//Make a place holder Instances //If you already have access to one, you can skip this step Instances dataset = new Instances("testdata", attr, 1); dataset.setClassIndex(classIdx); DenseInstance newInst = new DenseInstance(1.0,values); //To associate your instance with Instances object, in this case dataset newInst.setDataset(dataset);
ArrayList<Attribute> attributes = new ArrayList<Attribute>(); attributes.add(new Attribute("x")); attributes.add(new Attribute("y")); attributes.add(new Attribute("z")); Instances dataRaw = new Instances("TestInstances", attributes , 0); dataRaw.setClassIndex(dataRaw.numAttributes() - 1); // Assuming z (z on lastindex) as classindex for (Double[] a: myValues) { dataRaw.add(new DenseInstance(1.0, a)); } // Then train or build the algorithm/model on instances (dataRaw) created above. MultilayerPerceptron mlp = new MultilayerPerceptron(); // Sample algorithm, go through about neural networks to use this or replace with appropriate algorithm. mlp.buildClassifier(dataRaw); // Create a test instance,I think you can create testinstance without // classindex value but cross check in weka as I forgot about it. double[] values = new double[]{-818.84, 9186.82, 2436.73}; // sample values DenseInstance testInstance = new DenseInstance(1.0, values); testInstance.setDataset(dataRaw); // To associate with instances object // now you can clasify double classify = mlp.classifyInstance(testInstance);
vals[1] = Utils.missingValue(); DenseInstance metaI = new DenseInstance(inst.weight(), vals); metaI.setDataset(m_fitLogisticStructure); return m_svmProbs.distributionForInstance(metaI);
vals[1] = Utils.missingValue(); DenseInstance metaI = new DenseInstance(inst.weight(), vals); metaI.setDataset(m_fitLogisticStructure); return m_svmProbs.distributionForInstance(metaI);
vals[1] = instance.classValue(); DenseInstance metaI = new DenseInstance(instance.weight(), vals); metaI.setDataset(m_fitLogisticStructure); m_svmProbs.updateClassifier(metaI);
vals[1] = instance.classValue(); DenseInstance metaI = new DenseInstance(instance.weight(), vals); metaI.setDataset(m_fitLogisticStructure); m_svmProbs.updateClassifier(metaI);
inst.setDataset(m_structure);
inst.setDataset(m_structure);
example.setDataset(format);
example.setDataset(format); format.add(example);
example.setDataset(format);
example.setDataset(format); format.add(example);
example.setDataset(format);
example.setDataset(format); format.add(example);
example.setDataset(format); format.add(example);
instNew.setDataset(result);
instNew.setDataset(result);
instNew.setDataset(result);
instNew.setDataset(result);
newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst); d.setDataset(m_classifiers[0][1].m_calibrationDataHeader); return m_classifiers[0][1].m_calibrator.distributionForInstance(d); newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst); d.setDataset(m_classifiers[i][j].m_calibrationDataHeader); r[i][j] = m_classifiers[i][j].m_calibrator.distributionForInstance(d)[0]; n[i][j] = m_classifiers[i][j].m_sumOfWeights;