def test1():
print '============== test1 ============'
dimension = 3
target0 = make_target(dimension, 2, (0.75, 0.25), ((1, 1, 1), (2, 3, 4)), ((1, 1, 1), (0.5, 0.5, 1)))
target1 = make_target(dimension, 2, (0.5, 0.5), ((-1, -1, -1), (-2, -3, -4)), ((1, 1, 1), (0.5, 0.5, 1)))
target2 = make_target(dimension, 2, (0.1, 0.9), ((1, 1, -2), (3, 3, 5)), ((1, 1, 1), (0.5, 0.5, 1)))
print target0
print target1
print target2
GaussianModelBase.seed(0)
labels = ('A', 'B', 'C')
ncomps = (1, 2, 2)
sources = dict((('A', target0), ('B', target1), ('C', target2)))
GaussianMixtureModel.seed(0)
gmm_mgr = GmmMgr(ncomps, dimension, GaussianModelBase.DIAGONAL_COVARIANCE)
c0 = AdaptingGmmClassifier(gmm_mgr, izip(labels, count()))
print
print c0
result = list()
proc0 = AdaptingGmmClassProcessor(c0, result.append)
# Prime things a little bit to try to get a good start
c0.set_relevance(0.001)
c0.set_num_em_iterations(2)
for i in xrange(1):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
proc0.process((label, data))
# Now adapt on more data
c0.set_relevance(10)
c0.set_num_em_iterations(2)
for i in xrange(10):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
proc0.process((label, data))
print
print c0
print
print len(result)
# XXX Win32 gets values off in the last 2-3 hex digits. I'm not sure how to account for this in a
# logref test, so I'm disabling this printing for now.
# for training_label, scores in result[-10:]:
# print training_label, tuple(((label, float_to_readable_string(score)) for score, label in scores))
correct = tuple(label for label, scores in result)
guessed = tuple(scores[0][1] for l, scores in result)
print len(correct), len(guessed)
ind = [c == g for (c, g) in izip(correct, guessed)]
print ind.count(True)
print ind.count(True) / len(correct)
def test0():
print '============== test0 ============'
dimension = 3
target0 = make_target(dimension, 2, (0.75, 0.25), ((1, 1, 1), (2, 3, 4)),
((1, 1, 1), (0.5, 0.5, 1)))
target1 = make_target(dimension, 2, (0.5, 0.5),
((-1, -1, -1), (-2, -3, -4)),
((1, 1, 1), (0.5, 0.5, 1)))
target2 = make_target(dimension, 2, (0.1, 0.9), ((1, 1, -2), (3, 3, 5)),
((1, 1, 1), (0.5, 0.5, 1)))
print target0
print target1
print target2
GaussianModelBase.seed(0)
labels = ('A', 'B', 'C')
ncomps = (1, 2, 2)
sources = dict((('A', target0), ('B', target1), ('C', target2)))
GaussianMixtureModel.seed(0)
gmm_mgr = GmmMgr(ncomps, dimension, GaussianModelBase.DIAGONAL_COVARIANCE)
c0 = AdaptingGmmClassifier(gmm_mgr, izip(labels, count()))
print
print c0
# Prime things a little bit to try to get a good start
c0.set_relevance(0.001)
for i in xrange(1):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
c0.adapt_one_class(label, data)
# Now adapt on more data
c0.set_relevance(10)
for i in xrange(10):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
c0.adapt_one_class(label, data)
print
print c0
print
def test0():
print '============== test0 ============'
dimension = 3
target0 = make_target(dimension, 2, (0.75, 0.25), ((1, 1, 1), (2, 3, 4)), ((1, 1, 1), (0.5, 0.5, 1)))
target1 = make_target(dimension, 2, (0.5, 0.5), ((-1, -1, -1), (-2, -3, -4)), ((1, 1, 1), (0.5, 0.5, 1)))
target2 = make_target(dimension, 2, (0.1, 0.9), ((1, 1, -2), (3, 3, 5)), ((1, 1, 1), (0.5, 0.5, 1)))
print target0
print target1
print target2
GaussianModelBase.seed(0)
labels = ('A', 'B', 'C')
ncomps = (1, 2, 2)
sources = dict((('A', target0), ('B', target1), ('C', target2)))
GaussianMixtureModel.seed(0)
gmm_mgr = GmmMgr(ncomps, dimension, GaussianModelBase.DIAGONAL_COVARIANCE)
c0 = AdaptingGmmClassifier(gmm_mgr, izip(labels, count()))
print
print c0
# Prime things a little bit to try to get a good start
c0.set_relevance(0.001)
for i in xrange(1):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
c0.adapt_one_class(label, data)
# Now adapt on more data
c0.set_relevance(10)
for i in xrange(10):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
c0.adapt_one_class(label, data)
print
print c0
print
def test1():
print '============== test1 ============'
dimension = 3
target0 = make_target(dimension, 2, (0.75, 0.25), ((1, 1, 1), (2, 3, 4)),
((1, 1, 1), (0.5, 0.5, 1)))
target1 = make_target(dimension, 2, (0.5, 0.5),
((-1, -1, -1), (-2, -3, -4)),
((1, 1, 1), (0.5, 0.5, 1)))
target2 = make_target(dimension, 2, (0.1, 0.9), ((1, 1, -2), (3, 3, 5)),
((1, 1, 1), (0.5, 0.5, 1)))
print target0
print target1
print target2
GaussianModelBase.seed(0)
labels = ('A', 'B', 'C')
ncomps = (1, 2, 2)
sources = dict((('A', target0), ('B', target1), ('C', target2)))
GaussianMixtureModel.seed(0)
gmm_mgr = GmmMgr(ncomps, dimension, GaussianModelBase.DIAGONAL_COVARIANCE)
c0 = AdaptingGmmClassifier(gmm_mgr, izip(labels, count()))
print
print c0
result = list()
proc0 = AdaptingGmmClassProcessor(c0, result.append)
# Prime things a little bit to try to get a good start
c0.set_relevance(0.001)
c0.set_num_em_iterations(2)
for i in xrange(1):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
proc0.process((label, data))
# Now adapt on more data
c0.set_relevance(10)
c0.set_num_em_iterations(2)
for i in xrange(10):
for label in labels:
target = sources[label]
data = (target.sample() for i in xrange(100))
proc0.process((label, data))
print
print c0
print
print len(result)
# XXX Win32 gets values off in the last 2-3 hex digits. I'm not sure how to account for this in a
# logref test, so I'm disabling this printing for now.
# for training_label, scores in result[-10:]:
# print training_label, tuple(((label, float_to_readable_string(score)) for score, label in scores))
correct = tuple(label for label, scores in result)
guessed = tuple(scores[0][1] for l, scores in result)
print len(correct), len(guessed)
ind = [c == g for (c, g) in izip(correct, guessed)]
print ind.count(True)
print ind.count(True) / len(correct)
