#!/usr/bin/python3.7
# -*- coding: utf-8 -*-
# @Time : 2019/11/13 10:55
# @Author: Jtyoui@qq.com
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsRegressor
"""
KNN分类算法例子
"""
[文档]def knn_class_fit(train, label):
"""训练数据模型"""
binary = LabelBinarizer() # 二值化
y_ = binary.fit_transform(label)
clf = KNeighborsClassifier()
clf.fit(train, np.ravel(y_))
return clf, binary
[文档]def knn_class_predict(models, predict_value):
"""预测模型"""
predict = models[0].predict(predict_value)
return models[1].inverse_transform(predict)
[文档]def knn_regress_fit(train, label):
clf = KNeighborsRegressor()
clf.fit(train, label)
return clf
[文档]def knn_regress_predict(models, predict_value):
return models.predict(predict_value)
if __name__ == '__main__':
# 身高和体重去预测性别
x = np.array([
[156, 64],
[170, 86],
[183, 84],
[191, 80],
[155, 49],
[163, 59],
[180, 67],
[158, 54],
[170, 67]
])
y = np.array(['male', 'male', 'male', 'male', 'female', 'female', 'female', 'female', 'female'])
test = np.array([[155, 70]])
model = knn_class_fit(x, y)
print(knn_class_predict(model, test)) # 预测：['female']，正确：['female']
if __name__ == '__main__':
# 利用身高和性别来预测体重
x = np.array([
[158, 1],
[170, 1],
[183, 1],
[191, 1],
[155, 0],
[163, 0],
[180, 0],
[158, 0],
[170, 0],
[168, 1],
[180, 1],
[160, 0]
])
y = np.array([64, 86, 84, 80, 49, 59, 67, 54, 67, 65, 96, 52])
test = np.array([[169, 0]])
model = knn_regress_fit(x, y)
print(knn_regress_predict(model, test)) # 预测：[65.8]，正确：[67]