def test_matrix_sqrt_eigs(self):
     import spectral as spy
     from spectral.algorithms.spymath import matrix_sqrt
     stats = spy.calc_stats(self.data)
     (evals, evecs) = np.linalg.eig(stats.cov)
     S = matrix_sqrt(eigs=(evals, evecs))
     assert_allclose(S.dot(S), self.C, atol=1e-8)
Пример #2
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 def set_background(self, stats):
     '''Sets background statistics to be used when applying the detector.'''
     from spectral.algorithms.spymath import matrix_sqrt
     self.background = stats
     self.u_b = stats.mean
     # Matrix square root of inverse of cov: C**(-1/2)
     self.C_1_2 = matrix_sqrt(np.linalg.inv(stats.cov))
Пример #3
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    def whiten(self, X):
        '''Transforms data to the whitened space of the background.

        Arguments:

            `X` (ndarray):

                Size (M,N,K) or (M*N,K) array of length K vectors to transform.

        Returns an array of same size as `X` but linearly transformed to the
        whitened space of the filter.
        '''
        import math
        from spectral.algorithms.transforms import LinearTransform
        from spectral.algorithms.spymath import matrix_sqrt

        if self._whitening_transform is None:
            A = math.sqrt(self.coef) * matrix_sqrt(self.C_1, True)
            self._whitening_transform = LinearTransform(A, pre=-self.u_b)
        return self._whitening_transform(X)
Пример #4
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 def get_whitening_transform(self):
     '''Returns transform that centers and whitens data for these stats.'''
     from spectral.algorithms.transforms import LinearTransform
     from spectral.algorithms.spymath import matrix_sqrt
     C_1 = np.linalg.inv(self.cov)
     return LinearTransform(matrix_sqrt(C_1, True), pre=-self.mean)
Пример #5
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 def test_matrix_sqrt_eigs(self):
     stats = spy.calc_stats(self.data)
     (evals, evecs) = np.linalg.eig(stats.cov)
     S = matrix_sqrt(eigs=(evals, evecs))
     assert_allclose(S.dot(S), self.C, atol=1e-8)
Пример #6
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 def test_matrix_sqrt_sym_inv(self):
     S = matrix_sqrt(self.C, symmetric=True, inverse=True)
     assert_allclose(S.dot(S), np.linalg.inv(self.C), atol=1e-8)
Пример #7
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 def test_matrix_sqrt_sym(self):
     S = matrix_sqrt(self.C, symmetric=True)
     assert_allclose(S.dot(S), self.C, atol=1e-8)
Пример #8
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 def test_matrix_sqrt_inv(self):
     S = matrix_sqrt(self.X, inverse=True)
     assert_allclose(S.dot(S), np.linalg.inv(self.X))
Пример #9
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 def test_matrix_sqrt(self):
     S = matrix_sqrt(self.X)
     assert_allclose(S.dot(S), self.X)
 def test_matrix_sqrt_sym_inv(self):
     from spectral.algorithms.spymath import matrix_sqrt
     S = matrix_sqrt(self.C, symmetric=True, inverse=True)
     assert_allclose(S.dot(S), np.linalg.inv(self.C), atol=1e-8)
 def test_matrix_sqrt_sym(self):
     from spectral.algorithms.spymath import matrix_sqrt
     S = matrix_sqrt(self.C, symmetric=True)
     assert_allclose(S.dot(S), self.C, atol=1e-8)
 def test_matrix_sqrt_inv(self):
     from spectral.algorithms.spymath import matrix_sqrt
     S = matrix_sqrt(self.X, inverse=True)
     assert_allclose(S.dot(S), np.linalg.inv(self.X))
 def test_matrix_sqrt(self):
     from spectral.algorithms.spymath import matrix_sqrt
     S = matrix_sqrt(self.X)
     assert_allclose(S.dot(S), self.X)