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(),