def reset_weight(Data):
w0 = 1 / float(len(Data))
for row in Data:
row['weight'] = w0
# Q2.a
# Run AdaBoosting for T = 1-500, plot error-T relationship
outFile = open("ada_out.txt", 'w')
outFile.write("iter\terr_train\terr_test\n")
for T in [
1, 2, 3, 4, 5, 6, 8, 10, 15, 20, 30, 50, 70, 90, 120, 150, 200, 250,
300, 350, 400, 450, 500
]:
trees, alphas = AdaBoost.AdaBoost_train(Data_train, Attributes, Labels, T)
hit_train = AdaBoost.AdaBoost_test(Data_train, trees, alphas)
hit_test = AdaBoost.AdaBoost_test(Data_test, trees, alphas)
outFile.write(
str(T) + "\t" + str(1 - hit_train) + "\t" + str(1 - hit_test) + "\n")
reset_weight(Data_train)
outFile.close()
# for T=500, find training and test error in each iteration
e_t, e_r = AdaBoost.print_err_Ada(Data_train, Data_test, Attributes, Labels,
500)
t = [i + 1 for i in range(0, 500)]
fig, ax = plt.subplots(figsize=(6, 4))
ax.plot(t, e_r, label='test error', c='grey', alpha=0.3)
