active_inference_basic.py 文件源码

def rh_model_sweep_generate_input_grid_a(self):
        sweepsteps = 11 # 21
        # extero config
        dim_axes = [np.linspace(self.environment.conf.s_mins[i], self.environment.conf.s_maxs[i], sweepsteps) for i in range(self.environment.conf.s_ndims)]
        # dim_axes = [np.linspace(self.environment.conf.s_mins[i], self.environment.conf.s_maxs[i], sweepsteps) for i in range(self.mdl.idim)]
        print "rh_model_sweep_generate_input_grid: s_ndims = %d, dim_axes = %s" % (self.environment.conf.s_ndims, dim_axes,)
        full_axes = np.meshgrid(*tuple(dim_axes), indexing='ij')
        print "rh_model_sweep_generate_input_grid: full_axes = %s, %s" % (len(full_axes), full_axes,)

        for i in range(len(full_axes)):
            print i, full_axes[i].shape
            print i, full_axes[i].flatten()

        # return proxy
        error_grid = np.vstack([full_axes[i].flatten() for i in range(len(full_axes))])
        print "error_grid", error_grid.shape

        # draw state / goal configurations
        X_accum = []
        states = np.linspace(-1, 1, sweepsteps)
        # for state in range(1): # sweepsteps):
        for state in states:
            # randomize initial position
            # self.M_prop_pred = self.environment.compute_motor_command(np.random.uniform(-1.0, 1.0, (1, self.odim)))
            # draw random goal and keep it fixed
            # self.goal_prop = self.environment.compute_motor_command(np.random.uniform(-1.0, 1.0, (1, self.odim)))
            self.goal_prop = self.environment.compute_motor_command(np.ones((1, self.odim)) * state)
            # self.goal_prop = np.random.uniform(self.environment.conf.m_mins, self.environment.conf.m_maxs, (1, self.odim))
            GOALS = np.repeat(self.goal_prop, error_grid.shape[1], axis = 0) # as many goals as error components
            # FIXME: hacks for M1/M2
            if self.mdl.idim == 3:
                X = GOALS
            elif self.mdl.idim == 6:
                X = np.hstack((GOALS, error_grid.T))
            else:
                X = np.hstack((GOALS, error_grid.T))
            X_accum.append(X)

        X_accum = np.array(X_accum)

        # don't need this?
        # X_accum = X_accum.reshape((X_accum.shape[0] * X_accum.shape[1], X_accum.shape[2]))

        print "X_accum.shape = %s, mdl.idim = %d, mdl.odim = %d" % (X_accum.shape, self.mdl.idim, self.mdl.odim)
        # print X_accum
        X = X_accum
        # X's and pred's indices now mean: slowest: goal, e1, e2, fastest: e3
        self.X_model_sweep = X.copy()
        return X
        # print "self.X_model_sweep.shape", self.X_model_sweep.shape