except:
pass
os.mkdir(out_dir)
# Output parameters...
low = -5.0
high = 9.0
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
shutil.rmtree(out_dir)
except:
pass
os.mkdir(out_dir)
# Output parameters...
low = 1.0
high = 9.0
width = 400
height = 200
scale = 1.1 * gt.prob(gt.getMean())
# 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, 6)
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
# Now plot the estimated distribution against the actual distribution...
img = numpy.ones((height, width, 3))
draw = model.sampleMixture()
for _ in xrange(trainCount):
which = numpy.random.multinomial(1, mix).argmax()
covar = sd[which] * numpy.identity(3)
s = numpy.random.multivariate_normal(mean[which, :], covar)
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...
# 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))))
# Iterate a number of sample counts...
out = [8, 16, 32, 64, 128, 256, 512, 1024, 2048]
for dpc in out:
print '%i datapoints:' % (dpc)
# Fill in the model...
model = DPGMM(dims)
for point in samples[:dpc]:
model.add(point)
model.setPrior()
# Solve...
p = ProgBar()
model = model.multiGrowSolve(8)
del p
# Now plot the estimated distribution against the actual distribution...
img = numpy.ones((height, width, 3))
draw = model.sampleMixture()
for px in xrange(width):
x = float(px) / float(width) * (high - low) + low
