/*对已知数据进行测试,统计预测的正确率*/ KnnDatasetClassification(K); /*对位置数据进行预测*/ KnnPredict(K); return 1; } 代码输出如下: .KnnDatasetClassification. ..load training digits. ..Done. ..load testing digits. ..Done. ..Cal CalculationData begin. Correct radio: 0: ( 1 / 20 ) ...
简单KNN分类:param inX:待分类向量:param dataSet:训练样本集:param labels:训练样本标签:param k:K值:return:""" dataSetSize=dataSet.shape[0]# 训练样本个数 diffMat=np.tile(inX,(dataSetSize,1))-dataSet # 计算训练样本和待分类样本的数值差值 # 用于后面计算欧式距离,欧氏距离为各维度上数值差值的平方和...
": [9, 38, 2, "爱情片"], "新步步惊心": [8, 34, 17, "爱情片"]} return learning_dataset def kNN(learning_dataset,dataPoint,k): ''' kNN算法,返回k个邻居的类别和得到的测试数据的类别 ''' # s1:计算一个新样本与数据集中所有数据的距离 disList=[] for key,v in learning_dataset....
def kNNClassify(newInput, dataSet, labels, k): numSamples = dataSet.shape[0] # shape[0]表示行数 # tile(A, reps): 构造一个矩阵,通过A重复reps次得到 # the following copy numSamples rows for dataSet diff = np.tile(newInput, (numSamples, 1)) - dataSet # 按元素求差值 squaredDiff = d...
Therefore the privacy preserving k-NN classification is maintained over encrypted data in the cloud. KeywordsClassification, Dataset, Encryption, Regression, Privacy, Security.S. DhivyaM. MalarP. Anusuya
public KnnClassification(String paraFilename) { try { FileReader fileReader=new FileReader(paraFilename); dataset=new Instances(fileReader); dataset.setClassIndex(dataset.numAttributes()-1); fileReader.close(); }catch (Exception ee) { System.out.println("Error occurred while trying to read \'" ...
right) class KDTree(object): def __init__(self, data): k = len(data[0]) # 数据维度 def create(dataset, sp): if len(dataset) ==0: # 如果数据为空 return None # sort by current dimension dataset = sorted(dataset, key=lambda x: x[sp]) #以sp层做为目标点,作升序 mid = len(...
在机器学习领域,KNN(K-Nearest Neighbors,K近邻算法)是一种简单而有效的分类算法。本文将通过Scikit-Learn工具包实现KNN算法对鸢尾花数据集的分类任务,涵盖数据加载、预处理、模型构建、训练、测试及结果可视化等核心步骤。 一、数据加载与预处理 鸢尾花数据集(Iris Dataset)是机器学习领域中最经典的分类数据集之一,包...
importnumpy as npimportmatplotlib.pyplot as pltfromsklearnimportneighbors, datasetsfromsklearn.model_selectionimporttrain_test_splitdefload_classification_data():#使用 scikit-learn 自带的手写识别数据集 Digit Datasetdigits=datasets.load_digits()
plt.scatter(X[:,0],X[:,1],c=y,cmap=cmap_bold,edgecolor='k',s=20)plt.xlim(xx.min(),xx.max())plt.ylim(yy.min(),yy.max())plt.title("3-Class classification (k = %i)"%k)plt.show() Step4: 原理简析 如果选择较小的K值,就相当于用较小的领域中的训练实例进行预测,例如当k=1的...