reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/utils/arrayfuncs.pyx

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+"""A small collection of auxiliary functions that operate on arrays."""
+
+from cython cimport floating
+from cython.parallel cimport prange
+from libc.math cimport fabs
+from libc.float cimport DBL_MAX, FLT_MAX
+
+from ._cython_blas cimport _copy, _rotg, _rot
+from ._typedefs cimport float64_t
+
+
+ctypedef fused real_numeric:
+    short
+    int
+    long
+    long long
+    float
+    double
+
+
+def min_pos(const floating[:] X):
+    """Find the minimum value of an array over positive values.
+
+    Returns the maximum representable value of the input dtype if none of the
+    values are positive.
+
+    Parameters
+    ----------
+    X : ndarray of shape (n,)
+        Input array.
+
+    Returns
+    -------
+    min_val : float
+        The smallest positive value in the array, or the maximum representable value
+         of the input dtype if no positive values are found.
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.utils.arrayfuncs import min_pos
+    >>> X = np.array([0, -1, 2, 3, -4, 5])
+    >>> min_pos(X)
+    2.0
+    """
+    cdef Py_ssize_t i
+    cdef floating min_val = FLT_MAX if floating is float else DBL_MAX
+    for i in range(X.size):
+        if 0. < X[i] < min_val:
+            min_val = X[i]
+    return min_val
+
+
+def _all_with_any_reduction_axis_1(real_numeric[:, :] array, real_numeric value):
+    """Check whether any row contains all values equal to `value`.
+
+    It is equivalent to `np.any(np.all(X == value, axis=1))`, but it avoids to
+    materialize the temporary boolean matrices in memory.
+
+    Parameters
+    ----------
+    array: array-like
+        The array to be checked.
+    value: short, int, long, float, or double
+        The value to use for the comparison.
+
+    Returns
+    -------
+    any_all_equal: bool
+        Whether or not any rows contains all values equal to `value`.
+    """
+    cdef Py_ssize_t i, j
+
+    for i in range(array.shape[0]):
+        for j in range(array.shape[1]):
+            if array[i, j] != value:
+                break
+        else:  # no break
+            return True
+    return False
+
+
+# General Cholesky Delete.
+# Remove an element from the cholesky factorization
+# m = columns
+# n = rows
+#
+# TODO: put transpose as an option
+def cholesky_delete(floating[:, :] L, int go_out):
+    cdef:
+        int n = L.shape[0]
+        int m = L.strides[0]
+        floating c, s
+        floating *L1
+        int i
+
+    if floating is float:
+        m /= sizeof(float)
+    else:
+        m /= sizeof(double)
+
+    # delete row go_out
+    L1 = &L[0, 0] + (go_out * m)
+    for i in range(go_out, n-1):
+        _copy(i + 2, L1 + m, 1, L1, 1)
+        L1 += m
+
+    L1 = &L[0, 0] + (go_out * m)
+    for i in range(go_out, n-1):
+        _rotg(L1 + i, L1 + i + 1, &c, &s)
+        if L1[i] < 0:
+            # Diagonals cannot be negative
+            L1[i] = fabs(L1[i])
+            c = -c
+            s = -s
+
+        L1[i + 1] = 0.  # just for cleanup
+        L1 += m
+
+        _rot(n - i - 2, L1 + i, m, L1 + i + 1, m, c, s)
+
+
+def sum_parallel(const floating [:] array, int n_threads):
+    """Parallel sum, always using float64 internally."""
+    cdef:
+        float64_t out = 0.
+        int i = 0
+
+    for i in prange(
+        array.shape[0], schedule='static', nogil=True, num_threads=n_threads
+    ):
+        out += array[i]
+
+    return out