Python机器学习之底层实现KNN

import pandas as pd
def read_xlsx(csv_path):
    data = pd.read_csv(csv_path)
    print(data)
    return data

import numpy as np
def MinMaxScaler(data):
    col = data.shape[1]
    for i in range(0, col-1):
        arr = data.iloc[:, i]
        arr = np.array(arr) #将DataFrame形式转化为ndarray形式，方便后续用numpy计算
        min = np.min(arr)
        max = np.max(arr)
        arr = (arr-min)/(max-min)
        data.iloc[:, i] = arr
    return data

def train_test_split(data, test_size=0.2, random_state=None):
    col = data.shape[1]
    x = data.iloc[:, 0:col-1]
    y = data.iloc[:, -1]
    x = np.array(x)
    y = np.array(y)
    # 设置随机种子，当随机种子非空时，将锁定随机数
    if random_state:
        np.random.seed(random_state)
        # 将样本集的索引值进行随机打乱
        # permutation随机生成0-len(data)随机序列
    shuffle_indexs = np.random.permutation(len(x))
    # 提取位于样本集中20%的那个索引值
    test_size = int(len(x) * test_size)
    # 将随机打乱的20%的索引值赋值给测试索引
    test_indexs = shuffle_indexs[:test_size]
    # 将随机打乱的80%的索引值赋值给训练索引
    train_indexs = shuffle_indexs[test_size:]
    # 根据索引提取训练集和测试集
    x_train = x[train_indexs]
    y_train = y[train_indexs]
    x_test = x[test_indexs]
    y_test = y[test_indexs]
    # 将切分好的数据集返回出去
    # print(y_train)
    return x_train, x_test, y_train, y_test

def CountDistance(train,test,length):
    distance = 0
    for x in range(length):
        distance += pow(test[x] - train[x], 2)**0.5
    return distance

import numpy as np
#bincount（）：统计非负整数的个数，不能统计浮点数
counts = np.bincount(nums)
#返回众数
np.argmax(counts)

def getNeighbor(x_train,test,y_train,k):
    distance = []
    #测试集的维度
    length = x_train.shape[1]
    #测试集合所有训练集的距离
    for x in range(x_train.shape[0]):
        dist = CountDistance(test, x_train[x], length)
        distance.append(dist)
    distance = np.array(distance)
    #排序
    distanceSort = distance.argsort()
    # distance.sort(key= operator.itemgetter(1))
    # print(len(distance))
    # print(distanceSort[0])
    neighbors =[]
    for x in range(k):
        labels = y_train[distanceSort[x]]
        neighbors.append(labels)
        # print(labels)
    counts = np.bincount(neighbors)
    label = np.argmax(counts)
    # print(label)
    return label

getNeighbor(x_train,x_test[0],y_train,3)

def getAccuracy(x_test,x_train,y_train,y_test):
    result = []
    k = 3
    # arr_label = getNeighbor(x_train, x_test[0], y_train, k)
    for x in range(len(x_test)):
        arr_label = getNeighbor(x_train, x_test[x], y_train, k)
        result.append(arr_label)
    correct = 0
    for x in range(len(y_test)):
        if result[x] == y_test[x]:
           correct += 1
    # print(correct)
    accuracy = (correct / float(len(y_test))) * 100.0
    print("Accuracy:", accuracy, "%")
    return accuracy