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')
# Fit model for each part
for part in range(0, n_parts):
train_start_i = int(part * samp_per_part)
test_start_i = int(train_start_i + samp_per_part * ratio_train_validate)
test_end_i = int(train_start_i + samp_per_part)
training_input[part] = input_data[train_start_i:test_start_i, :]
training_output[part] = output_data[train_start_i:test_start_i, :]
validation_input[part] = input_data[test_start_i:test_end_i, :]
validation_output[part] = output_data[test_start_i:test_end_i, :]
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(),
