machine_learn/linear/train_xsquare.py

#!/usr/bin/python
# -*- coding: UTF-8 -*-
import sys

reload(sys)
sys.setdefaultencoding('utf-8')
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn import metrics


def extend_feature(x):
    # return [x[0]]
    return [x[0], x[0] * x[0]]


def read_data(path):
    with open(path) as f:
        lines = f.readlines()
    lines = [eval(line.strip()) for line in lines]
    X, y = zip(*lines)
    X = [extend_feature(x) for x in X]
    X = np.array(X)
    y = np.array(y)
    return X, y


if __name__ == '__main__':
    X_train, y_train = read_data("train_paracurve_data")
    X_test, y_test = read_data("test_paracurve_data")
    model = LinearRegression()
    model.fit(X_train, y_train)
    print model.coef_
    print model.intercept_

    y_pred_train = model.predict(X_train)
    train_mse = metrics.mean_squared_error(y_train, y_pred_train)
    print "特征+平方非线性"
    print "MSE:", train_mse
    y_pred_test = model.predict(X_test)
    test_mse = metrics.mean_squared_error(y_test, y_pred_test)
    print "MSE:", test_mse
    print "推广mse差", test_mse - train_mse