143 lines
3.9 KiB
Python
143 lines
3.9 KiB
Python
from __future__ import division, print_function, absolute_import
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import numpy as np
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from numpy.testing import (assert_almost_equal, assert_equal,
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assert_array_almost_equal, assert_)
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from scipy.special import logsumexp
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def test_logsumexp():
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# Test whether logsumexp() function correctly handles large inputs.
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a = np.arange(200)
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desired = np.log(np.sum(np.exp(a)))
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assert_almost_equal(logsumexp(a), desired)
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# Now test with large numbers
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b = [1000, 1000]
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desired = 1000.0 + np.log(2.0)
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assert_almost_equal(logsumexp(b), desired)
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n = 1000
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b = np.ones(n) * 10000
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desired = 10000.0 + np.log(n)
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assert_almost_equal(logsumexp(b), desired)
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x = np.array([1e-40] * 1000000)
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logx = np.log(x)
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X = np.vstack([x, x])
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logX = np.vstack([logx, logx])
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assert_array_almost_equal(np.exp(logsumexp(logX)), X.sum())
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assert_array_almost_equal(np.exp(logsumexp(logX, axis=0)), X.sum(axis=0))
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assert_array_almost_equal(np.exp(logsumexp(logX, axis=1)), X.sum(axis=1))
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# Handling special values properly
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assert_equal(logsumexp(np.inf), np.inf)
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assert_equal(logsumexp(-np.inf), -np.inf)
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assert_equal(logsumexp(np.nan), np.nan)
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assert_equal(logsumexp([-np.inf, -np.inf]), -np.inf)
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# Handling an array with different magnitudes on the axes
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assert_array_almost_equal(logsumexp([[1e10, 1e-10],
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[-1e10, -np.inf]], axis=-1),
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[1e10, -1e10])
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# Test keeping dimensions
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assert_array_almost_equal(logsumexp([[1e10, 1e-10],
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[-1e10, -np.inf]],
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axis=-1,
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keepdims=True),
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[[1e10], [-1e10]])
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# Test multiple axes
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assert_array_almost_equal(logsumexp([[1e10, 1e-10],
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[-1e10, -np.inf]],
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axis=(-1,-2)),
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1e10)
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def test_logsumexp_b():
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a = np.arange(200)
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b = np.arange(200, 0, -1)
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desired = np.log(np.sum(b*np.exp(a)))
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assert_almost_equal(logsumexp(a, b=b), desired)
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a = [1000, 1000]
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b = [1.2, 1.2]
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desired = 1000 + np.log(2 * 1.2)
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assert_almost_equal(logsumexp(a, b=b), desired)
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x = np.array([1e-40] * 100000)
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b = np.linspace(1, 1000, 100000)
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logx = np.log(x)
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X = np.vstack((x, x))
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logX = np.vstack((logx, logx))
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B = np.vstack((b, b))
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assert_array_almost_equal(np.exp(logsumexp(logX, b=B)), (B * X).sum())
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assert_array_almost_equal(np.exp(logsumexp(logX, b=B, axis=0)),
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(B * X).sum(axis=0))
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assert_array_almost_equal(np.exp(logsumexp(logX, b=B, axis=1)),
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(B * X).sum(axis=1))
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def test_logsumexp_sign():
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a = [1,1,1]
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b = [1,-1,-1]
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r, s = logsumexp(a, b=b, return_sign=True)
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assert_almost_equal(r,1)
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assert_equal(s,-1)
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def test_logsumexp_sign_zero():
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a = [1,1]
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b = [1,-1]
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r, s = logsumexp(a, b=b, return_sign=True)
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assert_(not np.isfinite(r))
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assert_(not np.isnan(r))
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assert_(r < 0)
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assert_equal(s,0)
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def test_logsumexp_sign_shape():
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a = np.ones((1,2,3,4))
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b = np.ones_like(a)
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r, s = logsumexp(a, axis=2, b=b, return_sign=True)
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assert_equal(r.shape, s.shape)
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assert_equal(r.shape, (1,2,4))
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r, s = logsumexp(a, axis=(1,3), b=b, return_sign=True)
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assert_equal(r.shape, s.shape)
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assert_equal(r.shape, (1,3))
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def test_logsumexp_shape():
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a = np.ones((1, 2, 3, 4))
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b = np.ones_like(a)
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r = logsumexp(a, axis=2, b=b)
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assert_equal(r.shape, (1, 2, 4))
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r = logsumexp(a, axis=(1, 3), b=b)
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assert_equal(r.shape, (1, 3))
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def test_logsumexp_b_zero():
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a = [1,10000]
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b = [1,0]
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assert_almost_equal(logsumexp(a, b=b), 1)
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def test_logsumexp_b_shape():
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a = np.zeros((4,1,2,1))
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b = np.ones((3,1,5))
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logsumexp(a, b=b)
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