我正在尝试使用MATLAB和WEKA API从WEKA中获取类别，但类别值总是0。有什么想法吗？
我的数据集有241个属性，使用WEKA对这个数据集进行处理时，我得到了正确的结果。
首先创建训练和测试对象，然后构建分类器并执行classifyInstance操作。但这会得到错误的结果
train = [xtrain ytrain]; test = [xtest]; save ('train.txt','train','-ASCII'); save ('test.txt','test','-ASCII');%## pathsWEKA_HOME = 'C:\Program Files\Weka-3-7';javaaddpath([WEKA_HOME '\weka.jar']);fName = 'train.txt';%## read fileloader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );train = loader.getDataSet();train.setClassIndex( train.numAttributes()-1 );% setting class as nominalv(1) = java.lang.String('-R');v(2) = java.lang.String('242');options = cat(1,v(1:end));filter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions(options); filter.setInputFormat(train); train = filter.useFilter(train, filter);fName = 'test.txt';%## read fileloader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );test = loader.getDataSet();%## datasetrelationName = char(test.relationName);numAttr = test.numAttributes;numInst = test.numInstances;%## classificationclassifier = weka.classifiers.trees.J48();classifier.buildClassifier( train );fprintf('Classifier: %s %s\n%s', ... char(classifier.getClass().getName()), ... char(weka.core.Utils.joinOptions(classifier.getOptions())), ... char(classifier.toString()) )classes =[];for i=1:numInst classes(i) = classifier.classifyInstance(test.instance(i-1));end这里有一个新的代码，但仍然不工作 – 类别值为0。使用相同算法和数据集的Weka输出是正常的
=== 按类别详细准确度 ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class 0.99 0.015 0.985 0.99 0.988 0.991 0 0.985 0.01 0.99 0.985 0.988 0.991 1Weighted Avg. 0.988 0.012 0.988 0.988 0.988 0.991
=== 混淆矩阵 ===
a b <-- classified as 1012 10 | a = 0 15 1003 | b = 1
ytest1 = ones(size(xtest,1),1); train = [xtrain ytrain]; test = [xtest ytest1]; save ('train.txt','train','-ASCII'); save ('test.txt','test','-ASCII');%## pathsWEKA_HOME = 'C:\Program Files\Weka-3-7';javaaddpath([WEKA_HOME '\weka.jar']);fName = 'train.txt';%## read fileloader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );train = loader.getDataSet();train.setClassIndex( train.numAttributes()-1 );v(1) = java.lang.String('-R');v(2) = java.lang.String('242');options = cat(1,v(1:end));filter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions(options); filter.setInputFormat(train); train = filter.useFilter(train, filter);fName = 'test.txt';%## read fileloader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );test = loader.getDataSet();filter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions( weka.core.Utils.splitOptions('-R last') );filter.setInputFormat(test); test = filter.useFilter(test, filter);%## datasetrelationName = char(test.relationName);numAttr = test.numAttributes;numInst = test.numInstances;%## classificationclassifier = weka.classifiers.trees.J48();classifier.buildClassifier( train );fprintf('Classifier: %s %s\n%s', ... char(classifier.getClass().getName()), ... char(weka.core.Utils.joinOptions(classifier.getOptions())), ... char(classifier.toString()) )classes = zeros(numInst,1);for i=1:numInst classes(i) = classifier.classifyInstance(test.instance(i-1)); end这里是Java中类别分布的代码片段
// output predictions System.out.println("# - actual - predicted - error - distribution"); for (int i = 0; i < test.numInstances(); i++) { double pred = cls.classifyInstance(test.instance(i)); double[] dist = cls.distributionForInstance(test.instance(i)); System.out.print((i+1)); System.out.print(" - "); System.out.print(test.instance(i).toString(test.classIndex())); System.out.print(" - "); System.out.print(test.classAttribute().value((int) pred)); System.out.print(" - "); if (pred != test.instance(i).classValue()) System.out.print("yes"); else System.out.print("no"); System.out.print(" - "); System.out.print(Utils.arrayToString(dist)); System.out.println();我将其转换为MATLAB代码如下
classes = zeros(numInst,1);for i=1:numInst pred = classifier.classifyInstance(test.instance(i-1)); classes(i) = str2num(char(test.classAttribute().value(( pred))));end但类别值输出不正确。
在你的回答中没有显示pred包含类别和predProb概率。请打印出来！！！
回答：
训练和测试数据必须具有相同数量的属性。因此在你的情况下，即使你不知道测试数据的实际类别，也可以使用虚拟值：
ytest = ones(size(xtest,1),1); %# dummy class values for test datatrain = [xtrain ytrain];test = [xtest ytest];save ('train.txt','train','-ASCII'); save ('test.txt','test','-ASCII');不要忘记在加载测试数据集时将其转换为名义属性（就像你对训练数据集所做的那样）：
filter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions( weka.core.Utils.splitOptions('-R last') );filter.setInputFormat(test); test = filter.useFilter(test, filter);最后，你可以调用训练好的J48分类器来预测测试实例的类别值：
classes = zeros(numInst,1);for i=1:numInst classes(i) = classifier.classifyInstance(test.instance(i-1));end编辑
如果不知道你正在处理的数据，很难判断…
所以让我用一个完整的例子来说明。我将使用MATLAB中的Fisher Iris数据（4个属性，150个实例，3个类别）来创建数据集。
%# load dataset (data + labels)load fisheririsX = meas;Y = grp2idx(species);%# partition the data into training/testingc = cvpartition(Y, 'holdout',1/3);xtrain = X(c.training,:);ytrain = Y(c.training);xtest = X(c.test,:);ytest = Y(c.test); %# or dummy values%# save as space-delimited text filetrain = [xtrain ytrain];test = [xtest ytest];save train.txt train -asciisave test.txt test -ascii在这里我应该提到，在使用NumericToNominal过滤器之前，确保两个数据集中完全表示了类别值是很重要的。否则，训练集和测试集可能会不兼容。我的意思是，你必须在每个数据集中至少有一个每个类别值的实例。因此，如果你使用虚拟值，我们可以这样做：
ytest = ones(size(xtest,1),1);v = unique(Y);ytest(1:numel(v)) = v;接下来，让我们使用Weka API读取新创建的文件。我们将最后一个属性从数值转换为名义属性（以启用分类）：
%# read train/test files using WekafName = 'train.txt';loader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );train = loader.getDataSet();train.setClassIndex( train.numAttributes()-1 );fName = 'test.txt';loader = weka.core.converters.MatlabLoader();loader.setFile( java.io.File(fName) );test = loader.getDataSet();test.setClassIndex( test.numAttributes()-1 );%# convert last attribute (class) from numeric to nominalfilter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions( weka.core.Utils.splitOptions('-R last') );filter.setInputFormat(train); train = filter.useFilter(train, filter);filter = weka.filters.unsupervised.attribute.NumericToNominal();filter.setOptions( weka.core.Utils.splitOptions('-R last') );filter.setInputFormat(test); test = filter.useFilter(test, filter);现在我们训练一个J48分类器，并使用它来预测测试实例的类别：
%# train a J48 treeclassifier = weka.classifiers.trees.J48();classifier.setOptions( weka.core.Utils.splitOptions('-c last -C 0.25 -M 2') );classifier.buildClassifier( train );%# classify test instancesnumInst = test.numInstances();pred = zeros(numInst,1);predProbs = zeros(numInst, train.numClasses());for i=1:numInst pred(i) = classifier.classifyInstance( test.instance(i-1) ); predProbs(i,:) = classifier.distributionForInstance( test.instance(i-1) );end最后，我们评估训练模型在测试数据上的表现（这应该与你在Weka Explorer中看到的类似）。显然，这只有在测试实例具有真实类别值（而不是虚拟值）时才有意义：
eval = weka.classifiers.Evaluation(train);eval.evaluateModel(classifier, test, javaArray('java.lang.Object',1));fprintf('=== Run information ===\n\n')fprintf('Scheme: %s %s\n', ... char(classifier.getClass().getName()), ... char(weka.core.Utils.joinOptions(classifier.getOptions())) )fprintf('Relation: %s\n', char(train.relationName))fprintf('Instances: %d\n', train.numInstances)fprintf('Attributes: %d\n\n', train.numAttributes)fprintf('=== Classifier model ===\n\n')disp( char(classifier.toString()) )fprintf('=== Summary ===\n')disp( char(eval.toSummaryString()) )disp( char(eval.toClassDetailsString()) )disp( char(eval.toMatrixString()) )