def test_ICA(self):
print ('Preprocessing -> Performing ICA test ...')
sys.stdout.flush()
fail = 0
for _ in range(100):
data, mixMat = generate_2d_mixtures(10000)
zca = ZCA(data.shape[1])
zca.train(data)
data_zca = zca.project(data)
mixMat_zca = zca.project(mixMat.T).T
mixMat_zca /= get_norms(mixMat_zca, axis=None)
model = ICA(2)
model.train(data_zca, 10)
assert numx.all(numx.abs(data_zca - model.unproject(model.project(data_zca))) < self.epsilon)
# Check the angle between mixing matrix vectors and resulting vectors of the projection matrix
# Theorectially the amari distnace could be used but it seems only to be rotation and scal invariant
# and not invariant through permutations of the colums.
res = model.projection_matrix / get_norms(model.projection_matrix, axis=None)
v1 = res
v2 = res * numx.array([[-1, 1], [-1, 1]])
v3 = res * numx.array([[1, -1], [1, -1]])
v4 = -res
res = numx.array([res[:, 1], res[:, 0]]).T
v5 = res
v6 = res * numx.array([[-1, 1], [-1, 1]])
v7 = res * numx.array([[1, -1], [1, -1]])
v8 = -res
v1d = numx.max([angle_between_vectors(v1[0], mixMat_zca[0]), angle_between_vectors(v1[1], mixMat_zca[1])])
v2d = numx.max([angle_between_vectors(v2[0], mixMat_zca[0]), angle_between_vectors(v2[1], mixMat_zca[1])])
v3d = numx.max([angle_between_vectors(v3[0], mixMat_zca[0]), angle_between_vectors(v3[1], mixMat_zca[1])])
v4d = numx.max([angle_between_vectors(v4[0], mixMat_zca[0]), angle_between_vectors(v4[1], mixMat_zca[1])])
v5d = numx.max([angle_between_vectors(v5[0], mixMat_zca[0]), angle_between_vectors(v5[1], mixMat_zca[1])])
v6d = numx.max([angle_between_vectors(v6[0], mixMat_zca[0]), angle_between_vectors(v6[1], mixMat_zca[1])])
v7d = numx.max([angle_between_vectors(v7[0], mixMat_zca[0]), angle_between_vectors(v7[1], mixMat_zca[1])])
v8d = numx.max([angle_between_vectors(v8[0], mixMat_zca[0]), angle_between_vectors(v8[1], mixMat_zca[1])])
dist = numx.min([v1d, v2d, v3d, v4d, v5d, v6d, v7d, v8d])
# biggest angle smaller than 5 degrees
if dist > 5.0:
fail += 1
assert fail < 5
print('successfully passed!')
sys.stdout.flush()
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
# Import numpy, numpy extensions, PCA, 2D linear mixture, and visualization module
import numpy as numx
from pydeep.preprocessing import PCA
from pydeep.misc.toyproblems import generate_2d_mixtures
import pydeep.misc.visualization as vis
# Set the random seed
# (optional, if stochastic processes are involved we get the same results)
numx.random.seed(42)
# Create 2D linear mixture, 50000 samples, mean = 0, std = 3
data, _ = generate_2d_mixtures(num_samples=50000,
mean=0.0,
scale=3.0)
# PCA
pca = PCA(data.shape[1])
pca.train(data)
data_pca = pca.project(data)
# Display results
# For better visualization the principal components are rescaled
scale_factor = 3
# Figure 1 - Data with estimated principal components
vis.figure(0, figsize=[7, 7])
vis.title("Data with estimated principal components")
"""
# Import numpy, numpy extensions, ZCA, ICA, 2D linear mixture, and visualization
import numpy as numx
import pydeep.base.numpyextension as numxext
from pydeep.preprocessing import ZCA, ICA
from pydeep.misc.toyproblems import generate_2d_mixtures
import pydeep.misc.visualization as vis
# Set the random seed
# (optional, if stochastic processes are involved we get the same results)
numx.random.seed(42)
# Create 2D linear mixture, 50000 samples, mean = 0, std = 3
data, mixing_matrix = generate_2d_mixtures(num_samples=50000,
mean=0.0,
scale=3.0)
# Zero Phase Component Analysis (ZCA) - Whitening in original space
zca = ZCA(data.shape[1])
zca.train(data)
whitened_data = zca.project(data)
# Independent Component Analysis (ICA)
ica = ICA(whitened_data.shape[1])
ica.train(whitened_data, iterations=100, status=False)
data_ica = ica.project(whitened_data)
# print the ll on the data
print("Log-likelihood on all data: " +
import pydeep.base.numpyextension as numxext
# Import models, trainers and estimators
import pydeep.rbm.model as model
import pydeep.rbm.trainer as trainer
import pydeep.rbm.estimator as estimator
# Import linear mixture, preprocessing, and visualization
from pydeep.misc.toyproblems import generate_2d_mixtures
import pydeep.preprocessing as pre
import pydeep.misc.visualization as vis
numx.random.seed(42)
# Create a 2D mxiture
data, mixing_matrix = generate_2d_mixtures(100000, 1, 1.0)
# Whiten data
zca = pre.ZCA(data.shape[1])
zca.train(data)
whitened_data = zca.project(data)
# split training test data
train_data = whitened_data[0:numx.int32(whitened_data.shape[0] / 2.0), :]
test_data = whitened_data[numx.int32(whitened_data.shape[0] /
2.0):whitened_data.shape[0], :]
# Input output dims
h1 = 2
h2 = 2
v1 = whitened_data.shape[1]