第一關：
#encoding=utf8 import numpy as np from collections import Counter class kNNClassifier(object): def __init__(self, k): ''' 初始化函式 :param k:kNN演算法中的k ''' self.k = k # 用來存放訓練資料，型別為ndarray self.train_feature = None # 用來存放訓練標籤，型別為ndarray self.train_label = None def fit(self, feature, label): ''' kNN演算法的訓練過程 :param feature: 訓練集資料，型別為ndarray :param label: 訓練集標籤，型別為ndarray :return: 無返回 ''' #********* Begin *********# self.train_feature = feature self.train_label = label #********* End *********# def predict(self, feature): ''' kNN演算法的預測過程 :param feature: 測試集資料，型別為ndarray :return: 預測結果，型別為ndarray或list ''' #********* Begin *********# result = [] for data in feature: dist = np.sqrt(np.sum((self.train_feature - data) ** 2, axis = 1)) # 歐氏距離 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個鄰居中出現次數最多的label不止一個，要取總距離最小的label，這裡直接取第一個（懶得寫了 result.append(key) return result #********* End *********#
第2關：
from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import StandardScaler def classification(train_feature, train_label, test_feature): ''' 對test_feature進行紅酒分類 :param train_feature: 訓練集資料，型別為ndarray :param train_label: 訓練集標籤，型別為ndarray :param test_feature: 測試集資料，型別為ndarray :return: 測試集資料的分類結果 ''' #********* Begin *********# #例項化StandardScaler函式 scaler = StandardScaler() train_feature = scaler.fit_transform(train_feature) test_feature = scaler.transform(test_feature) #生成K近鄰分類器 clf = KNeighborsClassifier() #訓練分類器 clf.fit(train_feature, train_label) #進行預測 predict_result = clf.predict(test_feature) return predict_result #********* End **********#