rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/skimage/metrics/_contingency_table.py

@@ -1,39 +0,0 @@
-import scipy.sparse as sparse
-import numpy as np
-
-__all__ = ['contingency_table']
-
-
-def contingency_table(im_true, im_test, *, ignore_labels=None,
-                      normalize=False):
-    """
-    Return the contingency table for all regions in matched segmentations.
-
-    Parameters
-    ----------
-    im_true : ndarray of int
-        Ground-truth label image, same shape as im_test.
-    im_test : ndarray of int
-        Test image.
-    ignore_labels : sequence of int, optional
-        Labels to ignore. Any part of the true image labeled with any of these
-        values will not be counted in the score.
-    normalize : bool
-        Determines if the contingency table is normalized by pixel count.
-
-    Returns
-    -------
-    cont : scipy.sparse.csr_matrix
-        A contingency table. `cont[i, j]` will equal the number of voxels
-        labeled `i` in `im_true` and `j` in `im_test`.
-    """
-
-    if ignore_labels is None:
-        ignore_labels = []
-    im_test_r = im_test.reshape(-1)
-    im_true_r = im_true.reshape(-1)
-    data = np.isin(im_true_r, ignore_labels, invert=True).astype(float)
-    if normalize:
-        data /= np.count_nonzero(data)
-    cont = sparse.coo_matrix((data, (im_true_r, im_test_r))).tocsr()
-    return cont