自己參考Bobo老師寫得程式碼：

主要分為四個檔案： knn.py中實現KNN演算法、model_selection.py封裝了樣本資料的一些工具方法，比如切分為訓練集和測試集；

metrics用來對模型進行評估、client用來呼叫演算法進行執行

# -*- encoding: utf-8 -*-
"""
實現KNN的分類演算法
"""
import numpy as np
from math import sqrt
from collections import Counter
from metrics import accuracy_score


class KnnClassifier(object):
    """
    K-近鄰演算法,（K Nearest Neighbour），簡稱KNN
    """

    def __init__(self, k):
        """
        K表示
        :param k: 表示參考的個數
        """
        self.k = k

    def fit(self, X_train, y_train):
        """
        利用輸入的樣本集進行訓練KNN演算法
        :param X_train: X 訓練樣本集
        :param y_train: y
        :return:
        """
        self.X_train = X_train
        self.y_train = y_train

        return self

    def predict(self, x_test):
        """
        對於輸入的測試樣本x進行預測
        :param x_test: 這個一個行向量
        :return:
        """

        assert x_test.shape[1] == self.X_train.shape[1], u'預測樣本和訓練樣本的特徵值不相等'
        # step1 用歐幾里得演算法計算x與周邊的距離
        pridect_list = []
        for one_x in x_test:
            distances = [sqrt(np.sum((x - one_x) ** 2)) for x in self.X_train]
            sorted_index = np.argsort(distances)
            fit_y = self.y_train[sorted_index[:self.k]]
            target_label = Counter(fit_y).most_common()[0][0]
            pridect_list.append(target_label)
        return np.asarray(pridect_list, dtype='int32')

    def scores(self, y_pridect, y_test):

        return accuracy_score(y_pridect, y_test)

    def __repr__(self):
        return 'knn(k=%s)' % self.k
 

Metrics檔案：

# -*- encoding: utf-8 -*-
"""
這個檔案主要是計算一些指標比如準確度，用來評估演算法的好還
"""

import numpy as np

def accuracy_score(y_test, y_pridect):
    """
    用來計算準確度
    :param y_test: 樣本的標記測試集和
    :param y_pridect: 預測集
    :return:
    """

    assert y_pridect.shape[0] == y_test.shape[0], u'測試集和預測集的資料個數不相等'

    cnt = np.sum(y_test==y_pridect)

    return cnt / len(y_pridect)

 

model_selection.py檔案：

# -*- encoding: utf-8 -*-
"""
這個檔案主要是計算一些指標比如準確度，用來評估演算法的好還
"""

import numpy as np

def accuracy_score(y_test, y_pridect):
    """
    用來計算準確度
    :param y_test: 樣本的標記測試集和
    :param y_pridect: 預測集
    :return:
    """

    assert y_pridect.shape[0] == y_test.shape[0], u'測試集和預測集的資料個數不相等'

    cnt = np.sum(y_test==y_pridect)

    return cnt / len(y_pridect)

 

client檔案進行測試：

from knn import KnnClassifier

from sklearn import datasets
from model_selection import train_test_split
from metrics import accuracy_score
import numpy as np

if __name__ == '__main__':
    knn = KnnClassifier(3)

    iris = datasets.load_iris()
    x_train, y_train, x_test, y_test = train_test_split(iris.data, iris.target, 0.7)
    classifier = knn.fit(x_train, y_train)
    y_pridect = classifier.predict(x_test)
    print(classifier.scores(y_pridect, y_test))