def test_fastica(add_noise=False):
""" Test the FastICA algorithm on very simple data.
"""
import scipy.stats as st
n_samples = 1000
# Generate two sources:
s1 = (2*np.sin(np.linspace(0, 100, n_samples))>0)-1
#s1 = 2*(np.random.rand(n_samples)>.5) - 1
s2 = st.t.rvs(1, size=n_samples)
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing angle
phi = 0.6
mixing = np.array([[np.cos(phi), np.sin(phi)],
[np.sin(phi), -np.cos(phi)]])
m = np.dot(mixing, s)
if add_noise:
m += 0.1*np.random.randn(2, 1000)
center_and_norm(m)
algorithm = ['parallel', 'deflation']
non_linearity = ['logcosh', 'exp', 'cube']
for nl in non_linearity:
for algo in algorithm:
k_, mixing_, s_ = fastica.fastica(
m, fun=nl, algorithm=algo)
# Check that the mixing model described in the docstring holds:
np.testing.assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if add_noise==False:
np.testing.assert_almost_equal(
np.dot(s1_, s1)/n_samples, 1, decimal=2)
np.testing.assert_almost_equal(
np.dot(s2_, s2)/n_samples, 1, decimal=2)
else:
np.testing.assert_almost_equal(
np.dot(s1_, s1)/n_samples, 1, decimal=1)
np.testing.assert_almost_equal(
np.dot(s2_, s2)/n_samples, 1, decimal=1)
# Test FastICA class
ica = fastica.FastICA(fun=nl, algorithm=algo)
ica.fit(m)
ica.get_mixing_matrix()
def test_non_square_fastica(add_noise=False):
""" Test the FastICA algorithm on very simple data.
"""
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi * t))
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing matrix
mixing = np.random.randn(6, 2)
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * np.random.randn(6, n_samples)
center_and_norm(m)
k_, mixing_, s_ = fastica.fastica(m, n_components=2)
# Check that the mixing model described in the docstring holds:
np.testing.assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if add_noise == False:
np.testing.assert_almost_equal(np.dot(s1_, s1) / n_samples,
1,
decimal=3)
np.testing.assert_almost_equal(np.dot(s2_, s2) / n_samples,
1,
decimal=3)
def test_non_square_fastica(add_noise=False):
""" Test the FastICA algorithm on very simple data.
"""
n_samples = 1000
# Generate two sources:
t = np.linspace(0, 100, n_samples)
s1 = np.sin(t)
s2 = np.ceil(np.sin(np.pi*t))
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing matrix
mixing = np.random.randn(6, 2)
m = np.dot(mixing, s)
if add_noise:
m += 0.1*np.random.randn(6, n_samples)
center_and_norm(m)
k_, mixing_, s_ = fastica.fastica(m, n_components=2)
# Check that the mixing model described in the docstring holds:
np.testing.assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if add_noise==False:
np.testing.assert_almost_equal(np.dot(s1_, s1)/n_samples, 1, decimal=3)
np.testing.assert_almost_equal(np.dot(s2_, s2)/n_samples, 1, decimal=3)
def test_fastica(add_noise=False):
""" Test the FastICA algorithm on very simple data.
"""
import scipy.stats as st
n_samples = 1000
# Generate two sources:
s1 = (2 * np.sin(np.linspace(0, 100, n_samples)) > 0) - 1
#s1 = 2*(np.random.rand(n_samples)>.5) - 1
s2 = st.t.rvs(1, size=n_samples)
s = np.c_[s1, s2].T
center_and_norm(s)
s1, s2 = s
# Mixing angle
phi = 0.6
mixing = np.array([[np.cos(phi), np.sin(phi)], [np.sin(phi),
-np.cos(phi)]])
m = np.dot(mixing, s)
if add_noise:
m += 0.1 * np.random.randn(2, 1000)
center_and_norm(m)
algorithm = ['parallel', 'deflation']
non_linearity = ['logcosh', 'exp', 'cube']
for nl in non_linearity:
for algo in algorithm:
k_, mixing_, s_ = fastica.fastica(m, fun=nl, algorithm=algo)
# Check that the mixing model described in the docstring holds:
np.testing.assert_almost_equal(s_, np.dot(np.dot(mixing_, k_), m))
center_and_norm(s_)
s1_, s2_ = s_
# Check to see if the sources have been estimated
# in the wrong order
if abs(np.dot(s1_, s2)) > abs(np.dot(s1_, s1)):
s2_, s1_ = s_
s1_ *= np.sign(np.dot(s1_, s1))
s2_ *= np.sign(np.dot(s2_, s2))
# Check that we have estimated the original sources
if add_noise == False:
np.testing.assert_almost_equal(np.dot(s1_, s1) / n_samples,
1,
decimal=2)
np.testing.assert_almost_equal(np.dot(s2_, s2) / n_samples,
1,
decimal=2)
else:
np.testing.assert_almost_equal(np.dot(s1_, s1) / n_samples,
1,
decimal=1)
np.testing.assert_almost_equal(np.dot(s2_, s2) / n_samples,
1,
decimal=1)
# Test FastICA class
ica = fastica.FastICA(fun=nl, algorithm=algo)
ica.fit(m)
ica.get_mixing_matrix()
