def __init__(self):
self.feature_vec = [
features.LinearX1(),
features.LinearX2(),
features.SquareX1(),
features.ExpX2(),
features.LogX1(),
features.Identity()
]
self.feature_weights = [1, 2, 1, 0.1, 10, 40]
self.noise_model = noise.NoiseModel()
self.max_x1 = 10
self.max_x2 = 10
self.saver = saver.DataSaver('data', 'data_samples.pkl')
lm[part] = model.LinearRegressionModel()
# TODO use and select the new features
lm[part].set_feature_vector([
features.LinearX1(),
features.LinearX2(),
features.LinearX3(),
features.LinearX4(),
features.SquareX1(),
features.SquareX2(),
features.SquareX3(),
features.SquareX4(),
features.ExpX1(),
features.ExpX2(),
features.ExpX3(),
features.ExpX4(),
features.LogX1(),
features.LogX2(),
features.LogX3(),
features.LogX4(),
features.SinX1(),
features.SinX2(),
features.SinX3(),
features.SinX4(),
# features.X1Cube(), features.X2Cube(),
# features.X3Cube(), features.X4Cube(),
# features.TanX1(), features.TanX2(),
# features.TanX3(), features.TanX4(),
# features.X1OverX2(), features.X1OverX3(),
# features.X1OverX4(), features.X2OverX3(),
# features.X2OverX4(), features.X3OverX4(),
features.CrossTermX1X2(),
