kNN(k近邻)算法代码实现

目标：预测未知数据(或测试数据)X的分类y
批量kNN算法
1.输入一个待预测的X(一维或多维)给训练数据集,计算出训练集X_train中的每一个样本与其的距离
2.找到前k个距离该数据最近的样本-->所属的分类y_train
3.将前k近的样本进行统计，哪个分类多，则我们将x分类为哪个分类

# 准备阶段：

import numpy as np
# import matplotlib.pyplot as plt

raw_data_X = [[3.393533211, 2.331273381],
              [3.110073483, 1.781539638],
              [1.343808831, 3.368360954],
              [3.582294042, 4.679179110],
              [2.280362439, 2.866990263],
              [7.423436942, 4.696522875],
              [5.745051997, 3.533989803],
              [9.172168622, 2.511101045],
              [7.792783481, 3.424088941],
              [7.939820817, 0.791637231]
             ]
raw_data_y = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]

X_train = np.array(raw_data_X)
y_train = np.array(raw_data_y)

x = np.array([8.093607318, 3.365731514])

核心代码：

 目标：预测未知数据(或测试数据)X的分类y  
批量kNN算法  
1.输入一个待预测的X(一维或多维)给训练数据集,计算出训练集X_train中的每一个样本与其的距离  
2.找到前k个距离该数据最近的样本-->所属的分类y_train  
3.将前k近的样本进行统计，哪个分类多，则我们将x分类为哪个分类

from math import sqrt
from collections import Counter

# 已知X_train，y_train
# 预测x的分类
def predict(x, k=5):
    # 计算训练集每个样本与x的距离
    distances = [sqrt(np.sum((x-x_train)**2)) for x_train in X_train]  # 这里用了numpy的fancy方法，np.sum((x-x_train)**2)
    # 获得距离对应的索引，可以通过这些索引找到其所属分类y_train
    nearest = np.argsort(distances)
    # 得到前k近的分类y
    topK_y = [y_train[neighbor] for neighbor in nearest[:k]]
    # 投票的方式，得到一个字典，key是分类，value数个数
    votes = Counter(topK_y)
    # 取出得票第一名的分类
    return votes.most_common(1)[0][0]   # 得到y_predict

predict(x, k=6)

面向对象的方式，模仿sklearn中的方法实现kNN算法：

import numpy as np
from math import sqrt
from collections import Counter


class kNN_classify:
    def __init__(self, n_neighbor=5):
        self.k = n_neighbor
        self._X_train = None
        self._y_train = None

    def fit(self, X_train, y_train):
        self._X_train = X_train
        self._y_train = y_train
        return self

    def predict(self, X):
        '''接收多维数据，返回y_predict也是多维的'''
        y_predict = [self._predict(x) for x in X]
        # return y_predict
        return np.array(y_predict)  # 返回array的格式

    def _predict(self, x):
        '''接收一个待预测的x，返回y_predict'''
        distances = [sqrt(np.sum((x-x_train)**2)) for x_train in self._X_train]
        nearest = np.argsort(distances)
        topK_y = [self._y_train[neighbor] for neighbor in nearest[:self.k]]
        votes = Counter(topK_y)
        return votes.most_common(1)[0][0]

    def __repr__(self):
        return 'kNN_clf(k=%d)' % self.k