msaf/algorithms/fmc2d/xmeans.py
def compute_bic(self, D, means, labels, K, R):
        """Computes the Bayesian Information Criterion."""
        D = vq.whiten(D)
        Rn = D.shape[0]
        M = D.shape[1]

        if R == K:
            return 1

        # Maximum likelihood estimate (MLE)
        mle_var = 0
        for k in range(len(means)):
            X = D[np.argwhere(labels == k)]
            X = X.reshape((X.shape[0], X.shape[-1]))
            for x in X:
                mle_var += distance.euclidean(x, means[k])
                #print x, means[k], mle_var
        mle_var /= float(R - K)

        # Log-likelihood of the data
        l_D = - Rn/2. * np.log(2*np.pi) - (Rn * M)/2. * np.log(mle_var) - \
            (Rn - K) / 2. + Rn * np.log(Rn) - Rn * np.log(R)

        # Params of BIC
        p = (K-1) + M * K + mle_var

        #print "BIC:", l_D, p, R, K

        # Return the bic
        return l_D - p / 2. * np.log(R)
def compute_bic(self, D, means, labels, K, R):
        """Computes the Bayesian Information Criterion."""
        D = vq.whiten(D)
        Rn = D.shape[0]
        M = D.shape[1]

        if R == K:
            return 1

        # Maximum likelihood estimate (MLE)
        mle_var = 0
        for k in range(len(means)):
            X = D[np.argwhere(labels == k)]
            X = X.reshape((X.shape[0], X.shape[-1]))
            for x in X:
                mle_var += distance.euclidean(x, means[k])
                #print x, means[k], mle_var
        mle_var /= float(R - K)

        # Log-likelihood of the data
        l_D = - Rn/2. * np.log(2*np.pi) - (Rn * M)/2. * np.log(mle_var) - \
            (Rn - K) / 2. + Rn * np.log(Rn) - Rn * np.log(R)

        # Params of BIC
        p = (K-1) + M * K + mle_var

        #print "BIC:", l_D, p, R, K

        # Return the bic
        return l_D - p / 2. * np.log(R)
Computes the Bayesian Information Criterion.