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
