8000 Test row_norms for float32 data · scikit-learn/scikit-learn@ad211a1 · GitHub
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Test row_norms for float32 data
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‎sklearn/utils/tests/test_extmath.py

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@@ -148,14 +148,16 @@ def test_norm_squared_norm():
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def test_row_norms():
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X = np.random.RandomState(42).randn(100, 100)
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sq_norm = (X ** 2).sum(axis=1)
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for dtype in (np.float32, np.float64):
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X = X.astype(dtype)
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sq_norm = (X ** 2).sum(axis=1)
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assert_array_almost_equal(sq_norm, row_norms(X, squared=True), 5)
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assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X))
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assert_array_almost_equal(sq_norm, row_norms(X, squared=True), 4)
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assert_array_almost_equal(np.sqrt(sq_norm), row_norms(X), 4)
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Xcsr = sparse.csr_matrix(X, dtype=np.float32)
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assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True), 5)
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assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr))
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Xcsr = sparse.csr_matrix(X, dtype=dtype)
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assert_array_almost_equal(sq_norm, row_norms(Xcsr, squared=True), 4)
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assert_array_almost_equal(np.sqrt(sq_norm), row_norms(Xcsr), 4)
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def test_randomized_svd_low_rank_with_noise():

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