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linedance-app/venv/lib/python3.12/site-packages/sklearn/tree/_criterion.pxd

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+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+# See _criterion.pyx for implementation details.
+from sklearn.utils._typedefs cimport float64_t, int8_t, intp_t
+
+
+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
+    # such as the mean in regression and class probabilities in classification.
+
+    # Internal structures
+    cdef const float64_t[:, ::1] y         # Values of y
+    cdef const float64_t[:] sample_weight  # Sample weights
+
+    cdef const intp_t[:] sample_indices    # Sample indices in X, y
+    cdef intp_t start                      # samples[start:pos] are the samples in the left node
+    cdef intp_t pos                        # samples[pos:end] are the samples in the right node
+    cdef intp_t end
+    cdef intp_t n_missing                  # Number of missing values for the feature being evaluated
+    cdef bint missing_go_to_left           # Whether missing values go to the left node
+
+    cdef intp_t n_outputs                  # Number of outputs
+    cdef intp_t n_samples                  # Number of samples
+    cdef intp_t n_node_samples             # Number of samples in the node (end-start)
+    cdef float64_t weighted_n_samples         # Weighted number of samples (in total)
+    cdef float64_t weighted_n_node_samples    # Weighted number of samples in the node
+    cdef float64_t weighted_n_left            # Weighted number of samples in the left node
+    cdef float64_t weighted_n_right           # Weighted number of samples in the right node
+    cdef float64_t weighted_n_missing         # Weighted number of samples that are missing
+
+    # The criterion object is maintained such that left and right collected
+    # statistics correspond to samples[start:pos] and samples[pos:end].
+
+    # Methods
+    cdef int init(
+        self,
+        const float64_t[:, ::1] y,
+        const float64_t[:] sample_weight,
+        float64_t weighted_n_samples,
+        const intp_t[:] sample_indices,
+        intp_t start,
+        intp_t end
+    ) except -1 nogil
+    cdef void init_sum_missing(self)
+    cdef void init_missing(self, intp_t n_missing) noexcept nogil
+    cdef int reset(self) except -1 nogil
+    cdef int reverse_reset(self) except -1 nogil
+    cdef int update(self, intp_t new_pos) except -1 nogil
+    cdef float64_t node_impurity(self) noexcept nogil
+    cdef void children_impurity(
+        self,
+        float64_t* impurity_left,
+        float64_t* impurity_right
+    ) noexcept nogil
+    cdef void node_value(
+        self,
+        float64_t* dest
+    ) noexcept nogil
+    cdef void clip_node_value(
+        self,
+        float64_t* dest,
+        float64_t lower_bound,
+        float64_t upper_bound
+    ) noexcept nogil
+    cdef float64_t middle_value(self) noexcept nogil
+    cdef float64_t impurity_improvement(
+        self,
+        float64_t impurity_parent,
+        float64_t impurity_left,
+        float64_t impurity_right
+    ) noexcept nogil
+    cdef float64_t proxy_impurity_improvement(self) noexcept nogil
+    cdef bint check_monotonicity(
+            self,
+            int8_t monotonic_cst,
+            float64_t lower_bound,
+            float64_t upper_bound,
+    ) noexcept nogil
+    cdef inline bint _check_monotonicity(
+            self,
+            int8_t monotonic_cst,
+            float64_t lower_bound,
+            float64_t upper_bound,
+            float64_t sum_left,
+            float64_t sum_right,
+    ) noexcept nogil
+
+cdef class ClassificationCriterion(Criterion):
+    """Abstract criterion for classification."""
+
+    cdef intp_t[::1] n_classes
+    cdef intp_t max_n_classes
+
+    cdef float64_t[:, ::1] sum_total    # The sum of the weighted count of each label.
+    cdef float64_t[:, ::1] sum_left     # Same as above, but for the left side of the split
+    cdef float64_t[:, ::1] sum_right    # Same as above, but for the right side of the split
+    cdef float64_t[:, ::1] sum_missing  # Same as above, but for missing values in X
+
+cdef class RegressionCriterion(Criterion):
+    """Abstract regression criterion."""
+
+    cdef float64_t sq_sum_total
+
+    cdef float64_t[::1] sum_total    # The sum of w*y.
+    cdef float64_t[::1] sum_left     # Same as above, but for the left side of the split
+    cdef float64_t[::1] sum_right    # Same as above, but for the right side of the split
+    cdef float64_t[::1] sum_missing  # Same as above, but for missing values in X