def change_test(self):
# Check to see if functions touch original data
# They should not
data_init = np.copy(self.data)
p = PCA(eps=0.)
p.fit(self.data)
assert np.allclose(self.data,data_init)
p.fit_transform(self.data)
assert np.allclose(self.data,data_init)
p.inv_transform(self.data)
assert np.allclose(self.data,data_init)
def dimreduce_test(self):
# Check to see if intrinsically 2D data
# can be transformed to 2D and back exactly
p = PCA(dim=2, eps=0.)
new = p.fit_transform(self.data)
new = p.inv_transform(new)
assert np.allclose(new,self.data)
def transform_test(self):
# Check to see if data can
# be transformed and inverse transformed exactly
p = PCA(eps=0.)
new = p.fit_transform(self.data)
new = p.inv_transform(new)
assert np.allclose(new,self.data)
def whiten_test(self):
data = self.data+self.rng.rand(*self.data.shape)
p = PCA(whiten=True, eps=0.)
new = p.fit_transform(data)
cov = new.T.dot(new)
assert np.allclose(cov,np.eye(data.shape[1]))
