KNN调包实现

import numpy as np
import pandas as pd
from sklearn import neighborsT = [[3, 104, -1],[2, 100, -1],[1, 81, -1],[101, 10, 1],[99, 5, 1],[98, 2, 1]]# 初始化待测样本
x = [[18, 90]]# 初始化K值
K = 3data = pd.DataFrame(T, columns=['A', 'B', 'label'])# print(data)# 训练数据提取
X_train = data.iloc[:, :-1] 
# print(X_train)
Y_train = data.iloc[:, -1]
# print(Y_train)# 激活KNN分类，不加权
KNN1 = neighbors.KNeighborsClassifier(n_neighbors=3)KNN1.fit(X_train, Y_train)
y_predict = KNN1.predict(x)
print(y_predict)score = KNN01.score(X=X_train, y=Y_train)
print(score)# 激活KNN，加权
KNN2 = neighbors.KNeighborsClassifier(n_neighbors=3, weights='distance')
KNN2.fit(X_train, Y_train)
y_predict = KNN2.predict(x)
print(y_predict)print（'---------------------------KNN回归-----------------------------'）T = [[3, 104, 98],[2, 100, 93],[1, 81, 95],[101, 10, 16],[99, 5, 8],[98, 2, 7]]# 初始化待测样本
x = [[18, 90]]
# x =[[50, 50]]# 初始化K值
K = 5data = pd.DataFrame(T, columns=['A', 'B', 'label'])
# print(data)X_train = data.iloc[:, :-1]
# print(X_train)
Y_train = data.iloc[:, -1]
# print(Y_train)# 激活KNN回归，不加权
KNN3 = neighbors.KNeighborsRegressor(n_neighbors=K)
KNN3.fit(X_train, Y_train)
y_predict = KNN3.predict(x)
print(y_predict)# 激活KNN回归，加权
KNN4 = neighbors.KNeighborsRegressor(n_neighbors=K, weights='distance')
KNN4.fit(X_train, Y_train)
y_predict = KNN4.predict(x)
print(y_predict)

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