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# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org> # Justin Vincent # Lars Buitinck # License: BSD 3 clause import pickle import numpy as np from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_false from sklearn.utils.testing import assert_true from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.utils.fixes import divide, expit from sklearn.utils.fixes import astype from sklearn.utils.fixes import MaskedArray def test_expit(): # Check numerical stability of expit (logistic function). # Simulate our previous Cython implementation, based on #http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression assert_almost_equal(expit(1000.), 1. / (1. + np.exp(-1000.)), decimal=16) assert_almost_equal(expit(-1000.), np.exp(-1000.) / (1. + np.exp(-1000.)), decimal=16) x = np.arange(10) out = np.zeros_like(x, dtype=np.float32) assert_array_almost_equal(expit(x), expit(x, out=out)) def test_divide(): assert_equal(divide(.6, 1), .600000000000) def test_astype_copy_memory(): a_int32 = np.ones(3, np.int32) # Check that dtype conversion works b_float32 = astype(a_int32, dtype=np.float32, copy=False) assert_equal(b_float32.dtype, np.float32) # Changing dtype forces a copy even if copy=False assert_false(np.may_share_memory(b_float32, a_int32)) # Check that copy can be skipped if requested dtype match c_int32 = astype(a_int32, dtype=np.int32, copy=False) assert_true(c_int32 is a_int32) # Check that copy can be forced, and is the case by default: d_int32 = astype(a_int32, dtype=np.int32, copy=True) assert_false(np.may_share_memory(d_int32, a_int32)) e_int32 = astype(a_int32, dtype=np.int32) assert_false(np.may_share_memory(e_int32, a_int32)) def test_masked_array_obj_dtype_pickleable(): marr = MaskedArray([1, None, 'a'], dtype=object) for mask in (True, False, [0, 1, 0]): marr.mask = mask marr_pickled = pickle.loads(pickle.dumps(marr)) assert_array_equal(marr.data, marr_pickled.data) assert_array_equal(marr.mask, marr_pickled.mask)
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