str_to_write = "lr.gradient_descent_linear_regression_alg W = \n" + str(W) + "\n"
str_to_write += "elapsed_time = " + str(elapsed_time) + "\n"
fout_t2r.write(str_to_write)
print(str_to_write)
"""
str_to_write = "\n\n\n\n\n\n\n\n" + "for training and validation \n\n ===========!!!!===========\n\n"
fout_t2r.write(str_to_write)
print(str_to_write)
start_time = time.time()
W_cf, str_to_write = lr.least_squares_estimate_linear_regression_alg(
X_training_set_Adv, Y_training_set_Adv)
#print("lr.least_squares_estimate_linear_regression_alg W = \n", W)
fout_t2r.write(str_to_write)
print(str_to_write)
str_to_write = "lr.least_squares_estimate_linear_regression_alg W_cf = \n" + str(
W_cf) + "\n"
elapsed_time = time.time() - start_time
str_to_write += "least_squares_estimate_linear_regression_alg elapsed_time = " + str(
elapsed_time) + "\n"
fout_t2r.write(str_to_write)
print(str_to_write)
#print("elapsed_time = ", elapsed_time)
with open("../training_set.json", "r") as rf_training_set:
data = json.load(rf_training_set)
X_training_set_Adv, Y_training_set_Adv, str_output = pf.generate_wordfeature_and_output(wordcount, data, False, 0, False, 0)
with open("../validation_set.json", "r") as rf_validation_set:
data = json.load(rf_validation_set)
X_validation_set_Adv, Y_validation_set_Adv, str_output = pf.generate_wordfeature_and_output(wordcount, data, False, 0, False, 0)
#測試Closed form的性能
start_time = time.time()
W_closed, str_to_write = lr.least_squares_estimate_linear_regression_alg(X_validation_set_Adv, Y_validation_set_Adv)
closed_running_time = time.time() - start_time
est_Y = np.dot(X_training_set_Adv, W_closed)
trn_mse_closed, diff_AB, Sigma_Square_of_diff_AB = lr.mean_squared_error(est_Y, Y_training_set_Adv)
est_Y = np.dot(X_validation_set_Adv, W_closed)
val_mse_closed, diff_AB, Sigma_Square_of_diff_AB = lr.mean_squared_error(est_Y, Y_validation_set_Adv)
#測試Gradient Descent的性能
list_T_Robbins_Monroe_pow = []
list_trn_mse = []
list_val_mse = []
list_elapsed_time = []
list_W_MSE = []
Y_training_set, Y_validation_set = np.split(Y, [8])
#print("Y_training_set = \n", Y_training_set)
#print("Y_validation_set = \n", Y_validation_set)
str_to_write = "Y_training_set = \n" + str(Y_training_set) + "\n"
str_to_write += "Y_validation_set = \n" + str(Y_validation_set) + "\n"
fout_t2r.write(str_to_write)
print(str_to_write)
start_time = time.time()
# your code
W, str_to_write = lr.least_squares_estimate_linear_regression_alg(X, Y)
fout_t2r.write(str_to_write)
print(str_to_write)
elapsed_time = time.time() - start_time
#print("lr.least_squares_estimate_linear_regression_alg W = \n", W)
#print("elapsed_time = ", elapsed_time)
str_to_write = "lr.least_squares_estimate_linear_regression_alg W = \n" + str(
W) + "\n"
str_to_write += "elapsed_time = " + str(elapsed_time) + "\n\n\n\n\n"
fout_t2r.write(str_to_write)
print(str_to_write)
"""