y_true = numpy.zeros(1024)
for i in range(len(gt)):
y_true += gt_weight[i] * gt[i].prob(x[:, None])
plt.figure(figsize=(16, 8))
plt.xlim(x_low, x_high)
plt.ylim(0.0, y_high)
plt.plot(x, y_true, c='g')
plt.savefig(os.path.join(out_dir, '0000.png'), bbox_inches='tight')
# 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)
model.setConcGamma(1 / 8., 1 / 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
# Calculate it's posterior distribution...
plt.figure(figsize=(16,8))
plt.xlim(x_low, x_high)
plt.ylim(0.0, y_high)
plt.plot(x, y_true, c='g')
plt.savefig(os.path.join(out_dir, '0000.png'), bbox_inches='tight')
# 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)
model.setConcGamma(1/8., 1/8.)
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
# Calculate it's posterior distribution...
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)
for i in xrange(len(catGuess)):
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)
