X_train = data[0:280, 0:len(data[0]) - 1]
y_train = data[0:280, len(data[0]) - 1:]
if algo == "gradient_descent":
W_temp.append(linreg.gradient_descent(X_train, y_train))
else:
W_temp.append(linreg.closed_form_solution(X_train, y_train))
W_temp = np.array(W_temp)
W = []
for j in range(W_temp.shape[1]):
sm = 0
for i in range(n_iter):
sm += W_temp[i][j][0]
sm /= n_iter
W.append(sm)
W = np.array(W)
W = W.reshape((len(W), 1))
X_test, y_pred = parse_data('test.csv', train=False), []
X_test = linreg.normalize_data(X_test)
X_test = linreg.add_features(X_test)
X_test = np.insert(X_test, 0, 1, axis=1)
y_pred = []
for x in X_test:
y_pred.append(linreg.predict(x, W)[0])
create_output_file(y_pred)
