List of usage examples for weka.core Instance stringValue
public String stringValue(Attribute att);
From source file:tubes1.myClassifiers.myC45.java
private Instances filterInstanceWithAttributeValue(Instances instances, Attribute attribute, String value) { Instances newInstances = new Instances(instances); newInstances.delete();//from w ww . ja v a 2s. c o m int numInstances = instances.numInstances(); for (int i = 0; i < numInstances; i++) { Instance instance = instances.instance(i); if (instance.stringValue(attribute).equals(value)) { newInstances.add(instance); } } return newInstances; }
From source file:tubesduaai.NB_030.java
@Override public double classifyInstance(Instance instnc) throws Exception { // Pengklasifikasi instance baru int NUM_CLASSES = datas.classAttribute().numValues(); // System.out.println("------------------------------"); // System.out.println(datas.get(1)); // System.out.println(datas.get(1).stringValue(2)); // Probability of classes int arg = 0;// w w w . j a va2 s .c o m Double argmax = 0.0; Double temp = 1.0; for (int i = 0; i < NUM_CLASSES; i++) { // arg max (vj E enum.attributes(datas)) // P(kelas)*P(atribut|kelas) temp = ((new Double(num[i])) / datas.numInstances()); for (int j = 0; j < datas.numAttributes(); j++) { if (j != datas.classIndex()) { // System.out.println("--------------"); // System.out.println(map.get(instnc.attribute(j).name()+instnc.stringValue(j)+datas.classAttribute().value(i))); temp *= map.get( instnc.attribute(j).name() + instnc.stringValue(j) + datas.classAttribute().value(i)); } } if (temp > argmax) { argmax = temp; arg = i; } } switch (arg) { case 0: return 0.0; case 1: return 1.0; case 2: return 2.0; default: return 0.0; } }
From source file:wedt.project.Common.java
public Instance extractFeature(Instance input) { Map<Integer, Double> map = new TreeMap<>(); List<Token> tokens = tagger.runPOSTagger(input.stringValue(0)); for (Token token : tokens) { switch (token.getPOS()) { case "A": case "V": case "R": case "#": String word = token.getWord().replaceAll("#", ""); if (featureWords.contains(word)) { map.put(featureWords.indexOf(word), 1.0); }/*from ww w . j a v a 2s .c o m*/ } } int indices[] = new int[map.size() + 1]; double values[] = new double[map.size() + 1]; int i = 0; for (Map.Entry<Integer, Double> entry : map.entrySet()) { indices[i] = entry.getKey(); values[i] = entry.getValue(); i++; } indices[i] = featureWords.size(); values[i] = (double) sentiment.indexOf(input.stringValue(1)); return new SparseInstance(1.0, values, indices, featureWords.size() + 1); }
From source file:wtute.engine.AnalysisEngine.java
private Instance convertInstance(Instance instance) { EssayParser ep = new EssayParser(); Tree pt = ep.getTreeOf(instance.stringValue(0)); List<Tree> tl = pt.getChildrenAsList(); for (Tree tree : tl) { }/*from w w w. j ava 2 s .c o m*/ return null; }
From source file:zhaop.textmining.proj.MyStringToWordVector.java
License:Open Source License
/** * determines the dictionary./*w ww .j a va 2 s .c o 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); }
From source file:zhaop.textmining.proj.MyStringToWordVector.java
License:Open Source License
/** * Converts the instance w/o normalization. * // w w w .j a v a 2 s . c o m * @oaram instance the instance to convert * @param v * @return the conerted instance */ private int convertInstancewoDocNorm(Instance instance, FastVector v) { // Convert the instance into a sorted set of indexes TreeMap contained = new TreeMap(); // Copy all non-converted attributes from input to output int firstCopy = 0; for (int i = 0; i < getInputFormat().numAttributes(); i++) { if (!m_SelectedRange.isInRange(i)) { if (getInputFormat().attribute(i).type() != Attribute.STRING) { // Add simple nominal and numeric attributes directly if (instance.value(i) != 0.0) { contained.put(new Integer(firstCopy), new Double(instance.value(i))); } } else { if (instance.isMissing(i)) { contained.put(new Integer(firstCopy), new Double(Instance.missingValue())); } else { // If this is a string attribute, we have to first add // this value to the range of possible values, then add // its new internal index. if (outputFormatPeek().attribute(firstCopy).numValues() == 0) { // Note that the first string value in a // SparseInstance doesn't get printed. outputFormatPeek().attribute(firstCopy) .addStringValue("Hack to defeat SparseInstance bug"); } int newIndex = outputFormatPeek().attribute(firstCopy) .addStringValue(instance.stringValue(i)); contained.put(new Integer(firstCopy), new Double(newIndex)); } } firstCopy++; } } for (int j = 0; j < instance.numAttributes(); j++) { // if ((getInputFormat().attribute(j).type() == Attribute.STRING) if (m_SelectedRange.isInRange(j) && (instance.isMissing(j) == false)) { m_Tokenizer.tokenize(instance.stringValue(j)); while (m_Tokenizer.hasMoreElements()) { String word = (String) m_Tokenizer.nextElement(); if (this.m_lowerCaseTokens == true) word = word.toLowerCase(); word = m_Stemmer.stem(word); Integer index = (Integer) m_Dictionary.get(word); if (index != null) { if (m_OutputCounts) { // Separate if here rather than two lines down // to avoid hashtable lookup Double count = (Double) contained.get(index); if (count != null) { contained.put(index, new Double(count.doubleValue() + 1.0)); } else { contained.put(index, new Double(1)); } } else { contained.put(index, new Double(1)); } } } } } // Doing TFTransform if (m_TFTransform == true) { Iterator it = contained.keySet().iterator(); for (int i = 0; it.hasNext(); i++) { Integer index = (Integer) it.next(); if (index.intValue() >= firstCopy) { double val = ((Double) contained.get(index)).doubleValue(); val = Math.log(val + 1); contained.put(index, new Double(val)); } } } // Doing IDFTransform if (m_IDFTransform == true) { Iterator it = contained.keySet().iterator(); for (int i = 0; it.hasNext(); i++) { Integer index = (Integer) it.next(); if (index.intValue() >= firstCopy) { double val = ((Double) contained.get(index)).doubleValue(); val = val * Math.log(m_NumInstances / (double) m_DocsCounts[index.intValue()]); contained.put(index, new Double(val)); } } } // Convert the set to structures needed to create a sparse instance. double[] values = new double[contained.size()]; int[] indices = new int[contained.size()]; Iterator it = contained.keySet().iterator(); for (int i = 0; it.hasNext(); i++) { Integer index = (Integer) it.next(); Double value = (Double) contained.get(index); values[i] = value.doubleValue(); indices[i] = index.intValue(); } Instance inst = new SparseInstance(instance.weight(), values, indices, outputFormatPeek().numAttributes()); inst.setDataset(outputFormatPeek()); v.addElement(inst); return firstCopy; }