def setup_method(self, method):
real = np.random.random((8,8))
imag = np.random.random((8,8))
s_complex_dtype = signals.ComplexSignal1D(real + 1j*imag - 1j*imag)
s_real_dtype = signals.ComplexSignal1D(real)
s_complex_dtype.decomposition()
s_real_dtype.decomposition()
self.s_complex_dtype = s_complex_dtype
self.s_real_dtype = s_real_dtype
def setup_method(self, method):
rng = np.random.RandomState(123)
real = rng.random_sample(size=(8, 8))
imag = rng.random_sample(size=(8, 8))
s_complex_dtype = signals.ComplexSignal1D(real + 1j * imag - 1j * imag)
s_real_dtype = signals.ComplexSignal1D(real)
s_complex_dtype.decomposition()
s_real_dtype.decomposition()
self.s_complex_dtype = s_complex_dtype
self.s_real_dtype = s_real_dtype
def test_first_r_values_of_scree_non_zero(self):
"""For low-rank matrix by creating a = RandomComplex(m, r) and
b = RandomComplex(n, r), then performing PCA on the result of a.b^T
(i.e. an m x n matrix).
The first r values of scree plot / singular values should be non-zero.
"""
m, n, r = 32, 32, 3
A = (np.random.random((m,r)) + 1j* np.random.random((m,r)))
B = (np.random.random((n,r)) + 1j* np.random.random((n,r)))
s = signals.ComplexSignal1D(np.dot(A,B.T))
s.decomposition()
np.testing.assert_almost_equal(
s.get_explained_variance_ratio().data[r:].sum(), 0)
