List of usage examples for weka.core Instance classValue
public double classValue();
From source file:xlong.urlclassify.others.SPegasos.java
License:Open Source License
/** * Updates the classifier with the given instance. * * @param instance the new training instance to include in the model * @exception Exception if the instance could not be incorporated in * the model.//from w w w. j av a 2 s. co m */ public void updateClassifier(Instance instance) throws Exception { if (!instance.classIsMissing()) { double learningRate = 1.0 / (m_lambda * m_t); //double scale = 1.0 - learningRate * m_lambda; double scale = 1.0 - 1.0 / m_t; double y = (instance.classValue() == 0) ? -1 : 1; double wx = dotProd(instance, m_weights, instance.classIndex()); double z = y * (wx + m_weights[m_weights.length - 1]); for (int j = 0; j < m_weights.length - 1; j++) { if (j != instance.classIndex()) { m_weights[j] *= scale; } } if (m_loss == LOGLOSS || (z < 1)) { double loss = dloss(z); int n1 = instance.numValues(); for (int p1 = 0; p1 < n1; p1++) { int indS = instance.index(p1); if (indS != instance.classIndex() && !instance.isMissingSparse(p1)) { double m = learningRate * loss * (instance.valueSparse(p1) * y); m_weights[indS] += m; } } // update the bias m_weights[m_weights.length - 1] += learningRate * loss * y; } double norm = 0; for (int k = 0; k < m_weights.length - 1; k++) { if (k != instance.classIndex()) { norm += (m_weights[k] * m_weights[k]); } } double scale2 = Math.min(1.0, (1.0 / (m_lambda * norm))); if (scale2 < 1.0) { scale2 = Math.sqrt(scale2); for (int j = 0; j < m_weights.length - 1; j++) { if (j != instance.classIndex()) { m_weights[j] *= scale2; } } } m_t++; } }
From source file:zhaop.textmining.proj.MyStringToWordVector.java
License:Open Source License
/** * determines the dictionary.//from w ww . j ava 2 s . co m */ private void determineDictionary() { // initialize stopwords Stopwords stopwords = new Stopwords(); if (getUseStoplist()) { try { if (getStopwords().exists() && !getStopwords().isDirectory()) stopwords.read(getStopwords()); } catch (Exception e) { e.printStackTrace(); } } // Operate on a per-class basis if class attribute is set int classInd = getInputFormat().classIndex(); int values = 1; if (!m_doNotOperateOnPerClassBasis && (classInd != -1)) { values = getInputFormat().attribute(classInd).numValues(); } // TreeMap dictionaryArr [] = new TreeMap[values]; TreeMap[] dictionaryArr = new TreeMap[values]; for (int i = 0; i < values; i++) { dictionaryArr[i] = new TreeMap(); } // Make sure we know which fields to convert determineSelectedRange(); // Tokenize all training text into an orderedMap of "words". long pruneRate = Math.round((m_PeriodicPruningRate / 100.0) * getInputFormat().numInstances()); for (int i = 0; i < getInputFormat().numInstances(); i++) { Instance instance = getInputFormat().instance(i); int vInd = 0; if (!m_doNotOperateOnPerClassBasis && (classInd != -1)) { vInd = (int) instance.classValue(); } // Iterate through all relevant string attributes of the current instance Hashtable h = new Hashtable(); for (int j = 0; j < instance.numAttributes(); j++) { if (m_SelectedRange.isInRange(j) && (instance.isMissing(j) == false)) { // Get tokenizer m_Tokenizer.tokenize(instance.stringValue(j)); // Iterate through tokens, perform stemming, and remove stopwords // (if required) while (m_Tokenizer.hasMoreElements()) { String word = ((String) m_Tokenizer.nextElement()).intern(); if (this.m_lowerCaseTokens == true) word = word.toLowerCase(); // stop first. if (this.m_useStoplist == true) if (stopwords.is(word)) { // System.out.println("a stop word: " + word); continue; } // stem next word = m_Stemmer.stem(word); if (!(h.contains(word))) h.put(word, new Integer(0)); Count count = (Count) dictionaryArr[vInd].get(word); if (count == null) { dictionaryArr[vInd].put(word, new Count(1)); } else { count.count++; } } } } // updating the docCount for the words that have occurred in this // instance(document). Enumeration e = h.keys(); while (e.hasMoreElements()) { String word = (String) e.nextElement(); Count c = (Count) dictionaryArr[vInd].get(word); if (c != null) { c.docCount++; } else System.err.println( "Warning: A word should definitely be in the " + "dictionary.Please check the code"); } if (pruneRate > 0) { if (i % pruneRate == 0 && i > 0) { for (int z = 0; z < values; z++) { Vector d = new Vector(1000); Iterator it = dictionaryArr[z].keySet().iterator(); while (it.hasNext()) { String word = (String) it.next(); Count count = (Count) dictionaryArr[z].get(word); if (count.count <= 1) { d.add(word); } } Iterator iter = d.iterator(); while (iter.hasNext()) { String word = (String) iter.next(); dictionaryArr[z].remove(word); } } } } } // Figure out the minimum required word frequency int totalsize = 0; int prune[] = new int[values]; for (int z = 0; z < values; z++) { totalsize += dictionaryArr[z].size(); int array[] = new int[dictionaryArr[z].size()]; int pos = 0; Iterator it = dictionaryArr[z].keySet().iterator(); while (it.hasNext()) { String word = (String) it.next(); Count count = (Count) dictionaryArr[z].get(word); array[pos] = count.count; pos++; } // sort the array sortArray(array); if (array.length < m_WordsToKeep) { // if there aren't enough words, set the threshold to // minFreq prune[z] = m_minTermFreq; } else { // otherwise set it to be at least minFreq prune[z] = Math.max(m_minTermFreq, array[array.length - m_WordsToKeep]); } } // Convert the dictionary into an attribute index // and create one attribute per word FastVector attributes = new FastVector(totalsize + getInputFormat().numAttributes()); // Add the non-converted attributes int classIndex = -1; for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (!m_SelectedRange.isInRange(i)) { if (getInputFormat().classIndex() == i) { classIndex = attributes.size(); } attributes.addElement(getInputFormat().attribute(i).copy()); } } // Add the word vector attributes (eliminating duplicates // that occur in multiple classes) TreeMap newDictionary = new TreeMap(); int index = attributes.size(); for (int z = 0; z < values; z++) { Iterator it = dictionaryArr[z].keySet().iterator(); while (it.hasNext()) { String word = (String) it.next(); Count count = (Count) dictionaryArr[z].get(word); if (count.count >= prune[z]) { if (newDictionary.get(word) == null) { newDictionary.put(word, new Integer(index++)); attributes.addElement(new Attribute(m_Prefix + word)); } } } } // Compute document frequencies m_DocsCounts = new int[attributes.size()]; Iterator it = newDictionary.keySet().iterator(); while (it.hasNext()) { String word = (String) it.next(); int idx = ((Integer) newDictionary.get(word)).intValue(); int docsCount = 0; for (int j = 0; j < values; j++) { Count c = (Count) dictionaryArr[j].get(word); if (c != null) docsCount += c.docCount; } m_DocsCounts[idx] = docsCount; } // Trim vector and set instance variables attributes.trimToSize(); m_Dictionary = newDictionary; m_NumInstances = getInputFormat().numInstances(); // Set the filter's output format Instances outputFormat = new Instances(getInputFormat().relationName(), attributes, 0); outputFormat.setClassIndex(classIndex); setOutputFormat(outputFormat); }