def model_fit(param): print "Training %s" % param[0] neural_shape = [param[0], 15, 1] X_train = param[1][0] y_train = param[1][1] X_test = param[1][2] y_test = param[1][3] acoNet = ACOEstimator(Q=0.08, epsilon=0.55) fit_param = {"neural_shape": neural_shape} acoNet.fit(X_train, y_train, **fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train, y_train, **fit_param) return param[0], np.sqrt(neuralNet.score(X_test, y_test))
def model_fit(param): print "Training %s"%param[0] X_train = param[1][0] y_train = param[1][1] X_test = param[1][2] y_test = param[1][3] neural_shape = [y_train.shape[1]*param[0],10,y_train.shape[1]] acoNet = ACOEstimator(Q=0.08,epsilon=0.55) fit_param = { "neural_shape":neural_shape } acoNet.fit(X_train,y_train,**fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train,y_train,**fit_param) return param[0],np.sqrt(neuralNet.score(X_test,y_test))
if __name__ == '__main__': best_estimator = None best_score = np.Inf for loop in np.arange(1, 20): n_input = 4 n_periodic = 1 n_hidden = 15 neural_shape = [n_input + n_periodic, n_hidden, 1] Q = 0.09 epsilon = 0.55 dataFeeder = TrafficFeeder() X_train, y_train = dataFeeder.fetch_traffic_training( n_input, 1, (40, 46)) X_test, y_test = dataFeeder.fetch_traffic_test(n_input, 1, (46, 48)) # retrieve = [n_input+1,(X_train,y_train,X_test,y_test)] acoNet = ACOEstimator(Q=Q, epsilon=epsilon) fit_param = {"neural_shape": neural_shape} acoNet.fit(X_train, y_train, **fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train, y_train, **fit_param) y_pred = dataFeeder.convert(neuralNet.predict(X_test)) score = np.sqrt(mean_squared_error(y_pred, y_test)) if (score < best_score): best_estimator = acoNet print score # plot_fig(y_pred,y_test) print best_score, best_estimator
best_estimator = None best_score = np.Inf for loop in np.arange(1,20): n_input = 4 n_periodic = 1 n_hidden = 15 neural_shape = [n_input+n_periodic,n_hidden,1] Q = 0.09 epsilon = 0.55 dataFeeder = TrafficFeeder() X_train,y_train = dataFeeder.fetch_traffic_training(n_input,1,(40,46)) X_test,y_test = dataFeeder.fetch_traffic_test(n_input,1,(46,48)) # retrieve = [n_input+1,(X_train,y_train,X_test,y_test)] acoNet = ACOEstimator(Q=Q,epsilon=epsilon) fit_param = { "neural_shape":neural_shape } acoNet.fit(X_train,y_train,**fit_param) fit_param["weights_matrix"] = acoNet.best_archive neuralNet = NeuralFlowRegressor() neuralNet.fit(X_train,y_train,**fit_param) y_pred = dataFeeder.convert(neuralNet.predict(X_test)) score = np.sqrt(mean_squared_error(y_pred,y_test)) if(score<best_score): best_estimator = acoNet print score # plot_fig(y_pred,y_test) print best_score,best_estimator