List of usage examples for weka.core Instances deleteWithMissingClass
public void deleteWithMissingClass()
From source file:org.ssase.debt.classification.OnlineMultilayerPerceptron.java
License:Open Source License
/** * Returns default capabilities of the classifier. * //ww w. j a va 2 s .c om * @return the capabilities of this classifier */ /* * public Capabilities getCapabilities() { Capabilities result = * super.getCapabilities(); result.disableAll(); * * // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); * result.enable(Capability.NUMERIC_ATTRIBUTES); * result.enable(Capability.DATE_ATTRIBUTES); * result.enable(Capability.MISSING_VALUES); * * // class result.enable(Capability.NOMINAL_CLASS); * result.enable(Capability.NUMERIC_CLASS); * result.enable(Capability.DATE_CLASS); * result.enable(Capability.MISSING_CLASS_VALUES); * * return result; } */ public void buildClassifier_old(Instances i) throws Exception { // can classifier handle the data? // getCapabilities().testWithFail(i); // remove instances with missing class i = new Instances(i); i.deleteWithMissingClass(); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(i); // only class? -> use ZeroR model if (i.numAttributes() == 1) { System.err.println( "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!"); m_useDefaultModel = true; return; } else { m_useDefaultModel = false; } m_epoch = 0; m_error = 0; m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_instances = new Instances(i); m_random = new Random(m_randomSeed); m_instances.randomize(m_random); if (m_useNomToBin) { m_nominalToBinaryFilter = new NominalToBinary(); m_nominalToBinaryFilter.setInputFormat(m_instances); m_instances = Filter.useFilter(m_instances, m_nominalToBinaryFilter); } m_numAttributes = m_instances.numAttributes() - 1; m_numClasses = m_instances.numClasses(); setClassType(m_instances); // this sets up the validation set. Instances valSet = null; // numinval is needed later int numInVal = (int) (m_valSize / 100.0 * m_instances.numInstances()); if (m_valSize > 0) { if (numInVal == 0) { numInVal = 1; } valSet = new Instances(m_instances, 0, numInVal); } // ///////// setupInputs(); setupOutputs(); if (m_autoBuild) { setupHiddenLayer(); } // /////////////////////////// // this sets up the gui for usage if (m_gui) { m_win = new JFrame(); m_win.addWindowListener(new WindowAdapter() { @Override public void windowClosing(WindowEvent e) { boolean k = m_stopIt; m_stopIt = true; int well = JOptionPane .showConfirmDialog(m_win, "Are You Sure...\n" + "Click Yes To Accept" + " The Neural Network" + "\n Click No To Return", "Accept Neural Network", JOptionPane.YES_NO_OPTION); if (well == 0) { m_win.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE); m_accepted = true; blocker(false); } else { m_win.setDefaultCloseOperation(JFrame.DO_NOTHING_ON_CLOSE); } m_stopIt = k; } }); m_win.getContentPane().setLayout(new BorderLayout()); m_win.setTitle("Neural Network"); m_nodePanel = new NodePanel(); // without the following two lines, the // NodePanel.paintComponents(Graphics) // method will go berserk if the network doesn't fit completely: it // will // get called on a constant basis, using 100% of the CPU // see the following forum thread: // http://forum.java.sun.com/thread.jspa?threadID=580929&messageID=2945011 m_nodePanel.setPreferredSize(new Dimension(640, 480)); m_nodePanel.revalidate(); JScrollPane sp = new JScrollPane(m_nodePanel, JScrollPane.VERTICAL_SCROLLBAR_ALWAYS, JScrollPane.HORIZONTAL_SCROLLBAR_NEVER); m_controlPanel = new ControlPanel(); m_win.getContentPane().add(sp, BorderLayout.CENTER); m_win.getContentPane().add(m_controlPanel, BorderLayout.SOUTH); m_win.setSize(640, 480); m_win.setVisible(true); } // This sets up the initial state of the gui if (m_gui) { blocker(true); m_controlPanel.m_changeEpochs.setEnabled(false); m_controlPanel.m_changeLearning.setEnabled(false); m_controlPanel.m_changeMomentum.setEnabled(false); } // For silly situations in which the network gets accepted before // training // commenses if (m_numeric) { setEndsToLinear(); } if (m_accepted) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; m_instances = new Instances(m_instances, 0); m_currentInstance = null; return; } // connections done. double right = 0; double driftOff = 0; double lastRight = Double.POSITIVE_INFINITY; double bestError = Double.POSITIVE_INFINITY; double tempRate; double totalWeight = 0; double totalValWeight = 0; double origRate = m_learningRate; // only used for when reset // ensure that at least 1 instance is trained through. if (numInVal == m_instances.numInstances()) { numInVal--; } if (numInVal < 0) { numInVal = 0; } for (int noa = numInVal; noa < m_instances.numInstances(); noa++) { if (!m_instances.instance(noa).classIsMissing()) { totalWeight += m_instances.instance(noa).weight(); } } if (m_valSize != 0) { for (int noa = 0; noa < valSet.numInstances(); noa++) { if (!valSet.instance(noa).classIsMissing()) { totalValWeight += valSet.instance(noa).weight(); } } } m_stopped = false; for (int noa = 1; noa < m_numEpochs + 1; noa++) { right = 0; for (int nob = numInVal; nob < m_instances.numInstances(); nob++) { m_currentInstance = m_instances.instance(nob); if (!m_currentInstance.classIsMissing()) { // this is where the network updating (and training occurs, // for the // training set resetNetwork(); calculateOutputs(); tempRate = m_learningRate * m_currentInstance.weight(); if (m_decay) { tempRate /= noa; } right += (calculateErrors() / m_instances.numClasses()) * m_currentInstance.weight(); updateNetworkWeights(tempRate, m_momentum); } } right /= totalWeight; if (Double.isInfinite(right) || Double.isNaN(right)) { if (!m_reset) { m_instances = null; throw new Exception("Network cannot train. Try restarting with a" + " smaller learning rate."); } else { // reset the network if possible if (m_learningRate <= Utils.SMALL) { throw new IllegalStateException( "Learning rate got too small (" + m_learningRate + " <= " + Utils.SMALL + ")!"); } m_learningRate /= 2; buildClassifier(i); m_learningRate = origRate; m_instances = new Instances(m_instances, 0); m_currentInstance = null; return; } } // //////////////////////do validation testing if applicable if (m_valSize != 0) { right = 0; for (int nob = 0; nob < valSet.numInstances(); nob++) { m_currentInstance = valSet.instance(nob); if (!m_currentInstance.classIsMissing()) { // this is where the network updating occurs, for the // validation set resetNetwork(); calculateOutputs(); right += (calculateErrors() / valSet.numClasses()) * m_currentInstance.weight(); // note 'right' could be calculated here just using // the calculate output values. This would be faster. // be less modular } } if (right < lastRight) { if (right < bestError) { bestError = right; // save the network weights at this point for (int noc = 0; noc < m_numClasses; noc++) { m_outputs[noc].saveWeights(); } driftOff = 0; } } else { driftOff++; } lastRight = right; if (driftOff > m_driftThreshold || noa + 1 >= m_numEpochs) { for (int noc = 0; noc < m_numClasses; noc++) { m_outputs[noc].restoreWeights(); } m_accepted = true; } right /= totalValWeight; } m_epoch = noa; m_error = right; // shows what the neuralnet is upto if a gui exists. updateDisplay(); // This junction controls what state the gui is in at the end of // each // epoch, Such as if it is paused, if it is resumable etc... if (m_gui) { while ((m_stopIt || (m_epoch >= m_numEpochs && m_valSize == 0)) && !m_accepted) { m_stopIt = true; m_stopped = true; if (m_epoch >= m_numEpochs && m_valSize == 0) { m_controlPanel.m_startStop.setEnabled(false); } else { m_controlPanel.m_startStop.setEnabled(true); } m_controlPanel.m_startStop.setText("Start"); m_controlPanel.m_startStop.setActionCommand("Start"); m_controlPanel.m_changeEpochs.setEnabled(true); m_controlPanel.m_changeLearning.setEnabled(true); m_controlPanel.m_changeMomentum.setEnabled(true); blocker(true); if (m_numeric) { setEndsToLinear(); } } m_controlPanel.m_changeEpochs.setEnabled(false); m_controlPanel.m_changeLearning.setEnabled(false); m_controlPanel.m_changeMomentum.setEnabled(false); m_stopped = false; // if the network has been accepted stop the training loop if (m_accepted) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; m_instances = new Instances(m_instances, 0); m_currentInstance = null; return; } } if (m_accepted) { m_instances = new Instances(m_instances, 0); m_currentInstance = null; return; } } if (m_gui) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; } m_instances = new Instances(m_instances, 0); m_currentInstance = null; }
From source file:org.ssase.debt.classification.OnlineMultilayerPerceptron.java
License:Open Source License
/** * Call this function to build and train a neural network for the training * data provided.//w w w.j av a2 s. co m * * @param i * The training data. * @throws Exception * if can't build classification properly. */ public void buildClassifier(Instances i) throws Exception { // can classifier handle the data? // getCapabilities().testWithFail(i); // remove instances with missing class i = new Instances(i); i.deleteWithMissingClass(); m_epoch = 0; m_error = 0; m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_instances = new Instances(i); m_random = new Random(m_randomSeed); // m_instances.randomize(m_random); //commented by me if (m_useNomToBin) { m_nominalToBinaryFilter = new NominalToBinary(); m_nominalToBinaryFilter.setInputFormat(m_instances); m_instances = Filter.useFilter(m_instances, m_nominalToBinaryFilter); } m_numAttributes = m_instances.numAttributes() - 1; m_numClasses = m_instances.numClasses(); setClassType(m_instances); int numInVal = 0; // added by me setupInputs(); setupOutputs(); if (m_autoBuild) { setupHiddenLayer(); } // connections done. double right = 0; double driftOff = 0; double lastRight = Double.POSITIVE_INFINITY; double bestError = Double.POSITIVE_INFINITY; double tempRate; double totalWeight = 0; double totalValWeight = 0; double origRate = m_learningRate; // only used for when reset for (int noa = numInVal; noa < m_instances.numInstances(); noa++) { if (!m_instances.instance(noa).classIsMissing()) { totalWeight += m_instances.instance(noa).weight(); } } m_stopped = false; for (int noa = 1; noa < m_numEpochs + 1; noa++) { right = 0; for (int nob = numInVal; nob < m_instances.numInstances(); nob++) { m_currentInstance = m_instances.instance(nob); if (!m_currentInstance.classIsMissing()) { // this is where the network updating (and training occurs, // for the // training set resetNetwork(); calculateOutputs(); tempRate = m_learningRate * m_currentInstance.weight(); if (m_decay) { tempRate /= noa; } // System.out.println(calculateErrors()); right += (calculateErrors() / m_instances.numClasses()) * m_currentInstance.weight(); updateNetworkWeights(tempRate, m_momentum); // System.out.println(right); /* * for(int t = 0; t < this.m_neuralNodes.length; t++){ * System.out.print("Node "+t+": "); double[] nodeweights = * ((NeuralNode)this.m_neuralNodes[t]).getWeights(); for(int * p = 0; p < 3; p++) System.out.println(nodeweights[p] + * "\t"); } System.out.println(); */ } } right /= totalWeight; if (Double.isInfinite(right) || Double.isNaN(right)) { if (!m_reset) { m_instances = null; throw new Exception("Network cannot train. Try restarting with a" + " smaller learning rate."); } else { // reset the network if possible if (m_learningRate <= Utils.SMALL) throw new IllegalStateException( "Learning rate got too small (" + m_learningRate + " <= " + Utils.SMALL + ")!"); m_learningRate /= 2; buildClassifier(i); m_learningRate = origRate; m_instances = new Instances(m_instances, 0); return; } } m_epoch = noa; m_error = right; } if (m_gui) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; } m_instances = new Instances(m_instances, 0); }
From source file:org.wkwk.classifier.MyC45.java
@Override public void buildClassifier(Instances data) throws Exception { getCapabilities().testWithFail(data); for (int i = 0; i < data.numAttributes(); i++) { Attribute attr = data.attribute(i); for (int j = 0; j < 10; j++) { Instance instance = data.instance(j); if (instance.isMissing(attr)) { instance.setValue(attr, fillMissingValue(data, attr)); }// w w w . j a v a 2 s. co m } } data.deleteWithMissingClass(); makeTree(data); }
From source file:smo2.SMO.java
License:Open Source License
/** * Method for building the classifier. Implements a one-against-one wrapper * for multi-class problems./*from ww w . j a va 2 s.c om*/ * * @param insts * the set of training instances * @exception Exception * if the classifier can't be built successfully */ public void buildClassifier(Instances insts) throws Exception { if (!m_checksTurnedOff) { if (insts.checkForStringAttributes()) { throw new UnsupportedAttributeTypeException("Cannot handle string attributes!"); } if (insts.classAttribute().isNumeric()) { throw new UnsupportedClassTypeException( "mySMO can't handle a numeric class! Use" + "SMOreg for performing regression."); } insts = new Instances(insts); insts.deleteWithMissingClass(); if (insts.numInstances() == 0) { throw new Exception("No training instances without a missing class!"); } /* * Removes all the instances with weight equal to 0. MUST be done * since condition (8) of Keerthi's paper is made with the assertion * Ci > 0 (See equation (3a). */ Instances data = new Instances(insts, insts.numInstances()); for (int i = 0; i < insts.numInstances(); i++) { if (insts.instance(i).weight() > 0) data.add(insts.instance(i)); } if (data.numInstances() == 0) { throw new Exception("No training instances left after removing " + "instance with either a weight null or a missing class!"); } insts = data; } m_onlyNumeric = true; if (!m_checksTurnedOff) { for (int i = 0; i < insts.numAttributes(); i++) { if (i != insts.classIndex()) { if (!insts.attribute(i).isNumeric()) { m_onlyNumeric = false; break; } } } } if (!m_checksTurnedOff) { m_Missing = new ReplaceMissingValues(); m_Missing.setInputFormat(insts); insts = Filter.useFilter(insts, m_Missing); } else { m_Missing = null; } if (!m_onlyNumeric) { m_NominalToBinary = new NominalToBinary(); m_NominalToBinary.setInputFormat(insts); insts = Filter.useFilter(insts, m_NominalToBinary); } else { m_NominalToBinary = null; } if (m_filterType == FILTER_STANDARDIZE) { m_Filter = new Standardize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter); } else if (m_filterType == FILTER_NORMALIZE) { m_Filter = new Normalize(); m_Filter.setInputFormat(insts); insts = Filter.useFilter(insts, m_Filter); } else { m_Filter = null; } m_classIndex = insts.classIndex(); m_classAttribute = insts.classAttribute(); // Generate subsets representing each class Instances[] subsets = new Instances[insts.numClasses()]; for (int i = 0; i < insts.numClasses(); i++) { subsets[i] = new Instances(insts, insts.numInstances()); } for (int j = 0; j < insts.numInstances(); j++) { Instance inst = insts.instance(j); subsets[(int) inst.classValue()].add(inst); } for (int i = 0; i < insts.numClasses(); i++) { subsets[i].compactify(); } // Build the binary classifiers Random rand = new Random(m_randomSeed); m_classifiers = new BinarymySMO[insts.numClasses()][insts.numClasses()]; for (int i = 0; i < insts.numClasses(); i++) { for (int j = i + 1; j < insts.numClasses(); j++) { m_classifiers[i][j] = new BinarymySMO(); Instances data = new Instances(insts, insts.numInstances()); for (int k = 0; k < subsets[i].numInstances(); k++) { data.add(subsets[i].instance(k)); } for (int k = 0; k < subsets[j].numInstances(); k++) { data.add(subsets[j].instance(k)); } data.compactify(); data.randomize(rand); m_classifiers[i][j].buildClassifier(data, i, j, m_fitLogisticModels, m_numFolds, m_randomSeed); } } }
From source file:test.org.moa.opencl.IBk.java
License:Open Source License
/** * Generates the classifier.//from w w w .j a va 2 s.c o m * * @param instances set of instances serving as training data * @throws Exception if the classifier has not been generated successfully */ public void buildClassifier(Instances instances) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(instances); // remove instances with missing class instances = new Instances(instances); instances.deleteWithMissingClass(); m_NumClasses = instances.numClasses(); m_ClassType = instances.classAttribute().type(); m_Train = new Instances(instances, 0, instances.numInstances()); // Throw away initial instances until within the specified window size if ((m_WindowSize > 0) && (instances.numInstances() > m_WindowSize)) { m_Train = new Instances(m_Train, m_Train.numInstances() - m_WindowSize, m_WindowSize); } m_NumAttributesUsed = 0.0; for (int i = 0; i < m_Train.numAttributes(); i++) { if ((i != m_Train.classIndex()) && (m_Train.attribute(i).isNominal() || m_Train.attribute(i).isNumeric())) { m_NumAttributesUsed += 1.0; } } m_NNSearch.setInstances(m_Train); // Invalidate any currently cross-validation selected k m_kNNValid = false; m_defaultModel = new ZeroR(); m_defaultModel.buildClassifier(instances); }
From source file:uzholdem.classifier.OnlineMultilayerPerceptron.java
License:Open Source License
/** * Call this function to build and train a neural network for the training * data provided./*w ww .j a v a 2 s .c om*/ * @param i The training data. * @throws Exception if can't build classification properly. */ public void buildClassifier(Instances i) throws Exception { // can classifier handle the data? getCapabilities().testWithFail(i); // remove instances with missing class i = new Instances(i); i.deleteWithMissingClass(); // only class? -> build ZeroR model if (i.numAttributes() == 1) { System.err.println( "Cannot build model (only class attribute present in data!), " + "using ZeroR model instead!"); m_ZeroR = new weka.classifiers.rules.ZeroR(); m_ZeroR.buildClassifier(i); return; } else { m_ZeroR = null; } m_epoch = 0; m_error = 0; m_instances = null; m_currentInstance = null; m_controlPanel = null; m_nodePanel = null; m_outputs = new NeuralEnd[0]; m_inputs = new NeuralEnd[0]; m_numAttributes = 0; m_numClasses = 0; m_neuralNodes = new NeuralConnection[0]; m_selected = new FastVector(4); m_graphers = new FastVector(2); m_nextId = 0; m_stopIt = true; m_stopped = true; m_accepted = false; m_instances = new Instances(i); m_random = new Random(m_randomSeed); m_instances.randomize(m_random); if (m_useNomToBin) { m_nominalToBinaryFilter = new NominalToBinary(); m_nominalToBinaryFilter.setInputFormat(m_instances); m_instances = Filter.useFilter(m_instances, m_nominalToBinaryFilter); } m_numAttributes = m_instances.numAttributes() - 1; m_numClasses = m_instances.numClasses(); setClassType(m_instances); //this sets up the validation set. Instances valSet = null; //numinval is needed later int numInVal = (int) (m_valSize / 100.0 * m_instances.numInstances()); if (m_valSize > 0) { if (numInVal == 0) { numInVal = 1; } valSet = new Instances(m_instances, 0, numInVal); } /////////// setupInputs(); setupOutputs(); if (m_autoBuild) { setupHiddenLayer(); } ///////////////////////////// //this sets up the gui for usage if (m_gui) { m_win = new JFrame(); m_win.addWindowListener(new WindowAdapter() { public void windowClosing(WindowEvent e) { boolean k = m_stopIt; m_stopIt = true; int well = JOptionPane .showConfirmDialog(m_win, "Are You Sure...\n" + "Click Yes To Accept" + " The Neural Network" + "\n Click No To Return", "Accept Neural Network", JOptionPane.YES_NO_OPTION); if (well == 0) { m_win.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE); m_accepted = true; blocker(false); } else { m_win.setDefaultCloseOperation(JFrame.DO_NOTHING_ON_CLOSE); } m_stopIt = k; } }); m_win.getContentPane().setLayout(new BorderLayout()); m_win.setTitle("Neural Network"); m_nodePanel = new NodePanel(); // without the following two lines, the NodePanel.paintComponents(Graphics) // method will go berserk if the network doesn't fit completely: it will // get called on a constant basis, using 100% of the CPU // see the following forum thread: // http://forum.java.sun.com/thread.jspa?threadID=580929&messageID=2945011 m_nodePanel.setPreferredSize(new Dimension(640, 480)); m_nodePanel.revalidate(); JScrollPane sp = new JScrollPane(m_nodePanel, JScrollPane.VERTICAL_SCROLLBAR_ALWAYS, JScrollPane.HORIZONTAL_SCROLLBAR_NEVER); m_controlPanel = new ControlPanel(); m_win.getContentPane().add(sp, BorderLayout.CENTER); m_win.getContentPane().add(m_controlPanel, BorderLayout.SOUTH); m_win.setSize(640, 480); m_win.setVisible(true); } //This sets up the initial state of the gui if (m_gui) { blocker(true); m_controlPanel.m_changeEpochs.setEnabled(false); m_controlPanel.m_changeLearning.setEnabled(false); m_controlPanel.m_changeMomentum.setEnabled(false); } //For silly situations in which the network gets accepted before training //commenses if (m_numeric) { setEndsToLinear(); } if (m_accepted) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; m_instances = new Instances(m_instances, 0); return; } //connections done. double right = 0; double driftOff = 0; double lastRight = Double.POSITIVE_INFINITY; double bestError = Double.POSITIVE_INFINITY; double tempRate; double totalWeight = 0; double totalValWeight = 0; double origRate = m_learningRate; //only used for when reset //ensure that at least 1 instance is trained through. if (numInVal == m_instances.numInstances()) { numInVal--; } if (numInVal < 0) { numInVal = 0; } for (int noa = numInVal; noa < m_instances.numInstances(); noa++) { if (!m_instances.instance(noa).classIsMissing()) { totalWeight += m_instances.instance(noa).weight(); } } if (m_valSize != 0) { for (int noa = 0; noa < valSet.numInstances(); noa++) { if (!valSet.instance(noa).classIsMissing()) { totalValWeight += valSet.instance(noa).weight(); } } } m_stopped = false; for (int noa = 1; noa < m_numEpochs + 1; noa++) { right = 0; for (int nob = numInVal; nob < m_instances.numInstances(); nob++) { m_currentInstance = m_instances.instance(nob); if (!m_currentInstance.classIsMissing()) { //this is where the network updating (and training occurs, for the //training set resetNetwork(); calculateOutputs(); tempRate = m_learningRate * m_currentInstance.weight(); if (m_decay) { tempRate /= noa; } right += (calculateErrors() / m_instances.numClasses()) * m_currentInstance.weight(); updateNetworkWeights(tempRate, m_momentum); } } right /= totalWeight; if (Double.isInfinite(right) || Double.isNaN(right)) { if (!m_reset) { m_instances = null; throw new Exception("Network cannot train. Try restarting with a" + " smaller learning rate."); } else { //reset the network if possible if (m_learningRate <= Utils.SMALL) throw new IllegalStateException( "Learning rate got too small (" + m_learningRate + " <= " + Utils.SMALL + ")!"); m_learningRate /= 2; buildClassifier(i); m_learningRate = origRate; m_instances = new Instances(m_instances, 0); return; } } ////////////////////////do validation testing if applicable if (m_valSize != 0) { right = 0; for (int nob = 0; nob < valSet.numInstances(); nob++) { m_currentInstance = valSet.instance(nob); if (!m_currentInstance.classIsMissing()) { //this is where the network updating occurs, for the validation set resetNetwork(); calculateOutputs(); right += (calculateErrors() / valSet.numClasses()) * m_currentInstance.weight(); //note 'right' could be calculated here just using //the calculate output values. This would be faster. //be less modular } } if (right < lastRight) { if (right < bestError) { bestError = right; // save the network weights at this point for (int noc = 0; noc < m_numClasses; noc++) { m_outputs[noc].saveWeights(); } driftOff = 0; } } else { driftOff++; } lastRight = right; if (driftOff > m_driftThreshold || noa + 1 >= m_numEpochs) { for (int noc = 0; noc < m_numClasses; noc++) { m_outputs[noc].restoreWeights(); } m_accepted = true; } right /= totalValWeight; } m_epoch = noa; m_error = right; //shows what the neuralnet is upto if a gui exists. updateDisplay(); //This junction controls what state the gui is in at the end of each //epoch, Such as if it is paused, if it is resumable etc... if (m_gui) { while ((m_stopIt || (m_epoch >= m_numEpochs && m_valSize == 0)) && !m_accepted) { m_stopIt = true; m_stopped = true; if (m_epoch >= m_numEpochs && m_valSize == 0) { m_controlPanel.m_startStop.setEnabled(false); } else { m_controlPanel.m_startStop.setEnabled(true); } m_controlPanel.m_startStop.setText("Start"); m_controlPanel.m_startStop.setActionCommand("Start"); m_controlPanel.m_changeEpochs.setEnabled(true); m_controlPanel.m_changeLearning.setEnabled(true); m_controlPanel.m_changeMomentum.setEnabled(true); blocker(true); if (m_numeric) { setEndsToLinear(); } } m_controlPanel.m_changeEpochs.setEnabled(false); m_controlPanel.m_changeLearning.setEnabled(false); m_controlPanel.m_changeMomentum.setEnabled(false); m_stopped = false; //if the network has been accepted stop the training loop if (m_accepted) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; m_instances = new Instances(m_instances, 0); return; } } if (m_accepted) { m_instances = new Instances(m_instances, 0); return; } } if (m_gui) { m_win.dispose(); m_controlPanel = null; m_nodePanel = null; } m_instances = new Instances(m_instances, 0); }
From source file:wekimini.DataManager.java
public Instances getTrainingDataForOutput(int which, boolean includeMetadataFields) { if (!includeMetadataFields) { try {//from ww w .ja v a 2s .c o m Instances in = Filter.useFilter(allInstances, outputFilters[which]); in.setClassIndex(in.numAttributes() - 1); in.deleteWithMissingClass(); return in; } catch (Exception ex) { Logger.getLogger(DataManager.class.getName()).log(Level.SEVERE, null, ex); return null; } } else { try { Instances in = Filter.useFilter(allInstances, savingFilters[which]); in.setClassIndex(in.numAttributes() - 1); in.deleteWithMissingClass(); return in; } catch (Exception ex) { Logger.getLogger(DataManager.class.getName()).log(Level.SEVERE, null, ex); return null; } } }
From source file:xlong.urlclassify.others.SPegasos.java
License:Open Source License
/** * Method for building the classifier.//from w w w. j a va 2s. c om * * @param data the set of training instances. * @throws Exception if the classifier can't be built successfully. */ public void buildClassifier(Instances data) throws Exception { reset(); // can classifier handle the data? getCapabilities().testWithFail(data); data = new Instances(data); data.deleteWithMissingClass(); if (data.numInstances() > 0 && !m_dontReplaceMissing) { m_replaceMissing = new ReplaceMissingValues(); m_replaceMissing.setInputFormat(data); data = Filter.useFilter(data, m_replaceMissing); } // check for only numeric attributes boolean onlyNumeric = true; for (int i = 0; i < data.numAttributes(); i++) { if (i != data.classIndex()) { if (!data.attribute(i).isNumeric()) { onlyNumeric = false; break; } } } if (!onlyNumeric) { m_nominalToBinary = new NominalToBinary(); m_nominalToBinary.setInputFormat(data); data = Filter.useFilter(data, m_nominalToBinary); } if (!m_dontNormalize && data.numInstances() > 0) { m_normalize = new Normalize(); m_normalize.setInputFormat(data); data = Filter.useFilter(data, m_normalize); } m_weights = new double[data.numAttributes() + 1]; m_data = new Instances(data, 0); if (data.numInstances() > 0) { train(data); } }