     end = n
 *     while True:             # <<<<<<<<<<<<<<
 *         sift_down(Xf, samples, start, end)
 *         if start == 0:
 */
  while (1) {

    /* "sklearn/tree/_splitter.pyx":634
 *     end = n
 *     while True:
 *         sift_down(Xf, samples, start, end)             # <<<<<<<<<<<<<<
 *         if start == 0:
 *             break
 */
    __pyx_f_7sklearn_4tree_9_splitter_sift_down(__pyx_v_Xf, __pyx_v_samples, __pyx_v_start, __pyx_v_end);

    /* "sklearn/tree/_splitter.pyx":635
 *     while True:
 *         sift_down(Xf, samples, start, end)
 *         if start == 0:             # <<<<<<<<<<<<<<
 *             break
 *         start -= 1
 */
    __pyx_t_1 = ((__pyx_v_start == 0) != 0);
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      /* "sklearn/tree/_splitter.pyx":636
 *         sift_down(Xf, samples, start, end)
 *         if start == 0:
 *             break             # <<<<<<<<<<<<<<
 *         start -= 1
 * 
 */
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      /* "sklearn/tree/_splitter.pyx":635
 *     while True:
 *         sift_down(Xf, samples, start, end)
 *         if start == 0:             # <<<<<<<<<<<<<<
 *             break
 *         start -= 1
 */
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    /* "sklearn/tree/_splitter.pyx":637
 *         if start == 0:
 *             break
 *         start -= 1             # <<<<<<<<<<<<<<
 * 
 *     # sort by shrinking the heap, putting the max element immediately after it
 */
    __pyx_v_start = (__pyx_v_start - 1);
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  __pyx_L4_break:;

  /* "sklearn/tree/_splitter.pyx":640
 * 
 *     # sort by shrinking the heap, putting the max element immediately after it
 *     end = n - 1             # <<<<<<<<<<<<<<
 *     while end > 0:
 *         swap(Xf, samples, 0, end)
 */
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 *     # sort by shrinking the heap, putting the max element immediately after it
 *     end = n - 1
 *     while end > 0:             # <<<<<<<<<<<<<<
 *         swap(Xf, samples, 0, end)
 *         sift_down(Xf, samples, 0, end)
 */
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    /* "sklearn/tree/_splitter.pyx":642
 *     end = n - 1
 *     while end > 0:
 *         swap(Xf, samples, 0, end)             # <<<<<<<<<<<<<<
 *         sift_down(Xf, samples, 0, end)
 *         end = end - 1
 */
    __pyx_f_7sklearn_4tree_9_splitter_swap(__pyx_v_Xf, __pyx_v_samples, 0, __pyx_v_end);

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 *     while end > 0:
 *         swap(Xf, samples, 0, end)
 *         sift_down(Xf, samples, 0, end)             # <<<<<<<<<<<<<<
 *         end = end - 1
 * 
 */
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 *         swap(Xf, samples, 0, end)
 *         sift_down(Xf, samples, 0, end)
 *         end = end - 1             # <<<<<<<<<<<<<<
 * 
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":627
 * 
 * 
 * cdef void heapsort(DTYPE_t* Xf, SIZE_t* samples, SIZE_t n) nogil:             # <<<<<<<<<<<<<<
 *     cdef SIZE_t start, end
 * 
 */

  /* function exit code */
}

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 *     """Splitter for finding the best random split."""
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 *     def __reduce__(self):
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 *                                  self.max_features,
 *                                  self.min_samples_leaf,
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 *                                  self.min_samples_leaf,
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 *                                  self.max_features,
 *                                  self.min_samples_leaf,             # <<<<<<<<<<<<<<
 *                                  self.min_weight_leaf,
 *                                  self.random_state,
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 *                                  self.random_state,
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 *     def __reduce__(self):
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 *                                  self.max_features,
 *                                  self.min_samples_leaf,
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 *     cdef int node_split(self, double impurity, SplitRecord* split,
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 *     def __reduce__(self):
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 *                                  self.min_samples_leaf,
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 *     def __reduce__(self):             # <<<<<<<<<<<<<<
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 *                                  self.max_features,
 */

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/* "sklearn/tree/_splitter.pyx":657
 *                                  self.presort), self.__getstate__())
 * 
 *     cdef int node_split(self, double impurity, SplitRecord* split,             # <<<<<<<<<<<<<<
 *                         SIZE_t* n_constant_features) nogil except -1:
 *         """Find the best random split on node samples[start:end]
 */

static int __pyx_f_7sklearn_4tree_9_splitter_14RandomSplitter_node_split(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *__pyx_v_self, double __pyx_v_impurity, struct __pyx_t_7sklearn_4tree_9_splitter_SplitRecord *__pyx_v_split, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_n_constant_features) {
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end;
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_features;
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  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_sample_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_X_feature_stride;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_max_features;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_min_samples_leaf;
  double __pyx_v_min_weight_leaf;
  __pyx_t_7sklearn_4tree_9_splitter_UINT32_t *__pyx_v_random_state;
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_i;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_f_j;
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_partition_end;
  int __pyx_r;
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_2;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_3;
  double __pyx_t_4;
  int __pyx_t_5;
  int __pyx_t_6;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_7;
  int __pyx_t_8;

  /* "sklearn/tree/_splitter.pyx":665
 *         """
 *         # Draw random splits and pick the best
 *         cdef SIZE_t* samples = self.samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
  __pyx_v_samples = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":666
 *         # Draw random splits and pick the best
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start             # <<<<<<<<<<<<<<
 *         cdef SIZE_t end = self.end
 * 
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
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  /* "sklearn/tree/_splitter.pyx":667
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t* features = self.features
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
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  /* "sklearn/tree/_splitter.pyx":669
 *         cdef SIZE_t end = self.end
 * 
 *         cdef SIZE_t* features = self.features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features
 */
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  /* "sklearn/tree/_splitter.pyx":670
 * 
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_features = self.n_features
 * 
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
  __pyx_v_constant_features = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":671
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features             # <<<<<<<<<<<<<<
 * 
 *         cdef DTYPE_t* X = self.X
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
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  /* "sklearn/tree/_splitter.pyx":673
 *         cdef SIZE_t n_features = self.n_features
 * 
 *         cdef DTYPE_t* X = self.X             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 */
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  /* "sklearn/tree/_splitter.pyx":674
 * 
 *         cdef DTYPE_t* X = self.X
 *         cdef DTYPE_t* Xf = self.feature_values             # <<<<<<<<<<<<<<
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 */
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  /* "sklearn/tree/_splitter.pyx":675
 *         cdef DTYPE_t* X = self.X
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride             # <<<<<<<<<<<<<<
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features
 */
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  /* "sklearn/tree/_splitter.pyx":676
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride             # <<<<<<<<<<<<<<
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.X_feature_stride;
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  /* "sklearn/tree/_splitter.pyx":677
 *         cdef SIZE_t X_sample_stride = self.X_sample_stride
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 */
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  __pyx_v_max_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":678
 *         cdef SIZE_t X_feature_stride = self.X_feature_stride
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf             # <<<<<<<<<<<<<<
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state
 */
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  /* "sklearn/tree/_splitter.pyx":679
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf             # <<<<<<<<<<<<<<
 *         cdef UINT32_t* random_state = &self.rand_r_state
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":680
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state             # <<<<<<<<<<<<<<
 * 
 *         cdef SplitRecord best, current
 */
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  /* "sklearn/tree/_splitter.pyx":683
 * 
 *         cdef SplitRecord best, current
 *         cdef double current_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 *         cdef double best_proxy_improvement = - INFINITY
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":684
 *         cdef SplitRecord best, current
 *         cdef double current_proxy_improvement = - INFINITY
 *         cdef double best_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t f_i = n_features
 */
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  /* "sklearn/tree/_splitter.pyx":686
 *         cdef double best_proxy_improvement = - INFINITY
 * 
 *         cdef SIZE_t f_i = n_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t f_j
 *         cdef SIZE_t p
 */
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  /* "sklearn/tree/_splitter.pyx":692
 *         cdef SIZE_t feature_stride
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0             # <<<<<<<<<<<<<<
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 */
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  /* "sklearn/tree/_splitter.pyx":694
 *         cdef SIZE_t n_found_constants = 0
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 */
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  /* "sklearn/tree/_splitter.pyx":695
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 *         cdef SIZE_t n_known_constants = n_constant_features[0]             # <<<<<<<<<<<<<<
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants
 */
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  /* "sklearn/tree/_splitter.pyx":697
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_visited_features = 0
 *         cdef DTYPE_t min_feature_value
 */
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  /* "sklearn/tree/_splitter.pyx":698
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants
 *         cdef SIZE_t n_visited_features = 0             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t min_feature_value
 *         cdef DTYPE_t max_feature_value
 */
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  /* "sklearn/tree/_splitter.pyx":704
 *         cdef SIZE_t partition_end
 * 
 *         _init_split(&best, end)             # <<<<<<<<<<<<<<
 * 
 *         # Sample up to max_features without replacement using a
 */
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  /* "sklearn/tree/_splitter.pyx":715
 *         # newly discovered constant features to spare computation on descendant
 *         # nodes.
 *         while (f_i > n_total_constants and  # Stop early if remaining features             # <<<<<<<<<<<<<<
 *                                             # are constant
 *                 (n_visited_features < max_features or
 */
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    } else {
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      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":717
 *         while (f_i > n_total_constants and  # Stop early if remaining features
 *                                             # are constant
 *                 (n_visited_features < max_features or             # <<<<<<<<<<<<<<
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 */
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    } else {
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      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":719
 *                 (n_visited_features < max_features or
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):             # <<<<<<<<<<<<<<
 *             n_visited_features += 1
 * 
 */
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    __pyx_L5_bool_binop_done:;
    if (!__pyx_t_5) break;

    /* "sklearn/tree/_splitter.pyx":720
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 *             n_visited_features += 1             # <<<<<<<<<<<<<<
 * 
 *             # Loop invariant: elements of features in
 */
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    /* "sklearn/tree/_splitter.pyx":734
 * 
 *             # Draw a feature at random
 *             f_j = rand_int(n_drawn_constants, f_i - n_found_constants,             # <<<<<<<<<<<<<<
 *                            random_state)
 * 
 */
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    /* "sklearn/tree/_splitter.pyx":737
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
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 *             if f_j < n_known_constants:
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]             # <<<<<<<<<<<<<<
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp
 */
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      /* "sklearn/tree/_splitter.pyx":740
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]             # <<<<<<<<<<<<<<
 *                 features[n_drawn_constants] = tmp
 * 
 */
      (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);

      /* "sklearn/tree/_splitter.pyx":741
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp             # <<<<<<<<<<<<<<
 * 
 *                 n_drawn_constants += 1
 */
      (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;

      /* "sklearn/tree/_splitter.pyx":743
 *                 features[n_drawn_constants] = tmp
 * 
 *                 n_drawn_constants += 1             # <<<<<<<<<<<<<<
 * 
 *             else:
 */
      __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);

      /* "sklearn/tree/_splitter.pyx":737
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
      goto __pyx_L8;
    }

    /* "sklearn/tree/_splitter.pyx":747
 *             else:
 *                 # f_j in the interval [n_known_constants, f_i - n_found_constants[
 *                 f_j += n_found_constants             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 */
    /*else*/ {
      __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);

      /* "sklearn/tree/_splitter.pyx":750
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 *                 current.feature = features[f_j]             # <<<<<<<<<<<<<<
 *                 feature_stride = X_feature_stride * current.feature
 * 
 */
      __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":751
 * 
 *                 current.feature = features[f_j]
 *                 feature_stride = X_feature_stride * current.feature             # <<<<<<<<<<<<<<
 * 
 *                 # Find min, max
 */
      __pyx_v_feature_stride = (__pyx_v_X_feature_stride * __pyx_v_current.feature);

      /* "sklearn/tree/_splitter.pyx":754
 * 
 *                 # Find min, max
 *                 min_feature_value = X[X_sample_stride * samples[start] + feature_stride]             # <<<<<<<<<<<<<<
 *                 max_feature_value = min_feature_value
 *                 Xf[start] = min_feature_value
 */
      __pyx_v_min_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_start])) + __pyx_v_feature_stride)]);

      /* "sklearn/tree/_splitter.pyx":755
 *                 # Find min, max
 *                 min_feature_value = X[X_sample_stride * samples[start] + feature_stride]
 *                 max_feature_value = min_feature_value             # <<<<<<<<<<<<<<
 *                 Xf[start] = min_feature_value
 * 
 */
      __pyx_v_max_feature_value = __pyx_v_min_feature_value;

      /* "sklearn/tree/_splitter.pyx":756
 *                 min_feature_value = X[X_sample_stride * samples[start] + feature_stride]
 *                 max_feature_value = min_feature_value
 *                 Xf[start] = min_feature_value             # <<<<<<<<<<<<<<
 * 
 *                 for p in range(start + 1, end):
 */
      (__pyx_v_Xf[__pyx_v_start]) = __pyx_v_min_feature_value;

      /* "sklearn/tree/_splitter.pyx":758
 *                 Xf[start] = min_feature_value
 * 
 *                 for p in range(start + 1, end):             # <<<<<<<<<<<<<<
 *                     current_feature_value = X[X_sample_stride * samples[p] + feature_stride]
 *                     Xf[p] = current_feature_value
 */
      __pyx_t_2 = __pyx_v_end;
      for (__pyx_t_7 = (__pyx_v_start + 1); __pyx_t_7 < __pyx_t_2; __pyx_t_7+=1) {
        __pyx_v_p = __pyx_t_7;

        /* "sklearn/tree/_splitter.pyx":759
 * 
 *                 for p in range(start + 1, end):
 *                     current_feature_value = X[X_sample_stride * samples[p] + feature_stride]             # <<<<<<<<<<<<<<
 *                     Xf[p] = current_feature_value
 * 
 */
        __pyx_v_current_feature_value = (__pyx_v_X[((__pyx_v_X_sample_stride * (__pyx_v_samples[__pyx_v_p])) + __pyx_v_feature_stride)]);

        /* "sklearn/tree/_splitter.pyx":760
 *                 for p in range(start + 1, end):
 *                     current_feature_value = X[X_sample_stride * samples[p] + feature_stride]
 *                     Xf[p] = current_feature_value             # <<<<<<<<<<<<<<
 * 
 *                     if current_feature_value < min_feature_value:
 */
        (__pyx_v_Xf[__pyx_v_p]) = __pyx_v_current_feature_value;

        /* "sklearn/tree/_splitter.pyx":762
 *                     Xf[p] = current_feature_value
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
        __pyx_t_5 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":763
 * 
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value
 */
          __pyx_v_min_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":762
 *                     Xf[p] = current_feature_value
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
          goto __pyx_L11;
        }

        /* "sklearn/tree/_splitter.pyx":764
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        __pyx_t_5 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":765
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 */
          __pyx_v_max_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":764
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        }
        __pyx_L11:;
      }

      /* "sklearn/tree/_splitter.pyx":767
 *                         max_feature_value = current_feature_value
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
      __pyx_t_5 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
      if (__pyx_t_5) {

        /* "sklearn/tree/_splitter.pyx":768
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]             # <<<<<<<<<<<<<<
 *                     features[n_total_constants] = current.feature
 * 
 */
        (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);

        /* "sklearn/tree/_splitter.pyx":769
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature             # <<<<<<<<<<<<<<
 * 
 *                     n_found_constants += 1
 */
        __pyx_t_2 = __pyx_v_current.feature;
        (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;

        /* "sklearn/tree/_splitter.pyx":771
 *                     features[n_total_constants] = current.feature
 * 
 *                     n_found_constants += 1             # <<<<<<<<<<<<<<
 *                     n_total_constants += 1
 * 
 */
        __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);

        /* "sklearn/tree/_splitter.pyx":772
 * 
 *                     n_found_constants += 1
 *                     n_total_constants += 1             # <<<<<<<<<<<<<<
 * 
 *                 else:
 */
        __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);

        /* "sklearn/tree/_splitter.pyx":767
 *                         max_feature_value = current_feature_value
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
        goto __pyx_L12;
      }

      /* "sklearn/tree/_splitter.pyx":775
 * 
 *                 else:
 *                     f_i -= 1             # <<<<<<<<<<<<<<
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]
 * 
 */
      /*else*/ {
        __pyx_v_f_i = (__pyx_v_f_i - 1);

        /* "sklearn/tree/_splitter.pyx":776
 *                 else:
 *                     f_i -= 1
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]             # <<<<<<<<<<<<<<
 * 
 *                     # Draw a random threshold
 */
        __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
        __pyx_t_7 = (__pyx_v_features[__pyx_v_f_i]);
        (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
        (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_7;

        /* "sklearn/tree/_splitter.pyx":779
 * 
 *                     # Draw a random threshold
 *                     current.threshold = rand_uniform(min_feature_value,             # <<<<<<<<<<<<<<
 *                                                      max_feature_value,
 *                                                      random_state)
 */
        __pyx_v_current.threshold = __pyx_f_7sklearn_4tree_6_utils_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);

        /* "sklearn/tree/_splitter.pyx":783
 *                                                      random_state)
 * 
 *                     if current.threshold == max_feature_value:             # <<<<<<<<<<<<<<
 *                         current.threshold = min_feature_value
 * 
 */
        __pyx_t_5 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0);
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":784
 * 
 *                     if current.threshold == max_feature_value:
 *                         current.threshold = min_feature_value             # <<<<<<<<<<<<<<
 * 
 *                     # Partition
 */
          __pyx_v_current.threshold = __pyx_v_min_feature_value;

          /* "sklearn/tree/_splitter.pyx":783
 *                                                      random_state)
 * 
 *                     if current.threshold == max_feature_value:             # <<<<<<<<<<<<<<
 *                         current.threshold = min_feature_value
 * 
 */
        }

        /* "sklearn/tree/_splitter.pyx":787
 * 
 *                     # Partition
 *                     partition_end = end             # <<<<<<<<<<<<<<
 *                     p = start
 *                     while p < partition_end:
 */
        __pyx_v_partition_end = __pyx_v_end;

        /* "sklearn/tree/_splitter.pyx":788
 *                     # Partition
 *                     partition_end = end
 *                     p = start             # <<<<<<<<<<<<<<
 *                     while p < partition_end:
 *                         current_feature_value = Xf[p]
 */
        __pyx_v_p = __pyx_v_start;

        /* "sklearn/tree/_splitter.pyx":789
 *                     partition_end = end
 *                     p = start
 *                     while p < partition_end:             # <<<<<<<<<<<<<<
 *                         current_feature_value = Xf[p]
 *                         if current_feature_value <= current.threshold:
 */
        while (1) {
          __pyx_t_5 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
          if (!__pyx_t_5) break;

          /* "sklearn/tree/_splitter.pyx":790
 *                     p = start
 *                     while p < partition_end:
 *                         current_feature_value = Xf[p]             # <<<<<<<<<<<<<<
 *                         if current_feature_value <= current.threshold:
 *                             p += 1
 */
          __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);

          /* "sklearn/tree/_splitter.pyx":791
 *                     while p < partition_end:
 *                         current_feature_value = Xf[p]
 *                         if current_feature_value <= current.threshold:             # <<<<<<<<<<<<<<
 *                             p += 1
 *                         else:
 */
          __pyx_t_5 = ((__pyx_v_current_feature_value <= __pyx_v_current.threshold) != 0);
          if (__pyx_t_5) {

            /* "sklearn/tree/_splitter.pyx":792
 *                         current_feature_value = Xf[p]
 *                         if current_feature_value <= current.threshold:
 *                             p += 1             # <<<<<<<<<<<<<<
 *                         else:
 *                             partition_end -= 1
 */
            __pyx_v_p = (__pyx_v_p + 1);

            /* "sklearn/tree/_splitter.pyx":791
 *                     while p < partition_end:
 *                         current_feature_value = Xf[p]
 *                         if current_feature_value <= current.threshold:             # <<<<<<<<<<<<<<
 *                             p += 1
 *                         else:
 */
            goto __pyx_L16;
          }

          /* "sklearn/tree/_splitter.pyx":794
 *                             p += 1
 *                         else:
 *                             partition_end -= 1             # <<<<<<<<<<<<<<
 * 
 *                             Xf[p] = Xf[partition_end]
 */
          /*else*/ {
            __pyx_v_partition_end = (__pyx_v_partition_end - 1);

            /* "sklearn/tree/_splitter.pyx":796
 *                             partition_end -= 1
 * 
 *                             Xf[p] = Xf[partition_end]             # <<<<<<<<<<<<<<
 *                             Xf[partition_end] = current_feature_value
 * 
 */
            (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]);

            /* "sklearn/tree/_splitter.pyx":797
 * 
 *                             Xf[p] = Xf[partition_end]
 *                             Xf[partition_end] = current_feature_value             # <<<<<<<<<<<<<<
 * 
 *                             tmp = samples[partition_end]
 */
            (__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_current_feature_value;

            /* "sklearn/tree/_splitter.pyx":799
 *                             Xf[partition_end] = current_feature_value
 * 
 *                             tmp = samples[partition_end]             # <<<<<<<<<<<<<<
 *                             samples[partition_end] = samples[p]
 *                             samples[p] = tmp
 */
            __pyx_v_tmp = (__pyx_v_samples[__pyx_v_partition_end]);

            /* "sklearn/tree/_splitter.pyx":800
 * 
 *                             tmp = samples[partition_end]
 *                             samples[partition_end] = samples[p]             # <<<<<<<<<<<<<<
 *                             samples[p] = tmp
 * 
 */
            (__pyx_v_samples[__pyx_v_partition_end]) = (__pyx_v_samples[__pyx_v_p]);

            /* "sklearn/tree/_splitter.pyx":801
 *                             tmp = samples[partition_end]
 *                             samples[partition_end] = samples[p]
 *                             samples[p] = tmp             # <<<<<<<<<<<<<<
 * 
 *                     current.pos = partition_end
 */
            (__pyx_v_samples[__pyx_v_p]) = __pyx_v_tmp;
          }
          __pyx_L16:;
        }

        /* "sklearn/tree/_splitter.pyx":803
 *                             samples[p] = tmp
 * 
 *                     current.pos = partition_end             # <<<<<<<<<<<<<<
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 */
        __pyx_v_current.pos = __pyx_v_partition_end;

        /* "sklearn/tree/_splitter.pyx":806
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        __pyx_t_6 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
        if (!__pyx_t_6) {
        } else {
          __pyx_t_5 = __pyx_t_6;
          goto __pyx_L18_bool_binop_done;
        }

        /* "sklearn/tree/_splitter.pyx":807
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or
 *                             ((end - current.pos) < min_samples_leaf)):             # <<<<<<<<<<<<<<
 *                         continue
 * 
 */
        __pyx_t_6 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
        __pyx_t_5 = __pyx_t_6;
        __pyx_L18_bool_binop_done:;

        /* "sklearn/tree/_splitter.pyx":806
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":808
 *                     if (((current.pos - start) < min_samples_leaf) or
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue             # <<<<<<<<<<<<<<
 * 
 *                     # Evaluate split
 */
          goto __pyx_L3_continue;

          /* "sklearn/tree/_splitter.pyx":806
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        }

        /* "sklearn/tree/_splitter.pyx":811
 * 
 *                     # Evaluate split
 *                     self.criterion.reset()             # <<<<<<<<<<<<<<
 *                     self.criterion.update(current.pos)
 * 
 */
        __pyx_t_8 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_8 == -1)) __PYX_ERR(0, 811, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":812
 *                     # Evaluate split
 *                     self.criterion.reset()
 *                     self.criterion.update(current.pos)             # <<<<<<<<<<<<<<
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 */
        __pyx_t_8 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); if (unlikely(__pyx_t_8 == -1)) __PYX_ERR(0, 812, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":815
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
        if (!__pyx_t_6) {
        } else {
          __pyx_t_5 = __pyx_t_6;
          goto __pyx_L21_bool_binop_done;
        }

        /* "sklearn/tree/_splitter.pyx":816
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):             # <<<<<<<<<<<<<<
 *                         continue
 * 
 */
        __pyx_t_6 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
        __pyx_t_5 = __pyx_t_6;
        __pyx_L21_bool_binop_done:;

        /* "sklearn/tree/_splitter.pyx":815
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":817
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue             # <<<<<<<<<<<<<<
 * 
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 */
          goto __pyx_L3_continue;

          /* "sklearn/tree/_splitter.pyx":815
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        }

        /* "sklearn/tree/_splitter.pyx":819
 *                         continue
 * 
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()             # <<<<<<<<<<<<<<
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:
 */
        __pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);

        /* "sklearn/tree/_splitter.pyx":821
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                         best_proxy_improvement = current_proxy_improvement
 *                         best = current  # copy
 */
        __pyx_t_5 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
        if (__pyx_t_5) {

          /* "sklearn/tree/_splitter.pyx":822
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:
 *                         best_proxy_improvement = current_proxy_improvement             # <<<<<<<<<<<<<<
 *                         best = current  # copy
 * 
 */
          __pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;

          /* "sklearn/tree/_splitter.pyx":823
 *                     if current_proxy_improvement > best_proxy_improvement:
 *                         best_proxy_improvement = current_proxy_improvement
 *                         best = current  # copy             # <<<<<<<<<<<<<<
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 */
          __pyx_v_best = __pyx_v_current;

          /* "sklearn/tree/_splitter.pyx":821
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                         best_proxy_improvement = current_proxy_improvement
 *                         best = current  # copy
 */
        }
      }
      __pyx_L12:;
    }
    __pyx_L8:;
    __pyx_L3_continue:;
  }

  /* "sklearn/tree/_splitter.pyx":826
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":948
 *         cdef SIZE_t p
 * 
 *         cdef DTYPE_t* Xf = self.feature_values             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.feature_values;
  __pyx_v_Xf = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":949
 * 
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t* samples = self.samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 * 
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.samples;
  __pyx_v_samples = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":950
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t* index_to_samples = self.index_to_samples             # <<<<<<<<<<<<<<
 * 
 *         if threshold < 0.:
 */
  __pyx_t_2 = __pyx_v_self->index_to_samples;
  __pyx_v_index_to_samples = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":952
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 * 
 *         if threshold < 0.:             # <<<<<<<<<<<<<<
 *             p = self.start
 *             partition_end = end_negative
 */
  __pyx_t_3 = ((__pyx_v_threshold < 0.) != 0);
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    /* "sklearn/tree/_splitter.pyx":953
 * 
 *         if threshold < 0.:
 *             p = self.start             # <<<<<<<<<<<<<<
 *             partition_end = end_negative
 *         elif threshold > 0.:
 */
    __pyx_t_4 = __pyx_v_self->__pyx_base.start;
    __pyx_v_p = __pyx_t_4;

    /* "sklearn/tree/_splitter.pyx":954
 *         if threshold < 0.:
 *             p = self.start
 *             partition_end = end_negative             # <<<<<<<<<<<<<<
 *         elif threshold > 0.:
 *             p = start_positive
 */
    __pyx_v_partition_end = __pyx_v_end_negative;

    /* "sklearn/tree/_splitter.pyx":952
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 * 
 *         if threshold < 0.:             # <<<<<<<<<<<<<<
 *             p = self.start
 *             partition_end = end_negative
 */
    goto __pyx_L3;
  }

  /* "sklearn/tree/_splitter.pyx":955
 *             p = self.start
 *             partition_end = end_negative
 *         elif threshold > 0.:             # <<<<<<<<<<<<<<
 *             p = start_positive
 *             partition_end = self.end
 */
  __pyx_t_3 = ((__pyx_v_threshold > 0.) != 0);
  if (__pyx_t_3) {

    /* "sklearn/tree/_splitter.pyx":956
 *             partition_end = end_negative
 *         elif threshold > 0.:
 *             p = start_positive             # <<<<<<<<<<<<<<
 *             partition_end = self.end
 *         else:
 */
    __pyx_v_p = __pyx_v_start_positive;

    /* "sklearn/tree/_splitter.pyx":957
 *         elif threshold > 0.:
 *             p = start_positive
 *             partition_end = self.end             # <<<<<<<<<<<<<<
 *         else:
 *             # Data are already split
 */
    __pyx_t_4 = __pyx_v_self->__pyx_base.end;
    __pyx_v_partition_end = __pyx_t_4;

    /* "sklearn/tree/_splitter.pyx":955
 *             p = self.start
 *             partition_end = end_negative
 *         elif threshold > 0.:             # <<<<<<<<<<<<<<
 *             p = start_positive
 *             partition_end = self.end
 */
    goto __pyx_L3;
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  /* "sklearn/tree/_splitter.pyx":960
 *         else:
 *             # Data are already split
 *             return zero_pos             # <<<<<<<<<<<<<<
 * 
 *         while p < partition_end:
 */
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    __pyx_r = __pyx_v_zero_pos;
    goto __pyx_L0;
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  __pyx_L3:;

  /* "sklearn/tree/_splitter.pyx":962
 *             return zero_pos
 * 
 *         while p < partition_end:             # <<<<<<<<<<<<<<
 *             value = Xf[p]
 * 
 */
  while (1) {
    __pyx_t_3 = ((__pyx_v_p < __pyx_v_partition_end) != 0);
    if (!__pyx_t_3) break;

    /* "sklearn/tree/_splitter.pyx":963
 * 
 *         while p < partition_end:
 *             value = Xf[p]             # <<<<<<<<<<<<<<
 * 
 *             if value <= threshold:
 */
    __pyx_v_value = (__pyx_v_Xf[__pyx_v_p]);

    /* "sklearn/tree/_splitter.pyx":965
 *             value = Xf[p]
 * 
 *             if value <= threshold:             # <<<<<<<<<<<<<<
 *                 p += 1
 * 
 */
    __pyx_t_3 = ((__pyx_v_value <= __pyx_v_threshold) != 0);
    if (__pyx_t_3) {

      /* "sklearn/tree/_splitter.pyx":966
 * 
 *             if value <= threshold:
 *                 p += 1             # <<<<<<<<<<<<<<
 * 
 *             else:
 */
      __pyx_v_p = (__pyx_v_p + 1);

      /* "sklearn/tree/_splitter.pyx":965
 *             value = Xf[p]
 * 
 *             if value <= threshold:             # <<<<<<<<<<<<<<
 *                 p += 1
 * 
 */
      goto __pyx_L6;
    }

    /* "sklearn/tree/_splitter.pyx":969
 * 
 *             else:
 *                 partition_end -= 1             # <<<<<<<<<<<<<<
 * 
 *                 Xf[p] = Xf[partition_end]
 */
    /*else*/ {
      __pyx_v_partition_end = (__pyx_v_partition_end - 1);

      /* "sklearn/tree/_splitter.pyx":971
 *                 partition_end -= 1
 * 
 *                 Xf[p] = Xf[partition_end]             # <<<<<<<<<<<<<<
 *                 Xf[partition_end] = value
 *                 sparse_swap(index_to_samples, samples, p, partition_end)
 */
      (__pyx_v_Xf[__pyx_v_p]) = (__pyx_v_Xf[__pyx_v_partition_end]);

      /* "sklearn/tree/_splitter.pyx":972
 * 
 *                 Xf[p] = Xf[partition_end]
 *                 Xf[partition_end] = value             # <<<<<<<<<<<<<<
 *                 sparse_swap(index_to_samples, samples, p, partition_end)
 * 
 */
      (__pyx_v_Xf[__pyx_v_partition_end]) = __pyx_v_value;

      /* "sklearn/tree/_splitter.pyx":973
 *                 Xf[p] = Xf[partition_end]
 *                 Xf[partition_end] = value
 *                 sparse_swap(index_to_samples, samples, p, partition_end)             # <<<<<<<<<<<<<<
 * 
 *         return partition_end
 */
      __pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_p, __pyx_v_partition_end);
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    __pyx_L6:;
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  /* "sklearn/tree/_splitter.pyx":975
 *                 sparse_swap(index_to_samples, samples, p, partition_end)
 * 
 *         return partition_end             # <<<<<<<<<<<<<<
 * 
 *     cdef inline void extract_nnz(self, SIZE_t feature,
 */
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  /* "sklearn/tree/_splitter.pyx":939
 *         return 0
 * 
 *     cdef inline SIZE_t _partition(self, double threshold,             # <<<<<<<<<<<<<<
 *                                   SIZE_t end_negative, SIZE_t start_positive,
 *                                   SIZE_t zero_pos) nogil:
 */

  /* function exit code */
  __pyx_L0:;
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/* "sklearn/tree/_splitter.pyx":977
 *         return partition_end
 * 
 *     cdef inline void extract_nnz(self, SIZE_t feature,             # <<<<<<<<<<<<<<
 *                                  SIZE_t* end_negative, SIZE_t* start_positive,
 *                                  bint* is_samples_sorted) nogil:
 */

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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_indptr_end;
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  int __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1009
 * 
 *         """
 *         cdef SIZE_t indptr_start = self.X_indptr[feature],             # <<<<<<<<<<<<<<
 *         cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
 *         cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
 */
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  /* "sklearn/tree/_splitter.pyx":1010
 *         """
 *         cdef SIZE_t indptr_start = self.X_indptr[feature],
 *         cdef SIZE_t indptr_end = self.X_indptr[feature + 1]             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
 *         cdef SIZE_t n_samples = self.end - self.start
 */
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  /* "sklearn/tree/_splitter.pyx":1011
 *         cdef SIZE_t indptr_start = self.X_indptr[feature],
 *         cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
 *         cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_samples = self.end - self.start
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":1012
 *         cdef SIZE_t indptr_end = self.X_indptr[feature + 1]
 *         cdef SIZE_t n_indices = <SIZE_t>(indptr_end - indptr_start)
 *         cdef SIZE_t n_samples = self.end - self.start             # <<<<<<<<<<<<<<
 * 
 *         # Use binary search if n_samples * log(n_indices) <
 */
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  /* "sklearn/tree/_splitter.pyx":1020
 *         # approach.
 *         if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
 *                 n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):             # <<<<<<<<<<<<<<
 *             extract_nnz_binary_search(self.X_indices, self.X_data,
 *                                       indptr_start, indptr_end,
 */
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  /* "sklearn/tree/_splitter.pyx":1019
 *         # search and O(n_indices) is the running time of index_to_samples
 *         # approach.
 *         if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +             # <<<<<<<<<<<<<<
 *                 n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
 *             extract_nnz_binary_search(self.X_indices, self.X_data,
 */
  if (__pyx_t_1) {

    /* "sklearn/tree/_splitter.pyx":1021
 *         if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +
 *                 n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
 *             extract_nnz_binary_search(self.X_indices, self.X_data,             # <<<<<<<<<<<<<<
 *                                       indptr_start, indptr_end,
 *                                       self.samples, self.start, self.end,
 */
    __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_binary_search(__pyx_v_self->X_indices, __pyx_v_self->X_data, __pyx_v_indptr_start, __pyx_v_indptr_end, __pyx_v_self->__pyx_base.samples, __pyx_v_self->__pyx_base.start, __pyx_v_self->__pyx_base.end, __pyx_v_self->index_to_samples, __pyx_v_self->__pyx_base.feature_values, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_self->sorted_samples, __pyx_v_is_samples_sorted);

    /* "sklearn/tree/_splitter.pyx":1019
 *         # search and O(n_indices) is the running time of index_to_samples
 *         # approach.
 *         if ((1 - is_samples_sorted[0]) * n_samples * log(n_samples) +             # <<<<<<<<<<<<<<
 *                 n_samples * log(n_indices) < EXTRACT_NNZ_SWITCH * n_indices):
 *             extract_nnz_binary_search(self.X_indices, self.X_data,
 */
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  /* "sklearn/tree/_splitter.pyx":1032
 *         # index_to_samples is a mapping from X_indices to samples
 *         else:
 *             extract_nnz_index_to_samples(self.X_indices, self.X_data,             # <<<<<<<<<<<<<<
 *                                          indptr_start, indptr_end,
 *                                          self.samples, self.start, self.end,
 */
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    /* "sklearn/tree/_splitter.pyx":1037
 *                                          self.index_to_samples,
 *                                          self.feature_values,
 *                                          end_negative, start_positive)             # <<<<<<<<<<<<<<
 * 
 * 
 */
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  /* "sklearn/tree/_splitter.pyx":977
 *         return partition_end
 * 
 *     cdef inline void extract_nnz(self, SIZE_t feature,             # <<<<<<<<<<<<<<
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 * 
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 * 
 * 
 * cdef int compare_SIZE_t(const void* a, const void* b) nogil:             # <<<<<<<<<<<<<<
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/* "sklearn/tree/_splitter.pyx":1045
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 * cdef inline void binary_search(INT32_t* sorted_array,             # <<<<<<<<<<<<<<
 *                                INT32_t start, INT32_t end,
 *                                SIZE_t value, SIZE_t* index,
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  __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_pivot;
  int __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1054
 *     """
 *     cdef INT32_t pivot
 *     index[0] = -1             # <<<<<<<<<<<<<<
 *     while start < end:
 *         pivot = start + (end - start) / 2
 */
  (__pyx_v_index[0]) = -1;

  /* "sklearn/tree/_splitter.pyx":1055
 *     cdef INT32_t pivot
 *     index[0] = -1
 *     while start < end:             # <<<<<<<<<<<<<<
 *         pivot = start + (end - start) / 2
 * 
 */
  while (1) {
    __pyx_t_1 = ((__pyx_v_start < __pyx_v_end) != 0);
    if (!__pyx_t_1) break;

    /* "sklearn/tree/_splitter.pyx":1056
 *     index[0] = -1
 *     while start < end:
 *         pivot = start + (end - start) / 2             # <<<<<<<<<<<<<<
 * 
 *         if sorted_array[pivot] == value:
 */
    __pyx_v_pivot = (__pyx_v_start + ((__pyx_v_end - __pyx_v_start) / 2));

    /* "sklearn/tree/_splitter.pyx":1058
 *         pivot = start + (end - start) / 2
 * 
 *         if sorted_array[pivot] == value:             # <<<<<<<<<<<<<<
 *             index[0] = pivot
 *             start = pivot + 1
 */
    __pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) == __pyx_v_value) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":1059
 * 
 *         if sorted_array[pivot] == value:
 *             index[0] = pivot             # <<<<<<<<<<<<<<
 *             start = pivot + 1
 *             break
 */
      (__pyx_v_index[0]) = __pyx_v_pivot;

      /* "sklearn/tree/_splitter.pyx":1060
 *         if sorted_array[pivot] == value:
 *             index[0] = pivot
 *             start = pivot + 1             # <<<<<<<<<<<<<<
 *             break
 * 
 */
      __pyx_v_start = (__pyx_v_pivot + 1);

      /* "sklearn/tree/_splitter.pyx":1061
 *             index[0] = pivot
 *             start = pivot + 1
 *             break             # <<<<<<<<<<<<<<
 * 
 *         if sorted_array[pivot] < value:
 */
      goto __pyx_L4_break;

      /* "sklearn/tree/_splitter.pyx":1058
 *         pivot = start + (end - start) / 2
 * 
 *         if sorted_array[pivot] == value:             # <<<<<<<<<<<<<<
 *             index[0] = pivot
 *             start = pivot + 1
 */
    }

    /* "sklearn/tree/_splitter.pyx":1063
 *             break
 * 
 *         if sorted_array[pivot] < value:             # <<<<<<<<<<<<<<
 *             start = pivot + 1
 *         else:
 */
    __pyx_t_1 = (((__pyx_v_sorted_array[__pyx_v_pivot]) < __pyx_v_value) != 0);
    if (__pyx_t_1) {

      /* "sklearn/tree/_splitter.pyx":1064
 * 
 *         if sorted_array[pivot] < value:
 *             start = pivot + 1             # <<<<<<<<<<<<<<
 *         else:
 *             end = pivot
 */
      __pyx_v_start = (__pyx_v_pivot + 1);

      /* "sklearn/tree/_splitter.pyx":1063
 *             break
 * 
 *         if sorted_array[pivot] < value:             # <<<<<<<<<<<<<<
 *             start = pivot + 1
 *         else:
 */
      goto __pyx_L6;
    }

    /* "sklearn/tree/_splitter.pyx":1066
 *             start = pivot + 1
 *         else:
 *             end = pivot             # <<<<<<<<<<<<<<
 *     new_start[0] = start
 * 
 */
    /*else*/ {
      __pyx_v_end = __pyx_v_pivot;
    }
    __pyx_L6:;
  }
  __pyx_L4_break:;

  /* "sklearn/tree/_splitter.pyx":1067
 *         else:
 *             end = pivot
 *     new_start[0] = start             # <<<<<<<<<<<<<<
 * 
 * 
 */
  (__pyx_v_new_start[0]) = __pyx_v_start;

  /* "sklearn/tree/_splitter.pyx":1045
 * 
 * 
 * cdef inline void binary_search(INT32_t* sorted_array,             # <<<<<<<<<<<<<<
 *                                INT32_t start, INT32_t end,
 *                                SIZE_t value, SIZE_t* index,
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":1070
 * 
 * 
 * cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices,             # <<<<<<<<<<<<<<
 *                                               DTYPE_t* X_data,
 *                                               INT32_t indptr_start,
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_index_to_samples(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive) {
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_k;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_index;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative_;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive_;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_t_1;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_t_2;
  int __pyx_t_3;
  int __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":1087
 *     cdef INT32_t k
 *     cdef SIZE_t index
 *     cdef SIZE_t end_negative_ = start             # <<<<<<<<<<<<<<
 *     cdef SIZE_t start_positive_ = end
 * 
 */
  __pyx_v_end_negative_ = __pyx_v_start;

  /* "sklearn/tree/_splitter.pyx":1088
 *     cdef SIZE_t index
 *     cdef SIZE_t end_negative_ = start
 *     cdef SIZE_t start_positive_ = end             # <<<<<<<<<<<<<<
 * 
 *     for k in range(indptr_start, indptr_end):
 */
  __pyx_v_start_positive_ = __pyx_v_end;

  /* "sklearn/tree/_splitter.pyx":1090
 *     cdef SIZE_t start_positive_ = end
 * 
 *     for k in range(indptr_start, indptr_end):             # <<<<<<<<<<<<<<
 *         if start <= index_to_samples[X_indices[k]] < end:
 *             if X_data[k] > 0:
 */
  __pyx_t_1 = __pyx_v_indptr_end;
  for (__pyx_t_2 = __pyx_v_indptr_start; __pyx_t_2 < __pyx_t_1; __pyx_t_2+=1) {
    __pyx_v_k = __pyx_t_2;

    /* "sklearn/tree/_splitter.pyx":1091
 * 
 *     for k in range(indptr_start, indptr_end):
 *         if start <= index_to_samples[X_indices[k]] < end:             # <<<<<<<<<<<<<<
 *             if X_data[k] > 0:
 *                 start_positive_ -= 1
 */
    __pyx_t_3 = (__pyx_v_start <= (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]));
    if (__pyx_t_3) {
      __pyx_t_3 = ((__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]) < __pyx_v_end);
    }
    __pyx_t_4 = (__pyx_t_3 != 0);
    if (__pyx_t_4) {

      /* "sklearn/tree/_splitter.pyx":1092
 *     for k in range(indptr_start, indptr_end):
 *         if start <= index_to_samples[X_indices[k]] < end:
 *             if X_data[k] > 0:             # <<<<<<<<<<<<<<
 *                 start_positive_ -= 1
 *                 Xf[start_positive_] = X_data[k]
 */
      __pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) > 0.0) != 0);
      if (__pyx_t_4) {

        /* "sklearn/tree/_splitter.pyx":1093
 *         if start <= index_to_samples[X_indices[k]] < end:
 *             if X_data[k] > 0:
 *                 start_positive_ -= 1             # <<<<<<<<<<<<<<
 *                 Xf[start_positive_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]
 */
        __pyx_v_start_positive_ = (__pyx_v_start_positive_ - 1);

        /* "sklearn/tree/_splitter.pyx":1094
 *             if X_data[k] > 0:
 *                 start_positive_ -= 1
 *                 Xf[start_positive_] = X_data[k]             # <<<<<<<<<<<<<<
 *                 index = index_to_samples[X_indices[k]]
 *                 sparse_swap(index_to_samples, samples, index, start_positive_)
 */
        (__pyx_v_Xf[__pyx_v_start_positive_]) = (__pyx_v_X_data[__pyx_v_k]);

        /* "sklearn/tree/_splitter.pyx":1095
 *                 start_positive_ -= 1
 *                 Xf[start_positive_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]             # <<<<<<<<<<<<<<
 *                 sparse_swap(index_to_samples, samples, index, start_positive_)
 * 
 */
        __pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]);

        /* "sklearn/tree/_splitter.pyx":1096
 *                 Xf[start_positive_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]
 *                 sparse_swap(index_to_samples, samples, index, start_positive_)             # <<<<<<<<<<<<<<
 * 
 * 
 */
        __pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_start_positive_);

        /* "sklearn/tree/_splitter.pyx":1092
 *     for k in range(indptr_start, indptr_end):
 *         if start <= index_to_samples[X_indices[k]] < end:
 *             if X_data[k] > 0:             # <<<<<<<<<<<<<<
 *                 start_positive_ -= 1
 *                 Xf[start_positive_] = X_data[k]
 */
        goto __pyx_L6;
      }

      /* "sklearn/tree/_splitter.pyx":1099
 * 
 * 
 *             elif X_data[k] < 0:             # <<<<<<<<<<<<<<
 *                 Xf[end_negative_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]
 */
      __pyx_t_4 = (((__pyx_v_X_data[__pyx_v_k]) < 0.0) != 0);
      if (__pyx_t_4) {

        /* "sklearn/tree/_splitter.pyx":1100
 * 
 *             elif X_data[k] < 0:
 *                 Xf[end_negative_] = X_data[k]             # <<<<<<<<<<<<<<
 *                 index = index_to_samples[X_indices[k]]
 *                 sparse_swap(index_to_samples, samples, index, end_negative_)
 */
        (__pyx_v_Xf[__pyx_v_end_negative_]) = (__pyx_v_X_data[__pyx_v_k]);

        /* "sklearn/tree/_splitter.pyx":1101
 *             elif X_data[k] < 0:
 *                 Xf[end_negative_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]             # <<<<<<<<<<<<<<
 *                 sparse_swap(index_to_samples, samples, index, end_negative_)
 *                 end_negative_ += 1
 */
        __pyx_v_index = (__pyx_v_index_to_samples[(__pyx_v_X_indices[__pyx_v_k])]);

        /* "sklearn/tree/_splitter.pyx":1102
 *                 Xf[end_negative_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]
 *                 sparse_swap(index_to_samples, samples, index, end_negative_)             # <<<<<<<<<<<<<<
 *                 end_negative_ += 1
 * 
 */
        __pyx_f_7sklearn_4tree_9_splitter_sparse_swap(__pyx_v_index_to_samples, __pyx_v_samples, __pyx_v_index, __pyx_v_end_negative_);

        /* "sklearn/tree/_splitter.pyx":1103
 *                 index = index_to_samples[X_indices[k]]
 *                 sparse_swap(index_to_samples, samples, index, end_negative_)
 *                 end_negative_ += 1             # <<<<<<<<<<<<<<
 * 
 *     # Returned values
 */
        __pyx_v_end_negative_ = (__pyx_v_end_negative_ + 1);

        /* "sklearn/tree/_splitter.pyx":1099
 * 
 * 
 *             elif X_data[k] < 0:             # <<<<<<<<<<<<<<
 *                 Xf[end_negative_] = X_data[k]
 *                 index = index_to_samples[X_indices[k]]
 */
      }
      __pyx_L6:;

      /* "sklearn/tree/_splitter.pyx":1091
 * 
 *     for k in range(indptr_start, indptr_end):
 *         if start <= index_to_samples[X_indices[k]] < end:             # <<<<<<<<<<<<<<
 *             if X_data[k] > 0:
 *                 start_positive_ -= 1
 */
    }
  }

  /* "sklearn/tree/_splitter.pyx":1106
 * 
 *     # Returned values
 *     end_negative[0] = end_negative_             # <<<<<<<<<<<<<<
 *     start_positive[0] = start_positive_
 * 
 */
  (__pyx_v_end_negative[0]) = __pyx_v_end_negative_;

  /* "sklearn/tree/_splitter.pyx":1107
 *     # Returned values
 *     end_negative[0] = end_negative_
 *     start_positive[0] = start_positive_             # <<<<<<<<<<<<<<
 * 
 * 
 */
  (__pyx_v_start_positive[0]) = __pyx_v_start_positive_;

  /* "sklearn/tree/_splitter.pyx":1070
 * 
 * 
 * cdef inline void extract_nnz_index_to_samples(INT32_t* X_indices,             # <<<<<<<<<<<<<<
 *                                               DTYPE_t* X_data,
 *                                               INT32_t indptr_start,
 */

  /* function exit code */
}

/* "sklearn/tree/_splitter.pyx":1110
 * 
 * 
 * cdef inline void extract_nnz_binary_search(INT32_t* X_indices,             # <<<<<<<<<<<<<<
 *                                            DTYPE_t* X_data,
 *                                            INT32_t indptr_start,
 */

static CYTHON_INLINE void __pyx_f_7sklearn_4tree_9_splitter_extract_nnz_binary_search(__pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_v_X_indices, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_X_data, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_start, __pyx_t_7sklearn_4tree_9_splitter_INT32_t __pyx_v_indptr_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_samples, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_index_to_samples, __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_v_Xf, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_end_negative, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_start_positive, __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_v_sorted_samples, int *__pyx_v_is_samples_sorted) {
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_samples;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_index;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_k;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative_;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive_;
  int __pyx_t_1;
  int __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1133
 *     cdef SIZE_t n_samples
 * 
 *     if not is_samples_sorted[0]:             # <<<<<<<<<<<<<<
 *         n_samples = end - start
 *         memcpy(sorted_samples + start, samples + start,
 */
  __pyx_t_1 = ((!((__pyx_v_is_samples_sorted[0]) != 0)) != 0);
  if (__pyx_t_1) {

    /* "sklearn/tree/_splitter.pyx":1134
 * 
 *     if not is_samples_sorted[0]:
 *         n_samples = end - start             # <<<<<<<<<<<<<<
 *         memcpy(sorted_samples + start, samples + start,
 *                n_samples * sizeof(SIZE_t))
 */
    __pyx_v_n_samples = (__pyx_v_end - __pyx_v_start);

    /* "sklearn/tree/_splitter.pyx":1135
 *     if not is_samples_sorted[0]:
 *         n_samples = end - start
 *         memcpy(sorted_samples + start, samples + start,             # <<<<<<<<<<<<<<
 *                n_samples * sizeof(SIZE_t))
 *         qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),
 */
    memcpy((__pyx_v_sorted_samples + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_n_samples * (sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t))));

    /* "sklearn/tree/_splitter.pyx":1137
 *         memcpy(sorted_samples + start, samples + start,
 *                n_samples * sizeof(SIZE_t))
 *         qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),             # <<<<<<<<<<<<<<
 *               compare_SIZE_t)
 *         is_samples_sorted[0] = 1
 */
    qsort((__pyx_v_sorted_samples + __pyx_v_start), __pyx_v_n_samples, (sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)), __pyx_f_7sklearn_4tree_9_splitter_compare_SIZE_t);

    /* "sklearn/tree/_splitter.pyx":1139
 *         qsort(sorted_samples + start, n_samples, sizeof(SIZE_t),
 *               compare_SIZE_t)
 *         is_samples_sorted[0] = 1             # <<<<<<<<<<<<<<
 * 
 *     while (indptr_start < indptr_end and
 */
    (__pyx_v_is_samples_sorted[0]) = 1;

    /* "sklearn/tree/_splitter.pyx":1133
 *     cdef SIZE_t n_samples
 * 
 *     if not is_samples_sorted[0]:             # <<<<<<<<<<<<<<
 *         n_samples = end - start
 *         memcpy(sorted_samples + start, samples + start,
 */
  }

  /* "sklearn/tree/_splitter.pyx":1141
 *         is_samples_sorted[0] = 1
 * 
 *     while (indptr_start < indptr_end and             # <<<<<<<<<<<<<<
 *            sorted_samples[start] > X_indices[indptr_start]):
 *         indptr_start += 1
 */
  while (1) {
    __pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0);
    if (__pyx_t_2) {
    } else {
      __pyx_t_1 = __pyx_t_2;
      goto __pyx_L6_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1142
 * 
 *     while (indptr_start < indptr_end and
 *            sorted_samples[start] > X_indices[indptr_start]):             # <<<<<<<<<<<<<<
 *         indptr_start += 1
 * 
 */
    __pyx_t_2 = (((__pyx_v_sorted_samples[__pyx_v_start]) > (__pyx_v_X_indices[__pyx_v_indptr_start])) != 0);
    __pyx_t_1 = __pyx_t_2;
    __pyx_L6_bool_binop_done:;
    if (!__pyx_t_1) break;

    /* "sklearn/tree/_splitter.pyx":1143
 *     while (indptr_start < indptr_end and
 *            sorted_samples[start] > X_indices[indptr_start]):
 *         indptr_start += 1             # <<<<<<<<<<<<<<
 * 
 *     while (indptr_start < indptr_end and
 */
    __pyx_v_indptr_start = (__pyx_v_indptr_start + 1);
  }

  /* "sklearn/tree/_splitter.pyx":1145
 *         indptr_start += 1
 * 
 *     while (indptr_start < indptr_end and             # <<<<<<<<<<<<<<
 *            sorted_samples[end - 1] < X_indices[indptr_end - 1]):
 *         indptr_end -= 1
 */
  while (1) {
    __pyx_t_2 = ((__pyx_v_indptr_start < __pyx_v_indptr_end) != 0);
    if (__pyx_t_2) {
    } else {
      __pyx_t_1 = __pyx_t_2;
      goto __pyx_L10_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1146
 * 
 *     while (indptr_start < indptr_end and
 *            sorted_samples[end - 1] < X_indices[indptr_end - 1]):             # <<<<<<<<<<<<<<
 *         indptr_end -= 1
 * 
 */
    __pyx_t_2 = (((__pyx_v_sorted_samples[(__pyx_v_end - 1)]) < (__pyx_v_X_indices[(__pyx_v_indptr_end - 1)])) != 0);
    __pyx_t_1 = __pyx_t_2;
    __pyx_L10_bool_binop_done:;
    if (!__pyx_t_1) break;

    /* "sklearn/tree/_splitter.pyx":1147
 *     while (indptr_start < indptr_end and
 *            sorted_samples[end - 1] < X_indices[indptr_end - 1]):
 *         indptr_end -= 1             # <<<<<<<<<<<<<<
 * 
 *     cdef SIZE_t p = start
 */
    __pyx_v_indptr_end = (__pyx_v_indptr_end - 1);
  }

  /* "sklearn/tree/_splitter.pyx":1149
 *         indptr_end -= 1
 * 
 *     cdef SIZE_t p = start             # <<<<<<<<<<<<<<
 *     cdef SIZE_t index
 *     cdef SIZE_t k
 */
  __pyx_v_p = __pyx_v_start;

  /* "sklearn/tree/_splitter.pyx":1152
 *     cdef SIZE_t index
 *     cdef SIZE_t k
 *     cdef SIZE_t end_negative_ = start             # <<<<<<<<<<<<<<
 *     cdef SIZE_t start_positive_ = end
 * 
 */
  __pyx_v_end_negative_ = __pyx_v_start;

  /* "sklearn/tree/_splitter.pyx":1153
 *     cdef SIZE_t k
 *     cdef SIZE_t end_negative_ = start
 *     cdef SIZE_t start_positive_ = end             # <<<<<<<<<<<<<<
 * 
 *     while (p < end and indptr_start < indptr_end):
 */
  __pyx_v_start_positive_ = __pyx_v_end;

  /* "sklearn/tree/_splitter.pyx":1155
 *     cdef SIZE_t start_positive_ = end
 * 
 *     while (p < end and indptr_start < indptr_end):             # <<<<<<<<<<<<<<
 *         # Find index of sorted_samples[p] in X_indices
 *         binary_search(X_indices, indptr_start, indptr_end,
 */
  while (1) {
    __pyx_t_2 = ((__pyx_v_p < __pyx_v_end) != 0);
    if (__pyx_t_2) {
    } else {
      __pyx_t_1 = __pyx_t_2;
      goto __pyx_L14_bool_binop_done;
    }
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 * 
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  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_found_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_drawn_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_known_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_n_total_constants;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p_next;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_p_prev;
  int __pyx_v_is_samples_sorted;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_start_positive;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_v_end_negative;
  int __pyx_r;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t *__pyx_t_1;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_2;
  __pyx_t_7sklearn_4tree_9_splitter_INT32_t *__pyx_t_3;
  __pyx_t_7sklearn_4tree_9_splitter_DTYPE_t *__pyx_t_4;
  double __pyx_t_5;
  int __pyx_t_6;
  int __pyx_t_7;
  __pyx_t_7sklearn_4tree_9_splitter_SIZE_t __pyx_t_8;
  int __pyx_t_9;

  /* "sklearn/tree/_splitter.pyx":1209
 *         """
 *         # Find the best split
 *         cdef SIZE_t* samples = self.samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.samples;
  __pyx_v_samples = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1210
 *         # Find the best split
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start             # <<<<<<<<<<<<<<
 *         cdef SIZE_t end = self.end
 * 
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.start;
  __pyx_v_start = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1211
 *         cdef SIZE_t* samples = self.samples
 *         cdef SIZE_t start = self.start
 *         cdef SIZE_t end = self.end             # <<<<<<<<<<<<<<
 * 
 *         cdef INT32_t* X_indices = self.X_indices
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.end;
  __pyx_v_end = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1213
 *         cdef SIZE_t end = self.end
 * 
 *         cdef INT32_t* X_indices = self.X_indices             # <<<<<<<<<<<<<<
 *         cdef INT32_t* X_indptr = self.X_indptr
 *         cdef DTYPE_t* X_data = self.X_data
 */
  __pyx_t_3 = __pyx_v_self->__pyx_base.X_indices;
  __pyx_v_X_indices = __pyx_t_3;

  /* "sklearn/tree/_splitter.pyx":1214
 * 
 *         cdef INT32_t* X_indices = self.X_indices
 *         cdef INT32_t* X_indptr = self.X_indptr             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t* X_data = self.X_data
 * 
 */
  __pyx_t_3 = __pyx_v_self->__pyx_base.X_indptr;
  __pyx_v_X_indptr = __pyx_t_3;

  /* "sklearn/tree/_splitter.pyx":1215
 *         cdef INT32_t* X_indices = self.X_indices
 *         cdef INT32_t* X_indptr = self.X_indptr
 *         cdef DTYPE_t* X_data = self.X_data             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t* features = self.features
 */
  __pyx_t_4 = __pyx_v_self->__pyx_base.X_data;
  __pyx_v_X_data = __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":1217
 *         cdef DTYPE_t* X_data = self.X_data
 * 
 *         cdef SIZE_t* features = self.features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.features;
  __pyx_v_features = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1218
 * 
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_features = self.n_features
 * 
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.__pyx_base.constant_features;
  __pyx_v_constant_features = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1219
 *         cdef SIZE_t* features = self.features
 *         cdef SIZE_t* constant_features = self.constant_features
 *         cdef SIZE_t n_features = self.n_features             # <<<<<<<<<<<<<<
 * 
 *         cdef DTYPE_t* Xf = self.feature_values
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.n_features;
  __pyx_v_n_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1221
 *         cdef SIZE_t n_features = self.n_features
 * 
 *         cdef DTYPE_t* Xf = self.feature_values             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* sorted_samples = self.sorted_samples
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 */
  __pyx_t_4 = __pyx_v_self->__pyx_base.__pyx_base.feature_values;
  __pyx_v_Xf = __pyx_t_4;

  /* "sklearn/tree/_splitter.pyx":1222
 * 
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t* sorted_samples = self.sorted_samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 *         cdef SIZE_t max_features = self.max_features
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.sorted_samples;
  __pyx_v_sorted_samples = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1223
 *         cdef DTYPE_t* Xf = self.feature_values
 *         cdef SIZE_t* sorted_samples = self.sorted_samples
 *         cdef SIZE_t* index_to_samples = self.index_to_samples             # <<<<<<<<<<<<<<
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 */
  __pyx_t_1 = __pyx_v_self->__pyx_base.index_to_samples;
  __pyx_v_index_to_samples = __pyx_t_1;

  /* "sklearn/tree/_splitter.pyx":1224
 *         cdef SIZE_t* sorted_samples = self.sorted_samples
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 *         cdef SIZE_t max_features = self.max_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
  __pyx_v_max_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1225
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf             # <<<<<<<<<<<<<<
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
  __pyx_v_min_samples_leaf = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1226
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf             # <<<<<<<<<<<<<<
 *         cdef UINT32_t* random_state = &self.rand_r_state
 * 
 */
  __pyx_t_5 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
  __pyx_v_min_weight_leaf = __pyx_t_5;

  /* "sklearn/tree/_splitter.pyx":1227
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state             # <<<<<<<<<<<<<<
 * 
 *         cdef SplitRecord best, current
 */
  __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);

  /* "sklearn/tree/_splitter.pyx":1230
 * 
 *         cdef SplitRecord best, current
 *         _init_split(&best, end)             # <<<<<<<<<<<<<<
 *         cdef double current_proxy_improvement = - INFINITY
 *         cdef double best_proxy_improvement = - INFINITY
 */
  __pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);

  /* "sklearn/tree/_splitter.pyx":1231
 *         cdef SplitRecord best, current
 *         _init_split(&best, end)
 *         cdef double current_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 *         cdef double best_proxy_improvement = - INFINITY
 * 
 */
  __pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":1232
 *         _init_split(&best, end)
 *         cdef double current_proxy_improvement = - INFINITY
 *         cdef double best_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 * 
 *         cdef SIZE_t f_i = n_features
 */
  __pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":1234
 *         cdef double best_proxy_improvement = - INFINITY
 * 
 *         cdef SIZE_t f_i = n_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t f_j, p, tmp
 *         cdef SIZE_t n_visited_features = 0
 */
  __pyx_v_f_i = __pyx_v_n_features;

  /* "sklearn/tree/_splitter.pyx":1236
 *         cdef SIZE_t f_i = n_features
 *         cdef SIZE_t f_j, p, tmp
 *         cdef SIZE_t n_visited_features = 0             # <<<<<<<<<<<<<<
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0
 */
  __pyx_v_n_visited_features = 0;

  /* "sklearn/tree/_splitter.pyx":1238
 *         cdef SIZE_t n_visited_features = 0
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0             # <<<<<<<<<<<<<<
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 */
  __pyx_v_n_found_constants = 0;

  /* "sklearn/tree/_splitter.pyx":1240
 *         cdef SIZE_t n_found_constants = 0
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 */
  __pyx_v_n_drawn_constants = 0;

  /* "sklearn/tree/_splitter.pyx":1241
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 *         cdef SIZE_t n_known_constants = n_constant_features[0]             # <<<<<<<<<<<<<<
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants
 */
  __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);

  /* "sklearn/tree/_splitter.pyx":1243
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants             # <<<<<<<<<<<<<<
 *         cdef DTYPE_t current_feature_value
 * 
 */
  __pyx_v_n_total_constants = __pyx_v_n_known_constants;

  /* "sklearn/tree/_splitter.pyx":1248
 *         cdef SIZE_t p_next
 *         cdef SIZE_t p_prev
 *         cdef bint is_samples_sorted = 0  # indicate is sorted_samples is             # <<<<<<<<<<<<<<
 *                                          # inititialized
 * 
 */
  __pyx_v_is_samples_sorted = 0;

  /* "sklearn/tree/_splitter.pyx":1265
 *         # newly discovered constant features to spare computation on descendant
 *         # nodes.
 *         while (f_i > n_total_constants and  # Stop early if remaining features             # <<<<<<<<<<<<<<
 *                                             # are constant
 *                 (n_visited_features < max_features or
 */
  while (1) {
    __pyx_t_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
    if (__pyx_t_7) {
    } else {
      __pyx_t_6 = __pyx_t_7;
      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1267
 *         while (f_i > n_total_constants and  # Stop early if remaining features
 *                                             # are constant
 *                 (n_visited_features < max_features or             # <<<<<<<<<<<<<<
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 */
    __pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
    if (!__pyx_t_7) {
    } else {
      __pyx_t_6 = __pyx_t_7;
      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1269
 *                 (n_visited_features < max_features or
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):             # <<<<<<<<<<<<<<
 * 
 *             n_visited_features += 1
 */
    __pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
    __pyx_t_6 = __pyx_t_7;
    __pyx_L5_bool_binop_done:;
    if (!__pyx_t_6) break;

    /* "sklearn/tree/_splitter.pyx":1271
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 * 
 *             n_visited_features += 1             # <<<<<<<<<<<<<<
 * 
 *             # Loop invariant: elements of features in
 */
    __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);

    /* "sklearn/tree/_splitter.pyx":1285
 * 
 *             # Draw a feature at random
 *             f_j = rand_int(n_drawn_constants, f_i - n_found_constants,             # <<<<<<<<<<<<<<
 *                            random_state)
 * 
 */
    __pyx_v_f_j = __pyx_f_7sklearn_4tree_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);

    /* "sklearn/tree/_splitter.pyx":1288
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
    __pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
    if (__pyx_t_6) {

      /* "sklearn/tree/_splitter.pyx":1290
 *             if f_j < n_known_constants:
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]             # <<<<<<<<<<<<<<
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp
 */
      __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":1291
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]             # <<<<<<<<<<<<<<
 *                 features[n_drawn_constants] = tmp
 * 
 */
      (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);

      /* "sklearn/tree/_splitter.pyx":1292
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp             # <<<<<<<<<<<<<<
 * 
 *                 n_drawn_constants += 1
 */
      (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;

      /* "sklearn/tree/_splitter.pyx":1294
 *                 features[n_drawn_constants] = tmp
 * 
 *                 n_drawn_constants += 1             # <<<<<<<<<<<<<<
 * 
 *             else:
 */
      __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);

      /* "sklearn/tree/_splitter.pyx":1288
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
      goto __pyx_L8;
    }

    /* "sklearn/tree/_splitter.pyx":1298
 *             else:
 *                 # f_j in the interval [n_known_constants, f_i - n_found_constants[
 *                 f_j += n_found_constants             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 */
    /*else*/ {
      __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);

      /* "sklearn/tree/_splitter.pyx":1301
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 *                 current.feature = features[f_j]             # <<<<<<<<<<<<<<
 *                 self.extract_nnz(current.feature,
 *                                  &end_negative, &start_positive,
 */
      __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":1302
 * 
 *                 current.feature = features[f_j]
 *                 self.extract_nnz(current.feature,             # <<<<<<<<<<<<<<
 *                                  &end_negative, &start_positive,
 *                                  &is_samples_sorted)
 */
      __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));

      /* "sklearn/tree/_splitter.pyx":1307
 * 
 *                 # Sort the positive and negative parts of `Xf`
 *                 sort(Xf + start, samples + start, end_negative - start)             # <<<<<<<<<<<<<<
 *                 sort(Xf + start_positive, samples + start_positive,
 *                      end - start_positive)
 */
      __pyx_f_7sklearn_4tree_9_splitter_sort((__pyx_v_Xf + __pyx_v_start), (__pyx_v_samples + __pyx_v_start), (__pyx_v_end_negative - __pyx_v_start));

      /* "sklearn/tree/_splitter.pyx":1308
 *                 # Sort the positive and negative parts of `Xf`
 *                 sort(Xf + start, samples + start, end_negative - start)
 *                 sort(Xf + start_positive, samples + start_positive,             # <<<<<<<<<<<<<<
 *                      end - start_positive)
 * 
 */
      __pyx_f_7sklearn_4tree_9_splitter_sort((__pyx_v_Xf + __pyx_v_start_positive), (__pyx_v_samples + __pyx_v_start_positive), (__pyx_v_end - __pyx_v_start_positive));

      /* "sklearn/tree/_splitter.pyx":1312
 * 
 *                 # Update index_to_samples to take into account the sort
 *                 for p in range(start, end_negative):             # <<<<<<<<<<<<<<
 *                     index_to_samples[samples[p]] = p
 *                 for p in range(start_positive, end):
 */
      __pyx_t_2 = __pyx_v_end_negative;
      for (__pyx_t_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
        __pyx_v_p = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1313
 *                 # Update index_to_samples to take into account the sort
 *                 for p in range(start, end_negative):
 *                     index_to_samples[samples[p]] = p             # <<<<<<<<<<<<<<
 *                 for p in range(start_positive, end):
 *                     index_to_samples[samples[p]] = p
 */
        (__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p;
      }

      /* "sklearn/tree/_splitter.pyx":1314
 *                 for p in range(start, end_negative):
 *                     index_to_samples[samples[p]] = p
 *                 for p in range(start_positive, end):             # <<<<<<<<<<<<<<
 *                     index_to_samples[samples[p]] = p
 * 
 */
      __pyx_t_2 = __pyx_v_end;
      for (__pyx_t_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
        __pyx_v_p = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1315
 *                     index_to_samples[samples[p]] = p
 *                 for p in range(start_positive, end):
 *                     index_to_samples[samples[p]] = p             # <<<<<<<<<<<<<<
 * 
 *                 # Add one or two zeros in Xf, if there is any
 */
        (__pyx_v_index_to_samples[(__pyx_v_samples[__pyx_v_p])]) = __pyx_v_p;
      }

      /* "sklearn/tree/_splitter.pyx":1318
 * 
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:             # <<<<<<<<<<<<<<
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.
 */
      __pyx_t_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":1319
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:
 *                     start_positive -= 1             # <<<<<<<<<<<<<<
 *                     Xf[start_positive] = 0.
 * 
 */
        __pyx_v_start_positive = (__pyx_v_start_positive - 1);

        /* "sklearn/tree/_splitter.pyx":1320
 *                 if end_negative < start_positive:
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.             # <<<<<<<<<<<<<<
 * 
 *                     if end_negative != start_positive:
 */
        (__pyx_v_Xf[__pyx_v_start_positive]) = 0.;

        /* "sklearn/tree/_splitter.pyx":1322
 *                     Xf[start_positive] = 0.
 * 
 *                     if end_negative != start_positive:             # <<<<<<<<<<<<<<
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1
 */
        __pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1323
 * 
 *                     if end_negative != start_positive:
 *                         Xf[end_negative] = 0.             # <<<<<<<<<<<<<<
 *                         end_negative += 1
 * 
 */
          (__pyx_v_Xf[__pyx_v_end_negative]) = 0.;

          /* "sklearn/tree/_splitter.pyx":1324
 *                     if end_negative != start_positive:
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1             # <<<<<<<<<<<<<<
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 */
          __pyx_v_end_negative = (__pyx_v_end_negative + 1);

          /* "sklearn/tree/_splitter.pyx":1322
 *                     Xf[start_positive] = 0.
 * 
 *                     if end_negative != start_positive:             # <<<<<<<<<<<<<<
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1
 */
        }

        /* "sklearn/tree/_splitter.pyx":1318
 * 
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:             # <<<<<<<<<<<<<<
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.
 */
      }

      /* "sklearn/tree/_splitter.pyx":1326
 *                         end_negative += 1
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
      __pyx_t_6 = (((__pyx_v_Xf[(__pyx_v_end - 1)]) <= ((__pyx_v_Xf[__pyx_v_start]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":1327
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]             # <<<<<<<<<<<<<<
 *                     features[n_total_constants] = current.feature
 * 
 */
        (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);

        /* "sklearn/tree/_splitter.pyx":1328
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature             # <<<<<<<<<<<<<<
 * 
 *                     n_found_constants += 1
 */
        __pyx_t_2 = __pyx_v_current.feature;
        (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;

        /* "sklearn/tree/_splitter.pyx":1330
 *                     features[n_total_constants] = current.feature
 * 
 *                     n_found_constants += 1             # <<<<<<<<<<<<<<
 *                     n_total_constants += 1
 * 
 */
        __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);

        /* "sklearn/tree/_splitter.pyx":1331
 * 
 *                     n_found_constants += 1
 *                     n_total_constants += 1             # <<<<<<<<<<<<<<
 * 
 *                 else:
 */
        __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);

        /* "sklearn/tree/_splitter.pyx":1326
 *                         end_negative += 1
 * 
 *                 if Xf[end - 1] <= Xf[start] + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
        goto __pyx_L15;
      }

      /* "sklearn/tree/_splitter.pyx":1334
 * 
 *                 else:
 *                     f_i -= 1             # <<<<<<<<<<<<<<
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]
 * 
 */
      /*else*/ {
        __pyx_v_f_i = (__pyx_v_f_i - 1);

        /* "sklearn/tree/_splitter.pyx":1335
 *                 else:
 *                     f_i -= 1
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]             # <<<<<<<<<<<<<<
 * 
 *                     # Evaluate all splits
 */
        __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
        __pyx_t_8 = (__pyx_v_features[__pyx_v_f_i]);
        (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
        (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1338
 * 
 *                     # Evaluate all splits
 *                     self.criterion.reset()             # <<<<<<<<<<<<<<
 *                     p = start
 * 
 */
        __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1338, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":1339
 *                     # Evaluate all splits
 *                     self.criterion.reset()
 *                     p = start             # <<<<<<<<<<<<<<
 * 
 *                     while p < end:
 */
        __pyx_v_p = __pyx_v_start;

        /* "sklearn/tree/_splitter.pyx":1341
 *                     p = start
 * 
 *                     while p < end:             # <<<<<<<<<<<<<<
 *                         if p + 1 != end_negative:
 *                             p_next = p + 1
 */
        while (1) {
          __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
          if (!__pyx_t_6) break;

          /* "sklearn/tree/_splitter.pyx":1342
 * 
 *                     while p < end:
 *                         if p + 1 != end_negative:             # <<<<<<<<<<<<<<
 *                             p_next = p + 1
 *                         else:
 */
          __pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
          if (__pyx_t_6) {

            /* "sklearn/tree/_splitter.pyx":1343
 *                     while p < end:
 *                         if p + 1 != end_negative:
 *                             p_next = p + 1             # <<<<<<<<<<<<<<
 *                         else:
 *                             p_next = start_positive
 */
            __pyx_v_p_next = (__pyx_v_p + 1);

            /* "sklearn/tree/_splitter.pyx":1342
 * 
 *                     while p < end:
 *                         if p + 1 != end_negative:             # <<<<<<<<<<<<<<
 *                             p_next = p + 1
 *                         else:
 */
            goto __pyx_L18;
          }

          /* "sklearn/tree/_splitter.pyx":1345
 *                             p_next = p + 1
 *                         else:
 *                             p_next = start_positive             # <<<<<<<<<<<<<<
 * 
 *                         while (p_next < end and
 */
          /*else*/ {
            __pyx_v_p_next = __pyx_v_start_positive;
          }
          __pyx_L18:;

          /* "sklearn/tree/_splitter.pyx":1347
 *                             p_next = start_positive
 * 
 *                         while (p_next < end and             # <<<<<<<<<<<<<<
 *                                Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p = p_next
 */
          while (1) {
            __pyx_t_7 = ((__pyx_v_p_next < __pyx_v_end) != 0);
            if (__pyx_t_7) {
            } else {
              __pyx_t_6 = __pyx_t_7;
              goto __pyx_L21_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":1348
 * 
 *                         while (p_next < end and
 *                                Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):             # <<<<<<<<<<<<<<
 *                             p = p_next
 *                             if p + 1 != end_negative:
 */
            __pyx_t_7 = (((__pyx_v_Xf[__pyx_v_p_next]) <= ((__pyx_v_Xf[__pyx_v_p]) + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
            __pyx_t_6 = __pyx_t_7;
            __pyx_L21_bool_binop_done:;
            if (!__pyx_t_6) break;

            /* "sklearn/tree/_splitter.pyx":1349
 *                         while (p_next < end and
 *                                Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p = p_next             # <<<<<<<<<<<<<<
 *                             if p + 1 != end_negative:
 *                                 p_next = p + 1
 */
            __pyx_v_p = __pyx_v_p_next;

            /* "sklearn/tree/_splitter.pyx":1350
 *                                Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p = p_next
 *                             if p + 1 != end_negative:             # <<<<<<<<<<<<<<
 *                                 p_next = p + 1
 *                             else:
 */
            __pyx_t_6 = (((__pyx_v_p + 1) != __pyx_v_end_negative) != 0);
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":1351
 *                             p = p_next
 *                             if p + 1 != end_negative:
 *                                 p_next = p + 1             # <<<<<<<<<<<<<<
 *                             else:
 *                                 p_next = start_positive
 */
              __pyx_v_p_next = (__pyx_v_p + 1);

              /* "sklearn/tree/_splitter.pyx":1350
 *                                Xf[p_next] <= Xf[p] + FEATURE_THRESHOLD):
 *                             p = p_next
 *                             if p + 1 != end_negative:             # <<<<<<<<<<<<<<
 *                                 p_next = p + 1
 *                             else:
 */
              goto __pyx_L23;
            }

            /* "sklearn/tree/_splitter.pyx":1353
 *                                 p_next = p + 1
 *                             else:
 *                                 p_next = start_positive             # <<<<<<<<<<<<<<
 * 
 * 
 */
            /*else*/ {
              __pyx_v_p_next = __pyx_v_start_positive;
            }
            __pyx_L23:;
          }

          /* "sklearn/tree/_splitter.pyx":1358
 *                         # (p_next >= end) or (X[samples[p_next], current.feature] >
 *                         #                     X[samples[p], current.feature])
 *                         p_prev = p             # <<<<<<<<<<<<<<
 *                         p = p_next
 *                         # (p >= end) or (X[samples[p], current.feature] >
 */
          __pyx_v_p_prev = __pyx_v_p;

          /* "sklearn/tree/_splitter.pyx":1359
 *                         #                     X[samples[p], current.feature])
 *                         p_prev = p
 *                         p = p_next             # <<<<<<<<<<<<<<
 *                         # (p >= end) or (X[samples[p], current.feature] >
 *                         #                X[samples[p_prev], current.feature])
 */
          __pyx_v_p = __pyx_v_p_next;

          /* "sklearn/tree/_splitter.pyx":1364
 * 
 * 
 *                         if p < end:             # <<<<<<<<<<<<<<
 *                             current.pos = p
 * 
 */
          __pyx_t_6 = ((__pyx_v_p < __pyx_v_end) != 0);
          if (__pyx_t_6) {

            /* "sklearn/tree/_splitter.pyx":1365
 * 
 *                         if p < end:
 *                             current.pos = p             # <<<<<<<<<<<<<<
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 */
            __pyx_v_current.pos = __pyx_v_p;

            /* "sklearn/tree/_splitter.pyx":1368
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            __pyx_t_7 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
            if (!__pyx_t_7) {
            } else {
              __pyx_t_6 = __pyx_t_7;
              goto __pyx_L26_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":1369
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or
 *                                     ((end - current.pos) < min_samples_leaf)):             # <<<<<<<<<<<<<<
 *                                 continue
 * 
 */
            __pyx_t_7 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
            __pyx_t_6 = __pyx_t_7;
            __pyx_L26_bool_binop_done:;

            /* "sklearn/tree/_splitter.pyx":1368
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":1370
 *                             if (((current.pos - start) < min_samples_leaf) or
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue             # <<<<<<<<<<<<<<
 * 
 *                             self.criterion.update(current.pos)
 */
              goto __pyx_L16_continue;

              /* "sklearn/tree/_splitter.pyx":1368
 * 
 *                             # Reject if min_samples_leaf is not guaranteed
 *                             if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                                     ((end - current.pos) < min_samples_leaf)):
 *                                 continue
 */
            }

            /* "sklearn/tree/_splitter.pyx":1372
 *                                 continue
 * 
 *                             self.criterion.update(current.pos)             # <<<<<<<<<<<<<<
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 */
            __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1372, __pyx_L1_error)

            /* "sklearn/tree/_splitter.pyx":1375
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
            if (!__pyx_t_7) {
            } else {
              __pyx_t_6 = __pyx_t_7;
              goto __pyx_L29_bool_binop_done;
            }

            /* "sklearn/tree/_splitter.pyx":1376
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):             # <<<<<<<<<<<<<<
 *                                 continue
 * 
 */
            __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
            __pyx_t_6 = __pyx_t_7;
            __pyx_L29_bool_binop_done:;

            /* "sklearn/tree/_splitter.pyx":1375
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":1377
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue             # <<<<<<<<<<<<<<
 * 
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 */
              goto __pyx_L16_continue;

              /* "sklearn/tree/_splitter.pyx":1375
 * 
 *                             # Reject if min_weight_leaf is not satisfied
 *                             if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                                     (self.criterion.weighted_n_right < min_weight_leaf)):
 *                                 continue
 */
            }

            /* "sklearn/tree/_splitter.pyx":1379
 *                                 continue
 * 
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()             # <<<<<<<<<<<<<<
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:
 */
            __pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);

            /* "sklearn/tree/_splitter.pyx":1381
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                                 best_proxy_improvement = current_proxy_improvement
 * 
 */
            __pyx_t_6 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
            if (__pyx_t_6) {

              /* "sklearn/tree/_splitter.pyx":1382
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:
 *                                 best_proxy_improvement = current_proxy_improvement             # <<<<<<<<<<<<<<
 * 
 *                                 current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
 */
              __pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;

              /* "sklearn/tree/_splitter.pyx":1384
 *                                 best_proxy_improvement = current_proxy_improvement
 * 
 *                                 current.threshold = (Xf[p_prev] + Xf[p]) / 2.0             # <<<<<<<<<<<<<<
 *                                 if current.threshold == Xf[p]:
 *                                     current.threshold = Xf[p_prev]
 */
              __pyx_v_current.threshold = (((__pyx_v_Xf[__pyx_v_p_prev]) + (__pyx_v_Xf[__pyx_v_p])) / 2.0);

              /* "sklearn/tree/_splitter.pyx":1385
 * 
 *                                 current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
 *                                 if current.threshold == Xf[p]:             # <<<<<<<<<<<<<<
 *                                     current.threshold = Xf[p_prev]
 * 
 */
              __pyx_t_6 = ((__pyx_v_current.threshold == (__pyx_v_Xf[__pyx_v_p])) != 0);
              if (__pyx_t_6) {

                /* "sklearn/tree/_splitter.pyx":1386
 *                                 current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
 *                                 if current.threshold == Xf[p]:
 *                                     current.threshold = Xf[p_prev]             # <<<<<<<<<<<<<<
 * 
 *                                 best = current
 */
                __pyx_v_current.threshold = (__pyx_v_Xf[__pyx_v_p_prev]);

                /* "sklearn/tree/_splitter.pyx":1385
 * 
 *                                 current.threshold = (Xf[p_prev] + Xf[p]) / 2.0
 *                                 if current.threshold == Xf[p]:             # <<<<<<<<<<<<<<
 *                                     current.threshold = Xf[p_prev]
 * 
 */
              }

              /* "sklearn/tree/_splitter.pyx":1388
 *                                     current.threshold = Xf[p_prev]
 * 
 *                                 best = current             # <<<<<<<<<<<<<<
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 */
              __pyx_v_best = __pyx_v_current;

              /* "sklearn/tree/_splitter.pyx":1381
 *                             current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                             if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                                 best_proxy_improvement = current_proxy_improvement
 * 
 */
            }

            /* "sklearn/tree/_splitter.pyx":1364
 * 
 * 
 *                         if p < end:             # <<<<<<<<<<<<<<
 *                             current.pos = p
 * 
 */
          }
          __pyx_L16_continue:;
        }
      }
      __pyx_L15:;
    }
    __pyx_L8:;
  }

  /* "sklearn/tree/_splitter.pyx":1391
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:             # <<<<<<<<<<<<<<
 *             self.extract_nnz(best.feature, &end_negative, &start_positive,
 *                              &is_samples_sorted)
 */
  __pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
  if (__pyx_t_6) {

    /* "sklearn/tree/_splitter.pyx":1392
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:
 *             self.extract_nnz(best.feature, &end_negative, &start_positive,             # <<<<<<<<<<<<<<
 *                              &is_samples_sorted)
 * 
 */
    __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));

    /* "sklearn/tree/_splitter.pyx":1395
 *                              &is_samples_sorted)
 * 
 *             self._partition(best.threshold, end_negative, start_positive,             # <<<<<<<<<<<<<<
 *                             best.pos)
 * 
 */
    __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.threshold, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_best.pos);

    /* "sklearn/tree/_splitter.pyx":1398
 *                             best.pos)
 * 
 *             self.criterion.reset()             # <<<<<<<<<<<<<<
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1398, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":1399
 * 
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)             # <<<<<<<<<<<<<<
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1399, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":1400
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)             # <<<<<<<<<<<<<<
 *             self.criterion.children_impurity(&best.impurity_left,
 *                                              &best.impurity_right)
 */
    __pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);

    /* "sklearn/tree/_splitter.pyx":1401
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,             # <<<<<<<<<<<<<<
 *                                              &best.impurity_right)
 * 
 */
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 *         cdef SIZE_t max_features = self.max_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.max_features;
  __pyx_v_max_features = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1455
 *         cdef SIZE_t* index_to_samples = self.index_to_samples
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf             # <<<<<<<<<<<<<<
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state
 */
  __pyx_t_2 = __pyx_v_self->__pyx_base.__pyx_base.min_samples_leaf;
  __pyx_v_min_samples_leaf = __pyx_t_2;

  /* "sklearn/tree/_splitter.pyx":1456
 *         cdef SIZE_t max_features = self.max_features
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf             # <<<<<<<<<<<<<<
 *         cdef UINT32_t* random_state = &self.rand_r_state
 * 
 */
  __pyx_t_5 = __pyx_v_self->__pyx_base.__pyx_base.min_weight_leaf;
  __pyx_v_min_weight_leaf = __pyx_t_5;

  /* "sklearn/tree/_splitter.pyx":1457
 *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
 *         cdef double min_weight_leaf = self.min_weight_leaf
 *         cdef UINT32_t* random_state = &self.rand_r_state             # <<<<<<<<<<<<<<
 * 
 *         cdef SplitRecord best, current
 */
  __pyx_v_random_state = (&__pyx_v_self->__pyx_base.__pyx_base.rand_r_state);

  /* "sklearn/tree/_splitter.pyx":1460
 * 
 *         cdef SplitRecord best, current
 *         _init_split(&best, end)             # <<<<<<<<<<<<<<
 *         cdef double current_proxy_improvement = - INFINITY
 *         cdef double best_proxy_improvement = - INFINITY
 */
  __pyx_f_7sklearn_4tree_9_splitter__init_split((&__pyx_v_best), __pyx_v_end);

  /* "sklearn/tree/_splitter.pyx":1461
 *         cdef SplitRecord best, current
 *         _init_split(&best, end)
 *         cdef double current_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 *         cdef double best_proxy_improvement = - INFINITY
 * 
 */
  __pyx_v_current_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":1462
 *         _init_split(&best, end)
 *         cdef double current_proxy_improvement = - INFINITY
 *         cdef double best_proxy_improvement = - INFINITY             # <<<<<<<<<<<<<<
 * 
 *         cdef DTYPE_t current_feature_value
 */
  __pyx_v_best_proxy_improvement = (-__pyx_v_7sklearn_4tree_9_splitter_INFINITY);

  /* "sklearn/tree/_splitter.pyx":1466
 *         cdef DTYPE_t current_feature_value
 * 
 *         cdef SIZE_t f_i = n_features             # <<<<<<<<<<<<<<
 *         cdef SIZE_t f_j, p, tmp
 *         cdef SIZE_t n_visited_features = 0
 */
  __pyx_v_f_i = __pyx_v_n_features;

  /* "sklearn/tree/_splitter.pyx":1468
 *         cdef SIZE_t f_i = n_features
 *         cdef SIZE_t f_j, p, tmp
 *         cdef SIZE_t n_visited_features = 0             # <<<<<<<<<<<<<<
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0
 */
  __pyx_v_n_visited_features = 0;

  /* "sklearn/tree/_splitter.pyx":1470
 *         cdef SIZE_t n_visited_features = 0
 *         # Number of features discovered to be constant during the split search
 *         cdef SIZE_t n_found_constants = 0             # <<<<<<<<<<<<<<
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 */
  __pyx_v_n_found_constants = 0;

  /* "sklearn/tree/_splitter.pyx":1472
 *         cdef SIZE_t n_found_constants = 0
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0             # <<<<<<<<<<<<<<
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 */
  __pyx_v_n_drawn_constants = 0;

  /* "sklearn/tree/_splitter.pyx":1473
 *         # Number of features known to be constant and drawn without replacement
 *         cdef SIZE_t n_drawn_constants = 0
 *         cdef SIZE_t n_known_constants = n_constant_features[0]             # <<<<<<<<<<<<<<
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants
 */
  __pyx_v_n_known_constants = (__pyx_v_n_constant_features[0]);

  /* "sklearn/tree/_splitter.pyx":1475
 *         cdef SIZE_t n_known_constants = n_constant_features[0]
 *         # n_total_constants = n_known_constants + n_found_constants
 *         cdef SIZE_t n_total_constants = n_known_constants             # <<<<<<<<<<<<<<
 *         cdef SIZE_t partition_end
 * 
 */
  __pyx_v_n_total_constants = __pyx_v_n_known_constants;

  /* "sklearn/tree/_splitter.pyx":1481
 *         cdef DTYPE_t max_feature_value
 * 
 *         cdef bint is_samples_sorted = 0  # indicate that sorted_samples is             # <<<<<<<<<<<<<<
 *                                          # inititialized
 * 
 */
  __pyx_v_is_samples_sorted = 0;

  /* "sklearn/tree/_splitter.pyx":1498
 *         # newly discovered constant features to spare computation on descendant
 *         # nodes.
 *         while (f_i > n_total_constants and  # Stop early if remaining features             # <<<<<<<<<<<<<<
 *                                             # are constant
 *                 (n_visited_features < max_features or
 */
  while (1) {
    __pyx_t_7 = ((__pyx_v_f_i > __pyx_v_n_total_constants) != 0);
    if (__pyx_t_7) {
    } else {
      __pyx_t_6 = __pyx_t_7;
      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1500
 *         while (f_i > n_total_constants and  # Stop early if remaining features
 *                                             # are constant
 *                 (n_visited_features < max_features or             # <<<<<<<<<<<<<<
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 */
    __pyx_t_7 = ((__pyx_v_n_visited_features < __pyx_v_max_features) != 0);
    if (!__pyx_t_7) {
    } else {
      __pyx_t_6 = __pyx_t_7;
      goto __pyx_L5_bool_binop_done;
    }

    /* "sklearn/tree/_splitter.pyx":1502
 *                 (n_visited_features < max_features or
 *                  # At least one drawn features must be non constant
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):             # <<<<<<<<<<<<<<
 * 
 *             n_visited_features += 1
 */
    __pyx_t_7 = ((__pyx_v_n_visited_features <= (__pyx_v_n_found_constants + __pyx_v_n_drawn_constants)) != 0);
    __pyx_t_6 = __pyx_t_7;
    __pyx_L5_bool_binop_done:;
    if (!__pyx_t_6) break;

    /* "sklearn/tree/_splitter.pyx":1504
 *                  n_visited_features <= n_found_constants + n_drawn_constants)):
 * 
 *             n_visited_features += 1             # <<<<<<<<<<<<<<
 * 
 *             # Loop invariant: elements of features in
 */
    __pyx_v_n_visited_features = (__pyx_v_n_visited_features + 1);

    /* "sklearn/tree/_splitter.pyx":1518
 * 
 *             # Draw a feature at random
 *             f_j = rand_int(n_drawn_constants, f_i - n_found_constants,             # <<<<<<<<<<<<<<
 *                            random_state)
 * 
 */
    __pyx_v_f_j = __pyx_f_7sklearn_4tree_6_utils_rand_int(__pyx_v_n_drawn_constants, (__pyx_v_f_i - __pyx_v_n_found_constants), __pyx_v_random_state);

    /* "sklearn/tree/_splitter.pyx":1521
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
    __pyx_t_6 = ((__pyx_v_f_j < __pyx_v_n_known_constants) != 0);
    if (__pyx_t_6) {

      /* "sklearn/tree/_splitter.pyx":1523
 *             if f_j < n_known_constants:
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]             # <<<<<<<<<<<<<<
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp
 */
      __pyx_v_tmp = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":1524
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]             # <<<<<<<<<<<<<<
 *                 features[n_drawn_constants] = tmp
 * 
 */
      (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_drawn_constants]);

      /* "sklearn/tree/_splitter.pyx":1525
 *                 tmp = features[f_j]
 *                 features[f_j] = features[n_drawn_constants]
 *                 features[n_drawn_constants] = tmp             # <<<<<<<<<<<<<<
 * 
 *                 n_drawn_constants += 1
 */
      (__pyx_v_features[__pyx_v_n_drawn_constants]) = __pyx_v_tmp;

      /* "sklearn/tree/_splitter.pyx":1527
 *                 features[n_drawn_constants] = tmp
 * 
 *                 n_drawn_constants += 1             # <<<<<<<<<<<<<<
 * 
 *             else:
 */
      __pyx_v_n_drawn_constants = (__pyx_v_n_drawn_constants + 1);

      /* "sklearn/tree/_splitter.pyx":1521
 *                            random_state)
 * 
 *             if f_j < n_known_constants:             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_drawn_constants, n_known_constants[
 *                 tmp = features[f_j]
 */
      goto __pyx_L8;
    }

    /* "sklearn/tree/_splitter.pyx":1531
 *             else:
 *                 # f_j in the interval [n_known_constants, f_i - n_found_constants[
 *                 f_j += n_found_constants             # <<<<<<<<<<<<<<
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 */
    /*else*/ {
      __pyx_v_f_j = (__pyx_v_f_j + __pyx_v_n_found_constants);

      /* "sklearn/tree/_splitter.pyx":1534
 *                 # f_j in the interval [n_total_constants, f_i[
 * 
 *                 current.feature = features[f_j]             # <<<<<<<<<<<<<<
 * 
 *                 self.extract_nnz(current.feature,
 */
      __pyx_v_current.feature = (__pyx_v_features[__pyx_v_f_j]);

      /* "sklearn/tree/_splitter.pyx":1536
 *                 current.feature = features[f_j]
 * 
 *                 self.extract_nnz(current.feature,             # <<<<<<<<<<<<<<
 *                                  &end_negative, &start_positive,
 *                                  &is_samples_sorted)
 */
      __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));

      /* "sklearn/tree/_splitter.pyx":1541
 * 
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:             # <<<<<<<<<<<<<<
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.
 */
      __pyx_t_6 = ((__pyx_v_end_negative < __pyx_v_start_positive) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":1542
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:
 *                     start_positive -= 1             # <<<<<<<<<<<<<<
 *                     Xf[start_positive] = 0.
 * 
 */
        __pyx_v_start_positive = (__pyx_v_start_positive - 1);

        /* "sklearn/tree/_splitter.pyx":1543
 *                 if end_negative < start_positive:
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.             # <<<<<<<<<<<<<<
 * 
 *                     if end_negative != start_positive:
 */
        (__pyx_v_Xf[__pyx_v_start_positive]) = 0.;

        /* "sklearn/tree/_splitter.pyx":1545
 *                     Xf[start_positive] = 0.
 * 
 *                     if end_negative != start_positive:             # <<<<<<<<<<<<<<
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1
 */
        __pyx_t_6 = ((__pyx_v_end_negative != __pyx_v_start_positive) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1546
 * 
 *                     if end_negative != start_positive:
 *                         Xf[end_negative] = 0.             # <<<<<<<<<<<<<<
 *                         end_negative += 1
 * 
 */
          (__pyx_v_Xf[__pyx_v_end_negative]) = 0.;

          /* "sklearn/tree/_splitter.pyx":1547
 *                     if end_negative != start_positive:
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1             # <<<<<<<<<<<<<<
 * 
 *                 # Find min, max in Xf[start:end_negative]
 */
          __pyx_v_end_negative = (__pyx_v_end_negative + 1);

          /* "sklearn/tree/_splitter.pyx":1545
 *                     Xf[start_positive] = 0.
 * 
 *                     if end_negative != start_positive:             # <<<<<<<<<<<<<<
 *                         Xf[end_negative] = 0.
 *                         end_negative += 1
 */
        }

        /* "sklearn/tree/_splitter.pyx":1541
 * 
 *                 # Add one or two zeros in Xf, if there is any
 *                 if end_negative < start_positive:             # <<<<<<<<<<<<<<
 *                     start_positive -= 1
 *                     Xf[start_positive] = 0.
 */
      }

      /* "sklearn/tree/_splitter.pyx":1550
 * 
 *                 # Find min, max in Xf[start:end_negative]
 *                 min_feature_value = Xf[start]             # <<<<<<<<<<<<<<
 *                 max_feature_value = min_feature_value
 * 
 */
      __pyx_v_min_feature_value = (__pyx_v_Xf[__pyx_v_start]);

      /* "sklearn/tree/_splitter.pyx":1551
 *                 # Find min, max in Xf[start:end_negative]
 *                 min_feature_value = Xf[start]
 *                 max_feature_value = min_feature_value             # <<<<<<<<<<<<<<
 * 
 *                 for p in range(start, end_negative):
 */
      __pyx_v_max_feature_value = __pyx_v_min_feature_value;

      /* "sklearn/tree/_splitter.pyx":1553
 *                 max_feature_value = min_feature_value
 * 
 *                 for p in range(start, end_negative):             # <<<<<<<<<<<<<<
 *                     current_feature_value = Xf[p]
 * 
 */
      __pyx_t_2 = __pyx_v_end_negative;
      for (__pyx_t_8 = __pyx_v_start; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
        __pyx_v_p = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1554
 * 
 *                 for p in range(start, end_negative):
 *                     current_feature_value = Xf[p]             # <<<<<<<<<<<<<<
 * 
 *                     if current_feature_value < min_feature_value:
 */
        __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);

        /* "sklearn/tree/_splitter.pyx":1556
 *                     current_feature_value = Xf[p]
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
        __pyx_t_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1557
 * 
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value
 */
          __pyx_v_min_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":1556
 *                     current_feature_value = Xf[p]
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
          goto __pyx_L13;
        }

        /* "sklearn/tree/_splitter.pyx":1558
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        __pyx_t_6 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1559
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 * 
 *                 # Update min, max given Xf[start_positive:end]
 */
          __pyx_v_max_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":1558
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        }
        __pyx_L13:;
      }

      /* "sklearn/tree/_splitter.pyx":1562
 * 
 *                 # Update min, max given Xf[start_positive:end]
 *                 for p in range(start_positive, end):             # <<<<<<<<<<<<<<
 *                     current_feature_value = Xf[p]
 * 
 */
      __pyx_t_2 = __pyx_v_end;
      for (__pyx_t_8 = __pyx_v_start_positive; __pyx_t_8 < __pyx_t_2; __pyx_t_8+=1) {
        __pyx_v_p = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1563
 *                 # Update min, max given Xf[start_positive:end]
 *                 for p in range(start_positive, end):
 *                     current_feature_value = Xf[p]             # <<<<<<<<<<<<<<
 * 
 *                     if current_feature_value < min_feature_value:
 */
        __pyx_v_current_feature_value = (__pyx_v_Xf[__pyx_v_p]);

        /* "sklearn/tree/_splitter.pyx":1565
 *                     current_feature_value = Xf[p]
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
        __pyx_t_6 = ((__pyx_v_current_feature_value < __pyx_v_min_feature_value) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1566
 * 
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value
 */
          __pyx_v_min_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":1565
 *                     current_feature_value = Xf[p]
 * 
 *                     if current_feature_value < min_feature_value:             # <<<<<<<<<<<<<<
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 */
          goto __pyx_L16;
        }

        /* "sklearn/tree/_splitter.pyx":1567
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        __pyx_t_6 = ((__pyx_v_current_feature_value > __pyx_v_max_feature_value) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1568
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:
 *                         max_feature_value = current_feature_value             # <<<<<<<<<<<<<<
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 */
          __pyx_v_max_feature_value = __pyx_v_current_feature_value;

          /* "sklearn/tree/_splitter.pyx":1567
 *                     if current_feature_value < min_feature_value:
 *                         min_feature_value = current_feature_value
 *                     elif current_feature_value > max_feature_value:             # <<<<<<<<<<<<<<
 *                         max_feature_value = current_feature_value
 * 
 */
        }
        __pyx_L16:;
      }

      /* "sklearn/tree/_splitter.pyx":1570
 *                         max_feature_value = current_feature_value
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
      __pyx_t_6 = ((__pyx_v_max_feature_value <= (__pyx_v_min_feature_value + __pyx_v_7sklearn_4tree_9_splitter_FEATURE_THRESHOLD)) != 0);
      if (__pyx_t_6) {

        /* "sklearn/tree/_splitter.pyx":1571
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]             # <<<<<<<<<<<<<<
 *                     features[n_total_constants] = current.feature
 * 
 */
        (__pyx_v_features[__pyx_v_f_j]) = (__pyx_v_features[__pyx_v_n_total_constants]);

        /* "sklearn/tree/_splitter.pyx":1572
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature             # <<<<<<<<<<<<<<
 * 
 *                     n_found_constants += 1
 */
        __pyx_t_2 = __pyx_v_current.feature;
        (__pyx_v_features[__pyx_v_n_total_constants]) = __pyx_t_2;

        /* "sklearn/tree/_splitter.pyx":1574
 *                     features[n_total_constants] = current.feature
 * 
 *                     n_found_constants += 1             # <<<<<<<<<<<<<<
 *                     n_total_constants += 1
 * 
 */
        __pyx_v_n_found_constants = (__pyx_v_n_found_constants + 1);

        /* "sklearn/tree/_splitter.pyx":1575
 * 
 *                     n_found_constants += 1
 *                     n_total_constants += 1             # <<<<<<<<<<<<<<
 * 
 *                 else:
 */
        __pyx_v_n_total_constants = (__pyx_v_n_total_constants + 1);

        /* "sklearn/tree/_splitter.pyx":1570
 *                         max_feature_value = current_feature_value
 * 
 *                 if max_feature_value <= min_feature_value + FEATURE_THRESHOLD:             # <<<<<<<<<<<<<<
 *                     features[f_j] = features[n_total_constants]
 *                     features[n_total_constants] = current.feature
 */
        goto __pyx_L17;
      }

      /* "sklearn/tree/_splitter.pyx":1578
 * 
 *                 else:
 *                     f_i -= 1             # <<<<<<<<<<<<<<
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]
 * 
 */
      /*else*/ {
        __pyx_v_f_i = (__pyx_v_f_i - 1);

        /* "sklearn/tree/_splitter.pyx":1579
 *                 else:
 *                     f_i -= 1
 *                     features[f_i], features[f_j] = features[f_j], features[f_i]             # <<<<<<<<<<<<<<
 * 
 *                     # Draw a random threshold
 */
        __pyx_t_2 = (__pyx_v_features[__pyx_v_f_j]);
        __pyx_t_8 = (__pyx_v_features[__pyx_v_f_i]);
        (__pyx_v_features[__pyx_v_f_i]) = __pyx_t_2;
        (__pyx_v_features[__pyx_v_f_j]) = __pyx_t_8;

        /* "sklearn/tree/_splitter.pyx":1582
 * 
 *                     # Draw a random threshold
 *                     current.threshold = rand_uniform(min_feature_value,             # <<<<<<<<<<<<<<
 *                                                      max_feature_value,
 *                                                      random_state)
 */
        __pyx_v_current.threshold = __pyx_f_7sklearn_4tree_6_utils_rand_uniform(__pyx_v_min_feature_value, __pyx_v_max_feature_value, __pyx_v_random_state);

        /* "sklearn/tree/_splitter.pyx":1586
 *                                                      random_state)
 * 
 *                     if current.threshold == max_feature_value:             # <<<<<<<<<<<<<<
 *                         current.threshold = min_feature_value
 * 
 */
        __pyx_t_6 = ((__pyx_v_current.threshold == __pyx_v_max_feature_value) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1587
 * 
 *                     if current.threshold == max_feature_value:
 *                         current.threshold = min_feature_value             # <<<<<<<<<<<<<<
 * 
 *                     # Partition
 */
          __pyx_v_current.threshold = __pyx_v_min_feature_value;

          /* "sklearn/tree/_splitter.pyx":1586
 *                                                      random_state)
 * 
 *                     if current.threshold == max_feature_value:             # <<<<<<<<<<<<<<
 *                         current.threshold = min_feature_value
 * 
 */
        }

        /* "sklearn/tree/_splitter.pyx":1590
 * 
 *                     # Partition
 *                     current.pos = self._partition(current.threshold,             # <<<<<<<<<<<<<<
 *                                                   end_negative,
 *                                                   start_positive,
 */
        __pyx_v_current.pos = __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_current.threshold, __pyx_v_end_negative, __pyx_v_start_positive, (__pyx_v_start_positive + ((__pyx_v_Xf[__pyx_v_start_positive]) == 0.)));

        /* "sklearn/tree/_splitter.pyx":1597
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        __pyx_t_7 = (((__pyx_v_current.pos - __pyx_v_start) < __pyx_v_min_samples_leaf) != 0);
        if (!__pyx_t_7) {
        } else {
          __pyx_t_6 = __pyx_t_7;
          goto __pyx_L20_bool_binop_done;
        }

        /* "sklearn/tree/_splitter.pyx":1598
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or
 *                             ((end - current.pos) < min_samples_leaf)):             # <<<<<<<<<<<<<<
 *                         continue
 * 
 */
        __pyx_t_7 = (((__pyx_v_end - __pyx_v_current.pos) < __pyx_v_min_samples_leaf) != 0);
        __pyx_t_6 = __pyx_t_7;
        __pyx_L20_bool_binop_done:;

        /* "sklearn/tree/_splitter.pyx":1597
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1599
 *                     if (((current.pos - start) < min_samples_leaf) or
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue             # <<<<<<<<<<<<<<
 * 
 *                     # Evaluate split
 */
          goto __pyx_L3_continue;

          /* "sklearn/tree/_splitter.pyx":1597
 * 
 *                     # Reject if min_samples_leaf is not guaranteed
 *                     if (((current.pos - start) < min_samples_leaf) or             # <<<<<<<<<<<<<<
 *                             ((end - current.pos) < min_samples_leaf)):
 *                         continue
 */
        }

        /* "sklearn/tree/_splitter.pyx":1602
 * 
 *                     # Evaluate split
 *                     self.criterion.reset()             # <<<<<<<<<<<<<<
 *                     self.criterion.update(current.pos)
 * 
 */
        __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1602, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":1603
 *                     # Evaluate split
 *                     self.criterion.reset()
 *                     self.criterion.update(current.pos)             # <<<<<<<<<<<<<<
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 */
        __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_current.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1603, __pyx_L1_error)

        /* "sklearn/tree/_splitter.pyx":1606
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_left < __pyx_v_min_weight_leaf) != 0);
        if (!__pyx_t_7) {
        } else {
          __pyx_t_6 = __pyx_t_7;
          goto __pyx_L23_bool_binop_done;
        }

        /* "sklearn/tree/_splitter.pyx":1607
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):             # <<<<<<<<<<<<<<
 *                         continue
 * 
 */
        __pyx_t_7 = ((__pyx_v_self->__pyx_base.__pyx_base.criterion->weighted_n_right < __pyx_v_min_weight_leaf) != 0);
        __pyx_t_6 = __pyx_t_7;
        __pyx_L23_bool_binop_done:;

        /* "sklearn/tree/_splitter.pyx":1606
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1608
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue             # <<<<<<<<<<<<<<
 * 
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 */
          goto __pyx_L3_continue;

          /* "sklearn/tree/_splitter.pyx":1606
 * 
 *                     # Reject if min_weight_leaf is not satisfied
 *                     if ((self.criterion.weighted_n_left < min_weight_leaf) or             # <<<<<<<<<<<<<<
 *                             (self.criterion.weighted_n_right < min_weight_leaf)):
 *                         continue
 */
        }

        /* "sklearn/tree/_splitter.pyx":1610
 *                         continue
 * 
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()             # <<<<<<<<<<<<<<
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:
 */
        __pyx_v_current_proxy_improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->proxy_impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion);

        /* "sklearn/tree/_splitter.pyx":1612
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                         best_proxy_improvement = current_proxy_improvement
 *                         current.improvement = self.criterion.impurity_improvement(impurity)
 */
        __pyx_t_6 = ((__pyx_v_current_proxy_improvement > __pyx_v_best_proxy_improvement) != 0);
        if (__pyx_t_6) {

          /* "sklearn/tree/_splitter.pyx":1613
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:
 *                         best_proxy_improvement = current_proxy_improvement             # <<<<<<<<<<<<<<
 *                         current.improvement = self.criterion.impurity_improvement(impurity)
 * 
 */
          __pyx_v_best_proxy_improvement = __pyx_v_current_proxy_improvement;

          /* "sklearn/tree/_splitter.pyx":1614
 *                     if current_proxy_improvement > best_proxy_improvement:
 *                         best_proxy_improvement = current_proxy_improvement
 *                         current.improvement = self.criterion.impurity_improvement(impurity)             # <<<<<<<<<<<<<<
 * 
 *                         self.criterion.children_impurity(&current.impurity_left,
 */
          __pyx_v_current.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);

          /* "sklearn/tree/_splitter.pyx":1616
 *                         current.improvement = self.criterion.impurity_improvement(impurity)
 * 
 *                         self.criterion.children_impurity(&current.impurity_left,             # <<<<<<<<<<<<<<
 *                                                          &current.impurity_right)
 *                         best = current
 */
          ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_current.impurity_left), (&__pyx_v_current.impurity_right));

          /* "sklearn/tree/_splitter.pyx":1618
 *                         self.criterion.children_impurity(&current.impurity_left,
 *                                                          &current.impurity_right)
 *                         best = current             # <<<<<<<<<<<<<<
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 */
          __pyx_v_best = __pyx_v_current;

          /* "sklearn/tree/_splitter.pyx":1612
 *                     current_proxy_improvement = self.criterion.proxy_impurity_improvement()
 * 
 *                     if current_proxy_improvement > best_proxy_improvement:             # <<<<<<<<<<<<<<
 *                         best_proxy_improvement = current_proxy_improvement
 *                         current.improvement = self.criterion.impurity_improvement(impurity)
 */
        }
      }
      __pyx_L17:;
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    __pyx_L8:;
    __pyx_L3_continue:;
  }

  /* "sklearn/tree/_splitter.pyx":1621
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:             # <<<<<<<<<<<<<<
 *             if current.feature != best.feature:
 *                 self.extract_nnz(best.feature, &end_negative, &start_positive,
 */
  __pyx_t_6 = ((__pyx_v_best.pos < __pyx_v_end) != 0);
  if (__pyx_t_6) {

    /* "sklearn/tree/_splitter.pyx":1622
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:
 *             if current.feature != best.feature:             # <<<<<<<<<<<<<<
 *                 self.extract_nnz(best.feature, &end_negative, &start_positive,
 *                                  &is_samples_sorted)
 */
    __pyx_t_6 = ((__pyx_v_current.feature != __pyx_v_best.feature) != 0);
    if (__pyx_t_6) {

      /* "sklearn/tree/_splitter.pyx":1623
 *         if best.pos < end:
 *             if current.feature != best.feature:
 *                 self.extract_nnz(best.feature, &end_negative, &start_positive,             # <<<<<<<<<<<<<<
 *                                  &is_samples_sorted)
 * 
 */
      __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter_extract_nnz(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.feature, (&__pyx_v_end_negative), (&__pyx_v_start_positive), (&__pyx_v_is_samples_sorted));

      /* "sklearn/tree/_splitter.pyx":1626
 *                                  &is_samples_sorted)
 * 
 *                 self._partition(best.threshold, end_negative, start_positive,             # <<<<<<<<<<<<<<
 *                                 best.pos)
 * 
 */
      __pyx_f_7sklearn_4tree_9_splitter_18BaseSparseSplitter__partition(((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)__pyx_v_self), __pyx_v_best.threshold, __pyx_v_end_negative, __pyx_v_start_positive, __pyx_v_best.pos);

      /* "sklearn/tree/_splitter.pyx":1622
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:
 *             if current.feature != best.feature:             # <<<<<<<<<<<<<<
 *                 self.extract_nnz(best.feature, &end_negative, &start_positive,
 *                                  &is_samples_sorted)
 */
    }

    /* "sklearn/tree/_splitter.pyx":1629
 *                                 best.pos)
 * 
 *             self.criterion.reset()             # <<<<<<<<<<<<<<
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->reset(__pyx_v_self->__pyx_base.__pyx_base.criterion); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1629, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":1630
 * 
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)             # <<<<<<<<<<<<<<
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,
 */
    __pyx_t_9 = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->update(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_best.pos); if (unlikely(__pyx_t_9 == -1)) __PYX_ERR(0, 1630, __pyx_L1_error)

    /* "sklearn/tree/_splitter.pyx":1631
 *             self.criterion.reset()
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)             # <<<<<<<<<<<<<<
 *             self.criterion.children_impurity(&best.impurity_left,
 *                                              &best.impurity_right)
 */
    __pyx_v_best.improvement = ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->impurity_improvement(__pyx_v_self->__pyx_base.__pyx_base.criterion, __pyx_v_impurity);

    /* "sklearn/tree/_splitter.pyx":1632
 *             self.criterion.update(best.pos)
 *             best.improvement = self.criterion.impurity_improvement(impurity)
 *             self.criterion.children_impurity(&best.impurity_left,             # <<<<<<<<<<<<<<
 *                                              &best.impurity_right)
 * 
 */
    ((struct __pyx_vtabstruct_7sklearn_4tree_10_criterion_Criterion *)__pyx_v_self->__pyx_base.__pyx_base.criterion->__pyx_vtab)->children_impurity(__pyx_v_self->__pyx_base.__pyx_base.criterion, (&__pyx_v_best.impurity_left), (&__pyx_v_best.impurity_right));

    /* "sklearn/tree/_splitter.pyx":1621
 * 
 *         # Reorganize into samples[start:best.pos] + samples[best.pos:end]
 *         if best.pos < end:             # <<<<<<<<<<<<<<
 *             if current.feature != best.feature:
 *                 self.extract_nnz(best.feature, &end_negative, &start_positive,
 */
  }

  /* "sklearn/tree/_splitter.pyx":1638
 *         # element in features[:n_known_constants] must be preserved for sibling
 *         # and child nodes
 *         memcpy(features, constant_features, sizeof(SIZE_t) * n_known_constants)             # <<<<<<<<<<<<<<
 * 
 *         # Copy newly found constant features
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  memcpy(__pyx_v_features, __pyx_v_constant_features, ((sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)) * __pyx_v_n_known_constants));

  /* "sklearn/tree/_splitter.pyx":1641
 * 
 *         # Copy newly found constant features
 *         memcpy(constant_features + n_known_constants,             # <<<<<<<<<<<<<<
 *                features + n_known_constants,
 *                sizeof(SIZE_t) * n_found_constants)
 */
  memcpy((__pyx_v_constant_features + __pyx_v_n_known_constants), (__pyx_v_features + __pyx_v_n_known_constants), ((sizeof(__pyx_t_7sklearn_4tree_9_splitter_SIZE_t)) * __pyx_v_n_found_constants));

  /* "sklearn/tree/_splitter.pyx":1646
 * 
 *         # Return values
 *         split[0] = best             # <<<<<<<<<<<<<<
 *         n_constant_features[0] = n_total_constants
 *         return 0
 */
  (__pyx_v_split[0]) = __pyx_v_best;

  /* "sklearn/tree/_splitter.pyx":1647
 *         # Return values
 *         split[0] = best
 *         n_constant_features[0] = n_total_constants             # <<<<<<<<<<<<<<
 *         return 0
 */
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  /* "sklearn/tree/_splitter.pyx":1648
 *         split[0] = best
 *         n_constant_features[0] = n_total_constants
 *         return 0             # <<<<<<<<<<<<<<
 */
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 *                                        self.presort), self.__getstate__())
 * 
 *     cdef int node_split(self, double impurity, SplitRecord* split,             # <<<<<<<<<<<<<<
 *                         SIZE_t* n_constant_features) nogil except -1:
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 */

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static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_Splitter(PyTypeObject *t, PyObject *a, PyObject *k) {
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  } else {
    o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0);
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  p->random_state = Py_None; Py_INCREF(Py_None);
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static void __pyx_tp_dealloc_7sklearn_4tree_9_splitter_Splitter(PyObject *o) {
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  {
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}

static int __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter(PyObject *o, visitproc v, void *a) {
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}

static int __pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter(PyObject *o) {
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static PyObject *__pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_criterion(PyObject *o, CYTHON_UNUSED void *x) {
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}

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  }
  else {
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}

static PyObject *__pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_max_features(PyObject *o, CYTHON_UNUSED void *x) {
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}

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}

static PyObject *__pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_min_samples_leaf(PyObject *o, CYTHON_UNUSED void *x) {
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}

static int __pyx_setprop_7sklearn_4tree_9_splitter_8Splitter_min_samples_leaf(PyObject *o, PyObject *v, CYTHON_UNUSED void *x) {
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static PyObject *__pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_min_weight_leaf(PyObject *o, CYTHON_UNUSED void *x) {
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}

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  else {
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}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_Splitter[] = {
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  {"__reduce_cython__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_8Splitter_9__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_8Splitter_11__setstate_cython__, METH_O, 0},
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};

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  {(char *)"min_samples_leaf", __pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_min_samples_leaf, __pyx_setprop_7sklearn_4tree_9_splitter_8Splitter_min_samples_leaf, (char *)0, 0},
  {(char *)"min_weight_leaf", __pyx_getprop_7sklearn_4tree_9_splitter_8Splitter_min_weight_leaf, __pyx_setprop_7sklearn_4tree_9_splitter_8Splitter_min_weight_leaf, (char *)0, 0},
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};

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  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_Splitter), /*tp_basicsize*/
  0, /*tp_itemsize*/
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  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
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  0, /*tp_repr*/
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  __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_Splitter, /*tp_methods*/
  0, /*tp_members*/
  __pyx_getsets_7sklearn_4tree_9_splitter_Splitter, /*tp_getset*/
  0, /*tp_base*/
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  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_Splitter, /*tp_new*/
  0, /*tp_free*/
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  0, /*tp_finalize*/
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};
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  return o;
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}

static void __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o) {
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static int __pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o, visitproc v, void *a) {
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static int __pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter(PyObject *o) {
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}

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  {"__setstate_cython__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_17BaseDenseSplitter_7__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

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  0, /*tp_itemsize*/
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  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
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  0, /*tp_str*/
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  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
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  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_methods*/
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  __pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_new*/
  0, /*tp_free*/
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  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BestSplitter;

static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *p;
  PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter *)o);
  p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSplitter;
  return o;
}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BestSplitter[] = {
  {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_12BestSplitter_1__reduce__, METH_NOARGS, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BestSplitter = {
  PyVarObject_HEAD_INIT(0, 0)
  "sklearn.tree._splitter.BestSplitter", /*tp_name*/
  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSplitter), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  0, /*tp_repr*/
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  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  0, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Splitter for finding the best split.", /*tp_doc*/
  __pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_BestSplitter, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_BestSplitter, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSplitter __pyx_vtable_7sklearn_4tree_9_splitter_RandomSplitter;

static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *p;
  PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseDenseSplitter(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter *)o);
  p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSplitter;
  return o;
}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_RandomSplitter[] = {
  {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_14RandomSplitter_1__reduce__, METH_NOARGS, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_RandomSplitter = {
  PyVarObject_HEAD_INIT(0, 0)
  "sklearn.tree._splitter.RandomSplitter", /*tp_name*/
  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSplitter), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
  #if PY_MAJOR_VERSION >= 3
  0, /*tp_as_async*/
  #endif
  0, /*tp_repr*/
  0, /*tp_as_number*/
  0, /*tp_as_sequence*/
  0, /*tp_as_mapping*/
  0, /*tp_hash*/
  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
  0, /*tp_as_buffer*/
  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Splitter for finding the best random split.", /*tp_doc*/
  __pyx_tp_traverse_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_BaseDenseSplitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_RandomSplitter, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
  0, /*tp_descr_get*/
  0, /*tp_descr_set*/
  0, /*tp_dictoffset*/
  0, /*tp_init*/
  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_RandomSplitter, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
  0, /*tp_weaklist*/
  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BaseSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BaseSparseSplitter;

static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *p;
  PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_Splitter(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter *)o);
  p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BaseSparseSplitter;
  if (unlikely(__pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_1__cinit__(o, a, k) < 0)) goto bad;
  return o;
  bad:
  Py_DECREF(o); o = 0;
  return NULL;
}

static void __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter(PyObject *o) {
  #if PY_VERSION_HEX >= 0x030400a1
  if (unlikely(PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE) && Py_TYPE(o)->tp_finalize) && !_PyGC_FINALIZED(o)) {
    if (PyObject_CallFinalizerFromDealloc(o)) return;
  }
  #endif
  PyObject_GC_UnTrack(o);
  {
    PyObject *etype, *eval, *etb;
    PyErr_Fetch(&etype, &eval, &etb);
    ++Py_REFCNT(o);
    __pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_3__dealloc__(o);
    --Py_REFCNT(o);
    PyErr_Restore(etype, eval, etb);
  }
  PyObject_GC_Track(o);
  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_Splitter(o);
}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BaseSparseSplitter[] = {
  {"__reduce_cython__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_5__reduce_cython__, METH_NOARGS, 0},
  {"__setstate_cython__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_18BaseSparseSplitter_7__setstate_cython__, METH_O, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BaseSparseSplitter = {
  PyVarObject_HEAD_INIT(0, 0)
  "sklearn.tree._splitter.BaseSparseSplitter", /*tp_name*/
  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BaseSparseSplitter), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
  0, /*tp_compare*/
  #endif
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  0, /*tp_call*/
  0, /*tp_str*/
  0, /*tp_getattro*/
  0, /*tp_setattro*/
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  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  0, /*tp_doc*/
  __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
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  0, /*tp_dictoffset*/
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  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_new*/
  0, /*tp_free*/
  0, /*tp_is_gc*/
  0, /*tp_bases*/
  0, /*tp_mro*/
  0, /*tp_cache*/
  0, /*tp_subclasses*/
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  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_BestSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_BestSparseSplitter;

static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *p;
  PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter *)o);
  p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_BestSparseSplitter;
  return o;
}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_BestSparseSplitter[] = {
  {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_18BestSparseSplitter_1__reduce__, METH_NOARGS, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_BestSparseSplitter = {
  PyVarObject_HEAD_INIT(0, 0)
  "sklearn.tree._splitter.BestSparseSplitter", /*tp_name*/
  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_BestSparseSplitter), /*tp_basicsize*/
  0, /*tp_itemsize*/
  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
  0, /*tp_print*/
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  0, /*tp_setattr*/
  #if PY_MAJOR_VERSION < 3
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  __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_BestSparseSplitter, /*tp_methods*/
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  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_BestSparseSplitter, /*tp_new*/
  0, /*tp_free*/
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  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
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};
static struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_RandomSparseSplitter __pyx_vtable_7sklearn_4tree_9_splitter_RandomSparseSplitter;

static PyObject *__pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter(PyTypeObject *t, PyObject *a, PyObject *k) {
  struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *p;
  PyObject *o = __pyx_tp_new_7sklearn_4tree_9_splitter_BaseSparseSplitter(t, a, k);
  if (unlikely(!o)) return 0;
  p = ((struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter *)o);
  p->__pyx_base.__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_7sklearn_4tree_9_splitter_Splitter*)__pyx_vtabptr_7sklearn_4tree_9_splitter_RandomSparseSplitter;
  return o;
}

static PyMethodDef __pyx_methods_7sklearn_4tree_9_splitter_RandomSparseSplitter[] = {
  {"__reduce__", (PyCFunction)__pyx_pw_7sklearn_4tree_9_splitter_20RandomSparseSplitter_1__reduce__, METH_NOARGS, 0},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_7sklearn_4tree_9_splitter_RandomSparseSplitter = {
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  "sklearn.tree._splitter.RandomSparseSplitter", /*tp_name*/
  sizeof(struct __pyx_obj_7sklearn_4tree_9_splitter_RandomSparseSplitter), /*tp_basicsize*/
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  __pyx_tp_dealloc_7sklearn_4tree_9_splitter_BaseSparseSplitter, /*tp_dealloc*/
  0, /*tp_print*/
  0, /*tp_getattr*/
  0, /*tp_setattr*/
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  0, /*tp_compare*/
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  0, /*tp_str*/
  0, /*tp_getattro*/
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  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
  "Splitter for finding a random split, using the sparse data.", /*tp_doc*/
  __pyx_tp_traverse_7sklearn_4tree_9_splitter_Splitter, /*tp_traverse*/
  __pyx_tp_clear_7sklearn_4tree_9_splitter_Splitter, /*tp_clear*/
  0, /*tp_richcompare*/
  0, /*tp_weaklistoffset*/
  0, /*tp_iter*/
  0, /*tp_iternext*/
  __pyx_methods_7sklearn_4tree_9_splitter_RandomSparseSplitter, /*tp_methods*/
  0, /*tp_members*/
  0, /*tp_getset*/
  0, /*tp_base*/
  0, /*tp_dict*/
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  0, /*tp_dictoffset*/
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  0, /*tp_alloc*/
  __pyx_tp_new_7sklearn_4tree_9_splitter_RandomSparseSplitter, /*tp_new*/
  0, /*tp_free*/
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  0, /*tp_del*/
  0, /*tp_version_tag*/
  #if PY_VERSION_HEX >= 0x030400a1
  0, /*tp_finalize*/
  #endif
};

static PyMethodDef __pyx_methods[] = {
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};

#if PY_MAJOR_VERSION >= 3
static struct PyModuleDef __pyx_moduledef = {
  #if PY_VERSION_HEX < 0x03020000
    { PyObject_HEAD_INIT(NULL) NULL, 0, NULL },
  #else
    PyModuleDef_HEAD_INIT,
  #endif
    "_splitter",
    0, /* m_doc */
    -1, /* m_size */
    __pyx_methods /* m_methods */,
    NULL, /* m_reload */
    NULL, /* m_traverse */
    NULL, /* m_clear */
    NULL /* m_free */
};
#endif

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typedef struct { int x; char c; } __Pyx_pad_int;
typedef struct { long x; char c; } __Pyx_pad_long;
typedef struct { float x; char c; } __Pyx_pad_float;
typedef struct { double x; char c; } __Pyx_pad_double;
typedef struct { long double x; char c; } __Pyx_pad_longdouble;
typedef struct { void *x; char c; } __Pyx_pad_void_p;
#ifdef HAVE_LONG_LONG
typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;
#endif
static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {
  switch (ch) {
    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;
    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);
    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);
    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);
#ifdef HAVE_LONG_LONG
    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);
#endif
    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);
    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);
    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);
    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);
    default:
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
}
static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {
  switch (ch) {
    case 'c':
        return 'H';
    case 'b': case 'h': case 'i':
    case 'l': case 'q': case 's': case 'p':
        return 'I';
    case 'B': case 'H': case 'I': case 'L': case 'Q':
        return 'U';
    case 'f': case 'd': case 'g':
        return (is_complex ? 'C' : 'R');
    case 'O':
        return 'O';
    case 'P':
        return 'P';
    default: {
      __Pyx_BufFmt_RaiseUnexpectedChar(ch);
      return 0;
    }
  }
}
static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {
  if (ctx->head == NULL || ctx->head->field == &ctx->root) {
    const char* expected;
    const char* quote;
    if (ctx->head == NULL) {
      expected = "end";
      quote = "";
    } else {
      expected = ctx->head->field->type->name;
      quote = "'";
    }
    PyErr_Format(PyExc_ValueError,
                 "Buffer dtype mismatch, expected %s%s%s but got %s",
                 quote, expected, quote,
                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));
  } else {
    __Pyx_StructField* field = ctx->head->field;
    __Pyx_StructField* parent = (ctx->head - 1)->field;
    PyErr_Format(PyExc_ValueError,
                 "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'",
                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),
                 parent->type->name, field->name);
  }
}
static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {
  char group;
  size_t size, offset, arraysize = 1;
  if (ctx->enc_type == 0) return 0;
  if (ctx->head->field->type->arraysize[0]) {
    int i, ndim = 0;
    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {
        ctx->is_valid_array = ctx->head->field->type->ndim == 1;
        ndim = 1;
        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {
            PyErr_Format(PyExc_ValueError,
                         "Expected a dimension of size %zu, got %zu",
                         ctx->head->field->type->arraysize[0], ctx->enc_count);
            return -1;
        }
    }
    if (!ctx->is_valid_array) {
      PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d",
                   ctx->head->field->type->ndim, ndim);
      return -1;
    }
    for (i = 0; i < ctx->head->field->type->ndim; i++) {
      arraysize *= ctx->head->field->type->arraysize[i];
    }
    ctx->is_valid_array = 0;
    ctx->enc_count = 1;
  }
  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);
  do {
    __Pyx_StructField* field = ctx->head->field;
    __Pyx_TypeInfo* type = field->type;
    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {
      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);
    } else {
      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);
    }
    if (ctx->enc_packmode == '@') {
      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);
      size_t align_mod_offset;
      if (align_at == 0) return -1;
      align_mod_offset = ctx->fmt_offset % align_at;
      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;
      if (ctx->struct_alignment == 0)
          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,
                                                                 ctx->is_complex);
    }
    if (type->size != size || type->typegroup != group) {
      if (type->typegroup == 'C' && type->fields != NULL) {
        size_t parent_offset = ctx->head->parent_offset + field->offset;
        ++ctx->head;
        ctx->head->field = type->fields;
        ctx->head->parent_offset = parent_offset;
        continue;
      }
      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {
      } else {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return -1;
      }
    }
    offset = ctx->head->parent_offset + field->offset;
    if (ctx->fmt_offset != offset) {
      PyErr_Format(PyExc_ValueError,
                   "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected",
                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);
      return -1;
    }
    ctx->fmt_offset += size;
    if (arraysize)
      ctx->fmt_offset += (arraysize - 1) * size;
    --ctx->enc_count;
    while (1) {
      if (field == &ctx->root) {
        ctx->head = NULL;
        if (ctx->enc_count != 0) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return -1;
        }
        break;
      }
      ctx->head->field = ++field;
      if (field->type == NULL) {
        --ctx->head;
        field = ctx->head->field;
        continue;
      } else if (field->type->typegroup == 'S') {
        size_t parent_offset = ctx->head->parent_offset + field->offset;
        if (field->type->fields->type == NULL) continue;
        field = field->type->fields;
        ++ctx->head;
        ctx->head->field = field;
        ctx->head->parent_offset = parent_offset;
        break;
      } else {
        break;
      }
    }
  } while (ctx->enc_count);
  ctx->enc_type = 0;
  ctx->is_complex = 0;
  return 0;
}
static CYTHON_INLINE PyObject *
__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)
{
    const char *ts = *tsp;
    int i = 0, number;
    int ndim = ctx->head->field->type->ndim;
;
    ++ts;
    if (ctx->new_count != 1) {
        PyErr_SetString(PyExc_ValueError,
                        "Cannot handle repeated arrays in format string");
        return NULL;
    }
    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
    while (*ts && *ts != ')') {
        switch (*ts) {
            case ' ': case '\f': case '\r': case '\n': case '\t': case '\v':  continue;
            default:  break;
        }
        number = __Pyx_BufFmt_ExpectNumber(&ts);
        if (number == -1) return NULL;
        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])
            return PyErr_Format(PyExc_ValueError,
                        "Expected a dimension of size %zu, got %d",
                        ctx->head->field->type->arraysize[i], number);
        if (*ts != ',' && *ts != ')')
            return PyErr_Format(PyExc_ValueError,
                                "Expected a comma in format string, got '%c'", *ts);
        if (*ts == ',') ts++;
        i++;
    }
    if (i != ndim)
        return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d",
                            ctx->head->field->type->ndim, i);
    if (!*ts) {
        PyErr_SetString(PyExc_ValueError,
                        "Unexpected end of format string, expected ')'");
        return NULL;
    }
    ctx->is_valid_array = 1;
    ctx->new_count = 1;
    *tsp = ++ts;
    return Py_None;
}
static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {
  int got_Z = 0;
  while (1) {
    switch(*ts) {
      case 0:
        if (ctx->enc_type != 0 && ctx->head == NULL) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return NULL;
        }
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        if (ctx->head != NULL) {
          __Pyx_BufFmt_RaiseExpected(ctx);
          return NULL;
        }
        return ts;
      case ' ':
      case '\r':
      case '\n':
        ++ts;
        break;
      case '<':
        if (!__Pyx_Is_Little_Endian()) {
          PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler");
          return NULL;
        }
        ctx->new_packmode = '=';
        ++ts;
        break;
      case '>':
      case '!':
        if (__Pyx_Is_Little_Endian()) {
          PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler");
          return NULL;
        }
        ctx->new_packmode = '=';
        ++ts;
        break;
      case '=':
      case '@':
      case '^':
        ctx->new_packmode = *ts++;
        break;
      case 'T':
        {
          const char* ts_after_sub;
          size_t i, struct_count = ctx->new_count;
          size_t struct_alignment = ctx->struct_alignment;
          ctx->new_count = 1;
          ++ts;
          if (*ts != '{') {
            PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'");
            return NULL;
          }
          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
          ctx->enc_type = 0;
          ctx->enc_count = 0;
          ctx->struct_alignment = 0;
          ++ts;
          ts_after_sub = ts;
          for (i = 0; i != struct_count; ++i) {
            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);
            if (!ts_after_sub) return NULL;
          }
          ts = ts_after_sub;
          if (struct_alignment) ctx->struct_alignment = struct_alignment;
        }
        break;
      case '}':
        {
          size_t alignment = ctx->struct_alignment;
          ++ts;
          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
          ctx->enc_type = 0;
          if (alignment && ctx->fmt_offset % alignment) {
            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);
          }
        }
        return ts;
      case 'x':
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        ctx->fmt_offset += ctx->new_count;
        ctx->new_count = 1;
        ctx->enc_count = 0;
        ctx->enc_type = 0;
        ctx->enc_packmode = ctx->new_packmode;
        ++ts;
        break;
      case 'Z':
        got_Z = 1;
        ++ts;
        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {
          __Pyx_BufFmt_RaiseUnexpectedChar('Z');
          return NULL;
        }
      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':
      case 'l': case 'L': case 'q': case 'Q':
      case 'f': case 'd': case 'g':
      case 'O': case 'p':
        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&
            ctx->enc_packmode == ctx->new_packmode) {
          ctx->enc_count += ctx->new_count;
          ctx->new_count = 1;
          got_Z = 0;
          ++ts;
          break;
        }
      case 's':
        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;
        ctx->enc_count = ctx->new_count;
        ctx->enc_packmode = ctx->new_packmode;
        ctx->enc_type = *ts;
        ctx->is_complex = got_Z;
        ++ts;
        ctx->new_count = 1;
        got_Z = 0;
        break;
      case ':':
        ++ts;
        while(*ts != ':') ++ts;
        ++ts;
        break;
      case '(':
        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;
        break;
      default:
        {
          int number = __Pyx_BufFmt_ExpectNumber(&ts);
          if (number == -1) return NULL;
          ctx->new_count = (size_t)number;
        }
    }
  }
}
static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) {
  buf->buf = NULL;
  buf->obj = NULL;
  buf->strides = __Pyx_zeros;
  buf->shape = __Pyx_zeros;
  buf->suboffsets = __Pyx_minusones;
}
static CYTHON_INLINE int __Pyx_GetBufferAndValidate(
        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,
        int nd, int cast, __Pyx_BufFmt_StackElem* stack)
{
  if (obj == Py_None || obj == NULL) {
    __Pyx_ZeroBuffer(buf);
    return 0;
  }
  buf->buf = NULL;
  if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail;
  if (buf->ndim != nd) {
    PyErr_Format(PyExc_ValueError,
                 "Buffer has wrong number of dimensions (expected %d, got %d)",
                 nd, buf->ndim);
    goto fail;
  }
  if (!cast) {
    __Pyx_BufFmt_Context ctx;
    __Pyx_BufFmt_Init(&ctx, stack, dtype);
    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;
  }
  if ((unsigned)buf->itemsize != dtype->size) {
    PyErr_Format(PyExc_ValueError,
      "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)",
      buf->itemsize, (buf->itemsize > 1) ? "s" : "",
      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : "");
    goto fail;
  }
  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;
  return 0;
fail:;
  __Pyx_ZeroBuffer(buf);
  return -1;
}
static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {
  if (info->buf == NULL) return;
  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;
  __Pyx_ReleaseBuffer(info);
}

/* PyFunctionFastCall */
  #if CYTHON_FAST_PYCALL
#include "frameobject.h"
static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
                                               PyObject *globals) {
    PyFrameObject *f;
    PyThreadState *tstate = PyThreadState_GET();
    PyObject **fastlocals;
    Py_ssize_t i;
    PyObject *result;
    assert(globals != NULL);
    /* XXX Perhaps we should create a specialized
       PyFrame_New() that doesn't take locals, but does
       take builtins without sanity checking them.
       */
    assert(tstate != NULL);
    f = PyFrame_New(tstate, co, globals, NULL);
    if (f == NULL) {
        return NULL;
    }
    fastlocals = f->f_localsplus;
    for (i = 0; i < na; i++) {
        Py_INCREF(*args);
        fastlocals[i] = *args++;
    }
    result = PyEval_EvalFrameEx(f,0);
    ++tstate->recursion_depth;
    Py_DECREF(f);
    --tstate->recursion_depth;
    return result;
}
#if 1 || PY_VERSION_HEX < 0x030600B1
static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {
    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
    PyObject *globals = PyFunction_GET_GLOBALS(func);
    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
    PyObject *closure;
#if PY_MAJOR_VERSION >= 3
    PyObject *kwdefs;
#endif
    PyObject *kwtuple, **k;
    PyObject **d;
    Py_ssize_t nd;
    Py_ssize_t nk;
    PyObject *result;
    assert(kwargs == NULL || PyDict_Check(kwargs));
    nk = kwargs ? PyDict_Size(kwargs) : 0;
    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
        return NULL;
    }
    if (
#if PY_MAJOR_VERSION >= 3
            co->co_kwonlyargcount == 0 &&
#endif
            likely(kwargs == NULL || nk == 0) &&
            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
        if (argdefs == NULL && co->co_argcount == nargs) {
            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
            goto done;
        }
        else if (nargs == 0 && argdefs != NULL
                 && co->co_argcount == Py_SIZE(argdefs)) {
            /* function called with no arguments, but all parameters have
               a default value: use default values as arguments .*/
            args = &PyTuple_GET_ITEM(argdefs, 0);
            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
            goto done;
        }
    }
    if (kwargs != NULL) {
        Py_ssize_t pos, i;
        kwtuple = PyTuple_New(2 * nk);
        if (kwtuple == NULL) {
            result = NULL;
            goto done;
        }
        k = &PyTuple_GET_ITEM(kwtuple, 0);
        pos = i = 0;
        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
            Py_INCREF(k[i]);
            Py_INCREF(k[i+1]);
            i += 2;
        }
        nk = i / 2;
    }
    else {
        kwtuple = NULL;
        k = NULL;
    }
    closure = PyFunction_GET_CLOSURE(func);
#if PY_MAJOR_VERSION >= 3
    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
#endif
    if (argdefs != NULL) {
        d = &PyTuple_GET_ITEM(argdefs, 0);
        nd = Py_SIZE(argdefs);
    }
    else {
        d = NULL;
        nd = 0;
    }
#if PY_MAJOR_VERSION >= 3
    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
                               args, nargs,
                               k, (int)nk,
                               d, (int)nd, kwdefs, closure);
#else
    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
                               args, nargs,
                               k, (int)nk,
                               d, (int)nd, closure);
#endif
    Py_XDECREF(kwtuple);
done:
    Py_LeaveRecursiveCall();
    return result;
}
#endif
#endif

/* PyCFunctionFastCall */
  #if CYTHON_FAST_PYCCALL
static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
    PyObject *self = PyCFunction_GET_SELF(func);
    int flags = PyCFunction_GET_FLAGS(func);
    assert(PyCFunction_Check(func));
    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)));
    assert(nargs >= 0);
    assert(nargs == 0 || args != NULL);
    /* _PyCFunction_FastCallDict() must not be called with an exception set,
       because it may clear it (directly or indirectly) and so the
       caller loses its exception */
    assert(!PyErr_Occurred());
    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {
        return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL);
    } else {
        return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs);
    }
}
#endif

/* PyObjectCall */
  #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
    PyObject *result;
    ternaryfunc call = func->ob_type->tp_call;
    if (unlikely(!call))
        return PyObject_Call(func, arg, kw);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = (*call)(func, arg, kw);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* GetItemInt */
  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
    PyObject *r;
    if (!j) return NULL;
    r = PyObject_GetItem(o, j);
    Py_DECREF(j);
    return r;
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    Py_ssize_t wrapped_i = i;
    if (wraparound & unlikely(i < 0)) {
        wrapped_i += PyList_GET_SIZE(o);
    }
    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {
        PyObject *r = PyList_GET_ITEM(o, wrapped_i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    Py_ssize_t wrapped_i = i;
    if (wraparound & unlikely(i < 0)) {
        wrapped_i += PyTuple_GET_SIZE(o);
    }
    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {
        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,
                                                     CYTHON_NCP_UNUSED int wraparound,
                                                     CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
            PyObject *r = PyList_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    }
    else if (PyTuple_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
            PyObject *r = PyTuple_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    } else {
        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
        if (likely(m && m->sq_item)) {
            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
                Py_ssize_t l = m->sq_length(o);
                if (likely(l >= 0)) {
                    i += l;
                } else {
                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))
                        return NULL;
                    PyErr_Clear();
                }
            }
            return m->sq_item(o, i);
        }
    }
#else
    if (is_list || PySequence_Check(o)) {
        return PySequence_GetItem(o, i);
    }
#endif
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
}

/* PyErrFetchRestore */
  #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    tmp_type = tstate->curexc_type;
    tmp_value = tstate->curexc_value;
    tmp_tb = tstate->curexc_traceback;
    tstate->curexc_type = type;
    tstate->curexc_value = value;
    tstate->curexc_traceback = tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->curexc_type;
    *value = tstate->curexc_value;
    *tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
}
#endif

/* ExtTypeTest */
  static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
    if (unlikely(!type)) {
        PyErr_SetString(PyExc_SystemError, "Missing type object");
        return 0;
    }
    if (likely(PyObject_TypeCheck(obj, type)))
        return 1;
    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
                 Py_TYPE(obj)->tp_name, type->tp_name);
    return 0;
}

/* RaiseException */
  #if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
                        CYTHON_UNUSED PyObject *cause) {
    __Pyx_PyThreadState_declare
    Py_XINCREF(type);
    if (!value || value == Py_None)
        value = NULL;
    else
        Py_INCREF(value);
    if (!tb || tb == Py_None)
        tb = NULL;
    else {
        Py_INCREF(tb);
        if (!PyTraceBack_Check(tb)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: arg 3 must be a traceback or None");
            goto raise_error;
        }
    }
    if (PyType_Check(type)) {
#if CYTHON_COMPILING_IN_PYPY
        if (!value) {
            Py_INCREF(Py_None);
            value = Py_None;
        }
#endif
        PyErr_NormalizeException(&type, &value, &tb);
    } else {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto raise_error;
        }
        value = type;
        type = (PyObject*) Py_TYPE(type);
        Py_INCREF(type);
        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: exception class must be a subclass of BaseException");
            goto raise_error;
        }
    }
    __Pyx_PyThreadState_assign
    __Pyx_ErrRestore(type, value, tb);
    return;
raise_error:
    Py_XDECREF(value);
    Py_XDECREF(type);
    Py_XDECREF(tb);
    return;
}
#else
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
    PyObject* owned_instance = NULL;
    if (tb == Py_None) {
        tb = 0;
    } else if (tb && !PyTraceBack_Check(tb)) {
        PyErr_SetString(PyExc_TypeError,
            "raise: arg 3 must be a traceback or None");
        goto bad;
    }
    if (value == Py_None)
        value = 0;
    if (PyExceptionInstance_Check(type)) {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto bad;
        }
        value = type;
        type = (PyObject*) Py_TYPE(value);
    } else if (PyExceptionClass_Check(type)) {
        PyObject *instance_class = NULL;
        if (value && PyExceptionInstance_Check(value)) {
            instance_class = (PyObject*) Py_TYPE(value);
            if (instance_class != type) {
                int is_subclass = PyObject_IsSubclass(instance_class, type);
                if (!is_subclass) {
                    instance_class = NULL;
                } else if (unlikely(is_subclass == -1)) {
                    goto bad;
                } else {
                    type = instance_class;
                }
            }
        }
        if (!instance_class) {
            PyObject *args;
            if (!value)
                args = PyTuple_New(0);
            else if (PyTuple_Check(value)) {
                Py_INCREF(value);
                args = value;
            } else
                args = PyTuple_Pack(1, value);
            if (!args)
                goto bad;
            owned_instance = PyObject_Call(type, args, NULL);
            Py_DECREF(args);
            if (!owned_instance)
                goto bad;
            value = owned_instance;
            if (!PyExceptionInstance_Check(value)) {
                PyErr_Format(PyExc_TypeError,
                             "calling %R should have returned an instance of "
                             "BaseException, not %R",
                             type, Py_TYPE(value));
                goto bad;
            }
        }
    } else {
        PyErr_SetString(PyExc_TypeError,
            "raise: exception class must be a subclass of BaseException");
        goto bad;
    }
#if PY_VERSION_HEX >= 0x03030000
    if (cause) {
#else
    if (cause && cause != Py_None) {
#endif
        PyObject *fixed_cause;
        if (cause == Py_None) {
            fixed_cause = NULL;
        } else if (PyExceptionClass_Check(cause)) {
            fixed_cause = PyObject_CallObject(cause, NULL);
            if (fixed_cause == NULL)
                goto bad;
        } else if (PyExceptionInstance_Check(cause)) {
            fixed_cause = cause;
            Py_INCREF(fixed_cause);
        } else {
            PyErr_SetString(PyExc_TypeError,
                            "exception causes must derive from "
                            "BaseException");
            goto bad;
        }
        PyException_SetCause(value, fixed_cause);
    }
    PyErr_SetObject(type, value);
    if (tb) {
#if CYTHON_COMPILING_IN_PYPY
        PyObject *tmp_type, *tmp_value, *tmp_tb;
        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
        Py_INCREF(tb);
        PyErr_Restore(tmp_type, tmp_value, tb);
        Py_XDECREF(tmp_tb);
#else
        PyThreadState *tstate = PyThreadState_GET();
        PyObject* tmp_tb = tstate->curexc_traceback;
        if (tb != tmp_tb) {
            Py_INCREF(tb);
            tstate->curexc_traceback = tb;
            Py_XDECREF(tmp_tb);
        }
#endif
    }
bad:
    Py_XDECREF(owned_instance);
    return;
}
#endif

/* PyObjectCallMethO */
    #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
    PyObject *self, *result;
    PyCFunction cfunc;
    cfunc = PyCFunction_GET_FUNCTION(func);
    self = PyCFunction_GET_SELF(func);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = cfunc(self, arg);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* PyObjectCallOneArg */
    #if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_New(1);
    if (unlikely(!args)) return NULL;
    Py_INCREF(arg);
    PyTuple_SET_ITEM(args, 0, arg);
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, &arg, 1);
    }
#endif
    if (likely(PyCFunction_Check(func))) {
        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
            return __Pyx_PyObject_CallMethO(func, arg);
#if CYTHON_FAST_PYCCALL
        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
#endif
        }
    }
    return __Pyx__PyObject_CallOneArg(func, arg);
}
#else
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_Pack(1, arg);
    if (unlikely(!args)) return NULL;
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
#endif

/* PyObjectCallNoArg */
    #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, NULL, 0);
    }
#endif
#ifdef __Pyx_CyFunction_USED
    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
    if (likely(PyCFunction_Check(func))) {
#endif
        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
            return __Pyx_PyObject_CallMethO(func, NULL);
        }
    }
    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
}
#endif

/* GetModuleGlobalName */
      static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
    PyObject *result;
#if !CYTHON_AVOID_BORROWED_REFS
    result = PyDict_GetItem(__pyx_d, name);
    if (likely(result)) {
        Py_INCREF(result);
    } else {
#else
    result = PyObject_GetItem(__pyx_d, name);
    if (!result) {
        PyErr_Clear();
#endif
        result = __Pyx_GetBuiltinName(name);
    }
    return result;
}

/* RaiseTooManyValuesToUnpack */
        static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
    PyErr_Format(PyExc_ValueError,
                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}

/* RaiseNeedMoreValuesToUnpack */
        static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
    PyErr_Format(PyExc_ValueError,
                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
                 index, (index == 1) ? "" : "s");
}

/* RaiseNoneIterError */
        static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {
    PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
}

/* SaveResetException */
        #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->exc_type;
    *value = tstate->exc_value;
    *tb = tstate->exc_traceback;
    Py_XINCREF(*type);
    Py_XINCREF(*value);
    Py_XINCREF(*tb);
}
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = type;
    tstate->exc_value = value;
    tstate->exc_traceback = tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
#endif

/* PyErrExceptionMatches */
        #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
    PyObject *exc_type = tstate->curexc_type;
    if (exc_type == err) return 1;
    if (unlikely(!exc_type)) return 0;
    return PyErr_GivenExceptionMatches(exc_type, err);
}
#endif

/* GetException */
        #if CYTHON_FAST_THREAD_STATE
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
#else
static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
#endif
    PyObject *local_type, *local_value, *local_tb;
#if CYTHON_FAST_THREAD_STATE
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    local_type = tstate->curexc_type;
    local_value = tstate->curexc_value;
    local_tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
#else
    PyErr_Fetch(&local_type, &local_value, &local_tb);
#endif
    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
#if CYTHON_FAST_THREAD_STATE
    if (unlikely(tstate->curexc_type))
#else
    if (unlikely(PyErr_Occurred()))
#endif
        goto bad;
    #if PY_MAJOR_VERSION >= 3
    if (local_tb) {
        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
            goto bad;
    }
    #endif
    Py_XINCREF(local_tb);
    Py_XINCREF(local_type);
    Py_XINCREF(local_value);
    *type = local_type;
    *value = local_value;
    *tb = local_tb;
#if CYTHON_FAST_THREAD_STATE
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = local_type;
    tstate->exc_value = local_value;
    tstate->exc_traceback = local_tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
#else
    PyErr_SetExcInfo(local_type, local_value, local_tb);
#endif
    return 0;
bad:
    *type = 0;
    *value = 0;
    *tb = 0;
    Py_XDECREF(local_type);
    Py_XDECREF(local_value);
    Py_XDECREF(local_tb);
    return -1;
}

/* SetVTable */
          static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
#if PY_VERSION_HEX >= 0x02070000
    PyObject *ob = PyCapsule_New(vtable, 0, 0);
#else
    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
#endif
    if (!ob)
        goto bad;
    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
        goto bad;
    Py_DECREF(ob);
    return 0;
bad:
    Py_XDECREF(ob);
    return -1;
}

/* SetupReduce */
          static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) {
  int ret;
  PyObject *name_attr;
  name_attr = __Pyx_PyObject_GetAttrStr(meth, __pyx_n_s_name);
  if (likely(name_attr)) {
      ret = PyObject_RichCompareBool(name_attr, name, Py_EQ);
  } else {
      ret = -1;
  }
  if (unlikely(ret < 0)) {
      PyErr_Clear();
      ret = 0;
  }
  Py_XDECREF(name_attr);
  return ret;
}
static int __Pyx_setup_reduce(PyObject* type_obj) {
    int ret = 0;
    PyObject *object_reduce = NULL;
    PyObject *object_reduce_ex = NULL;
    PyObject *reduce = NULL;
    PyObject *reduce_ex = NULL;
    PyObject *reduce_cython = NULL;
    PyObject *setstate = NULL;
    PyObject *setstate_cython = NULL;
#if CYTHON_USE_PYTYPE_LOOKUP
    if (_PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate)) goto GOOD;
#else
    if (PyObject_HasAttr(type_obj, __pyx_n_s_getstate)) goto GOOD;
#endif
#if CYTHON_USE_PYTYPE_LOOKUP
    object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
#else
    object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto BAD;
#endif
    reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto BAD;
    if (reduce_ex == object_reduce_ex) {
#if CYTHON_USE_PYTYPE_LOOKUP
        object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
#else
        object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto BAD;
#endif
        reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto BAD;
        if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) {
            reduce_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_cython); if (unlikely(!reduce_cython)) goto BAD;
            ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto BAD;
            ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto BAD;
            setstate = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate);
            if (!setstate) PyErr_Clear();
            if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) {
                setstate_cython = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_setstate_cython); if (unlikely(!setstate_cython)) goto BAD;
                ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto BAD;
                ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto BAD;
            }
            PyType_Modified((PyTypeObject*)type_obj);
        }
    }
    goto GOOD;
BAD:
    if (!PyErr_Occurred())
        PyErr_Format(PyExc_RuntimeError, "Unable to initialize pickling for %s", ((PyTypeObject*)type_obj)->tp_name);
    ret = -1;
GOOD:
#if !CYTHON_COMPILING_IN_CPYTHON
    Py_XDECREF(object_reduce);
    Py_XDECREF(object_reduce_ex);
#endif
    Py_XDECREF(reduce);
    Py_XDECREF(reduce_ex);
    Py_XDECREF(reduce_cython);
    Py_XDECREF(setstate);
    Py_XDECREF(setstate_cython);
    return ret;
}

/* GetVTable */
          static void* __Pyx_GetVtable(PyObject *dict) {
    void* ptr;
    PyObject *ob = PyObject_GetItem(dict, __pyx_n_s_pyx_vtable);
    if (!ob)
        goto bad;
#if PY_VERSION_HEX >= 0x02070000
    ptr = PyCapsule_GetPointer(ob, 0);
#else
    ptr = PyCObject_AsVoidPtr(ob);
#endif
    if (!ptr && !PyErr_Occurred())
        PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type");
    Py_DECREF(ob);
    return ptr;
bad:
    Py_XDECREF(ob);
    return NULL;
}

/* Import */
          static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
    PyObject *empty_list = 0;
    PyObject *module = 0;
    PyObject *global_dict = 0;
    PyObject *empty_dict = 0;
    PyObject *list;
    #if PY_VERSION_HEX < 0x03030000
    PyObject *py_import;
    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
    if (!py_import)
        goto bad;
    #endif
    if (from_list)
        list = from_list;
    else {
        empty_list = PyList_New(0);
        if (!empty_list)
            goto bad;
        list = empty_list;
    }
    global_dict = PyModule_GetDict(__pyx_m);
    if (!global_dict)
        goto bad;
    empty_dict = PyDict_New();
    if (!empty_dict)
        goto bad;
    {
        #if PY_MAJOR_VERSION >= 3
        if (level == -1) {
            if (strchr(__Pyx_MODULE_NAME, '.')) {
                #if PY_VERSION_HEX < 0x03030000
                PyObject *py_level = PyInt_FromLong(1);
                if (!py_level)
                    goto bad;
                module = PyObject_CallFunctionObjArgs(py_import,
                    name, global_dict, empty_dict, list, py_level, NULL);
                Py_DECREF(py_level);
                #else
                module = PyImport_ImportModuleLevelObject(
                    name, global_dict, empty_dict, list, 1);
                #endif
                if (!module) {
                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
                        goto bad;
                    PyErr_Clear();
                }
            }
            level = 0;
        }
        #endif
        if (!module) {
            #if PY_VERSION_HEX < 0x03030000
            PyObject *py_level = PyInt_FromLong(level);
            if (!py_level)
                goto bad;
            module = PyObject_CallFunctionObjArgs(py_import,
                name, global_dict, empty_dict, list, py_level, NULL);
            Py_DECREF(py_level);
            #else
            module = PyImport_ImportModuleLevelObject(
                name, global_dict, empty_dict, list, level);
            #endif
        }
    }
bad:
    #if PY_VERSION_HEX < 0x03030000
    Py_XDECREF(py_import);
    #endif
    Py_XDECREF(empty_list);
    Py_XDECREF(empty_dict);
    return module;
}

/* ImportFrom */
          static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
        PyErr_Format(PyExc_ImportError,
        #if PY_MAJOR_VERSION < 3
            "cannot import name %.230s", PyString_AS_STRING(name));
        #else
            "cannot import name %S", name);
        #endif
    }
    return value;
}

/* CLineInTraceback */
          static int __Pyx_CLineForTraceback(int c_line) {
#ifdef CYTHON_CLINE_IN_TRACEBACK
    return ((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0;
#else
    PyObject *use_cline;
#if CYTHON_COMPILING_IN_CPYTHON
    PyObject **cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);
    if (likely(cython_runtime_dict)) {
      use_cline = PyDict_GetItem(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);
    } else
#endif
    {
      PyObject *ptype, *pvalue, *ptraceback;
      PyObject *use_cline_obj;
      PyErr_Fetch(&ptype, &pvalue, &ptraceback);
      use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);
      if (use_cline_obj) {
        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;
        Py_DECREF(use_cline_obj);
      } else {
        use_cline = NULL;
      }
      PyErr_Restore(ptype, pvalue, ptraceback);
    }
    if (!use_cline) {
        c_line = 0;
        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);
    }
    else if (PyObject_Not(use_cline) != 0) {
        c_line = 0;
    }
    return c_line;
#endif
}

/* CodeObjectCache */
          static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
    int start = 0, mid = 0, end = count - 1;
    if (end >= 0 && code_line > entries[end].code_line) {
        return count;
    }
    while (start < end) {
        mid = start + (end - start) / 2;
        if (code_line < entries[mid].code_line) {
            end = mid;
        } else if (code_line > entries[mid].code_line) {
             start = mid + 1;
        } else {
            return mid;
        }
    }
    if (code_line <= entries[mid].code_line) {
        return mid;
    } else {
        return mid + 1;
    }
}
static PyCodeObject *__pyx_find_code_object(int code_line) {
    PyCodeObject* code_object;
    int pos;
    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
        return NULL;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
        return NULL;
    }
    code_object = __pyx_code_cache.entries[pos].code_object;
    Py_INCREF(code_object);
    return code_object;
}
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
    int pos, i;
    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
    if (unlikely(!code_line)) {
        return;
    }
    if (unlikely(!entries)) {
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
        if (likely(entries)) {
            __pyx_code_cache.entries = entries;
            __pyx_code_cache.max_count = 64;
            __pyx_code_cache.count = 1;
            entries[0].code_line = code_line;
            entries[0].code_object = code_object;
            Py_INCREF(code_object);
        }
        return;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
        PyCodeObject* tmp = entries[pos].code_object;
        entries[pos].code_object = code_object;
        Py_DECREF(tmp);
        return;
    }
    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
        int new_max = __pyx_code_cache.max_count + 64;
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
        if (unlikely(!entries)) {
            return;
        }
        __pyx_code_cache.entries = entries;
        __pyx_code_cache.max_count = new_max;
    }
    for (i=__pyx_code_cache.count; i>pos; i--) {
        entries[i] = entries[i-1];
    }
    entries[pos].code_line = code_line;
    entries[pos].code_object = code_object;
    __pyx_code_cache.count++;
    Py_INCREF(code_object);
}

/* AddTraceback */
          #include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
            const char *funcname, int c_line,
            int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyObject *py_srcfile = 0;
    PyObject *py_funcname = 0;
    #if PY_MAJOR_VERSION < 3
    py_srcfile = PyString_FromString(filename);
    #else
    py_srcfile = PyUnicode_FromString(filename);
    #endif
    if (!py_srcfile) goto bad;
    if (c_line) {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #else
        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #endif
    }
    else {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromString(funcname);
        #else
        py_funcname = PyUnicode_FromString(funcname);
        #endif
    }
    if (!py_funcname) goto bad;
    py_code = __Pyx_PyCode_New(
        0,
        0,
        0,
        0,
        0,
        __pyx_empty_bytes, /*PyObject *code,*/
        __pyx_empty_tuple, /*PyObject *consts,*/
        __pyx_empty_tuple, /*PyObject *names,*/
        __pyx_empty_tuple, /*PyObject *varnames,*/
        __pyx_empty_tuple, /*PyObject *freevars,*/
        __pyx_empty_tuple, /*PyObject *cellvars,*/
        py_srcfile,   /*PyObject *filename,*/
        py_funcname,  /*PyObject *name,*/
        py_line,
        __pyx_empty_bytes  /*PyObject *lnotab*/
    );
    Py_DECREF(py_srcfile);
    Py_DECREF(py_funcname);
    return py_code;
bad:
    Py_XDECREF(py_srcfile);
    Py_XDECREF(py_funcname);
    return NULL;
}
static void __Pyx_AddTraceback(const char *funcname, int c_line,
                               int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyFrameObject *py_frame = 0;
    if (c_line) {
        c_line = __Pyx_CLineForTraceback(c_line);
    }
    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);
    if (!py_code) {
        py_code = __Pyx_CreateCodeObjectForTraceback(
            funcname, c_line, py_line, filename);
        if (!py_code) goto bad;
        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);
    }
    py_frame = PyFrame_New(
        PyThreadState_GET(), /*PyThreadState *tstate,*/
        py_code,             /*PyCodeObject *code,*/
        __pyx_d,      /*PyObject *globals,*/
        0                    /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
    PyTraceBack_Here(py_frame);
bad:
    Py_XDECREF(py_code);
    Py_XDECREF(py_frame);
}

#if PY_MAJOR_VERSION < 3
static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {
    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);
        if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);
    PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name);
    return -1;
}
static void __Pyx_ReleaseBuffer(Py_buffer *view) {
    PyObject *obj = view->obj;
    if (!obj) return;
    if (PyObject_CheckBuffer(obj)) {
        PyBuffer_Release(view);
        return;
    }
    if ((0)) {}
        else if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view);
    view->obj = NULL;
    Py_DECREF(obj);
}
#endif


          /* CIntFromPyVerify */
          #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
    {\
        func_type value = func_value;\
        if (sizeof(target_type) < sizeof(func_type)) {\
            if (unlikely(value != (func_type) (target_type) value)) {\
                func_type zero = 0;\
                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
                    return (target_type) -1;\
                if (is_unsigned && unlikely(value < zero))\
                    goto raise_neg_overflow;\
                else\
                    goto raise_overflow;\
            }\
        }\
        return (target_type) value;\
    }

/* CIntToPy */
          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
    const int neg_one = (int) -1, const_zero = (int) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(int) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
    const long neg_one = (long) -1, const_zero = (long) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(long) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(long) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(long) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(long),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_Py_intptr_t(Py_intptr_t value) {
    const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(Py_intptr_t) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(Py_intptr_t) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(Py_intptr_t),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value) {
    const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_int32) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_int32) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_int32) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_int32),
                                     little, !is_unsigned);
    }
}

/* Declarations */
          #if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      return ::std::complex< float >(x, y);
    }
  #else
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      return x + y*(__pyx_t_float_complex)_Complex_I;
    }
  #endif
#else
    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {
      __pyx_t_float_complex z;
      z.real = x;
      z.imag = y;
      return z;
    }
#endif

/* Arithmetic */
          #if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
       return (a.real == b.real) && (a.imag == b.imag);
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real + b.real;
        z.imag = a.imag + b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real - b.real;
        z.imag = a.imag - b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        __pyx_t_float_complex z;
        z.real = a.real * b.real - a.imag * b.imag;
        z.imag = a.real * b.imag + a.imag * b.real;
        return z;
    }
    #if 1
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        if (b.imag == 0) {
            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else if (fabsf(b.real) >= fabsf(b.imag)) {
            if (b.real == 0 && b.imag == 0) {
                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);
            } else {
                float r = b.imag / b.real;
                float s = 1.0 / (b.real + b.imag * r);
                return __pyx_t_float_complex_from_parts(
                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
            }
        } else {
            float r = b.real / b.imag;
            float s = 1.0 / (b.imag + b.real * r);
            return __pyx_t_float_complex_from_parts(
                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
        }
    }
    #else
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
        if (b.imag == 0) {
            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else {
            float denom = b.real * b.real + b.imag * b.imag;
            return __pyx_t_float_complex_from_parts(
                (a.real * b.real + a.imag * b.imag) / denom,
                (a.imag * b.real - a.real * b.imag) / denom);
        }
    }
    #endif
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {
        __pyx_t_float_complex z;
        z.real = -a.real;
        z.imag = -a.imag;
        return z;
    }
    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {
        __pyx_t_float_complex z;
        z.real =  a.real;
        z.imag = -a.imag;
        return z;
    }
    #if 1
        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {
          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
            return sqrtf(z.real*z.real + z.imag*z.imag);
          #else
            return hypotf(z.real, z.imag);
          #endif
        }
        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {
            __pyx_t_float_complex z;
            float r, lnr, theta, z_r, z_theta;
            if (b.imag == 0 && b.real == (int)b.real) {
                if (b.real < 0) {
                    float denom = a.real * a.real + a.imag * a.imag;
                    a.real = a.real / denom;
                    a.imag = -a.imag / denom;
                    b.real = -b.real;
                }
                switch ((int)b.real) {
                    case 0:
                        z.real = 1;
                        z.imag = 0;
                        return z;
                    case 1:
                        return a;
                    case 2:
                        z = __Pyx_c_prod_float(a, a);
                        return __Pyx_c_prod_float(a, a);
                    case 3:
                        z = __Pyx_c_prod_float(a, a);
                        return __Pyx_c_prod_float(z, a);
                    case 4:
                        z = __Pyx_c_prod_float(a, a);
                        return __Pyx_c_prod_float(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                } else if (b.imag == 0) {
                    z.real = powf(a.real, b.real);
                    z.imag = 0;
                    return z;
                } else if (a.real > 0) {
                    r = a.real;
                    theta = 0;
                } else {
                    r = -a.real;
                    theta = atan2f(0, -1);
                }
            } else {
                r = __Pyx_c_abs_float(a);
                theta = atan2f(a.imag, a.real);
            }
            lnr = logf(r);
            z_r = expf(lnr * b.real - theta * b.imag);
            z_theta = theta * b.real + lnr * b.imag;
            z.real = z_r * cosf(z_theta);
            z.imag = z_r * sinf(z_theta);
            return z;
        }
    #endif
#endif

/* Declarations */
          #if CYTHON_CCOMPLEX
  #ifdef __cplusplus
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      return ::std::complex< double >(x, y);
    }
  #else
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      return x + y*(__pyx_t_double_complex)_Complex_I;
    }
  #endif
#else
    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {
      __pyx_t_double_complex z;
      z.real = x;
      z.imag = y;
      return z;
    }
#endif

/* Arithmetic */
          #if CYTHON_CCOMPLEX
#else
    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
       return (a.real == b.real) && (a.imag == b.imag);
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real + b.real;
        z.imag = a.imag + b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real - b.real;
        z.imag = a.imag - b.imag;
        return z;
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        __pyx_t_double_complex z;
        z.real = a.real * b.real - a.imag * b.imag;
        z.imag = a.real * b.imag + a.imag * b.real;
        return z;
    }
    #if 1
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        if (b.imag == 0) {
            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else if (fabs(b.real) >= fabs(b.imag)) {
            if (b.real == 0 && b.imag == 0) {
                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);
            } else {
                double r = b.imag / b.real;
                double s = 1.0 / (b.real + b.imag * r);
                return __pyx_t_double_complex_from_parts(
                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);
            }
        } else {
            double r = b.real / b.imag;
            double s = 1.0 / (b.imag + b.real * r);
            return __pyx_t_double_complex_from_parts(
                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);
        }
    }
    #else
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
        if (b.imag == 0) {
            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);
        } else {
            double denom = b.real * b.real + b.imag * b.imag;
            return __pyx_t_double_complex_from_parts(
                (a.real * b.real + a.imag * b.imag) / denom,
                (a.imag * b.real - a.real * b.imag) / denom);
        }
    }
    #endif
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {
        __pyx_t_double_complex z;
        z.real = -a.real;
        z.imag = -a.imag;
        return z;
    }
    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {
       return (a.real == 0) && (a.imag == 0);
    }
    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {
        __pyx_t_double_complex z;
        z.real =  a.real;
        z.imag = -a.imag;
        return z;
    }
    #if 1
        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {
          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)
            return sqrt(z.real*z.real + z.imag*z.imag);
          #else
            return hypot(z.real, z.imag);
          #endif
        }
        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {
            __pyx_t_double_complex z;
            double r, lnr, theta, z_r, z_theta;
            if (b.imag == 0 && b.real == (int)b.real) {
                if (b.real < 0) {
                    double denom = a.real * a.real + a.imag * a.imag;
                    a.real = a.real / denom;
                    a.imag = -a.imag / denom;
                    b.real = -b.real;
                }
                switch ((int)b.real) {
                    case 0:
                        z.real = 1;
                        z.imag = 0;
                        return z;
                    case 1:
                        return a;
                    case 2:
                        z = __Pyx_c_prod_double(a, a);
                        return __Pyx_c_prod_double(a, a);
                    case 3:
                        z = __Pyx_c_prod_double(a, a);
                        return __Pyx_c_prod_double(z, a);
                    case 4:
                        z = __Pyx_c_prod_double(a, a);
                        return __Pyx_c_prod_double(z, z);
                }
            }
            if (a.imag == 0) {
                if (a.real == 0) {
                    return a;
                } else if (b.imag == 0) {
                    z.real = pow(a.real, b.real);
                    z.imag = 0;
                    return z;
                } else if (a.real > 0) {
                    r = a.real;
                    theta = 0;
                } else {
                    r = -a.real;
                    theta = atan2(0, -1);
                }
            } else {
                r = __Pyx_c_abs_double(a);
                theta = atan2(a.imag, a.real);
            }
            lnr = log(r);
            z_r = exp(lnr * b.real - theta * b.imag);
            z_theta = theta * b.real + lnr * b.imag;
            z.real = z_r * cos(z_theta);
            z.imag = z_r * sin(z_theta);
            return z;
        }
    #endif
#endif

/* CIntToPy */
          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {
    const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(enum NPY_TYPES) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(enum NPY_TYPES) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES),
                                     little, !is_unsigned);
    }
}

/* CIntFromPy */
          static CYTHON_INLINE Py_intptr_t __Pyx_PyInt_As_Py_intptr_t(PyObject *x) {
    const Py_intptr_t neg_one = (Py_intptr_t) -1, const_zero = (Py_intptr_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(Py_intptr_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (Py_intptr_t) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (Py_intptr_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(Py_intptr_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(Py_intptr_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(Py_intptr_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) >= 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (Py_intptr_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(Py_intptr_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(Py_intptr_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (Py_intptr_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(Py_intptr_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(Py_intptr_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(Py_intptr_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(Py_intptr_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(Py_intptr_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(Py_intptr_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((((Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(Py_intptr_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) (((Py_intptr_t)-1)*(((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(Py_intptr_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(Py_intptr_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(Py_intptr_t) - 1 > 4 * PyLong_SHIFT) {
                            return (Py_intptr_t) ((((((((((Py_intptr_t)digits[3]) << PyLong_SHIFT) | (Py_intptr_t)digits[2]) << PyLong_SHIFT) | (Py_intptr_t)digits[1]) << PyLong_SHIFT) | (Py_intptr_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(Py_intptr_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(Py_intptr_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(Py_intptr_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            Py_intptr_t val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (Py_intptr_t) -1;
        }
    } else {
        Py_intptr_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (Py_intptr_t) -1;
        val = __Pyx_PyInt_As_Py_intptr_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to Py_intptr_t");
    return (Py_intptr_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to Py_intptr_t");
    return (Py_intptr_t) -1;
}

/* CIntFromPy */
          static CYTHON_INLINE npy_uint32 __Pyx_PyInt_As_npy_uint32(PyObject *x) {
    const npy_uint32 neg_one = (npy_uint32) -1, const_zero = (npy_uint32) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_uint32) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_uint32, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_uint32) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint32) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_uint32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 2 * PyLong_SHIFT) {
                            return (npy_uint32) (((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 3 * PyLong_SHIFT) {
                            return (npy_uint32) (((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 4 * PyLong_SHIFT) {
                            return (npy_uint32) (((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_uint32) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_uint32) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint32) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint32) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_uint32, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint32,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_uint32) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_uint32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_uint32) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint32) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_uint32 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (npy_uint32) -1;
        }
    } else {
        npy_uint32 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_uint32) -1;
        val = __Pyx_PyInt_As_npy_uint32(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_uint32");
    return (npy_uint32) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_uint32");
    return (npy_uint32) -1;
}

/* CIntFromPy */
          static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) {
    const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_int32) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_int32) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int32) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 2 * PyLong_SHIFT) {
                            return (npy_int32) (((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 3 * PyLong_SHIFT) {
                            return (npy_int32) (((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 4 * PyLong_SHIFT) {
                            return (npy_int32) (((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_int32) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_int32) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int32) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_int32, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_int32,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_int32) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int32) ((((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int32) ((((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int32) ((((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_int32) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_int32 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (npy_int32) -1;
        }
    } else {
        npy_int32 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_int32) -1;
        val = __Pyx_PyInt_As_npy_int32(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_int32");
    return (npy_int32) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_int32");
    return (npy_int32) -1;
}

/* CIntFromPy */
          static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
    const int neg_one = (int) -1, const_zero = (int) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (int) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (int) -1;
        }
    } else {
        int val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (int) -1;
        val = __Pyx_PyInt_As_int(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int");
    return (int) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int");
    return (int) -1;
}

/* CIntFromPy */
          static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
    const long neg_one = (long) -1, const_zero = (long) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(long) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (long) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            long val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (long) -1;
        }
    } else {
        long val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (long) -1;
        val = __Pyx_PyInt_As_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to long");
    return (long) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to long");
    return (long) -1;
}

/* CheckBinaryVersion */
          static int __Pyx_check_binary_version(void) {
    char ctversion[4], rtversion[4];
    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
        char message[200];
        PyOS_snprintf(message, sizeof(message),
                      "compiletime version %s of module '%.100s' "
                      "does not match runtime version %s",
                      ctversion, __Pyx_MODULE_NAME, rtversion);
        return PyErr_WarnEx(NULL, message, 1);
    }
    return 0;
}

/* ModuleImport */
          #ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
    PyObject *py_name = 0;
    PyObject *py_module = 0;
    py_name = __Pyx_PyIdentifier_FromString(name);
    if (!py_name)
        goto bad;
    py_module = PyImport_Import(py_name);
    Py_DECREF(py_name);
    return py_module;
bad:
    Py_XDECREF(py_name);
    return 0;
}
#endif

/* TypeImport */
          #ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
    size_t size, int strict)
{
    PyObject *py_module = 0;
    PyObject *result = 0;
    PyObject *py_name = 0;
    char warning[200];
    Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
    PyObject *py_basicsize;
#endif
    py_module = __Pyx_ImportModule(module_name);
    if (!py_module)
        goto bad;
    py_name = __Pyx_PyIdentifier_FromString(class_name);
    if (!py_name)
        goto bad;
    result = PyObject_GetAttr(py_module, py_name);
    Py_DECREF(py_name);
    py_name = 0;
    Py_DECREF(py_module);
    py_module = 0;
    if (!result)
        goto bad;
    if (!PyType_Check(result)) {
        PyErr_Format(PyExc_TypeError,
            "%.200s.%.200s is not a type object",
            module_name, class_name);
        goto bad;
    }
#ifndef Py_LIMITED_API
    basicsize = ((PyTypeObject *)result)->tp_basicsize;
#else
    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
    if (!py_basicsize)
        goto bad;
    basicsize = PyLong_AsSsize_t(py_basicsize);
    Py_DECREF(py_basicsize);
    py_basicsize = 0;
    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
        goto bad;
#endif
    if (!strict && (size_t)basicsize > size) {
        PyOS_snprintf(warning, sizeof(warning),
            "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
    }
    else if ((size_t)basicsize != size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        goto bad;
    }
    return (PyTypeObject *)result;
bad:
    Py_XDECREF(py_module);
    Py_XDECREF(result);
    return NULL;
}
#endif

/* VoidPtrImport */
          #ifndef __PYX_HAVE_RT_ImportVoidPtr
#define __PYX_HAVE_RT_ImportVoidPtr
static int __Pyx_ImportVoidPtr(PyObject *module, const char *name, void **p, const char *sig) {
    PyObject *d = 0;
    PyObject *cobj = 0;
    d = PyObject_GetAttrString(module, (char *)"__pyx_capi__");
    if (!d)
        goto bad;
    cobj = PyDict_GetItemString(d, name);
    if (!cobj) {
        PyErr_Format(PyExc_ImportError,
            "%.200s does not export expected C variable %.200s",
                PyModule_GetName(module), name);
        goto bad;
    }
#if PY_VERSION_HEX >= 0x02070000
    if (!PyCapsule_IsValid(cobj, sig)) {
        PyErr_Format(PyExc_TypeError,
            "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), name, sig, PyCapsule_GetName(cobj));
        goto bad;
    }
    *p = PyCapsule_GetPointer(cobj, sig);
#else
    {const char *desc, *s1, *s2;
    desc = (const char *)PyCObject_GetDesc(cobj);
    if (!desc)
        goto bad;
    s1 = desc; s2 = sig;
    while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
    if (*s1 != *s2) {
        PyErr_Format(PyExc_TypeError,
            "C variable %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), name, sig, desc);
        goto bad;
    }
    *p = PyCObject_AsVoidPtr(cobj);}
#endif
    if (!(*p))
        goto bad;
    Py_DECREF(d);
    return 0;
bad:
    Py_XDECREF(d);
    return -1;
}
#endif

/* FunctionImport */
          #ifndef __PYX_HAVE_RT_ImportFunction
#define __PYX_HAVE_RT_ImportFunction
static int __Pyx_ImportFunction(PyObject *module, const char *funcname, void (**f)(void), const char *sig) {
    PyObject *d = 0;
    PyObject *cobj = 0;
    union {
        void (*fp)(void);
        void *p;
    } tmp;
    d = PyObject_GetAttrString(module, (char *)"__pyx_capi__");
    if (!d)
        goto bad;
    cobj = PyDict_GetItemString(d, funcname);
    if (!cobj) {
        PyErr_Format(PyExc_ImportError,
            "%.200s does not export expected C function %.200s",
                PyModule_GetName(module), funcname);
        goto bad;
    }
#if PY_VERSION_HEX >= 0x02070000
    if (!PyCapsule_IsValid(cobj, sig)) {
        PyErr_Format(PyExc_TypeError,
            "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), funcname, sig, PyCapsule_GetName(cobj));
        goto bad;
    }
    tmp.p = PyCapsule_GetPointer(cobj, sig);
#else
    {const char *desc, *s1, *s2;
    desc = (const char *)PyCObject_GetDesc(cobj);
    if (!desc)
        goto bad;
    s1 = desc; s2 = sig;
    while (*s1 != '\0' && *s1 == *s2) { s1++; s2++; }
    if (*s1 != *s2) {
        PyErr_Format(PyExc_TypeError,
            "C function %.200s.%.200s has wrong signature (expected %.500s, got %.500s)",
             PyModule_GetName(module), funcname, sig, desc);
        goto bad;
    }
    tmp.p = PyCObject_AsVoidPtr(cobj);}
#endif
    *f = tmp.fp;
    if (!(*f))
        goto bad;
    Py_DECREF(d);
    return 0;
bad:
    Py_XDECREF(d);
    return -1;
}
#endif

/* InitStrings */
          static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
    while (t->p) {
        #if PY_MAJOR_VERSION < 3
        if (t->is_unicode) {
            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
        } else if (t->intern) {
            *t->p = PyString_InternFromString(t->s);
        } else {
            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
        }
        #else
        if (t->is_unicode | t->is_str) {
            if (t->intern) {
                *t->p = PyUnicode_InternFromString(t->s);
            } else if (t->encoding) {
                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
            } else {
                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
            }
        } else {
            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
        }
        #endif
        if (!*t->p)
            return -1;
        if (PyObject_Hash(*t->p) == -1)
            PyErr_Clear();
        ++t;
    }
    return 0;
}

static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
}
static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {
    Py_ssize_t ignore;
    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT)
    if (
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
            __Pyx_sys_getdefaultencoding_not_ascii &&
#endif
            PyUnicode_Check(o)) {
#if PY_VERSION_HEX < 0x03030000
        char* defenc_c;
        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
        if (!defenc) return NULL;
        defenc_c = PyBytes_AS_STRING(defenc);
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
        {
            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
            char* c;
            for (c = defenc_c; c < end; c++) {
                if ((unsigned char) (*c) >= 128) {
                    PyUnicode_AsASCIIString(o);
                    return NULL;
                }
            }
        }
#endif
        *length = PyBytes_GET_SIZE(defenc);
        return defenc_c;
#else
        if (__Pyx_PyUnicode_READY(o) == -1) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
        if (PyUnicode_IS_ASCII(o)) {
            *length = PyUnicode_GET_LENGTH(o);
            return PyUnicode_AsUTF8(o);
        } else {
            PyUnicode_AsASCIIString(o);
            return NULL;
        }
#else
        return PyUnicode_AsUTF8AndSize(o, length);
#endif
#endif
    } else
#endif
#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
    if (PyByteArray_Check(o)) {
        *length = PyByteArray_GET_SIZE(o);
        return PyByteArray_AS_STRING(o);
    } else
#endif
    {
        char* result;
        int r = PyBytes_AsStringAndSize(o, &result, length);
        if (unlikely(r < 0)) {
            return NULL;
        } else {
            return result;
        }
    }
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
   int is_true = x == Py_True;
   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
   else return PyObject_IsTrue(x);
}
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {
#if CYTHON_USE_TYPE_SLOTS
  PyNumberMethods *m;
#endif
  const char *name = NULL;
  PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
  if (PyInt_Check(x) || PyLong_Check(x))
#else
  if (PyLong_Check(x))
#endif
    return __Pyx_NewRef(x);
#if CYTHON_USE_TYPE_SLOTS
  m = Py_TYPE(x)->tp_as_number;
  #if PY_MAJOR_VERSION < 3
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Int(x);
  }
  else if (m && m->nb_long) {
    name = "long";
    res = PyNumber_Long(x);
  }
  #else
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Long(x);
  }
  #endif
#else
  res = PyNumber_Int(x);
#endif
  if (res) {
#if PY_MAJOR_VERSION < 3
    if (!PyInt_Check(res) && !PyLong_Check(res)) {
#else
    if (!PyLong_Check(res)) {
#endif
      PyErr_Format(PyExc_TypeError,
                   "__%.4s__ returned non-%.4s (type %.200s)",
                   name, name, Py_TYPE(res)->tp_name);
      Py_DECREF(res);
      return NULL;
    }
  }
  else if (!PyErr_Occurred()) {
    PyErr_SetString(PyExc_TypeError,
                    "an integer is required");
  }
  return res;
}
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
  Py_ssize_t ival;
  PyObject *x;
#if PY_MAJOR_VERSION < 3
  if (likely(PyInt_CheckExact(b))) {
    if (sizeof(Py_ssize_t) >= sizeof(long))
        return PyInt_AS_LONG(b);
    else
        return PyInt_AsSsize_t(x);
  }
#endif
  if (likely(PyLong_CheckExact(b))) {
    #if CYTHON_USE_PYLONG_INTERNALS
    const digit* digits = ((PyLongObject*)b)->ob_digit;
    const Py_ssize_t size = Py_SIZE(b);
    if (likely(__Pyx_sst_abs(size) <= 1)) {
        ival = likely(size) ? digits[0] : 0;
        if (size == -1) ival = -ival;
        return ival;
    } else {
      switch (size) {
         case 2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
      }
    }
    #endif
    return PyLong_AsSsize_t(b);
  }
  x = PyNumber_Index(b);
  if (!x) return -1;
  ival = PyInt_AsSsize_t(x);
  Py_DECREF(x);
  return ival;
}
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
    return PyInt_FromSize_t(ival);
}


#endif /* Py_PYTHON_H */
