机器学习 --- kNN算法_头歌实践平台:机器学习之knn算法

第1关：实现kNN算法

#encoding=utf8
import numpy as np
 
class kNNClassifier(object):
    def __init__(self, k):
        '''
        初始化函数
        :param k:kNN算法中的k
        '''
        self.k = k
        # 用来存放训练数据，类型为ndarray
        self.train_feature = None
        # 用来存放训练标签，类型为ndarray
        self.train_label = None
 
 
    def fit(self, feature, label):
        '''
        kNN算法的训练过程
        :param feature: 训练集数据，类型为ndarray
        :param label: 训练集标签，类型为ndarray
        :return: 无返回
        '''
 
        #********* Begin *********#
        self.train_feature = np.array(feature)
        self.train_label = np.array(label)
        #********* End *********#
 
 
    def predict(self, feature):
        '''
        kNN算法的预测过程
        :param feature: 测试集数据，类型为ndarray
        :return: 预测结果，类型为ndarray或list
        '''
 
        #********* Begin *********#
        def _predict(test_data):
            distances = [np.sqrt(np.sum((test_data - vec) ** 2)) for vec in self.train_feature]
            nearest = np.argsort(distances)
            topK = [self.train_label[i] for i in nearest[:self.k]]
            votes = {}
            result = None
            max_count = 0
            for label in topK:
                if label in votes.keys():
                    votes[label] += 1
                    if votes[label] > max_count:
                        max_count = votes[label]
                        result = label
                else:
                    votes[label] = 1
                    if votes[label] > max_count:
                        max_count = votes[label]
                        result = label
            return result
        predict_result = [_predict(test_data) for test_data in feature]
        return predict_result
 
        #********* End *********#

第2关：红酒分类

from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
 
def classification(train_feature, train_label, test_feature):
    '''
    对test_feature进行红酒分类
    :param train_feature: 训练集数据，类型为ndarray
    :param train_label: 训练集标签，类型为ndarray
    :param test_feature: 测试集数据，类型为ndarray
    :return: 测试集数据的分类结果
    '''
 
    #********* Begin *********#
    scaler = StandardScaler()
    train_feature = scaler.fit_transform(train_feature)
    test_feature = scaler.transform(test_feature)
 
    clf = KNeighborsClassifier()
    clf.fit(train_feature, train_label)
    return clf.predict(test_feature)
 
    #********* End **********#