def main():
if len(sys.argv) < 2:
print("Usage:\n\t{} [housing-data]".format(sys.argv[0]))
sys.exit(1)
dataset = reader.read(sys.argv[1], delim=' ')
# Exapand features with nonlinear functions
# Need to put them back together to handle
features, labels = util.fldivide(dataset)
features, scale = scaling.unit_scale(features)
features = util.basis_expand(features, lambda x: x ** 2, lambda x: x ** 3)
features = np.hstack([features, np.ones((len(features), 1))])
dataset = util.fljoin(features, labels)
reg = NormalEquationLinearRegressor(regularization=1e-8)
cv = CrossValidator(reg)
feat_indices, feat_errors = cv.best_3features_topN(dataset, n=5)
for indices, err in zip(feat_indices, feat_errors):
bestfeats = np.dstack([features[:, i] for i in indices]).squeeze()
data = util.fljoin(bestfeats, labels)
reg.train(data)
print(reg.w)
print("indices = {}, err = {}".format(indices, err))
def main():
if len(sys.argv) < 2:
print("Usage:\n\t{} [trainfile] [testfile]".format(sys.argv[0]))
sys.exit(1)
train_file, test_file = sys.argv[1:]
train_data, train_labels = util.fldivide(read(train_file))
test_data, test_labels = util.fldivide(read(test_file))
for i in range(5):
nth_train_data = util.make_nth_order(train_data, i)
nth_train = np.hstack((nth_train_data, train_labels.reshape((len(train_labels), 1))))
nth_test_data = util.make_nth_order(test_data, i)
model = GradientDescentLinearRegressor(learn_rate=0.4, regularization=1e1)
model.train(nth_train)
predicted = model.predict(nth_test_data)
mse = model.error(predicted, test_labels)
plot_scatter_curve(test_data, test_labels, model.w, fignum=i,
title="Gradient Descent, order {}, alpha={}, lambda={}, mse={}".format(i, model.learn_rate, model.l, mse))
plt.show()
