python - numpy matrix algebra error -

i have following class in python:

import numpy class kalmanfilterlinear:     def __init__(self,_a, _b, _h, _x, _p, _q, _r):         self.a = _a                      # state transition matrix.         self.b = _b                      # control matrix.         self.h = _h                      # observation matrix.         self.current_state_estimate = _x # initial state estimate.         self.current_prob_estimate = _p  # initial covariance estimate.         self.q = _q                      # estimated error in process.         self.r = _r                      # estimated error in measurements.       def getcurrentstate(self):         return self.current_state_estimate      def step(self,control_vector,measurement_vector):         #---------------------------prediction step-----------------------------         print self.a         print self.current_state_estimate         print self.b         print control_vector         predicted_state_estimate = self.a * self.current_state_estimate + self.b * control_vector         predicted_prob_estimate = (self.a * self.current_prob_estimate) * numpy.transpose(self.a) + self.q         #--------------------------observation step-----------------------------         innovation = measurement_vector - self.h*predicted_state_estimate         innovation_covariance = self.h*predicted_prob_estimate*numpy.transpose(self.h) + self.r         #-----------------------------update step-------------------------------         kalman_gain = predicted_prob_estimate * numpy.transpose(self.h) * numpy.linalg.inv(innovation_covariance)         self.current_state_estimate = predicted_state_estimate + kalman_gain * innovation         # need size of matrix can make identity matrix.         size = self.current_prob_estimate.shape[0]         # eye(n) = nxn identity matrix.         self.current_prob_estimate = (numpy.eye(size)-kalman_gain*self.h)*predicted_prob_estimate 

i initialize this:

import numpy np state_transition = np.matrix([[1,-1.0/400],[0,1]]) control_matrix = np.matrix([ [1.0/400, 0.0], [0.0, 0.0] ])  observation_matrix = np.eye(2) observation_matrix[1][1] = 0.0  initial_probability = np.eye(2)  process_covariance = np.zeros(2) measurement_covariance = np.eye(2)*0.2 initial_state = np.matrix([[ins_h],[0.0]])  kf = kalmanfilterlinear(state_transition, control_matrix, observation_matrix, initial_state, initial_probability, process_covariance, measurement_covariance) 

then run code check works:

kf.step(initial_state, initial_state ) 

and error:

predicted_state_estimate = self.a * self.current_state_es... error: can't multiply sequence non-int of type 'float' 

i don't have experience numpy , i'd grateful help.