回归大杂烩

输入：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model,tree,svm,neighbors,ensemble
def f(x1, x2):
    y = 0.5 * np.sin(x1) + 0.5 * np.cos(x2) + 3 + 0.1 * x1 
    return y

def load_data():
    x1_train = np.linspace(0,50,500)
    x2_train = np.linspace(-10,10,500)
    data_train = np.array([[x1,x2,f(x1,x2) + (np.random.random(1)-0.5)] for x1,x2 in zip(x1_train, x2_train)])
    x1_test = np.linspace(0,50,100)+ 0.5 * np.random.random(100)
    x2_test = np.linspace(-10,10,100) + 0.02 * np.random.random(100)
    data_test = np.array([[x1,x2,f(x1,x2)] for x1,x2 in zip(x1_test, x2_test)])
    return data_train, data_test

def try_different_method(clf):
    clf.fit(x_train,y_train)
    score = clf.score(x_test, y_test)
    result = clf.predict(x_test)
    plt.figure()
    plt.plot(np.arange(len(result)), y_test,'go-',label='true value')
    plt.plot(np.arange(len(result)),result,'ro-',label='predict value')
    plt.title('score: %f'%score)
    plt.legend()

clfs = {'linear_reg': linear_model.LinearRegression(),\
        'tree_reg':tree.DecisionTreeRegressor(),
        'svr': svm.SVR(), \
        'knn':neighbors.KNeighborsRegressor(),\
        'rf' : ensemble.RandomForestRegressor(n_estimators=20),\
        'ada': ensemble.AdaBoostRegressor(n_estimators=50),
        'gbrt' : ensemble.GradientBoostingRegressor(n_estimators=100)
        }
    
    
train, test = load_data()
x_train, y_train = train[:,:2], train[:,2] #数据前两列是x1,x2 第三列是y,这里的y有随机噪声
x_test ,y_test = test[:,:2], test[:,2] # 同上,不过这里的y没有噪声

for clf_key in clfs.keys():
    print('the regression is :',clf_key)
    clf = clfs[clf_key]
    try_different_method(clf)
    
plt.show()

输出：