class TestZMGauss(object):
def setUp(self):
self.Sigma = np.eye(4)
self.distr = ZMGaussDistr(Sigma=self.Sigma.copy())
def test_dimension(self):
assert self.distr.D == self.Sigma.shape[0]
def test_cholSigma(self):
chol = self.distr.cholSigma()
assert np.allclose(np.dot(chol, chol.T), self.distr.Sigma)
def test_logdetSigma(self):
logdetSigma = self.distr.logdetSigma()
assert np.allclose( np.log(np.linalg.det(self.Sigma)), logdetSigma)
def test_get_log_norm_const(self):
logZ = self.distr.get_log_norm_const()
logdetSigma = np.log(np.linalg.det(self.Sigma))
mylogZ = 0.5*self.Sigma.shape[0]*np.log(2*np.pi) + 0.5 * logdetSigma
def test_dist_mahalanobis(self, N=10):
X = np.random.randn(N, self.distr.D)
Dist = self.distr.dist_mahalanobis(X)
invSigma = np.linalg.inv(self.Sigma)
MyDist = np.zeros(N)
for ii in range(N):
x = X[ii]
MyDist[ii] = np.dot(x.T, np.dot(invSigma, x))
#if error, we print it out
print MyDist[ii], Dist[ii]
assert np.allclose(MyDist, Dist)
def set_global_params(self, hmodel=None, Sigma=None, **kwargs):
''' Set global parameters to provided values
'''
if hmodel is not None:
self.comp = [copy.deepcopy(c) for c in hmodel.obsModel.comp]
self.K = hmodel.obsModel.K
if Sigma is not None:
self.K = Sigma.shape[0]
self.comp = [None for k in range(self.K)]
for k in range(self.K):
self.comp[k] = ZMGaussDistr(Sigma=Sigma[k])
def CreateWithAllComps(cls, oDict, obsPrior, compDictList):
if 'min_covar' in oDict:
mc = oDict['min_covar']
self = cls(oDict['inferType'], obsPrior=obsPrior, min_covar=mc)
else:
self = cls(oDict['inferType'], obsPrior=obsPrior)
self.K = len(compDictList)
self.comp = [None for k in range(self.K)]
if self.inferType == 'EM':
for k in xrange(self.K):
self.comp[k] = ZMGaussDistr(**compDictList[k])
self.D = self.comp[k].D
elif self.inferType.count('VB') > 0:
for k in xrange(self.K):
self.comp[k] = WishartDistr(**compDictList[k])
self.D = self.comp[k].D
return self
def update_obs_params_EM(self, SS, **kwargs):
for k in xrange(self.K):
covMat = SS.xxT[k] / SS.N[k]
covMat += self.min_covar * np.eye(self.D)
self.comp[k] = ZMGaussDistr(Sigma=covMat)
def setUp(self): self.Sigma = np.eye(4) self.distr = ZMGaussDistr(Sigma=self.Sigma.copy())
def setUp(self): R = np.random.rand(5,5) self.Sigma = np.dot(R, R.T) + 0.02*np.eye(5) self.distr = ZMGaussDistr(Sigma=self.Sigma)
def setUp(self): self.Sigma = np.asarray([[42.0]]) self.distr = ZMGaussDistr(Sigma=self.Sigma)
