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All Samples(6)  |  Call(3)  |  Derive(0)  |  Import(3)

src/v/b/vbihmm-1.03/vb_ihmm/model/sensors.py   vbihmm(Download)
from numpy.linalg.linalg import inv, det
from scipy.special.basic import digamma
from util import inv0
import sys
import pickle
    def expC(self):
        #calculate expected covariance matrix (for each component)
        return array([inv0(Wk*vk) for (Wk,vk) in zip(self._W,self._vk)])
 
    def _mW(self,K,W0,xd,NK,m0,XDim,beta0,S):
        Winv = [None for _ in range(K)]
        for k in range(K): 
            Winv[k]  = NK[k]*S[k] + inv0(W0[k])
        for k in range(K):
            try:
                W.append(inv0(Winv[k]))
            except linalg.linalg.LinAlgError:
                #print 'Winv[%i]'%k, Winv[k]

src/v/b/vbihmm-1.03/vb_ihmm/model/common_sensors.py   vbihmm(Download)
from accuracy_sensors import ReportedGaussian
from numpy.linalg.linalg import inv
from util import inv0, inv00
 
#aggregates a set of multivariate Gaussian sensors (reported or not reported)

src/v/b/vbihmm-1.03/vb_ihmm/model/visualise.py   vbihmm(Download)
from numpy import *
from vb_ihmm.testing.util import create_cov_ellipse
from util import inv0
from scipy import stats
import os