width = 400
height = 200
scale = 1.2 * max(
map(lambda i: gt_weight[i] * gt[i].prob(gt[i].getMean()), xrange(len(gt))))
# Iterate, slowlly building up the number of samples used and outputting the fit for each...
out = [8, 16, 32, 64, 128, 256, 512, 1024, 2048]
model = DPGMM(dims, 8)
for i, point in enumerate(samples):
model.add(point)
if (i + 1) in out:
print '%i datapoints:' % (i + 1)
# First fit the model...
model.setPrior()
p = ProgBar()
it = model.solve()
del p
print 'Updated fitting in %i iterations' % it
# Some information...
#print 'a:'
#print model.alpha
#print 'v:'
#print model.v
#print 'stick breaking weights:'
#print model.v[:,0] / model.v.sum(axis=1)
#print 'stick weights:'
#print model.intMixture()[0]
#print 'z sums:'
test = []
for _ in xrange(testCount):
which = numpy.random.multinomial(1, mix).argmax()
covar = sd[which] * numpy.identity(3)
s = numpy.random.multivariate_normal(mean[which, :], covar)
test.append((s, which))
# Train a model...
print "Trainning model..."
model = DPGMM(3)
for feat in train:
model.add(feat)
model.setPrior() # This sets the models prior using the data that has already been added.
model.setConcGamma(1.0, 0.25) # Make the model a little less conservative about creating new categories..
p = ProgBar()
iters = model.solveGrow()
del p
print "Solved model with %i iterations" % iters
# Classify the test set...
probs = model.stickProb(numpy.array(map(lambda t: t[0], test)))
catGuess = probs.argmax(axis=1)
catTruth = numpy.array(map(lambda t: t[1], test))
confusion_matrix = numpy.zeros((count, model.getStickCap() + 1), dtype=numpy.int32)
# Output parameters... low = -2.0 high = 14.0 width = 800 height = 400 scale = 1.5 * max(map(lambda i: gt_weight[i]*gt[i].prob(gt[i].getMean()), xrange(len(gt)))) # Fill in the model... model = DPGMM(dims) for point in samples: model.add(point) model.setPrior() # Iterate over the number of sticks, increasing till it stops getting better... prev = None while True: print 'Stick count = %i'%model.getStickCap() p = ProgBar() it = model.solve() del p print 'Updated fitting in %i iterations'%it # Now plot the estimated distribution against the actual distribution... img = numpy.ones((height,width,3)) draw = model.sampleMixture()
train.append(s)
test = []
for _ in xrange(testCount):
which = numpy.random.multinomial(1, mix).argmax()
covar = sd[which] * numpy.identity(3)
s = numpy.random.multivariate_normal(mean[which, :], covar)
test.append((s, which))
# Train a model...
print 'Trainning model...'
model = DPGMM(3)
for feat in train:
model.add(feat)
model.setPrior(
) # This sets the models prior using the data that has already been added.
model.setConcGamma(
1.0, 0.25
) # Make the model a little less conservative about creating new categories..
p = ProgBar()
iters = model.solveGrow()
del p
print 'Solved model with %i iterations' % iters
# Classify the test set...
probs = model.stickProb(numpy.array(map(lambda t: t[0], test)))
catGuess = probs.argmax(axis=1)
catTruth = numpy.array(map(lambda t: t[1], test))
confusion_matrix = numpy.zeros((count, model.getStickCap() + 1),
