def fit_one(args):
x, name = args
print "fitting", name, "of size", x.shape
m = DPMixtureModel(nclusts=8, iter=100, burnin=0, last=1)
r = m.fit(x, verbose=10)
print 'done fitting', name
return r
def test_list_fitting(self):
true1, data1 = self.generate_data()
true2, data2 = self.generate_data()
model = DPMixtureModel(3, 2000, 100, 1, type='BEM')
rs = model.fit([data1, data2])
assert(len(rs) == 2)
for r in rs:
diffs = {}
for i in gen_mean:
diffs[i] = np.min(np.abs(r.mus-gen_mean[i]), 0)
assert(np.vdot(diffs[i], diffs[i]) < 1)
fcm1 = FCMdata('test_fcm1', data1, ['fsc', 'ssc'], [0, 1])
fcm2 = FCMdata('test_fcm2', data2, ['fsc', 'ssc'], [0, 1])
c = FCMCollection('fcms', [fcm1, fcm2])
rs = model.fit(c)
assert(len(rs) == 2)
for r in rs:
diffs = {}
for i in gen_mean:
diffs[i] = np.min(np.abs(r.mus-gen_mean[i]), 0)
assert(np.vdot(diffs[i], diffs[i]) < 1)
def setUp(self):
self.mu = array([0, 0])
self.sig = eye(2)
self.pnts = multivariate_normal(self.mu, self.sig, size=(1000,))
self.k = 16
self.niter = 10
self.model = DPMixtureModel(self.k, self.niter, 0, 1)
def test_mcmc_fitting(self):
true, data = self.generate_data()
model = DPMixtureModel(3, 100, 100, 1)
model.seed = 1
r = model.fit(data, verbose=10)
diffs = {}
for i in gen_mean:
diffs[i] = np.min(np.abs(r.mus-gen_mean[i]), 0)
assert(np.vdot(diffs[i], diffs[i]) < 1)
def test_fit(self):
from fcm.statistics import DPMixtureModel, DPMixture
k = 16
niter = 10
model = DPMixtureModel(k, niter, 0, 1)
results = self.fcms.fit(model)
assert_equal(len(results.keys()), 2,
'fitting produced wrong shaped dict')
for i in self.fcms:
assert isinstance(
results[i],
DPMixture), 'fitting produced a non-DPMixture object'
def test_reference(self):
true, data = self.generate_data()
model = DPMixtureModel(3, 100, 100, 1)
model.seed = 1
model.load_ref(array(true))
r = model.fit(data, verbose=True)
diffs = {}
for i in gen_mean:
diffs[i] = np.abs(r.mus[i]-gen_mean[i])
assert(np.vdot(diffs[i], diffs[i]) < 1)
model.load_ref(r)
r = model.fit(data, verbose=True)
diffs = {}
for i in gen_mean:
diffs[i] = np.abs(r.mus[i]-gen_mean[i])
assert(np.vdot(diffs[i], diffs[i]) < 1)
def testBEMFitting(self):
print 'starting BEM'
true, data = self.generate_data()
m = data.mean(0)
s = data.std(0)
# true_mean = {}
# for i in gen_mean:
# true_mean[i] = (gen_mean[i]-m)/s
model = DPMixtureModel(3,2000,100,1, type='BEM')
model.seed = 1
start = time()
r = model.fit(data, verbose=False)
end = time() - start
diffs = {}
for i in gen_mean:
diffs[i] = np.min(np.abs(r.mus-gen_mean[i]),0)
#print i, gen_mean[i], diffs[i], np.vdot(diffs[i],diffs[i])
assert( np.vdot(diffs[i],diffs[i]) < 1)
print 'BEM fitting took %0.3f' % (end)
def testRefernce(self):
print "starting mcmc"
true, data = self.generate_data()
m = data.mean(0)
s = data.std(0)
# true_mean = {}
# for i in gen_mean:
# true_mean[i] = (gen_mean[i]-m)/s
model = DPMixtureModel(3,100,100,1)
model.seed = 1
model.load_ref(array(true))
start = time()
r = model.fit(data, verbose=True)
end = time() - start
diffs = {}
#print 'r.mus:', r.mus
for i in gen_mean:
#diffs[i] = np.min(np.abs(r.mus-gen_mean[i]),0)
diffs[i] = np.abs(r.mus[i]-gen_mean[i])
#print i, gen_mean[i],r.mus[i], diffs[i], np.vdot(diffs[i],diffs[i])
assert( np.vdot(diffs[i],diffs[i]) < 1)
#print diffs
print 'MCMC fitting took %0.3f' % (end)
model.load_ref(r)
start = time()
r = model.fit(data, verbose=True)
end = time() - start
diffs = {}
#print 'r.mus:', r.mus
for i in gen_mean:
#diffs[i] = np.min(np.abs(r.mus-gen_mean[i]),0)
diffs[i] = np.abs(r.mus[i]-gen_mean[i])
#print i, gen_mean[i],r.mus[i], diffs[i], np.vdot(diffs[i],diffs[i])
assert( np.vdot(diffs[i],diffs[i]) < 1)
def testMCMCFitting(self):
print "starting mcmc"
true, data = self.generate_data()
m = data.mean(0)
s = data.std(0)
# true_mean = {}
# for i in gen_mean:
# true_mean[i] = (gen_mean[i]-m)/s
model = DPMixtureModel(3,100,100,1)
model.seed = 1
start = time()
r = model.fit(data, verbose=10)
end = time() - start
diffs = {}
#print 'r.mus:', r.mus
for i in gen_mean:
diffs[i] = np.min(np.abs(r.mus-gen_mean[i]),0)
#print i, gen_mean[i], diffs[i], np.vdot(diffs[i],diffs[i])
assert( np.vdot(diffs[i],diffs[i]) < 1)
#print diffs
print r.classify(data)
print 'MCMC fitting took %0.3f' % (end)
xs[200:300, :] = numpy.random.multivariate_normal(mus[2, :], sigma, 100)
xs[300:400, :] = numpy.random.multivariate_normal(mus[3, :], sigma, 100)
xs[400:500, :] = numpy.random.multivariate_normal(mus[4, :], sigma, 100)
xs[500:600, :] = numpy.random.multivariate_normal(mus[5, :], sigma, 100)
true_z = numpy.zeros((600, ))
true_z[0:100] = 0
true_z[100:200] = 1
true_z[200:300] = 2
true_z[300:400] = 3
true_z[400:500] = 4
true_z[500:600] = 5
pylab.subplot(1, 3, 1)
pylab.scatter(xs[:, 0], xs[:, 1], c=true_z)
pylab.title("True")
#pylab.show()
model = DPMixtureModel(xs, 6, 5000, 100, 1)
model.gamma = 1.0
model.fit(verbose=True)
pylab.subplot(1, 3, 2)
model_z = model.get_class()
results = model.get_results()
model_class = results.classify(xs)
pylab.scatter(xs[:, 0], xs[:, 1], c=model_class)
pylab.scatter(results.mus()[:, 0], results.mus()[:, 1], c='red')
pylab.title("component")
modal = results.make_modal()
z = modal.classify(xs)
print z.min(), z.max()
pylab.subplot(1, 3, 3)
pylab.scatter(xs[:, 0], xs[:, 1], c=z)
pylab.title("modal")
def fit_one(args):
x, name = args
m = DPMixtureModel(nclusts=8, niter=100, burnin=0, last=1)
r = m.fit(x, verbose=10)
return r
