Java Code Examples for weka.core.Instances#remove()
The following examples show how to use
weka.core.Instances#remove() .
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Example 1
| Source File: DataProcessing.java From tsml with GNU General Public License v3.0 | 5 votes |
|
public static void mergeEpilepsy(){
Instances x,y,z;
Instances all;
String sourcePath="C:\\Users\\ajb\\Dropbox\\TSC Problems\\EpilepsyX\\";
String destPath="C:\\Users\\ajb\\Dropbox\\Multivariate TSC Problems\\HAR\\Epilepsy\\";
x=DatasetLoading.loadDataNullable(sourcePath+"EpilepsyX_ALL");
y=DatasetLoading.loadDataNullable(sourcePath+"EpilepsyY_ALL");
z=DatasetLoading.loadDataNullable(sourcePath+"EpilepsyZ_ALL");
//Delete the use ID, will reinsert manually after
x.deleteAttributeAt(0);
y.deleteAttributeAt(0);
z.deleteAttributeAt(0);
all=utilities.multivariate_tools.MultivariateInstanceTools.mergeToMultivariateInstances(new Instances[]{x,y,z});
// OutFile out=new OutFile(destPath+"EpilepsyNew.arff");
// out.writeString(all.toString());
//Create train test splits so participant 1,2,3 in train and 4,5,6 in test
int trainSize=149;
int testSize=126;
Instances train= new Instances(all,0);
Instances test= new Instances(all);
for(int i=0;i<trainSize;i++){
Instance t= test.remove(0);
train.add(t);
}
OutFile tr=new OutFile(destPath+"Epilepsy_TRAIN.arff");
OutFile te=new OutFile(destPath+"Epilepsy_TEST.arff");
tr.writeString(train.toString());
te.writeString(test.toString());
}
Example 2
| Source File: MultivariateProcessing.java From tsml with GNU General Public License v3.0 | 5 votes |
|
public static void mergeEpilepsy(){
Instances x,y,z;
Instances all;
String sourcePath="C:\\Users\\ajb\\Dropbox\\TSC Problems\\EpilepsyX\\";
String destPath="C:\\Users\\ajb\\Dropbox\\Multivariate TSC Problems\\HAR\\Epilepsy\\";
x=DatasetLoading.loadData(sourcePath+"EpilepsyX_ALL");
y=DatasetLoading.loadData(sourcePath+"EpilepsyY_ALL");
z=DatasetLoading.loadData(sourcePath+"EpilepsyZ_ALL");
//Delete the use ID, will reinsert manually after
x.deleteAttributeAt(0);
y.deleteAttributeAt(0);
z.deleteAttributeAt(0);
all=utilities.multivariate_tools.MultivariateInstanceTools.mergeToMultivariateInstances(new Instances[]{x,y,z});
// OutFile out=new OutFile(destPath+"EpilepsyNew.arff");
// out.writeString(all.toString());
//Create train test splits so participant 1,2,3 in train and 4,5,6 in test
int trainSize=149;
int testSize=126;
Instances train= new Instances(all,0);
Instances test= new Instances(all);
for(int i=0;i<trainSize;i++){
Instance t= test.remove(0);
train.add(t);
}
OutFile tr=new OutFile(destPath+"Epilepsy_TRAIN.arff");
OutFile te=new OutFile(destPath+"Epilepsy_TEST.arff");
tr.writeString(train.toString());
te.writeString(test.toString());
}
Example 3
| Source File: LeaveOneOutCV.java From NLIWOD with GNU Affero General Public License v3.0 | 4 votes |
|
public static void main(String[] args) throws Exception {
/*
* For multilable classification:
*/
//load the data
Path datapath= Paths.get("./src/main/resources/old/Qald6Logs.arff");
BufferedReader reader = new BufferedReader(new FileReader(datapath.toString()));
ArffReader arff = new ArffReader(reader);
/*
* Test the trained system
*/
// JSONObject qald6test = loadTestQuestions();
// JSONArray questions = (JSONArray) qald6test.get("questions");
// ArrayList<String> testQuestions = Lists.newArrayList();
// for(int i = 0; i < questions.size(); i++){
// JSONObject questionData = (JSONObject) questions.get(i);
// JSONArray questionStrings = (JSONArray) questionData.get("question");
// JSONObject questionEnglish = (JSONObject) questionStrings.get(0);
// testQuestions.add((String) questionEnglish.get("string"));
// }
Instances data = arff.getData();
data.setClassIndex(6);
System.out.println();
double cv_ave = 0;
ArrayList<String> systems = Lists.newArrayList("KWGAnswer", "NbFramework", "PersianQA", "SemGraphQA", "UIQA_withoutManualEntries", "UTQA_English" );
for(int i = 0; i < 100; i++){
Instance testquestion = data.get(i);
data.remove(i);
PSt classifier = new PSt();
classifier.buildClassifier(data);
double[] confidences = classifier.distributionForInstance(testquestion);
int argmax = -1;
double max = -1;
for(int j = 0; j < 6; j++){
if(confidences[j]>max){
max = confidences[j];
argmax = j;
}
}
String sys2ask = systems.get(systems.size() - argmax -1);
float p = Float.parseFloat(loadSystemP(sys2ask).get(i));
float r = Float.parseFloat(loadSystemR(sys2ask).get(i));
double f = 0;
if(p>0&&r>0){f = 2*p*r/(p + r);}
cv_ave += f;
data.add(i, testquestion);
}
System.out.println(cv_ave/100);
}
Example 4
| Source File: CNode.java From meka with GNU General Public License v3.0 | 4 votes |
|
/**
* Main - run some tests.
*/
public static void main(String args[]) throws Exception {
Instances D = new Instances(new FileReader(args[0]));
Instance x = D.lastInstance();
D.remove(D.numInstances()-1);
int L = Integer.parseInt(args[1]);
D.setClassIndex(L);
double y[] = new double[L];
Random r = new Random();
int s[] = new int[]{1,0,2};
int PA_J[][] = new int[][]{
{},{},{0,1},
};
//MLUtils.randomize(s,r);
// MUST GO IN TREE ORDER !!
for(int j : s) {
int pa_j[] = PA_J[j];
System.out.println("PARENTS = "+Arrays.toString(pa_j));
//MLUtils.randomize(pa_j,r);
System.out.println("**** TRAINING ***");
CNode n = new CNode(j,null,pa_j);
n.build(D,new SMO());
/*
*/
//Instances D_ = n.transform(D);
//n.T = D_;
System.out.println("============== D_"+j+" / class = "+n.T.classIndex()+" =");
System.out.println(""+n.T);
System.out.println("**** TESTING ****");
/*
Instance x_ = MLUtils.setTemplate(x,(Instance)D_.firstInstance().copy(),D_);
for(int pa : pa_j) {
//System.out.println(""+map[pa]);
x_.setValue(n.map[pa],y[pa]);
}
//x_.setDataset(T);
x_.setClassMissing();
*/
//n.T = D_;
Instance x_ = n.transform(x,y);
System.out.println(""+x_);
y[j] = 1;
}
}
