第一关：
#encoding=utf8
import numpy as np
from collections import Counter
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 = feature
self.train_label = 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 *********#
result = []
for data in feature:
dist = np.sqrt(np.sum((self.train_feature - data) ** 2, axis = 1)) # 欧氏距离
neighbor = np.argsort(dist)[0 : self.k]
kLabel = (self.train_label[i] for i in neighbor)
key, value = Counter(kLabel).most_common(1)[0] # 如果k个邻居中出现次数最多的label不止一个，要取总距离最小的label，这里直接取第一个（懒得写了
result.append(key)
return result
#********* End *********#
第2关：
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 *********#
#实例化StandardScaler函数
scaler = StandardScaler()
train_feature = scaler.fit_transform(train_feature)
test_feature = scaler.transform(test_feature)
#生成K近邻分类器
clf = KNeighborsClassifier()
#训练分类器
clf.fit(train_feature, train_label)
#进行预测
predict_result = clf.predict(test_feature)
return predict_result
#********* End **********#
标签：kNN,educoder,self,feature,param,label,---,train,ndarray From： https://www.cnblogs.com/kafuuchino/p/18278862