def test_correlation(self):
# data: 20 samples, 80 features
X = np.random.rand(20, 80)
C = 1 - one_minus_correlation(X, X)
# get nsample x nssample correlation matrix
self.assertTrue(C.shape == (20, 20))
# diagonal is 1
self.assertTrue((np.abs(np.diag(C) - 1).mean() < 0.00001).all())
# now two different
Y = np.random.rand(5, 80)
C2 = 1 - one_minus_correlation(X, Y)
# get nsample x nssample correlation matrix
self.assertTrue(C2.shape == (20, 5))
# external validity check -- we are dealing with correlations
self.assertTrue(C2[10, 2] - np.corrcoef(X[10], Y[2])[0, 1] < 0.000001)
def test_correlation(self):
# data: 20 samples, 80 features
X = np.random.rand(20, 80)
C = 1 - one_minus_correlation(X, X)
# get nsample x nssample correlation matrix
self.assertTrue(C.shape == (20, 20))
# diagonal is 1
self.assertTrue((np.abs(np.diag(C) - 1).mean() < 0.00001).all())
# now two different
Y = np.random.rand(5, 80)
C2 = 1 - one_minus_correlation(X, Y)
# get nsample x nssample correlation matrix
self.assertTrue(C2.shape == (20, 5))
# external validity check -- we are dealing with correlations
self.assertTrue(C2[10, 2] - np.corrcoef(X[10], Y[2])[0, 1] < 0.000001)
