sklearn实战：使用knn进行回归拟合

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np

# 生成训练样本
n_dots = 40
X = 5 * np.random.rand(n_dots, 1)

y = np.cos(X).ravel()

# 添加一些噪声
y += 0.2 * np.random.rand(n_dots) - 0.1

# 训练模型
from sklearn.neighbors import KNeighborsRegressor
k = 5
knn = KNeighborsRegressor(k)
knn.fit(X, y);

# 生成足够密集的点并进行预测
# T = np.linspace(0, 5, 500)[:, np.newaxis]

# y_pred = knn.predict(T)
knn.score(X, y)
# knn.score(T,y_pred)

0.9878569013063102

#生成足够密集的点并进行预测
T = np.linspace(0, 5, 500)[:, np.newaxis]

y_pred = knn.predict(T)

# 画出拟合曲线
plt.figure(figsize=(16, 10), dpi=144)
plt.scatter(X, y, c='g', label='data', s=100)         # 画出训练样本
plt.plot(T, y_pred, c='k', label='prediction', lw=4)  # 画出拟合曲线
plt.axis('tight')
plt.title("KNeighborsRegressor (k = %i)" % k)
plt.show()