KNN to use Euclidean distance
The following disadvantages KNN classification is easy to make an error (such as black dots below)
The following are three examples demo KNN algorithm,
The first example is implemented in accordance with the principles of the algorithm
import matplotlib.pyplot as plt import numpy as np import operator # 已知分类的数据 x1 = np.array([3,2,1]) y1 = np.array([104,100,81]) x2 = np.array([101,99,98]) y2 = np.array([10,5,2]) scatter1 = plt.scatter(x1,y1,c='r') scatter2 = plt.scatter(x2,y2,c='b') # 未知数据 x = np.array([18]) y = np.array([90]) scatter3 = plt.scatter(x,y,c='k') #画图例 plt.legend(handles=[scatter1,scatter2,scatter3],labels=['labelA','labelB','X'],loc='best') plt.show() # 已知分类的数据 x_data = np.array([[3,104], [2,100], [1,81], [101,10], [99,5], [81,2]]) y_data = np.array(['A','A','A','B','B','B']) x_test = np.array([18,90]) # 计算样本数量 x_data_size = x_data.shape[0] print(x_data_size) # 复制x_test print(np.tile(x_test, (x_data_size,1))) # 计算x_test与每一个样本的差值 diffMat = np.tile(x_test, (x_data_size,1)) - x_data diffMat # 计算差值的平方 sqDiffMat = diffMat**2 sqDiffMat # 求和 sqDistances = sqDiffMat.sum(axis=1) sqDistances # 开方 distances = sqDistances**0.5 print(distances) # 从小到大排序 sortedDistances = distances.argsort()#返回distances里的数据从小到大的下标数组 print(sortedDistances) classCount = {} # 设置k k = 5 for i in range(k): # 获取标签 votelabel = y_data[sortedDistances[i]] # 统计标签数量 classCount[votelabel] = classCount.get(votelabel,0) + 1#)0表示没有该字典里没有该值时默认为0 classCount # 根据operator.itemgetter(1)-第1个值对classCount排序，然后再取倒序 sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True) print(sortedClassCount) # 获取数量最多的标签 knnclass = sortedClassCount[0][0]#第一个0表示取第一个键值对('A', 3)，第二个0表示取('A', 3)的‘A’ print(knnclass)
1 import numpy as np#对iris数据集进行训练分类 2 from sklearn import datasets 3 from sklearn.model_selection import train_test_split 4 from sklearn.metrics import classification_report,confusion_matrix#对模型分类结果进行评估的两个模型 5 import operator#https://blog.csdn.net/u010339879/article/details/98304292，关于operator的使用 6 import random 7 def knn(x_test, x_data, y_data, k): 8 x_data_size = x_data.shape[0] # 计算样本数量 9 diffMat = np.tile(x_test,(x_data_size,1)) - x_data# 复制x_test，计算x_test与每一个样本的差值 10 sqDiffMat = diffMat**2# # 计算差值的平方 11 sqDistance = sqDiffMat.sum(axis= 1) # 求和 12 distances = sqDistance**0.5 # 开方 13 sortedDistance = distances.argsort()# 从小到大排序 14 classCount = {} 15 for i in range(k): 16 vlabel = y_data[sortedDistance[i]] # 获取标签 17 classCount[vlabel] = classCount.get(vlabel,0)+1# 统计标签数量 18 sortedClassCount = sorted(classCount.items(),key = operator.itemgetter(1), reverse = True) # 根据operator.itemgetter(1)-第1个值对classCount排序，然后再取倒序 19 return sortedClassCount[0][0] 20 iris = datasets.load_iris()# 载入数据 21 x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target, test_size=0.3) 22 #打乱数据 23 # data_size = iris.data.shape[0] 24 # index = [i for i in range(data_size)] 25 # random.shuffle(index) 26 # iris.data = iris.data[index] 27 # iris.target = iris.target[index] 28 # test_size = 40#切分数据集 29 # x_train = iris.data[test_size:] 30 # x_test = iris.data[:test_size] 31 # y_train = iris.target[test_size:] 32 # y_test = iris.target[:test_size] 33 prodictions = [] 34 for i in range(x_test.shape[0]): 35 prodictions.append(knn(x_test[i],x_train,y_train,5)) 36 print(prodictions) 37 print(classification_report(y_test, prodictions)) 38 print(confusion_matrix(y_test,prodictions)) 39 #关于混淆矩阵可以看这篇博客，#https://www.cnblogs.com/missidiot/p/9450662.html
1 # 导入算法包以及数据集 2 from sklearn import neighbors 3 from sklearn import datasets 4 from sklearn.model_selection import train_test_split 5 from sklearn.metrics import classification_report 6 import random 7 # 载入数据 8 iris = datasets.load_iris() 9 #print(iris) 10 # 打乱数据切分数据集 11 # x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target, test_size=0.2) #分割数据0.2为测试数据，0.8为训练数据 12 13 #打乱数据 14 data_size = iris.data.shape[0] 15 index = [i for i in range(data_size)] 16 random.shuffle(index) 17 iris.data = iris.data[index] 18 iris.target = iris.target[index] 19 20 #切分数据集 21 test_size = 40 22 x_train = iris.data[test_size:] 23 x_test = iris.data[:test_size] 24 y_train = iris.target[test_size:] 25 y_test = iris.target[:test_size] 26 27 # 构建模型 28 model = neighbors.KNeighborsClassifier(n_neighbors=3) 29 model.fit(x_train, y_train) 30 prediction = model.predict(x_test) 31 print(prediction) 32 print(classification_report(y_test, prediction))
这三个代码第一个，第二个是根据底层原理实现knn算法，第三个则是调用库函数处理数据。
下面一个代码是利用第三个代码中用到的库实现第一个代码功能，可以发现使用系统提供的库，简单许多
1 from sklearn import neighbors 2 from sklearn.model_selection import train_test_split 3 from sklearn.metrics import classification_report 4 import numpy as np 5 x_data = np.array([[3,104], 6 [2,100], 7 [1,81], 8 [101,10], 9 [99,5], 10 [81,2]]) 11 y_data = np.array(['A','A','A','B','B','B']) 12 x_test1 = np.array([[18,90]]) 13 x_train, x_test, y_train,y_test = train_test_split(x_data, y_data,test_size= 0.3) 14 model = neighbors.KNeighborsClassifier(n_neighbors=3) 15 model.fit(x_train, y_train) 16 print(x_test1) 17 prediction = model.predict(x_test1) 18 print(prediction)