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()
for px in xrange(width):
x = float(px)/float(width) * (high-low) + low
y_gt = 0.0
for ii in xrange(len(gt)):
y_gt += gt_weight[ii] * gt[ii].prob([x])
y_gu = model.prob([x])
y_gd = 0.0
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:'
#print model.z.sum(axis=0)
dims=2
numpy.random.seed(1);
gt = Gaussian(dims)
gt.setMean([1.0,0.0])
gt.setCovariance([[1.0,0.8],[0.8,1.0]])
sample_count = 30000
sample=[]
for _ in xrange(sample_count):
sample.append(gt.sample())
f=open('data.txt','w')
for x in sample:
f.write('%lf,%lf\n'%(x[0],x[1]))
f.close()
model = DPGMM(dims, 1)
for i,data in enumerate(sample):
model.add(data)
start = time.time()
model.setPrior()
elapsed_time = time.time() - start
num=model.solve()
print elapsed_time
print num
print "%f"%(model.prob([2.0,1.0]))
#for i in range(10):
# x=i*0.4-2.0
#print "%f,%f"%(x,model.prob([x]))
