def plot_scatter_curve(x, y, model, fignum=0, title=''):
plt.figure(fignum)
xmin, xmax = np.min(x), np.max(x)
ymin, ymax = np.min(y), np.max(y)
plt.xlim(0.70*xmin, 1.3*xmax)
plt.ylim(0.70*ymin, 1.3*ymax)
plt.scatter(x, y)
r = np.linspace(xmin, xmax, num=100).reshape((100, 1))
s = util.make_nth_order(r, len(model)-1).dot(model)
plt.plot(r, s, 'g-')
plt.axis([xmin, xmax, ymin, ymax])
plt.title(title)
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()
