Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/optimize/tests/test_linear_assignment.py

@@ -0,0 +1,102 @@
+# Author: Brian M. Clapper, G. Varoquaux, Lars Buitinck
+# License: BSD
+
+from numpy.testing import assert_array_equal
+from pytest import raises as assert_raises
+
+import numpy as np
+
+from scipy.optimize import linear_sum_assignment
+from scipy.sparse.sputils import matrix
+
+
+def test_linear_sum_assignment():
+    for sign in [-1, 1]:
+        for cost_matrix, expected_cost in [
+            # Square
+            ([[400, 150, 400],
+              [400, 450, 600],
+              [300, 225, 300]],
+             [150, 400, 300]
+             ),
+
+            # Rectangular variant
+            ([[400, 150, 400, 1],
+              [400, 450, 600, 2],
+              [300, 225, 300, 3]],
+             [150, 2, 300]),
+
+            # Square
+            ([[10, 10, 8],
+              [9, 8, 1],
+              [9, 7, 4]],
+             [10, 1, 7]),
+
+            # Rectangular variant
+            ([[10, 10, 8, 11],
+              [9, 8, 1, 1],
+              [9, 7, 4, 10]],
+             [10, 1, 4]),
+
+            # n == 2, m == 0 matrix
+            ([[], []],
+             []),
+
+            # Square with positive infinities
+            ([[10, float("inf"), float("inf")],
+              [float("inf"), float("inf"), 1],
+              [float("inf"), 7, float("inf")]],
+             [10, 1, 7]),
+        ]:
+
+            maximize = sign == -1
+            cost_matrix = sign * np.array(cost_matrix)
+            expected_cost = sign * np.array(expected_cost)
+
+            row_ind, col_ind = linear_sum_assignment(cost_matrix,
+                                                     maximize=maximize)
+            assert_array_equal(row_ind, np.sort(row_ind))
+            assert_array_equal(expected_cost, cost_matrix[row_ind, col_ind])
+
+            cost_matrix = cost_matrix.T
+            row_ind, col_ind = linear_sum_assignment(cost_matrix,
+                                                     maximize=maximize)
+            assert_array_equal(row_ind, np.sort(row_ind))
+            assert_array_equal(np.sort(expected_cost),
+                               np.sort(cost_matrix[row_ind, col_ind]))
+
+
+def test_linear_sum_assignment_input_validation():
+
+    assert_raises(ValueError, linear_sum_assignment, [1, 2, 3])
+
+    C = [[1, 2, 3], [4, 5, 6]]
+    assert_array_equal(linear_sum_assignment(C),
+                       linear_sum_assignment(np.asarray(C)))
+    assert_array_equal(linear_sum_assignment(C),
+                       linear_sum_assignment(matrix(C)))
+
+    I = np.identity(3)
+    assert_array_equal(linear_sum_assignment(I.astype(np.bool_)),
+                       linear_sum_assignment(I))
+    assert_raises(ValueError, linear_sum_assignment, I.astype(str))
+
+    I[0][0] = np.nan
+    assert_raises(ValueError, linear_sum_assignment, I)
+
+    I = np.identity(3)
+    I[1][1] = -np.inf
+    assert_raises(ValueError, linear_sum_assignment, I)
+
+    I = np.identity(3)
+    I[:, 0] = np.inf
+    assert_raises(ValueError, linear_sum_assignment, I)
+
+
+def test_constant_cost_matrix():
+    # Fixes #11602
+    n = 8
+    C = np.ones((n, n))
+    row_ind, col_ind = linear_sum_assignment(C)
+    assert_array_equal(row_ind, np.arange(n))
+    assert_array_equal(col_ind, np.arange(n))