def __init__(self):
self.feature_vec = [features.CrossTermX1X3(), features.SinX2(),
features.SquareX4(), features.Identity()]
self.feature_weights = [0.1, -2, -0.3, 3]
self.noise_model = noise.NoiseModel()
self.max_x1 = 10
self.max_x2 = 10
self.max_x3 = 10
self.max_x4 = 10
self.saver = saver.DataSaver('data', 'submission_data.pkl')
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')
#randomly shuffle input and output data
input_data, output_data = shuffle_data(input_data, output_data)
training_input = input_data[:idx_switch, :]
training_output = output_data[:idx_switch, :]
validation_input = input_data[idx_switch:, :]
validation_output = output_data[idx_switch:, :]
# Fit model
lm = model.LinearRegressionModel()
# TODO use and select the new features
lm.set_feature_vector([
features.CrossTermX1X3(),
features.SinX2(),
features.SquareX4(),
features.Identity()
])
lm.fit(training_input, training_output)
# Validation
mse = lm.validate(validation_input, validation_output)
print('MSE: {}'.format(mse))
print(' ')
print('feature weights \n{}'.format(lm.beta))
# load submission data
submission_loader = saver.DataSaver('data', 'submission_data.pkl')
submission_input = submission_loader.load_submission()
# predict output
submission_output = lm.predict(submission_input)
