educoder 机器学习 --- kNN算法

第一关：

#encoding=utf8
import numpy as np

from collections import Counter

class kNNClassifier(object):
    def __init__(self, k):
        '''
        初始化函数
        :param k:kNN算法中的k
        '''
        self.k = k
        # 用来存放训练数据，类型为ndarray
        self.train_feature = None
        # 用来存放训练标签，类型为ndarray
        self.train_label = None


    def fit(self, feature, label):
        '''
        kNN算法的训练过程
        :param feature: 训练集数据，类型为ndarray
        :param label: 训练集标签，类型为ndarray
        :return: 无返回
        '''

        #********* Begin *********#
        self.train_feature = feature
        self.train_label = label
        #********* End *********#


    def predict(self, feature):
        '''
        kNN算法的预测过程
        :param feature: 测试集数据，类型为ndarray
        :return: 预测结果，类型为ndarray或list
        '''

        #********* Begin *********#
        result = []
        for data in feature:
            dist = np.sqrt(np.sum((self.train_feature - data) ** 2, axis = 1)) # 欧氏距离
            neighbor = np.argsort(dist)[0 : self.k]
            kLabel = (self.train_label[i] for i in neighbor)
            key, value = Counter(kLabel).most_common(1)[0] # 如果k个邻居中出现次数最多的label不止一个，要取总距离最小的label，这里直接取第一个（懒得写了
            result.append(key)
        return result
        #********* End *********#

第2关：

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 *********#
    #实例化StandardScaler函数
    scaler = StandardScaler()
    train_feature = scaler.fit_transform(train_feature)
    test_feature = scaler.transform(test_feature)
   
    #生成K近邻分类器
    clf = KNeighborsClassifier()
    #训练分类器
    clf.fit(train_feature, train_label)
    #进行预测
    predict_result = clf.predict(test_feature)
    return predict_result 
    #********* End **********#