Python mniw_log_partitionfunction 예제들

예제 #1

파일: regression.py 프로젝트: satpreetsingh/pybasicbayes

    def get_vlb(self):
        E_Sigmainv, E_Sigmainv_A, E_AT_Sigmainv_A, E_logdetSigmainv = \
            mniw_expectedstats(*self._natural_to_standard(self.mf_natural_hypparam))
        A, B, C, d = self.natural_hypparam - self.mf_natural_hypparam
        bilinear_term = -1./2 * np.trace(A.dot(E_Sigmainv)) \
            + np.trace(B.T.dot(E_Sigmainv_A)) \
            - 1./2 * np.trace(C.dot(E_AT_Sigmainv_A)) \
            + 1./2 * d * E_logdetSigmainv

        # log normalizer term
        Z = mniw_log_partitionfunction(
            *self._natural_to_standard(self.natural_hypparam))
        Z_mf = mniw_log_partitionfunction(
            *self._natural_to_standard(self.mf_natural_hypparam))

        return bilinear_term - (Z - Z_mf)

예제 #2

파일: regression.py 프로젝트: mattjj/pybasicbayes

    def get_vlb(self):
        E_Sigmainv, E_Sigmainv_A, E_AT_Sigmainv_A, E_logdetSigmainv = \
            mniw_expectedstats(*self._natural_to_standard(self.mf_natural_hypparam))
        A, B, C, d = self.natural_hypparam - self.mf_natural_hypparam
        bilinear_term = -1./2 * np.trace(A.dot(E_Sigmainv)) \
            + np.trace(B.T.dot(E_Sigmainv_A)) \
            - 1./2 * np.trace(C.dot(E_AT_Sigmainv_A)) \
            + 1./2 * d * E_logdetSigmainv

        # log normalizer term
        Z = mniw_log_partitionfunction(*self._natural_to_standard(
            self.natural_hypparam))
        Z_mf = mniw_log_partitionfunction(*self._natural_to_standard(
            self.mf_natural_hypparam))

        return bilinear_term - (Z - Z_mf)

예제 #3

def dense_regression_logprior(regression):
    A = regression.A
    Sigmainv = np.linalg.inv(regression.sigma)
    Sigmainv_A = Sigmainv.dot(A)
    AT_Sigmainv_A = A.T.dot(Sigmainv_A)
    logdetSigmainv = np.linalg.slogdet(Sigmainv)[1]

    A, B, C, d = regression.natural_hypparam
    bilinear_term = -1./2 * np.trace(A.dot(Sigmainv)) \
        + np.trace(B.T.dot(Sigmainv_A)) \
        - 1./2 * np.trace(C.dot(AT_Sigmainv_A)) \
        + 1./2 * d * logdetSigmainv

    # log normalizer term
    from pybasicbayes.util.stats import mniw_log_partitionfunction
    Z = mniw_log_partitionfunction(
        *regression._natural_to_standard(regression.natural_hypparam))

    return bilinear_term - Z

예제 #4

파일: util.py 프로젝트: mattjj/pyhsmm-slds

def dense_regression_logprior(regression):
    A = regression.A
    Sigmainv = np.linalg.inv(regression.sigma)
    Sigmainv_A = Sigmainv.dot(A)
    AT_Sigmainv_A = A.T.dot(Sigmainv_A)
    logdetSigmainv = np.linalg.slogdet(Sigmainv)[1]

    A, B, C, d = regression.natural_hypparam
    bilinear_term = -1./2 * np.trace(A.dot(Sigmainv)) \
        + np.trace(B.T.dot(Sigmainv_A)) \
        - 1./2 * np.trace(C.dot(AT_Sigmainv_A)) \
        + 1./2 * d * logdetSigmainv

    # log normalizer term
    from pybasicbayes.util.stats import mniw_log_partitionfunction
    Z = mniw_log_partitionfunction(
        *regression._natural_to_standard(regression.natural_hypparam))

    return bilinear_term - Z