width = 800
height = 400
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])
# 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)):
confusion_matrix[catTruth[i], catGuess[i]] += 1
print 'Confusion matrix [truth, guess], noting that class labels will not match:'
print confusion_matrix
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)):
confusion_matrix[catTruth[i], catGuess[i]] += 1
print "Confusion matrix [truth, guess], noting that class labels will not match:"
print confusion_matrix
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))))
# 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])
