#print 'stick weights:'
#print model.intMixture()[0]
#print 'z sums:'
#print model.z.sum(axis=0)
# 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
for ind, gauss in enumerate(draw[1]):
y_gd += draw[0][ind] * gauss.prob([x])
py_gt = int((1.0 - y_gt / scale) * height)
py_gu = int((1.0 - y_gu / scale) * height)
py_gd = numpy.clip(int((1.0 - y_gd / scale) * height), 0,
height - 1)
img[py_gt, px, :] = [0.0, 1.0, 0.0]
img[py_gu, px, :] = [1.0, 0.0, 0.0]
img[py_gd, px, :] = [0.0, 0.0, 1.0]
# Save plot out...
img = cvarray.array2cv(img * 255.0)
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
for ind,gauss in enumerate(draw[1]):
y_gd += draw[0][ind] * gauss.prob([x])
py_gt = int((1.0 - y_gt/scale) * height)
py_gu = int((1.0 - y_gu/scale) * height)
py_gd = numpy.clip(int((1.0 - y_gd/scale) * height),0,height-1)
img[py_gt,px,:] = [0.0,1.0,0.0]
img[py_gu,px,:] = [1.0,0.0,0.0]
img[py_gd,px,:] = [0.0,0.0,1.0]
# Save plot out...
img = cvarray.array2cv(img*255.0)
cv.SaveImage('%s/plot_%i.png'%(out_dir,model.getStickCap()),img)
# 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...
y_post = model.prob(x[:, None])
# Generate and save a nice figure...
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.plot(x, y_post, c='b')
plt.savefig(os.path.join(out_dir, '{:04d}.png'.format(i + 1)),
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...
y_post = model.prob(x[:,None])
# Generate and save a nice figure...
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.plot(x, y_post, c='b')
plt.savefig(os.path.join(out_dir, '{:04d}.png'.format(i+1)), bbox_inches='tight')
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]))
out = [8,16,32,64,128,256,512,1024,2048]
model = DPGMM(dims, 6)
for i,point in enumerate(samples):
model.add(point)
model.setPrior()
#p = ProgBar()
it = model.solve()
#del p
img = ones((height,width,3))
draw = model.sampleMixture()
for px in xrange(width):
x = float(px)/float(width) * (high-low) + low
for py in xrange(width):
y = float(py)/float(width) * (high-low) + low
# print "%f,%f"%(x,y)
print "%f\t%f\t%f"%(x,y,model.prob([x,y]))
sys.exit()
# print [x,x]
#norm_pdf_multivariate([x,x],mu1,cov)
# if (i+1) in out:
# print '%i datapoints:'%(i+1)
# 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 = ones((height,width,3))
# draw = model.sampleMixture()
# for px in xrange(width):
