def test():
"""
This is a combination of two unsupervised learning techniques, principal
component analysis (PCA) and independent component analysis (ICA). PCA is
a technique for dimensionality reduction, i.e. an algorithm to explain the
observed variance in your data using less dimensions. ICA is a source
separation technique, for example to unmix multiple signals that have been
recorded through multiple sensors. Doing a PCA first and then an ICA can
be useful if you have more sensors than signals. For more information see:
the FastICA example from scikit-learn.
"""
data = np.random.random((5000, 100))
u, s, v = linalg.svd(data)
pca = np.dot(u[:, :10].T, data)
results = fastica(pca.T, whiten=False)
return results
def test():
data = np.random.random((5000, 100))
u, s, v = linalg.svd(data, full_matrices=False)
pca = np.dot(u[:, :10].T, data)
results = fastica(pca.T, whiten=False)
def test():
data = np.random.random((5000, 100))
u, s, v = linalg.svd(data)
pca = np.dot(u[:10, :], data)
results = fastica(pca.T, whiten=False)
def test():
data = np.random.random((5000, 100))
u, s, v = linalg.svd(data, full_matrices=False)
pca = np.dot(u[:, :10].T, data)
results = fastica(pca.T, whiten=False)