Python用sklearn拟合带有随机噪声的sin(x)随机数据，然后输入一个x预测y。用scatter作图
提供一个以前写的一个线性回归预测例子，楼主可以参考：
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
experiences = np.array([0,1,2,3,4,5,6,7,8,9,10])
salaries = np.array([103100, 104900, 106800, 108700, 110400, 112300, 114200, 116100, 117800, 119700, 121600])
# 将特征数据集分为训练集和测试集，除了最后 4 个作为测试用例，其他都用于训练
X_train = experiences[:7]
X_train = X_train.reshape(-1,1)
X_test = experiences[7:]
X_test = X_test.reshape(-1,1)
# 把目标数据（特征对应的真实值）也分为训练集和测试集
y_train = salaries[:7]
y_test = salaries[7:]
# 创建线性回归模型
regr = linear_model.LinearRegression()
# 用训练集训练模型——看就这么简单，一行搞定训练过程
regr.fit(X_train, y_train)
# 用训练得出的模型进行预测
diabetes_y_pred = regr.predict(X_test)