output_dict = dict.fromkeys(np.arange(0, 49), np.array([[]], dtype="int64"))
k_levels = np.arange(0, 12).reshape([12, 1])
for key in np.arange(0, 49):
current_values = np.arange(key * 12, key * 12 + 12).reshape([12, 1])
output_dict[key] = np.concatenate((current_values, k_levels), axis=1)
X_speed, _, _ = EnelWindSpeedTransformation().transform(X)
print("wind speed computed")
enel_transf = Enel_directionPowerCurveTransformation()
X_transf, dict_sample_turb = enel_transf.transform(X_angle, angles_coord_turb, X_speed, power_curve, Coord, Coord_turb,
threshold_dir=threshold_dir)
print("single transformation done")
X_transf, output_dict = enel_transf.transformPerTurbineLevel(dict_sample_turb, enel_dict, X, power_curve, X_transf,
output_dict)
print("transformation per turbine done")
XTrain_transf = X_transf[:XTrain.shape[0], :]
XTest_transf = X_transf[XTrain.shape[0]:, :]
##center data
values_TM = []
start_loss, _ = compute_lasso(XTrain_, YTrain_, XVal_, YVal_, score="mean_squared_error", values_TM=values_TM)
print("loss", start_loss)
n_features_transf = XTrain_.shape[1]
####generation blocks
X_transf_no_levels = np.concatenate((X_transf,X_transf_1), axis = 1)
XTrain_transf = X_transf_no_levels[:XTrain.shape[0],:]
XTest_transf = X_transf_no_levels[XTrain.shape[0]:,:]
print("loss mean turbina solo sul validation")
XTrain_, XVal_, YTrain_, YVal_ = train_test_split(XTrain_transf, YTrain, test_size=0.33,random_state=0)
Linear_fit().fitting(XTrain_, YTrain_, XVal_,YVal_, [])
print("loss mean turbina solo sul test")
Linear_fit().fitting(XTrain_transf, YTrain, XTest_transf,YTest,values_TM)
print("-------------")
if compute_levels:
X_transf_2,_ = enel_transf.transformPerTurbineLevel(matrix_turbs, enel_dict, X, power_curve, np.array([[]]),output_dict)
print("transformation per turbine done")
if compute_single_mean:
XTrain_transf = X_transf_2[:XTrain.shape[0],:]
XTest_transf = X_transf_2[XTrain.shape[0]:,:]
print("loss mean turbina levels solo sul validation")
XTrain_, XVal_, YTrain_, YVal_ = train_test_split(XTrain_transf, YTrain, test_size=0.33,random_state=0)
Linear_fit().fitting(XTrain_, YTrain_, XVal_,YVal_, [])
print("loss mean turbina levels solo sul test")
Linear_fit().fitting(XTrain_transf, YTrain, XTest_transf,YTest,values_TM)
X_transf = np.concatenate((X_transf,X_transf_2), axis = 1)
XTrain_transf = X_transf[:XTrain.shape[0],:]
