Fixed database typo and removed unnecessary class identifier.

venv/Lib/site-packages/scipy/special/tests/test_log_softmax.py

@@ -0,0 +1,109 @@
+import numpy as np
+from numpy.testing import assert_allclose
+
+import pytest
+
+import scipy.special as sc
+
+
+@pytest.mark.parametrize('x, expected', [
+    (np.array([1000, 1]), np.array([0, -999])),
+
+    # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
+    # converted to float.
+    (np.arange(4), np.array([-3.4401896985611953,
+                             -2.4401896985611953,
+                             -1.4401896985611953,
+                             -0.44018969856119533]))
+])
+def test_log_softmax(x, expected):
+    assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
+
+
+@pytest.fixture
+def log_softmax_x():
+    x = np.arange(4)
+    return x
+
+
+@pytest.fixture
+def log_softmax_expected():
+    # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
+    # converted to float.
+    expected = np.array([-3.4401896985611953,
+                         -2.4401896985611953,
+                         -1.4401896985611953,
+                         -0.44018969856119533])
+    return expected
+
+
+def test_log_softmax_translation(log_softmax_x, log_softmax_expected):
+    # Translation property.  If all the values are changed by the same amount,
+    # the softmax result does not change.
+    x = log_softmax_x + 100
+    expected = log_softmax_expected
+    assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
+
+
+def test_log_softmax_noneaxis(log_softmax_x, log_softmax_expected):
+    # When axis=None, softmax operates on the entire array, and preserves
+    # the shape.
+    x = log_softmax_x.reshape(2, 2)
+    expected = log_softmax_expected.reshape(2, 2)
+    assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
+
+
+@pytest.mark.parametrize('axis_2d, expected_2d', [
+    (0, np.log(0.5) * np.ones((2, 2))),
+    (1, np.array([[0, -999], [0, -999]]))
+])
+def test_axes(axis_2d, expected_2d):
+    assert_allclose(
+        sc.log_softmax([[1000, 1], [1000, 1]], axis=axis_2d),
+        expected_2d,
+        rtol=1e-13,
+    )
+
+
+@pytest.fixture
+def log_softmax_2d_x():
+    x = np.arange(8).reshape(2, 4)
+    return x
+
+
+@pytest.fixture
+def log_softmax_2d_expected():
+    # Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
+    # converted to float.
+    expected = np.array([[-3.4401896985611953,
+                         -2.4401896985611953,
+                         -1.4401896985611953,
+                         -0.44018969856119533],
+                        [-3.4401896985611953,
+                         -2.4401896985611953,
+                         -1.4401896985611953,
+                         -0.44018969856119533]])
+    return expected
+
+
+def test_log_softmax_2d_axis1(log_softmax_2d_x, log_softmax_2d_expected):
+    x = log_softmax_2d_x
+    expected = log_softmax_2d_expected
+    assert_allclose(sc.log_softmax(x, axis=1), expected, rtol=1e-13)
+
+
+def test_log_softmax_2d_axis0(log_softmax_2d_x, log_softmax_2d_expected):
+    x = log_softmax_2d_x.T
+    expected = log_softmax_2d_expected.T
+    assert_allclose(sc.log_softmax(x, axis=0), expected, rtol=1e-13)
+
+
+def test_log_softmax_3d(log_softmax_2d_x, log_softmax_2d_expected):
+    # 3-d input, with a tuple for the axis.
+    x_3d = log_softmax_2d_x.reshape(2, 2, 2)
+    expected_3d = log_softmax_2d_expected.reshape(2, 2, 2)
+    assert_allclose(sc.log_softmax(x_3d, axis=(1, 2)), expected_3d, rtol=1e-13)
+
+
+def test_log_softmax_scalar():
+    assert_allclose(sc.log_softmax(1.0), 0.0, rtol=1e-13)