fimix.py 文件源码

def expectation(self, dataSplit, coefficients, variances):

        assignment_weights = np.ones(
            (len(dataSplit), self.num_components), dtype=float)

        self.Q = len(self.endoVar)

        for k in range(self.num_components):

            coef_ = coefficients[k]

            Beta = coef_.ix[self.endoVar][self.endoVar]
            Gamma = coef_.ix[self.endoVar][self.exoVar]

            a_ = (np.dot(Beta, self.fscores[
                  self.endoVar].T) + np.dot(Gamma, self.fscores[self.exoVar].T))

            invert_ = np.linalg.inv(np.array(variances[k]))

            exponential = np.exp(-0.5 * np.dot(np.dot(a_.T, invert_), a_))

            den = (((2 * np.pi)**(self.Q / 2)) *
                   np.sqrt(np.linalg.det(variances[k])))
            probabilities = exponential / den
            probabilities = probabilities[0]
            assignment_weights[:, k] = probabilities

        assignment_weights /= assignment_weights.sum(axis=1)[:, np.newaxis]
 #       print(assignment_weights)
        return assignment_weights