def ica(X):
"""
Wrapper function for Independent Component Analysis of scikits.learn.decomposition
"""
from scikits.learn.decomposition import FastICA
model = FastICA()
model.fit(X.T)
Y = model.transform(X.T).T
return Y / Y.std(0)
from scikits.learn.decomposition import PCA, FastICA
###############################################################################
# Generate sample data
S = np.random.standard_t(1.5, size=(10000, 2))
S[0] *= 2.
# Mix data
A = np.array([[1, 1], [0, 2]]) # Mixing matrix
X = np.dot(S, A.T) # Generate observations
pca = PCA()
S_pca_ = pca.fit(X).transform(X)
ica = FastICA()
S_ica_ = ica.fit(X).transform(X) # Estimate the sources
S_ica_ /= S_ica_.std(axis=0)
###############################################################################
# Plot results
def plot_samples(S, axis_list=None):
pl.scatter(S[:,0], S[:,1], s=2, marker='o', linewidths=0, zorder=10)
if axis_list is not None:
colors = [(0, 0.6, 0), (0.6, 0, 0)]
for color, axis in zip(colors, axis_list):
axis /= axis.std()
x_axis, y_axis = axis
def run_ica(data, ncomponents):
logging.debug("running ica: %i" % ncomponents)
ica = FastICA(ncomponents)
ica.fit(data)
return ica
