def get_pca_transform(blocks, numpcs=2, feature='axes_correlation'):
# Put all blocks together into one big array
vectors = concatenate(
[b[feature] for b in blocks if b[feature].shape != (0, )]).T
# coeff, score, latent = princomp(vectors, numpcs)
Pcomponents, Trans, fracVar = prepca(vectors)
return Trans
def get_pca_transform(blocks, numpcs=2, feature='axes_correlation'):
# Put all blocks together into one big array
vectors = concatenate([b[feature] for b in blocks
if b[feature].shape!=(0,)]).T
# coeff, score, latent = princomp(vectors, numpcs)
Pcomponents, Trans, fracVar = prepca(vectors)
return Trans
'running', 'walking']
all_f = numpy.empty((0, 45), 'float32')
for action in action_names:
flow_dir = path.join(output_dir, action)
flow_names = os.listdir(flow_dir)
i = 0;
for flow_name in flow_names:
if i < 500:
flow_path = path.join(flow_dir, flow_name)
f = numpy.load(flow_path)
all_f = numpy.vstack((all_f, f.reshape(-1, 45)))
i += 1
else:
break
all_f = all_f.transpose()
[pcomponent, trans, fracVar] = mlab.prepca(all_f)
# take first 3 component
pca = pcomponent[0:3, :]
fig = pyplot.figure()
c = ['red', 'blue', 'purple', 'green', 'black', 'pink']
ax = p3.Axes3D(fig)
for i in xrange(6):
ax.scatter(pca[0, i*500:(i+1)*500], pca[1, i*500:(i+1)*500], pca[2, i*500:(i+1)*500], marker='x', color=c[i])
pyplot.show()
random.seed(options.randSeed)
# Load up the data
f = open(options.inFile)
try:
dataset = LoadDescriptors(f, prob=options.fractionUse)
finally:
f.close()
print 'Opened up %i datapoints' % dataset.shape[0]
# Perform PCA
#pcaObj = mlab.PCA(dataset)
dataset = dataset.transpose()
(pcaMat, pcaWeights, fracVar) = mlab.prepca(dataset)
print 'The top fractions are: %s' % str(fracVar[0:10])
if options.ndims == 2:
figure(1)
scatter(np.dot(pcaMat[:, 0], dataset), np.dot(pcaMat[:, 1], dataset))
elif options.ndims == 3:
fig = figure(1)
ax = Axes3D(fig)
ax.scatter(np.dot(pcaMat[:, 0],
dataset), np.dot(pcaMat[:, 1], dataset),
np.dot(pcaMat[:, 2], dataset))
show()
def computeConsistency(config):
'''Computes the consistency, assuming that the deviation has already been computed'''
try:
devFile = DeviationReader(deviationFile(config))
except:
raise IOError(
"Unable to load the deviation file for computing consistency")
T = int(config['consistencyWindow'])
if (T % 2 != 1 and T <= 0):
raise ValueError(
"The window size for consistency calculation must be positive and odd. Given %d."
% T)
if (T > devFile.frameCount):
raise ValueError(
"The consistency window size is larger than the number of frames: T = %d, frame count = %d"
% (T, devFile.frameCount))
conFile = ConsistencyWriter(consistencyFile(config))
conFile.setWindowSize(T)
conFile.setAgentCount(devFile.agtCount)
print "\nCONSISTENCY"
print "\tWindow:", T
print "\tTotal frames:", devFile.frameCount
print "\tTotal agents:", devFile.agtCount
deviations = np.zeros((devFile.agtCount, 2, T), dtype=np.float32)
print "\tLoading %d deviations:" % (T)
for t in xrange(T):
# no need to put this in a try block, because I know that I will be able to read all of these.
deviations[:, :, t] = devFile.next()
print "\t\t", deviations[:, :, t]
replaceID = 0
consistency = np.zeros((devFile.agtCount, 6), dtype=np.float32)
print "\tComputing consistency"
count = 0
while (True):
# The PCA class performs the mean for me -- in fact, I can't stop it.
# So, although I'm sure it would be cheaper to compute the mean myself in one huge
# block, I can't stop PCA from doing so.
# NOTE: Not currently using PCA because I can't figure out what the damn AXES are
mean = deviations.mean(axis=2)
print "\t\tMean:", mean
mean.shape = (-1, 2, 1)
centered = deviations - mean
for a in xrange(devFile.agtCount):
## p = mlab.PCA( centered[ a, :, : ].T )
## print p.Wt
with warnings.catch_warnings():
warnings.simplefilter("ignore")
components, trace, fracVar = mlab.prepca(centered[a, :, :].T)
sig2 = np.sqrt(np.sum(components**2, axis=1))
sig = np.sqrt(sig2)
a0 = components[0] / sig[0]
a1 = components[1] / sig[1]
consistency[a, :2] = a0
consistency[a, 2:4] = a1
consistency[a, 4:] = fracVar
conFile.write(consistency)
count += 1
try:
deviations[:, :, replaceID] = devFile.next()
except StopIteration:
break
else:
replaceID = (replaceID + 1) % T
conFile.setFrameCount(count)
conFile.close()