Java TDoubleArrayList类代码示例

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public void read(String inputDir, CotrainOutputData data) throws Exception {
    java.io.File f = new java.io.File(inputDir);
    if (!f.exists())
        throw new FileNotFoundException("The input directory " + inputDir
                + " does not exist!");

    String fname = inputDir + Os.pathSeparator() + "cotraining.db";
    DataInputStream is = new DataInputStream(
            new java.io.BufferedInputStream(new FileInputStream(fname)));

    data.catsThreshold = new TDoubleArrayList();
    int numCats = is.readInt();
    for (int i = 0; i < numCats; i++) {
        double threshold = is.readDouble();
        data.catsThreshold.add(threshold);
    }

    is.close();
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public void testSample ()
  {
    Variable v1 = new Variable (Variable.CONTINUOUS);
    Variable v2 = new Variable (Variable.CONTINUOUS);
    Randoms r = new Randoms (2343);

    Vector mu = new DenseVector (new double[] { 1.0, 2.0 });
    Matrix var = new DenseMatrix (new double[][] {{ 0.5, 2.0 }, { 0, 1 }});
//    Matrix var = new DenseMatrix (new double[][] {{ 0.5, 2.0 }, { 2.0, 0.75 }});

    VarSet vars = new HashVarSet (new Variable[] { v1, v2 });
    Factor f = new NormalFactor (vars, mu, var);

    TDoubleArrayList v1lst = new TDoubleArrayList ();
    TDoubleArrayList v2lst = new TDoubleArrayList ();
    for (int i = 0; i < 100000; i++) {
      Assignment assn = f.sample (r);
      v1lst.add (assn.getDouble (v1));
      v2lst.add (assn.getDouble (v2));
    }

    checkMeanStd (v1lst, 1.0, Math.sqrt (1/0.5));
    checkMeanStd (v2lst, 2.0, Math.sqrt (1/0.75));
  }
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public void testSample ()
{
  Variable var = new Variable (Variable.CONTINUOUS);
  Randoms r = new Randoms (2343);
  Factor f = new UniNormalFactor (var, -1.0, 2.0);
  TDoubleArrayList lst = new TDoubleArrayList ();
  for (int i = 0; i < 10000; i++) {
    Assignment assn = f.sample (r);
    lst.add (assn.getDouble (var));
  }

  double[] vals = lst.toNativeArray ();
  double mean = MatrixOps.mean (vals);
  double std = MatrixOps.stddev (vals);
  assertEquals (-1.0, mean, 0.025);
  assertEquals (Math.sqrt(2.0), std, 0.01);
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
private DirectedModel createDirectedModel ()
{
  int NUM_OUTCOMES = 2;
  cc.mallet.util.Randoms random = new cc.mallet.util.Randoms (13413);

  Dirichlet dirichlet = new Dirichlet (NUM_OUTCOMES, 1.0);
  double[] pA = dirichlet.randomVector (random);
  double[] pB = dirichlet.randomVector (random);

  TDoubleArrayList pC = new TDoubleArrayList (NUM_OUTCOMES * NUM_OUTCOMES * NUM_OUTCOMES);
  for (int i = 0; i < (NUM_OUTCOMES * NUM_OUTCOMES); i++) {
    pC.add (dirichlet.randomVector (random));
  }

  Variable[] vars = new Variable[] { new Variable (NUM_OUTCOMES), new Variable (NUM_OUTCOMES),
          new Variable (NUM_OUTCOMES) };
  DirectedModel mdl = new DirectedModel ();
  mdl.addFactor (new CPT (new TableFactor (vars[0], pA), vars[0]));
  mdl.addFactor (new CPT (new TableFactor (vars[1], pB), vars[1]));
  mdl.addFactor (new CPT (new TableFactor (vars, pC.toNativeArray ()), vars[2]));

  return mdl;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
private SparseMatrixn make3dMatrix ()
{
  int[] sizes = new int[]{2, 3, 4};
  TIntArrayList idxs = new TIntArrayList ();
  TDoubleArrayList vals = new TDoubleArrayList ();

  for (int i = 0; i < 24; i++) {
    if (i % 3 != 0) {
      idxs.add (i);
      vals.add (2.0 * i);
    }
  }

  SparseMatrixn a = new SparseMatrixn (sizes, idxs.toNativeArray (), vals.toNativeArray ());
  return a;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public static double[] readDoubleArray(JsonReaderEx reader) {
  checkIsNull(reader, null);
  reader.beginArray();
  if (!reader.hasNext()) {
    reader.endArray();
    return new double[]{0};
  }

  TDoubleArrayList result = new TDoubleArrayList();
  do {
    result.add(reader.nextDouble());
  }
  while (reader.hasNext());
  reader.endArray();
  return result.toNativeArray();
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
protected double classify()
{
	int numCorrect = 0;
	for (int j = 0; j < m_trainingData.size(); j++)
	{
		TDoubleArrayList currDatum = m_trainingData.get(j);
		// classify using current weights
		double pos = generateTestVal(currDatum);
		pos = new LDouble(pos).exponentiate();
		if ((pos >= 0.5 && m_trainingLabels.get(j) == 1) || (pos < 0.5 && m_trainingLabels.get(j) == 0)) {
			numCorrect++;
		}
	}
	double acc = ((double)numCorrect) / ((double)m_trainingData.size());
	System.out.println("Train: " + numCorrect + " / " + m_trainingData.size() + " = " + acc);
	return acc;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
protected double classifyDev()
{
	int numCorrect = 0;
	for (int j = 0; j < m_devData.size(); j++) {
		TDoubleArrayList currDatum = m_devData.get(j);
		// classify using current weights
		double pos = generateTestVal(currDatum);
		pos = new LDouble(pos).exponentiate();
		if ((pos >= 0.5 && m_devLabels.get(j) == 1) || (pos < 0.5 && m_devLabels.get(j) == -1)) {
			numCorrect++;
		}
	}
	double acc = ((double)numCorrect) / ((double)m_devData.size());
	System.out.println("Dev: " + numCorrect + " / " + m_devData.size() + " = " + acc);
	return acc;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
protected double classifyTest()
{
	int numCorrect = 0;
	for (int j = 0; j < m_testData.size(); j++) {
		TDoubleArrayList currDatum = m_testData.get(j);
		// classify using current weights
		double pos = generateTestVal(currDatum);
		pos = new LDouble(pos).exponentiate();
		if ((pos >= 0.5 && m_testLabels.get(j) == 1) || (pos < 0.5 && m_testLabels.get(j) == -1)) {
			numCorrect++;
		}
	}
	double acc = ((double)numCorrect) / ((double)m_testData.size());
	System.out.println("Test: " + numCorrect + " / " + m_testData.size() + " = " + acc);
	return acc;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public double classifyRavine(String outputFile)
{
	setParametersWhileTest(outputFile);
	int correct = 0;
	for (int j = 0; j < m_testData.size(); j++) {
		TDoubleArrayList currDatum = m_testData.get(j);
		// classify using current weights
		double pos = generateTestVal(currDatum);
		pos = new LDouble(pos).exponentiate();
		System.out.println(pos);
		if(pos>=0.5&&m_testLabels.get(j)==1)
			correct++;
		if(pos<0.5&&m_testLabels.get(j)==-1)
			correct++;
	}
	double acc = (double)correct/m_testData.size();
	System.out.println("Accuracy="+acc);
	return 0;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
protected void initializeParameterIndexes() {
	A = new Alphabet();
	V = new LDouble[PARAMETER_TABLE_INITIAL_CAPACITY];
	G = new LDouble[PARAMETER_TABLE_INITIAL_CAPACITY];
	m_trainingData = new ArrayList<TDoubleArrayList>(1000);
	m_trainingLabels = new TIntArrayList(1000);
	m_testData = new ArrayList<TDoubleArrayList>(100);
	m_testLabels = new TIntArrayList(100);
	m_devData = new ArrayList<TDoubleArrayList>(100);
	m_devLabels = new TIntArrayList(100);
	savedValues = new TObjectDoubleHashMap<String>(1000);
	m_savedFormulas = new ArrayList<LogFormula>(FORMULA_LIST_INITIAL_CAPACITY);
	m_current = 0;
	m_savedLLFormulas = new ArrayList<LazyLookupLogFormula>(LLFORMULA_LIST_INITIAL_CAPACITY);
	m_llcurrent = 0;
	mLookupChart = new THashMap<Integer,LogFormula>(PARAMETER_TABLE_INITIAL_CAPACITY);
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public ClassificationResult computeVariance(IIndex index, int doc) {
    ClassificationResult bagres = new ClassificationResult();
    bagres.documentID = doc;

    double[] avg = null;
    TDoubleArrayList[] values = null;
    for (int i = 0; i < _classifiers.length; ++i) {
        ClassificationResult res = _classifiers[i].classify(index, doc);
        if (bagres.categoryID.size() == 0) {
            avg = new double[res.categoryID.size()];
            values = new TDoubleArrayList[res.categoryID.size()];
            for (int j = 0; j < res.categoryID.size(); ++j) {
                bagres.categoryID.add(res.categoryID.getQuick(j));
                bagres.score.add(0);
                avg[j] = 0;
                values[j] = new TDoubleArrayList();
            }
        }
        for (int j = 0; j < res.score.size(); ++j) {
            double value = res.score.getQuick(j);
            values[j].add(value);
            avg[j] += value;
        }
    }

    for (int j = 0; j < bagres.score.size(); ++j) {
        avg[j] /= _classifiers.length;
    }

    for (int j = 0; j < bagres.score.size(); ++j) {
        for (int i = 0; i < values[j].size(); ++i) {
            bagres.score.setQuick(j, bagres.score.getQuick(j) + Math.pow(avg[j] - values[j].getQuick(i), 2.0));
        }
    }
    return bagres;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public static TableFactor retainMass (DiscreteFactor ptl, double alpha)
{
  int[] idxs = new int [ptl.numLocations ()];
  double[] vals = new double [ptl.numLocations ()];
  for (int i = 0; i < idxs.length; i++) {
    idxs[i] = ptl.indexAtLocation (i);
    vals[i] = ptl.logValue (i);
  }

  RankedFeatureVector rfv = new RankedFeatureVector (new Alphabet(), idxs, vals);
  TIntArrayList idxList = new TIntArrayList ();
  TDoubleArrayList valList = new TDoubleArrayList ();

  double mass = Double.NEGATIVE_INFINITY;
  double logAlpha = Math.log (alpha);
  for (int rank = 0; rank < rfv.numLocations (); rank++) {
    int idx = rfv.getIndexAtRank (rank);
    double val = rfv.value (idx);
    mass = Maths.sumLogProb (mass, val);
    idxList.add (idx);
    valList.add (val);
    if (mass > logAlpha) {
      break;
    }
  }

  int[] szs = computeSizes (ptl);
  SparseMatrixn m = new SparseMatrixn (szs, idxList.toNativeArray (), valList.toNativeArray ());

  TableFactor result = new TableFactor (computeVars (ptl));
  result.setValues (m);

  return result;
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
void checkMeanStd (TDoubleArrayList ell, double mu, double sigma)
{
  double[] vals = ell.toNativeArray ();
  double mean1 = MatrixOps.mean (vals);
  double std1 = MatrixOps.stddev (vals);
  assertEquals (mu, mean1, 0.025);
  assertEquals (sigma, std1, 0.01);
}
 

import gnu.trove.TDoubleArrayList; //导入依赖的package包/类
public void testSample ()
{
  Variable var = new Variable (Variable.CONTINUOUS);
  Randoms r = new Randoms (2343);
  Factor f = new BetaFactor (var, 0.7, 0.5);
  TDoubleArrayList lst = new TDoubleArrayList ();
  for (int i = 0; i < 100000; i++) {
    Assignment assn = f.sample (r);
    lst.add (assn.getDouble (var));
  }

  double[] vals = lst.toNativeArray ();
  double mean = MatrixOps.mean (vals);
  assertEquals (0.7 / (0.5 + 0.7), mean, 0.01);
}