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