KNN简单实现

 

KNN算是机器学习入门算法中比较容易理解的了，要注意和K-means的一些区别

KNN	K-Means
1.KNN是分类算法  2.监督学习  3.喂给它的数据集是带label的数据，已经是完全正确的数据	1.K-Means是聚类算法  2.非监督学习  3.喂给它的数据集是无label的数据，是杂乱无章的，经过聚类后才变得有点顺序，先无序，后有序
没有明显的前期训练过程，属于memory-based learning	有明显的前期训练过程
K的含义：来了一个样本x，要给它分类，即求出它的y，就从数据集中，在x附近找离它最近的K个数据点，这K个数据点，类别c占的个数最多，就把x的label设为c	K的含义：K是人工固定好的数字，假设数据集合可以分为K个簇，由于是依靠人工定好，需要一点先验知识

python简单实现

import numpy as np
from math import sqrt
from collections import Counter
from sklearn.metrics import accuracy_score


class kNNClassifier:

    def __init__(self, k):
        """初始化kNN分类器"""
        assert k >= 1,"k must be valid"
        self.k = k
        #前面加_的是私有变量,不能修改
        self._X_train = None
        self._y_train = None

    def fit(self, X_train, y_train):
        """根据训练数据集X_train和y_train训练kNN分类器"""
        assert X_train.shape[0] == y_train.shape[0],\
            "the size of X_train must be equal to the size of y_train"
        assert self.k <= X_train.shape[0],\
            "the size of X_train must be at least k"

        self._X_train = X_train
        self._y_train = y_train
        return self

    def predict(self, X_predict):
        """给定待预测数据集X_predict.返回表示X_predict的结果向量"""
        assert self._X_train is not None and self._y_train is not None,\
            "must fit before predict"
        assert X_predict.shape[1] == self._X_train.shape[1],\
            "the feature number of X_predict must be equal to X_train"
        y_predict = [self._predict(x) for x in X_predict]
        return np.array(y_predict)

    def _predict(self, x):
        """给定单个待预测数据x,返回x的预测结果值"""
        assert x.shape[0] == self._X_train.shape[1],\
            "the feature number of x must be equal to X_train"
        distance = [sqrt(np.sum((x_train - x)**2))
                    for x_train in self._X_train]
        nearest = np.argsort(distance)

        topK_y = [self._y_train[i] for i in nearest[:self.k]]
        votes = Counter(topK_y)

        return votes.most_common(1)[0][0]
    def score(self,X_test, y_test):
        """根据测试数据集X_test 和 y_test 确定当前模型的准确度"""
        y_predict = self.predict(X_test)
        return accuracy_score(y_test, y_predict)

    def __repr__(self):
        return "KNN(k=%d)"%self.k