我正在尝试使用 MATLAB 和 WEKA API 从 WEKA 检索类。一切看起来都很好，但课程总是0。有什么想法吗？？
我的数据集有 241 个属性，将 WEKA 应用到这个数据集我得到了正确的结果。
创建第一个训练和测试对象，然后构建分类器并执行分类实例。但这给出了错误的结果
train = [xtrain ytrain];
test = [xtest];
save ('train.txt','train','-ASCII');
save ('test.txt','test','-ASCII');
%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);
fName = 'train.txt';
%## read file
loader = weka.core.converters.MatlabLoader();
loader.setFile( java.io.File(fName) );
train = loader.getDataSet();
train.setClassIndex( train.numAttributes()-1 );
% setting class as nominal
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 file
loader = weka.core.converters.MatlabLoader();
loader.setFile( java.io.File(fName) );
test = loader.getDataSet();
%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;
%## classification
classifier = 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
这是一个新代码，但仍然无法正常工作 - 类 = 0。来自 Weka 的相同算法和数据集的输出正常
=== 按等级划分的详细准确度 ===
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 1
加权平均 0.988 0.012 0.988 0.988 0.988 0.991
=== 混淆矩阵 ===
ab <--分类为 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');
%## paths
WEKA_HOME = 'C:\Program Files\Weka-3-7';
javaaddpath([WEKA_HOME '\weka.jar']);
fName = 'train.txt';
%## read file
loader = 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 file
loader = 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);
%## dataset
relationName = char(test.relationName);
numAttr = test.numAttributes;
numInst = test.numInstances;
%## classification
classifier = 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
这是Java中类分发的代码片段
// 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();
我将它转换为这样的 MATLAB 代码
classes = zeros(numInst,1);
for i=1:numInst
pred = classifier.classifyInstance(test.instance(i-1));
classes(i) = str2num(char(test.classAttribute().value(( pred))));
end
但类输出不正确。
在您的回答中，您没有表明 pred 包含类和 predProb 概率。只需打印它！