def testSinglePointMultipleComponent(self):
for i in range(100):
x = array([uniform(-4, 4), uniform(-4, 4)])
mu = array([[uniform(-4, 4), uniform(-4, 4)],
[uniform(-4, 4), uniform(-4, 4)]])
a = uniform(0, 4)
b = uniform(0, 4)
pi = array([1, 1])
sigma = array([eye(2) + a, eye(2) + b])
# x = array([1,1])
# mu = array([[1,1],[1,1]])
# sigma = array([eye(2),eye(2)])
presult = array([
float(pmvnormpdf(x, mu[0, :], sigma[0, :, :])),
float(pmvnormpdf(x, mu[1, :], sigma[1, :, :]))
])
cresult = compmixnormpdf(x, pi, mu, sigma)
# print result, compmixnormpdf(x,pi,mu,sigma)
self.assertAlmostEqual(
presult[0], cresult[0], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[0], cresult[0], i, str(x), str(mu[0]), str(
sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(
presult[1], cresult[1], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[1], cresult[1], i, str(x), str(mu[1]), str(
sigma[1]).replace('\n', ',')))
def testMultiplePointMultipleComponent(self):
for i in range(100):
x = array([[uniform(-4, 4), uniform(-4, 4)]] * 2)
mu = array([[uniform(-4, 4), uniform(-4, 4)]] * 2)
a = uniform(0, 4)
b = uniform(0, 4)
pi = array([1] * 2)
sigma = array([eye(2) + a, eye(2) + b])
# x = array([[1,0],[1,1]])
# mu = array([[1,1],[1,1]])
# sigma = array([eye(2)]*2)
presult = array([[float(pmvnormpdf(x[0, :], mu[0, :], sigma[0, :, :])), float(pmvnormpdf(x[0, :], mu[1, :], sigma[1, :, :]))], [
float(pmvnormpdf(x[1, :], mu[0, :], sigma[0, :, :])), float(pmvnormpdf(x[1, :], mu[1, :], sigma[1, :, :]))]])
cresult = compmixnormpdf(x, pi, mu, sigma)
# print result, compmixnormpdf(x,pi,mu,sigma)
# print mixnormpdf(x,pi,mu,sigma), sum(result,1)
self.assertAlmostEqual(presult[0, 0], cresult[0, 0], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (
presult[0, 0], cresult[0, 0], i, str(x[0, :]), str(mu[0]), str(sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(presult[1, 0], cresult[1, 0], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (
presult[1, 0], cresult[1, 0], i, str(x[0, :]), str(mu[1]), str(sigma[1]).replace('\n', ',')))
self.assertAlmostEqual(presult[0, 0], cresult[0, 0], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (
presult[0, 1], cresult[0, 1], i, str(x[0, :]), str(mu[0]), str(sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(presult[1, 0], cresult[1, 0], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (
presult[1, 1], cresult[1, 0], i, str(x[0, :]), str(mu[1]), str(sigma[1]).replace('\n', ',')))
self.assertAlmostEqual(sum(presult, 1)[0], mixnormpdf(
x, pi, mu, sigma)[0], 6, '') # what are these two checking?
self.assertAlmostEqual(
sum(presult, 1)[1], mixnormpdf(x, pi, mu, sigma)[1], 6, '')
def prob(self, x, logged=False, **kwargs):
"""
DPCluster.prob(x):
returns probability of x belonging to this mixture component
"""
d = self.mu.shape[0]
return compmixnormpdf(x, self.pi, self.mu.reshape(1,-1), self.sigma.reshape(1,d,d), logged=logged, **kwargs)
def prob(self, x, logged=False, **kwargs):
"""
ModalDPMixture.prob(x)
returns an array of probabilities of x being in each mode of the modal
mixture
"""
probs = compmixnormpdf(x, self.pis, self.mus, self.sigmas, logged=logged, **kwargs)
#can't sum in log prob space
try:
n, j = x.shape # check we're more then 1 point
rslt = zeros((n, len(self.cmap.keys())))
for j in self.cmap.keys():
if logged:
rslt[:,j] = logsumexp([probs[:,i] for i in self.cmap[j]], 0)
else:
rslt[:, j] = sum([probs[:, i] for i in self.cmap[j]], 0)
except ValueError:
#single point
rslt = zeros((len(self.cmap.keys())))
for j in self.cmap.keys():
if logged:
rslt[j] = logsumexp([self.clusters[i].prob(x, logged=logged) for i in self.cmap[j]])
else:
rslt[j] = sum([self.clusters[i].prob(x) for i in self.cmap[j]])
return rslt
def prob(self, x, logged=False, **kwargs):
"""
DPMixture.prob(x)
returns an array of probabilities of x being in each component of the
mixture
"""
return compmixnormpdf(x, self.pis, self.mus, self.sigmas, logged=logged, **kwargs)
def prob(self, x, logged=False, **kwargs):
"""
DPCluster.prob(x):
returns probability of x belonging to this mixture component
"""
# return self.pi * mvnormpdf(x, self.mu, self.sigma)
d = self.mu.shape[0]
return compmixnormpdf(x, self.pi, self.mu.reshape(
1, -1), self.sigma.reshape(1, d, d), logged=logged, **kwargs)
def prob(self, x, logged=False, **kwargs):
"""
DPMixture.prob(x)
returns an array of probabilities of x being in each component of the
mixture
"""
return compmixnormpdf(
x,
self.pis,
self.mus,
self.sigmas,
logged=logged,
**kwargs)
def prob(self, x, logged=False, **kwargs):
'''
DPMixture.prob(x)
returns an array of probabilities of x being in each component of the
mixture
'''
# return array([i.prob(x) for i in self.clusters])
return compmixnormpdf(x,
self.pis,
self.mus,
self.sigmas,
logged=logged,
**kwargs)
def testMultiplePointMultipleComponent(self):
for i in range(100):
x = array([[uniform(-4, 4), uniform(-4, 4)]] * 2)
mu = array([[uniform(-4, 4), uniform(-4, 4)]] * 2)
a = uniform(0, 4)
b = uniform(0, 4)
pi = array([1] * 2)
sigma = array([eye(2) + a, eye(2) + b])
# x = array([[1,0],[1,1]])
# mu = array([[1,1],[1,1]])
# sigma = array([eye(2)]*2)
presult = array(
[[
float(pmvnormpdf(x[0, :], mu[0, :], sigma[0, :, :])),
float(pmvnormpdf(x[0, :], mu[1, :], sigma[1, :, :]))
],
[
float(pmvnormpdf(x[1, :], mu[0, :], sigma[0, :, :])),
float(pmvnormpdf(x[1, :], mu[1, :], sigma[1, :, :]))
]])
cresult = compmixnormpdf(x, pi, mu, sigma)
# print result, compmixnormpdf(x,pi,mu,sigma)
# print mixnormpdf(x,pi,mu,sigma), sum(result,1)
self.assertAlmostEqual(
presult[0, 0], cresult[0, 0], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[0, 0], cresult[0, 0], i, str(x[0, :]), str(
mu[0]), str(sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(
presult[1, 0], cresult[1, 0], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[1, 0], cresult[1, 0], i, str(x[0, :]), str(
mu[1]), str(sigma[1]).replace('\n', ',')))
self.assertAlmostEqual(
presult[0, 0], cresult[0, 0], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[0, 1], cresult[0, 1], i, str(x[0, :]), str(
mu[0]), str(sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(
presult[1, 0], cresult[1, 0], 6,
'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s '
% (presult[1, 1], cresult[1, 0], i, str(x[0, :]), str(
mu[1]), str(sigma[1]).replace('\n', ',')))
self.assertAlmostEqual(
sum(presult, 1)[0],
mixnormpdf(x, pi, mu, sigma)[0], 6,
'') # what are these two checking?
self.assertAlmostEqual(
sum(presult, 1)[1],
mixnormpdf(x, pi, mu, sigma)[1], 6, '')
def prob(self, x, logged=False, **kwargs):
'''
DPMixture.prob(x)
returns an array of probabilities of x being in each component of the
mixture
'''
# return array([i.prob(x) for i in self.clusters])
return compmixnormpdf(
x,
self.pis,
self.mus,
self.sigmas,
logged=logged,
**kwargs)
def testSinglePointMultipleComponent(self):
for i in range(100):
x = array([uniform(-4, 4), uniform(-4, 4)])
mu = array([[uniform(-4, 4), uniform(-4, 4)],
[uniform(-4, 4), uniform(-4, 4)]
])
a = uniform(0, 4)
b = uniform(0, 4)
pi = array([1, 1])
sigma = array([eye(2) + a, eye(2) + b])
presult = array([float(pmvnormpdf(x, mu[0, :], sigma[0, :, :])), float(pmvnormpdf(x, mu[1, :], sigma[1, :, :]))])
cresult = compmixnormpdf(x, pi, mu, sigma)
self.assertAlmostEqual(presult[0], cresult[0], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (presult[0], cresult[0], i, str(x), str(mu[0]), str(sigma[0]).replace('\n', ',')))
self.assertAlmostEqual(presult[1], cresult[1], 6, 'pmvnormpdf and mvnormpdf differ in result, %f != %f, (%d): %s, %s, %s ' % (presult[1], cresult[1], i, str(x), str(mu[1]), str(sigma[1]).replace('\n', ',')))
def _mode_search(pi, mu, sigma, nk=0, tol=0.000001, maxiter=20):
"""Search for modes in mixture of Gaussians"""
k, unused_p = mu.shape
omega = np.copy(sigma)
a = np.copy(mu)
for j in range(k):
omega[j] = inv(sigma[j])
a[j] = solve(sigma[j], mu[j])
if nk > 0:
allx = np.concatenate([mu, mixnormrnd(pi, mu, sigma, nk)])
else:
allx = np.copy(mu)
allpx = mixnormpdf(allx, pi, mu, sigma, use_gpu=False)
nk += k
mdict = {} # modes
sm = [] # starting point of mode search
spm = [] # density at starting points
etol = np.exp(tol)
# rnd = int(-1*np.floor(np.log10(tol)))
rnd = 1
for js in range(nk):
x = allx[js]
px = allpx[js]
sm.append(x)
spm.append(px)
# w = compmixnormpdf(allx,pi,mu,sigma)
h = 0
eps = 1 + etol
while ((h <= maxiter) and (eps > etol)):
w = compmixnormpdf(x, pi, mu, sigma, use_gpu=False)
Y = np.sum([w[j] * omega[j] for j in range(k)], 0)
yy = np.dot(w, a)
y = solve(Y, yy)
py = mixnormpdf(y, pi, mu, sigma, use_gpu=False)
eps = py / px
x = y
px = py
h += 1
mdict[(js, tuple(x))] = [x, px] # eliminate duplicates
return mdict, sm, spm
def _mode_search(pi, mu, sigma, nk=0, tol=0.000001, maxiter=20):
"""Search for modes in mixture of Gaussians"""
k, unused_p = mu.shape
omega = np.copy(sigma)
a = np.copy(mu)
for j in range(k):
omega[j] = inv(sigma[j])
a[j] = solve(sigma[j], mu[j])
if nk > 0:
allx = np.concatenate([mu, mixnormrnd(pi, mu, sigma, nk)])
else:
allx = np.copy(mu)
allpx = mixnormpdf(allx, pi, mu, sigma, use_gpu=False)
nk += k
mdict = {} # modes
sm = [] # starting point of mode search
spm = [] # density at starting points
etol = np.exp(tol)
# rnd = int(-1*np.floor(np.log10(tol)))
rnd = 1
for js in range(nk):
x = allx[js]
px = allpx[js]
sm.append(x)
spm.append(px)
# w = compmixnormpdf(allx,pi,mu,sigma)
h = 0
eps = 1 + etol
while ((h <= maxiter) and (eps > etol)):
w = compmixnormpdf(x, pi, mu, sigma, use_gpu=False)
Y = np.sum([w[j] * omega[j] for j in range(k)], 0)
yy = np.dot(w, a)
y = solve(Y, yy)
py = mixnormpdf(y, pi, mu, sigma, use_gpu=False)
eps = py / px
x = y
px = py
h += 1
mdict[(js, tuple(x))] = [x, px] # eliminate duplicates
return mdict, sm, spm
def prob(self, x, logged=False, **kwargs):
"""
ModalDPMixture.prob(x)
returns an array of probabilities of x being in each mode of the modal
mixture
"""
probs = compmixnormpdf(
x,
self.pis,
self.mus,
self.sigmas,
logged=logged,
**kwargs)
# can't sum in log prob space
try:
n, j = x.shape # check we're more then 1 point
rslt = zeros((n, len(self.cmap.keys())))
for j in self.cmap.keys():
if logged:
rslt[:, j] = logsumexp([probs[:, i]
for i in list(self.cmap[j])], 0)
else:
rslt[:, j] = sum([probs[:, list(i)]
for i in self.cmap[j]], 0)
except ValueError as e:
print ">>>", e
# single point
rslt = zeros((len(self.cmap.keys())))
for j in self.cmap.keys():
if logged:
rslt[j] = logsumexp(
[self.clusters[i].prob(x, logged=logged) for i in self.cmap[j]])
else:
rslt[j] = sum([self.clusters[i].prob(x)
for i in list(self.cmap[j])])
return rslt
