从0开始的机器学习——knn算法篇（2）

scikit-learn中的knn算法：

from sklearn.neighbors import KNeighborsClassfier  //KNeighborsClassifier里面包含了封装好的knn算法

//sklearn里面的所有算法都是以面向对象的形式封装的，所以使用时候需要先创建一个对象。

 KNN_classifier = KNeighborsClassfier(n_neighbors=6)  //创建对象。n_neighbors就是k值。

 KNN_classifier.fit(X_train,y_train) //fit就是拟合过程。任何一个机器学习算法都需要fit。fit里面传入的参数就是训练集和标签集

KNN_classifier.predict() //这样模型就存在于该对象中了。括号内需要传入一个矩阵。如果需要预测的是一个数值，也要写成矩阵形式传入。

x_predict = x.reshape(1,-1)  //只有一个数据的话写成矩阵形式

y_predict = KNN_classifier.predict(x_predict) //进行预测

y_predict(0) //即可输出预测值

 

 

自己写并且封装一个knn算法：

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


class KNNClasssifier:
    def __init__(self,k):
        """初始化knn分类器"""
        assert k>=1,    "k must be valid"
        self.k = k
        self._X_train = None
        "_表示私有"
        self._y_train = None

    def fit(self,X_train,y_train):
        """根据训练数据集X_train和y_train来训练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
        "接收用户传入的参数并赋值给变量"
        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):
        """给定单个带预测数据x，返回x的预测值"""
        assert x.shape[0] == self._X_train.shape[1]
        "the feature number of x must be equal to X_train"

        distances = [sqrt(np.num((x_train - x) ** 2))

                    for x_train in self.X_train]

        nearest = np.argsort(distances)

        topK_y = [self.y_train[i] for i in nearest[:self.k]]

        votes = Counter(topK_y)

        return votes.most_common(1)[0][0]

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


然后就可以在Anaconda里用魔法命令调用了。