def _compute_mixture_probabilities((cache_dir, images, gmm, meanvector, loadings)):
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
mean_centered = normalizeddata - meanvector
projected = np.dot(mean_centered, loadings)
mixture_probabilities = gmm.predict_proba(projected)
return mixture_probabilities.mean(0)
def _compute_group_subsample((cache_dir, images)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
np.random.shuffle(normalizeddata)
normalizeddata_sample = [x for i, x in enumerate(normalizeddata) if i % 1000 == 0]
return normalizeddata_sample
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
def _compute_group_subsample((cache_dir, images)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
np.random.shuffle(normalizeddata)
normalizeddata_sample = [x for i, x in enumerate(normalizeddata) if i % 100 == 0]
return normalizeddata_sample
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
def _compute_group_projection_and_mean((cache_dir, images, fa_node, mean, stdev)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
normalizeddata = (normalizeddata - mean) / stdev
normalizeddata_projected = fa_node.execute(normalizeddata)
normalizeddata_projected_mean = np.mean(normalizeddata_projected, axis = 0)
return normalizeddata_projected_mean
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
def _compute_group_projection_and_mean((cache_dir, images, fa_node, mean, stdev)):
try:
import numpy as np
from cpa.util import cache
cache = Cache(cache_dir)
normalizeddata, normalized_colnames = cache.load(images, normalization=RobustLinearNormalization)
normalizeddata = (normalizeddata - mean) / stdev
normalizeddata_projected = fa_node.execute(normalizeddata)
normalizeddata_projected_mean = np.mean(normalizeddata_projected, axis = 0)
return normalizeddata_projected_mean
except: # catch *all* exceptions
from traceback import print_exc
print_exc(None, sys.stderr)
e = sys.exc_info()[1]
print >>sys.stderr, "Error: %s" % (e,)
return None
