33 lines
768 B
Python
33 lines
768 B
Python
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import numpy as np
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from sklearn.utils.optimize import newton_cg
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from scipy.optimize import fmin_ncg
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from sklearn.utils.testing import assert_array_almost_equal
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def test_newton_cg():
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# Test that newton_cg gives same result as scipy's fmin_ncg
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rng = np.random.RandomState(0)
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A = rng.normal(size=(10, 10))
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x0 = np.ones(10)
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def func(x):
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Ax = A.dot(x)
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return .5 * (Ax).dot(Ax)
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def grad(x):
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return A.T.dot(A.dot(x))
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def hess(x, p):
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return p.dot(A.T.dot(A.dot(x.all())))
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def grad_hess(x):
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return grad(x), lambda x: A.T.dot(A.dot(x))
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assert_array_almost_equal(
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newton_cg(grad_hess, func, grad, x0, tol=1e-10)[0],
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fmin_ncg(f=func, x0=x0, fprime=grad, fhess_p=hess)
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)
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