fd1ba604a3ca228cebefe590a48e31893b664a66,examples/kalman_filter.py,KalmanFilter,initialize_covariances,#KalmanFilter#Any#Any#Any#,243

Before Change

    def initialize_covariances(self, prior_values, process_values, obs_values):
        Set the values of the covariances.
        values = [prior_values, process_values, obs_values]
        attributes = ["covariance", "process_noise", "measurement_noise"]
        for (index, val) in enumerate(values):
            setattr(self, attributes[index], val)

    def propagate(self, sensor_input):
        Propagate the estimate and its covariance.
        prop_noise = self.process_noise
        prop_jac = self.model.propagation_jacobian(self.state, sensor_input)

After Change

            "covariance": prior_values,
            "process_noise": process_values,
            "measurement_noise": obs_values}
        for key in cov_dict:
            setattr(self, key, cov_dict[key])

    def propagate(self, sensor_input):
        Propagate the estimate and its covariance.

        Given the propagation Jacobian F and the noise Jacobian G, the

Instances