# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause # See _utils.pyx for details. cimport numpy as cnp from sklearn.tree._tree cimport Node from sklearn.neighbors._quad_tree cimport Cell from sklearn.utils._typedefs cimport float32_t, float64_t, intp_t, uint8_t, int32_t, uint32_t 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 # particularly tiny on Windows/MSVC. # It corresponds to the maximum representable value for # 32-bit signed integers (i.e. 2^31 - 1). RAND_R_MAX = 2147483647 # safe_realloc(&p, n) resizes the allocation of p to n * sizeof(*p) bytes or # raises a MemoryError. It never calls free, since that's __dealloc__'s job. # cdef float32_t *p = NULL # safe_realloc(&p, n) # is equivalent to p = malloc(n * sizeof(*p)) with error checking. ctypedef fused realloc_ptr: # Add pointer types here as needed. (float32_t*) (intp_t*) (uint8_t*) (float64_t*) (float64_t**) (Node*) (Cell*) (Node**) cdef int safe_realloc(realloc_ptr* p, size_t nelems) except -1 nogil cdef cnp.ndarray sizet_ptr_to_ndarray(intp_t* data, intp_t size) cdef intp_t rand_int(intp_t low, intp_t high, uint32_t* random_state) noexcept nogil cdef float64_t rand_uniform(float64_t low, float64_t high, uint32_t* random_state) noexcept nogil cdef float64_t log(float64_t x) noexcept nogil cdef class WeightedFenwickTree: cdef intp_t size # number of leaves (ranks) cdef float64_t* tree_w # BIT for weights cdef float64_t* tree_wy # BIT for weighted targets cdef intp_t max_pow2 # highest power of two <= n cdef float64_t total_w # running total weight cdef float64_t total_wy # running total weighted target cdef void reset(self, intp_t size) noexcept nogil cdef void add(self, intp_t idx, float64_t y, float64_t w) noexcept nogil cdef intp_t search( self, float64_t t, float64_t* cw_out, float64_t* cwy_out, intp_t* prev_idx_out, ) noexcept nogil