第1关：kNN算法原理

第2关：使用sklearn中的kNN算法进行分类

from sklearn.neighbors import KNeighborsClassifier
 
def classification(train_feature, train_label, test_feature):
    '''
    使用KNeighborsClassifier对test_feature进行分类
    :param train_feature: 训练集数据
    :param train_label: 训练集标签
    :param test_feature: 测试集数据
    :return: 测试集预测结果
    '''
 
    #********* Begin *********#
    clf = KNeighborsClassifier()
    clf.fit(train_feature, train_label)
    return clf.predict(test_feature)
    #********* End *********#

第3关：使用sklearn中的kNN算法进行回归

from sklearn.neighbors import KNeighborsRegressor
 
def regression(train_feature, train_label, test_feature):
    '''
    使用KNeighborsRegressor对test_feature进行分类
    :param train_feature: 训练集数据
    :param train_label: 训练集标签
    :param test_feature: 测试集数据
    :return: 测试集预测结果
    '''
 
    #********* Begin *********#
    clf=KNeighborsRegressor() 
    clf.fit(train_feature, train_label)               
    return clf.predict(test_feature)
    #********* End *********#

第4关：分析红酒数据

import numpy as np
def alcohol_mean(data):
    '''
    返回红酒数据中红酒的酒精平均含量
    :param data: 红酒数据对象
    :return: 酒精平均含量，类型为float
    '''

    #********* Begin *********#
    return data.data[:,0].mean()
    #********* End **********#

第5关：对数据进行标准化

from sklearn.preprocessing import StandardScaler

def scaler(data):
    '''
    返回标准化后的红酒数据
    :param data: 红酒数据对象
    :return: 标准化后的红酒数据，类型为ndarray
    '''
    #********* Begin *********#
    scaler=StandardScaler()
    after_scaler=scaler.fit_transform(data['data'])
    return after_scaler
    #********* End **********#

第6关：使用kNN算法进行预测

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler

def classification(train_feature, train_label, test_feature):
    '''
    对test_feature进行红酒分类
    :param train_feature: 训练集数据，类型为ndarray
    :param train_label: 训练集标签，类型为ndarray
    :param test_feature: 测试集数据，类型为ndarray
    :return: 测试集数据的分类结果
    '''

    #********* Begin *********#
    scaler=StandardScaler()
    train_feature=scaler.fit_transform(train_feature)
    test_feature=scaler.transform(test_feature)
    clf=KNeighborsClassifier()
    clf.fit(train_feature,train_label)
    return clf.predict(test_feature)
    #********* End **********#