samplers.py 文件源码

def distribution_parameters(self, parameter_name):
        if parameter_name=='trend':
            E = dot(dot(self.derivative_matrix.T,inv(diag(self.parameters.list['omega'].current_value))),self.derivative_matrix)
            mean = dot(inv(eye(self.size)+E),self.data)
            cov = (self.parameters.list['sigma2'].current_value)*inv(eye(self.size)+E)
            return {'mean' : mean, 'cov' : cov}
        elif parameter_name=='sigma2':
            E = dot(dot(self.derivative_matrix.T,inv(diag(self.parameters.list['omega'].current_value))),self.derivative_matrix)
            pos = self.size
            loc = 0
            scale = 0.5*dot((self.data-dot(eye(self.size),self.parameters.list['trend'].current_value)).T,(self.data-dot(eye(self.size),self.parameters.list['trend'].current_value)))+0.5*dot(dot(self.parameters.list['trend'].current_value.T,E),self.parameters.list['trend'].current_value)
        elif parameter_name=='lambda2':
            pos = self.size-self.total_variation_order-1+self.alpha
            loc = 0.5*(norm(dot(self.derivative_matrix,self.parameters.list['trend'].current_value),ord=1))/self.parameters.list['sigma2'].current_value+self.rho
            scale = 1
        elif parameter_name==str('omega'):
            pos = [sqrt(((self.parameters.list['lambda2'].current_value**2)*self.parameters.list['sigma2'].current_value)/(dj**2)) for dj in dot(self.derivative_matrix,self.parameters.list['trend'].current_value)]
            loc = 0
            scale = self.parameters.list['lambda2'].current_value**2
        return {'pos' : pos, 'loc' : loc, 'scale' : scale}