用 岭回归 预测交通流量

1、数据介绍

数据为某路口的交通流量监测数据，记录全年小时级别的车流量。

2、实验目的

根据已有的数据创建多项式特征，使用岭回归模型代替一般的线性模型，对 车流量的信息进行多项式回归。

3、代码

import numpy as npimport pandas as pdfrom sklearn import cross_validationfrom sklearn.linear_model import Ridgeimport matplotlib.pyplot as pltfrom sklearn.preprocessing import PolynomialFeaturesdata = pd.read_csv('ridge.csv')a = np.array(data)plt.plot(a[:, 5])plt.show()x = a[:, 1:5]y = a[:, 5]poly = PolynomialFeatures(6)x = poly.fit_transform(x)x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size = 0.2,random_state = 0)clf = Ridge(alpha = 1.0, fit_intercept = True)clf.fit(x_train, y_train)clf.score(x_test, y_test)start = 200end = 300time = np.arange(start, end)timey_pre = clf.predict(x)plt.plot(time, y[start:end], 'b', label = 'real')plt.plot(time, y_pre[start:end], 'r', label = 'predict')plt.legend(loc = 'upper left')plt.show()

输出：