最邻近规则KNN分类应用

数据集介绍

虹膜

150个实例

萼片长度，萼片宽度，花瓣长度，花瓣宽度 (sepal length, sepal width, petal length and petal width）

类别： Iris setosa, Iris versicolor, Iris virginica.

利用python机器学习库sklearn:SkLearnExample.py

# 从sklearn中导入neighbors模块 from sklearn import neighbors # 导入已经存在的数据集 from sklearn import datasets # 调用KNN分类器 knn = neighbors.KNeighborsClassifier() # 得到iris数据库 iris = datasets.load_iris() print iris # 第一个参数为特征值,第二个参数为前面每一行对应的分类结果;建立模型 knn.fit(iris.data, iris.target) # 通过建立好的模型,对新的花瓣类别进行预测 predictedLabel = knn.predict([[0.1, 0.2, 0.3, 0.4]]) print predictedLabel

Pyhton sklearn中datasets的Iris数据集

可以看到数据集主要是一个字典主要分为两部分,第一部分data是一个矩阵包含了:萼片长度，萼片宽度，花瓣长度，花瓣宽度(sepal length, sepal width, petal length and petal width),共四个纬度,150个数据;第二部部分是target是一个一维的结果数组

{'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='|S10'), 'data': array([[ 5.1, 3.5, 1.4, 0.2], [ 4.9, 3. , 1.4, 0.2], [ 4.7, 3.2, 1.3, 0.2], [ 4.6, 3.1, 1.5, 0.2], [ 5. , 3.6, 1.4, 0.2], [ 5.4, 3.9, 1.7, 0.4], [ 4.6, 3.4, 1.4, 0.3], [ 5. , 3.4, 1.5, 0.2], [ 4.4, 2.9, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5.4, 3.7, 1.5, 0.2], [ 4.8, 3.4, 1.6, 0.2], [ 4.8, 3. , 1.4, 0.1], [ 4.3, 3. , 1.1, 0.1], [ 5.8, 4. , 1.2, 0.2], [ 5.7, 4.4, 1.5, 0.4], [ 5.4, 3.9, 1.3, 0.4], [ 5.1, 3.5, 1.4, 0.3], [ 5.7, 3.8, 1.7, 0.3], [ 5.1, 3.8, 1.5, 0.3], [ 5.4, 3.4, 1.7, 0.2], [ 5.1, 3.7, 1.5, 0.4], [ 4.6, 3.6, 1. , 0.2], [ 5.1, 3.3, 1.7, 0.5], [ 4.8, 3.4, 1.9, 0.2], [ 5. , 3. , 1.6, 0.2], [ 5. , 3.4, 1.6, 0.4], [ 5.2, 3.5, 1.5, 0.2], [ 5.2, 3.4, 1.4, 0.2], [ 4.7, 3.2, 1.6, 0.2], [ 4.8, 3.1, 1.6, 0.2], [ 5.4, 3.4, 1.5, 0.4], [ 5.2, 4.1, 1.5, 0.1], [ 5.5, 4.2, 1.4, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 5. , 3.2, 1.2, 0.2], [ 5.5, 3.5, 1.3, 0.2], [ 4.9, 3.1, 1.5, 0.1], [ 4.4, 3. , 1.3, 0.2], [ 5.1, 3.4, 1.5, 0.2], [ 5. , 3.5, 1.3, 0.3], [ 4.5, 2.3, 1.3, 0.3], [ 4.4, 3.2, 1.3, 0.2], [ 5. , 3.5, 1.6, 0.6], [ 5.1, 3.8, 1.9, 0.4], [ 4.8, 3. , 1.4, 0.3], [ 5.1, 3.8, 1.6, 0.2], [ 4.6, 3.2, 1.4, 0.2], [ 5.3, 3.7, 1.5, 0.2], [ 5. , 3.3, 1.4, 0.2], [ 7. , 3.2, 4.7, 1.4], [ 6.4, 3.2, 4.5, 1.5], [ 6.9, 3.1, 4.9, 1.5], [ 5.5, 2.3, 4. , 1.3], [ 6.5, 2.8, 4.6, 1.5], [ 5.7, 2.8, 4.5, 1.3], [ 6.3, 3.3, 4.7, 1.6], [ 4.9, 2.4, 3.3, 1. ], [ 6.6, 2.9, 4.6, 1.3], [ 5.2, 2.7, 3.9, 1.4], [ 5. , 2. , 3.5, 1. ], [ 5.9, 3. , 4.2, 1.5], [ 6. , 2.2, 4. , 1. ], [ 6.1, 2.9, 4.7, 1.4], [ 5.6, 2.9, 3.6, 1.3], [ 6.7, 3.1, 4.4, 1.4], [ 5.6, 3. , 4.5, 1.5], [ 5.8, 2.7, 4.1, 1. ], [ 6.2, 2.2, 4.5, 1.5], [ 5.6, 2.5, 3.9, 1.1], [ 5.9, 3.2, 4.8, 1.8], [ 6.1, 2.8, 4. , 1.3], [ 6.3, 2.5, 4.9, 1.5], [ 6.1, 2.8, 4.7, 1.2], [ 6.4, 2.9, 4.3, 1.3], [ 6.6, 3. , 4.4, 1.4], [ 6.8, 2.8, 4.8, 1.4], [ 6.7, 3. , 5. , 1.7], [ 6. , 2.9, 4.5, 1.5], [ 5.7, 2.6, 3.5, 1. ], [ 5.5, 2.4, 3.8, 1.1], [ 5.5, 2.4, 3.7, 1. ], [ 5.8, 2.7, 3.9, 1.2], [ 6. , 2.7, 5.1, 1.6], [ 5.4, 3. , 4.5, 1.5], [ 6. , 3.4, 4.5, 1.6], [ 6.7, 3.1, 4.7, 1.5], [ 6.3, 2.3, 4.4, 1.3], [ 5.6, 3. , 4.1, 1.3], [ 5.5, 2.5, 4. , 1.3], [ 5.5, 2.6, 4.4, 1.2], [ 6.1, 3. , 4.6, 1.4], [ 5.8, 2.6, 4. , 1.2], [ 5. , 2.3, 3.3, 1. ], [ 5.6, 2.7, 4.2, 1.3], [ 5.7, 3. , 4.2, 1.2], [ 5.7, 2.9, 4.2, 1.3], [ 6.2, 2.9, 4.3, 1.3], [ 5.1, 2.5, 3. , 1.1], [ 5.7, 2.8, 4.1, 1.3], [ 6.3, 3.3, 6. , 2.5], [ 5.8, 2.7, 5.1, 1.9], [ 7.1, 3. , 5.9, 2.1], [ 6.3, 2.9, 5.6, 1.8], [ 6.5, 3. , 5.8, 2.2], [ 7.6, 3. , 6.6, 2.1], [ 4.9, 2.5, 4.5, 1.7], [ 7.3, 2.9, 6.3, 1.8], [ 6.7, 2.5, 5.8, 1.8], [ 7.2, 3.6, 6.1, 2.5], [ 6.5, 3.2, 5.1, 2. ], [ 6.4, 2.7, 5.3, 1.9], [ 6.8, 3. , 5.5, 2.1], [ 5.7, 2.5, 5. , 2. ], [ 5.8, 2.8, 5.1, 2.4], [ 6.4, 3.2, 5.3, 2.3], [ 6.5, 3. , 5.5, 1.8], [ 7.7, 3.8, 6.7, 2.2], [ 7.7, 2.6, 6.9, 2.3], [ 6. , 2.2, 5. , 1.5], [ 6.9, 3.2, 5.7, 2.3], [ 5.6, 2.8, 4.9, 2. ], [ 7.7, 2.8, 6.7, 2. ], [ 6.3, 2.7, 4.9, 1.8], [ 6.7, 3.3, 5.7, 2.1], [ 7.2, 3.2, 6. , 1.8], [ 6.2, 2.8, 4.8, 1.8], [ 6.1, 3. , 4.9, 1.8], [ 6.4, 2.8, 5.6, 2.1], [ 7.2, 3. , 5.8, 1.6], [ 7.4, 2.8, 6.1, 1.9], [ 7.9, 3.8, 6.4, 2. ], [ 6.4, 2.8, 5.6, 2.2], [ 6.3, 2.8, 5.1, 1.5], [ 6.1, 2.6, 5.6, 1.4], [ 7.7, 3. , 6.1, 2.3], [ 6.3, 3.4, 5.6, 2.4], [ 6.4, 3.1, 5.5, 1.8], [ 6. , 3. , 4.8, 1.8], [ 6.9, 3.1, 5.4, 2.1], [ 6.7, 3.1, 5.6, 2.4], [ 6.9, 3.1, 5.1, 2.3], [ 5.8, 2.7, 5.1, 1.9], [ 6.8, 3.2, 5.9, 2.3], [ 6.7, 3.3, 5.7, 2.5], [ 6.7, 3. , 5.2, 2.3], [ 6.3, 2.5, 5. , 1.9], [ 6.5, 3. , 5.2, 2. ], [ 6.2, 3.4, 5.4, 2.3], [ 5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'DESCR': 'Iris Plants Database\n====================\n\nNotes\n-----\nData Set Characteristics:\n :Number of Instances: 150 (50 in each of three classes)\n :Number of Attributes: 4 numeric, predictive attributes and the class\n :Attribute Information:\n - sepal length in cm\n - sepal width in cm\n - petal length in cm\n - petal width in cm\n - class:\n - Iris-Setosa\n - Iris-Versicolour\n - Iris-Virginica\n :Summary Statistics:\n\n ============== ==== ==== ======= ===== ====================\n Min Max Mean SD Class Correlation\n ============== ==== ==== ======= ===== ====================\n sepal length: 4.3 7.9 5.84 0.83 0.7826\n sepal width: 2.0 4.4 3.05 0.43 -0.4194\n petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n ============== ==== ==== ======= ===== ====================\n\n :Missing Attribute Values: None\n :Class Distribution: 33.3% for each of 3 classes.\n :Creator: R.A. Fisher\n :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n :Date: July, 1988\n\nThis is a copy of UCI ML iris datasets.\nhttp://archive.ics.uci.edu/ml/datasets/Iris\n\nThe famous Iris database, first used by Sir R.A Fisher\n\nThis is perhaps the best known database to be found in the\npattern recognition literature. Fisher\'s paper is a classic in the field and\nis referenced frequently to this day. (See Duda & Hart, for example.) The\ndata set contains 3 classes of 50 instances each, where each class refers to a\ntype of iris plant. One class is linearly separable from the other 2; the\nlatter are NOT linearly separable from each other.\n\nReferences\n----------\n - Fisher,R.A. "The use of multiple measurements in taxonomic problems"\n Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to\n Mathematical Statistics" (John Wiley, NY, 1950).\n - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System\n Structure and Classification Rule for Recognition in Partially Exposed\n Environments". IEEE Transactions on Pattern Analysis and Machine\n Intelligence, Vol. PAMI-2, No. 1, 67-71.\n - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions\n on Information Theory, May 1972, 431-433.\n - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II\n conceptual clustering system finds 3 classes in the data.\n - Many, many more ...\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}

预测结果 predictedLabel = knn.predict([[0.1, 0.2, 0.3, 0.4]]) print predictedLabel

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独自实现KNN算法

# coding=utf-8 import csv import random import math import operator # 加载数据集,将原始数据集分为训练集和测试集 # filename数据集所在的文件名 # split根据此数值将数据集分为测试集和训练集 # trainingSet训练集,testSet测试集 def loadDataset(filename, split, trainingSet = [], testSet = []): # 读取文件为csvfile with open(filename, 'rb') as csvfile: # 把读进来的文件转为行的格式 lines = csv.reader(csvfile) # 把读进来的所有行转换成list的格式 dataset = list(lines) # 将数据集分为训练集与测试集 for x in range(len(dataset)-1): # y :0,1,2,3 for y in range(4): # 将加载数据由string转为double dataset[x][y] = float(dataset[x][y]) if random.random() < split: trainingSet.append(dataset[x]) else: testSet.append(dataset[x]) # 计算距离 # instance12分别为两个实例 # length为要计算的维度 def euclideanDistance(instance1, instance2, length): distance = 0 for x in range(length): distance += pow((instance1[x]-instance2[x]), 2) return math.sqrt(distance) # 返回最近的K个label,从训练集中选出k个离测试实例最近的实例 # trainingSet训练集 # testInstance测试实例 # k选出k个 def getNeighbors(trainingSet, testInstance, k): distances = [] length = len(testInstance)-1 for x in range(len(trainingSet)): #testinstance dist = euclideanDistance(testInstance, trainingSet[x], length) distances.append((trainingSet[x], dist)) #distances.append(dist) # 从小到大排序 distances.sort(key=operator.itemgetter(1)) neighbors = [] for x in range(k): neighbors.append(distances[x][0]) return neighbors # 根据邻居得到邻居所属类别最多分类 def getResponse(neighbors): # print neighbors classVotes = {} for x in range(len(neighbors)): # -1代表取最后一个值 response = neighbors[x][-1] if response in classVotes: classVotes[response] += 1 else: classVotes[response] = 1 sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True) return sortedVotes[0][0] # 预测分类后正确率 def getAccuracy(testSet, predictions): correct = 0 for x in range(len(testSet)): if testSet[x][-1] == predictions[x]: correct += 1 return (correct/float(len(testSet)))*100.0 def main(): # 准备数据和数据预处理 trainingSet = [] testSet = [] split = 0.67 loadDataset(r'irisdata.txt', split, trainingSet, testSet) print 'Train set: ' + repr(len(trainingSet)) print 'Test set: ' + repr(len(testSet)) # 预测结果 predictions = [] k = 3 for x in range(len(testSet)): # trainingsettrainingSet[x] neighbors = getNeighbors(trainingSet, testSet[x], k) result = getResponse(neighbors) predictions.append(result) print ('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1])) print ('predictions: ' + repr(predictions)) accuracy = getAccuracy(testSet, predictions) print('Accuracy: ' + repr(accuracy) + '%') if __name__ == '__main__': main()

运行结果

Train set: 100 Test set: 50 >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-setosa', actual='Iris-setosa' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-virginica', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-versicolor', actual='Iris-versicolor' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-versicolor', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' >predicted='Iris-virginica', actual='Iris-virginica' predictions: ['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-versicolor', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-versicolor', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica', 'Iris-virginica'] Accuracy: 96.0%