def test_errors():
cca = CCA()
with pytest.raises(ValueError):
cca.fit([train1, train2, train2])
with pytest.raises(ValueError):
cca.fit([train1, train2])
cca.stats([train1, train1, train1])
with pytest.raises(AssertionError):
cca.fit([train1, train2])
_ = cca.stats([train1, train2])
with pytest.raises(KeyError):
cca.fit([train1, train2])
_ = cca.stats([train1, train1], 'FAIL')
# Split each dataset into a training set and test set # (10% of dataset is training data) train1 = data1[:int(nSamples / 10)] train2 = data2[:int(nSamples / 10)] test1 = data1[int(nSamples / 10):] test2 = data2[int(nSamples / 10):] n_components = 4 # Initialize CCA cca = CCA(regs=0.001, n_components=n_components) # Use the methods to find a CCA mapping and transform the views of data cca_ft = cca.fit_transform([train1, train2]) cca_f = cca.fit([train1, train2]) cca_t = cca.transform([train1, train2]) # gaussian related data N = 100 t = np.random.uniform(-np.pi, np.pi, N) e1 = np.random.normal(0, 0.05, (N, 2)) e2 = np.random.normal(0, 0.05, (N, 2)) x = np.zeros((N, 2)) x[:, 0] = t x[:, 1] = np.sin(3 * t) x += e1 y = np.zeros((N, 2)) y[:, 0] = np.exp(t / 4) * np.cos(2 * t)
def test_n_components(n_components):
cca = CCA(n_components=n_components)
with pytest.raises(ValueError, match="n_components must be an integer"):
cca = cca.fit([train1, train2])