def learnCameraParameters(f_file, v_file):
# load features
X = np.loadtxt(f_file, unpack=True)
X = np.transpose(X)
print X.shape
min_max_scaler1 = preprocessing.MinMaxScaler()
X = min_max_scaler1.fit_transform(X)
np.savetxt("fv_scale.list", X, fmt='%0.6f')
X = np.transpose(X)
scaler1 = preprocessing.StandardScaler()
X = scaler1.fit_transform(X)
X = np.transpose(X)
scaler2 = preprocessing.StandardScaler()
X = scaler2.fit_transform(X)
y = np.loadtxt(v_file, unpack=True)
# y = np.transpose(y)
# min_max_scaler2 = preprocessing.MinMaxScaler()
# y = min_max_scaler2.fit_transform(y)
# np.savetxt("cam_scale.list", y, fmt='%0.6f')
# y = np.transpose(y)
scaler3 = preprocessing.StandardScaler()
y = scaler3.fit_transform(y)
y = np.transpose(y)
scaler4 = preprocessing.StandardScaler()
y = scaler4.fit_transform(y)
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=0.25, random_state=0)
regressor = DecisionTreeRegressor()
# regressor = DecisionTreeRegressor(max_features='log2', min_samples_split=20, min_samples_leaf=20, random_state=71)
# scores = cross_validation.cross_val_score(regressor, X, y, cv=4)
regressor.fit(X_train, y_train)
X_new = regressor.fit_transform(X_train, y_train)
regressor2 = DecisionTreeRegressor()
regressor2.fit(X_new, y_train)
print regressor2.score(X_new, y_train)
X_new = regressor.transform(X_test)
print regressor2.score(X_new, y_test)
yy = regressor.predict(X)
# print "Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() / 2)
y = scaler4.inverse_transform(yy)
y = np.transpose(y)
y = scaler3.inverse_transform(y)
y = np.transpose(y)
# y = min_max_scaler2.inverse_transform(y)
np.savetxt("cam_back.list", y, fmt='%0.6f')
