(self, pts)
return mu0, Phi, m, n0
Evaluate dynamics prior.
        Args:
            pts: A N x D array of points.
Evaluate dynamics prior.
        Args:
            pts: A N x D array of points.
Evaluate dynamics prior . Args : pts : A N x D array of points .
def inference(self, pts):
        """
        Evaluate dynamics prior.
        Args:
            pts: A N x D array of points.
        """
        # Compute posterior cluster weights.
        logwts = self.clusterwts(pts)

        # Compute posterior mean and covariance.
        mu0, Phi = self.moments(logwts)

        # Set hyperparameters.
        m = self.N
        n0 = m - 2 - mu0.shape[0]

        # Normalize.
        m = float(m) / self.N
        n0 = float(n0) / self.N
        return mu0, Phi, m, n0
def inference ( self , pts ) : # Compute posterior cluster weights. logwts = self . clusterwts ( pts ) # Compute posterior mean and covariance. mu0 , Phi = self . moments ( logwts ) # Set hyperparameters. m = self . N n0 = m - 2 - mu0 . shape [ 0 ] # Normalize. m = float ( m ) / self . N n0 = float ( n0 ) / self . N return mu0 , Phi , m , n0