Почему код в конце дает ошибку единственной матрицы? - PullRequest
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Почему код в конце дает ошибку единственной матрицы?

0 голосов
/ 10 ноября 2018

Я попробовал смесь модели Гаусса и столкнулся с несколькими проблемами.Я вставил весь код в ideone.Код по адресу: https://ideone.com/dNYtZ2

Когда я пытаюсь запустить fitMixGauss (data, k)), я получаю единственную матричную ошибку из функции, приведенной ниже. 

    def fitMixGauss(data, k):
"""
Estimate a k MoG model that would fit the data. Incremently plots the outcome.


Keyword arguments:
data -- d by n matrix containing data points.
k -- scalar representing the number of gaussians to use in the MoG model.

Returns: 
mixGaussEst -- dict containing the estimated MoG parameters.

"""

#     MAIN E-M ROUTINE  
#     In the E-M algorithm, we calculate a complete posterior distribution over                                  
#     the (nData) hidden variables in the E-Step.  
#     In the M-Step, we update the parameters of the Gaussians (mean, cov, w).   

nDims, nData = data.shape


postHidden = np.zeros(shape=(k, nData))

# we will initialize the values to random values
mixGaussEst = dict()
mixGaussEst['d'] = nDims
mixGaussEst['k'] = k
mixGaussEst['weight'] = (1 / k) * np.ones(shape=(k))
mixGaussEst['mean'] = 2 * np.random.randn(nDims, k)
mixGaussEst['cov'] = np.zeros(shape=(nDims, nDims, k))
for cGauss in range(k):
    mixGaussEst['cov'][:, :, cGauss] = 2.5 + 1.5 * np.random.uniform() * np.eye(nDims)


# calculate current likelihood
# TO DO - fill in this routine
logLike = getMixGaussLogLike(data, mixGaussEst)
print('Log Likelihood Iter 0 : {:4.3f}\n'.format(logLike))

nIter = 30;

logLikeVec = np.zeros(shape=(2 * nIter))
boundVec = np.zeros(shape=(2 * nIter))

fig, ax = plt.subplots(1, 1)

for cIter in range(nIter):

    # ===================== =====================
    # Expectation step
    # ===================== =====================
    curCov = mixGaussEst['cov']                                                                                  
    curWeight = mixGaussEst['weight']                                                                            
    curMean = mixGaussEst['mean']
    num= np.zeros(shape=(k,nData))
    for cData in range(nData):
        # TO DO (g) : fill in column of 'hidden' - calculate posterior probability that
        # this data point came from each of the Gaussians
        # replace this:


        thisData = data[:,cData]
        #for c in range(k):
        #    num[c] = mixGaussEst['weight'][c] * (1/((2*np.pi)**(nDims)*np.linalg.det(mixGaussEst['cov'][:,:,c]))**(1/2))*np.exp(-0.5*(np.transpose(thisData-mixGaussEst['mean'][:,c])))@np.linalg.inv(mixGaussEst['cov'][:,:,c])@(thisData-mixGaussEst['mean'][:,c])


        thisdata = data[:,cData];
        denominatorExp = 0
        for j in range(k):
            mu = curMean[:,j]
            sigma = curCov[:,:,j]
            curNorm = (1/((2*np.pi)**(nDims)*np.linalg.det(sigma))**(1/2))*np.exp(-0.5*(np.transpose(thisData-mu)))@np.linalg.inv(sigma)@(mu)
            num[j,cData] = curWeight[j]*curNorm
            denominatorExp = denominatorExp + num[j,cData]

        postHidden[:, cData] = num[:,cData]/denominatorExp


    # ===================== =====================
    # Maximization Step
    # ===================== =====================
    # for each constituent Gaussian
    for cGauss in range(k):
        # TO DO (h):  Update weighting parameters mixGauss.weight based on the total
        # posterior probability associated with each Gaussian. Replace this:
        #mixGaussEst['weight'][cGauss] = mixGaussEst['weight'][cGauss]
        sum_Kth_Gauss_Resp = np.sum(postHidden[cGauss,:])

        mixGaussEst['weight'][cGauss] = sum_Kth_Gauss_Resp /np.sum(postHidden)




        #mixGaussEst['weight'][cGauss] = np.sum(postHidden[cGauss,:])/sum(sum(postHidden[:,:]));






        # TO DO (i):  Update mean parameters mixGauss.mean by weighted average
        # where weights are given by posterior probability associated with
        # Gaussian.  Replace this:
        #mixGaussEst['mean'][:,cGauss] = mixGaussEst['mean'][:,cGauss]
        numerator = 0
        for j in range(nData):
            numerator = numerator + postHidden[cGauss,j]*data[:,j]
        numerator = np.dot( postHidden[cGauss,:],data[0,:])
        mixGaussEst['mean'][:,cGauss] = numerator / sum_Kth_Gauss_Resp


        # TO DO (j):  Update covarance parameter based on weighted average of
        # square distance from update mean, where weights are given by
        # posterior probability associated with Gaussian
        #mixGaussEst['cov'][:,:,cGauss] = mixGaussEst['cov'][:,:,cGauss]
        muMatrix = mixGaussEst['mean'][:,cGauss]
        muMatrix = muMatrix.reshape((2,1))
        numerator = 0
        for j in range(nData):

            kk=data[:,j]
            kk.reshape((2,1))
            numerator_i = postHidden[cGauss,j]*(kk-muMatrix)@np.transpose(kk-muMatrix)
            numerator = numerator + numerator_i

        mixGaussEst['cov'][:,:,cGauss] = numerator /sum_Kth_Gauss_Resp

        # draw the new solution

    drawEMData2d(data, mixGaussEst)
    time.sleep(0.7)
    fig.canvas.draw()

    # calculate the log likelihood
    logLike = getMixGaussLogLike(data, mixGaussEst)
    print('Log Likelihood After Iter {} : {:4.3f}\n'.format(cIter, logLike))


return mixGaussEst

Лог вероятности даетnan и почему весь код (в ideone) в конце выдает единственную матричную ошибку.

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