Examples of SparseVector

ariba.util.core.SparseVector
Object subclass that manages an array of objects. Unlike a Vector, a SparseVector can contain null. @aribaapi documented
ca.uwo.csd.ai.nlp.common.SparseVector
SparseVector stores a sparse vector in a memory efficient manner. It stores the elements of a vector as a list of index:value pairs. @author Syeed Ibn Faiz
cc.mallet.types.SparseVector
A vector that allocates memory only for non-zero values. When you create a SparseVector, you pass in a list of indices. These are the only elements of the vector you will be allowed to change. The rest are fixed at 0. The interface to Sparse vector uses the concept of a location, which is an integer in the range 0..numLocations which can be mapped to the index (and value) of a non zero element of the vector. A SparseVector can be sparse or dense depending on whether or not an array if indices is specified at construction time. If the SparseVector is dense, the mapping from location to index is the identity mapping. The type of the value an element in a SparseVector (or FeatureVector) can be double or binary (0.0 or 1.0), depending on whether an array of doubles is specified at contruction time. @author Andrew McCallum mccallum@cs.umass.edu
ch.akuhn.hapax.linalg.SparseVector
com.googlecode.gaal.data.impl.SparseVector
cz.zcu.fav.liks.math.SparseVector
dmt.tools.SparseVector
edu.ucla.sspace.vector.SparseVector
An interface for {@code Vector} implementations whose values are sparse andthat support access to only those indices with non-zero values. @author Keith Stevens
no.uib.cipr.matrix.sparse.SparseVector
Sparse vector
org.apache.mahout.matrix.SparseVector
Implements vector that only stores non-zero doubles
org.ar.redoexperiments.tools.SparseVector
org.data2semantics.proppred.learners.SparseVector
org.fnlp.ml.types.sv.SparseVector
稀疏向量，并实现各种向量运算 @version 1.0 @since 1.0
org.grouplens.lenskit.vectors.SparseVector
uplens.org">GroupLens Research @compat Public
types.SparseVector
a sparse vector implementation. Not necessarily the most efficient. @author kuzman

Examples of cc.mallet.types.SparseVector

      cachedValueStale = cachedGradientStale = true;


      int idx = 0;
      for (int tidx = 0; tidx < templates.length; tidx++) {
        ACRF.Template tmpl = templates [tidx];
        SparseVector defaults = tmpl.getDefaultWeights();
        double[] values = defaults.getValues ();
        System.arraycopy (params, idx, values, 0, values.length);
        idx += values.length;
      }


      for (int tidx = 0; tidx < templates.length; tidx++) {

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Examples of cc.mallet.types.SparseVector

      /* Index into current element of cachedGradient[] array. */
      int gidx = 0;


      // First do gradient wrt defaultWeights
      for (int tidx = 0; tidx < templates.length; tidx++) {
        SparseVector theseWeights = templates[tidx].getDefaultWeights ();
        SparseVector theseConstraints = defaultConstraints [tidx];
        SparseVector theseExpectations = defaultExpectations [tidx];
        for (int j = 0; j < theseWeights.numLocations(); j++) {
          double weight = theseWeights.valueAtLocation (j);
          double constraint = theseConstraints.valueAtLocation (j);
          double expectation = theseExpectations.valueAtLocation (j);
          if (printGradient) {
            System.out.println(" gradient ["+gidx+"] = "+constraint+" (ctr) - "+expectation+" (exp) - "+
                             (weight / gaussianPriorVariance)+" (reg)  [feature=DEFAULT]");
          }
          grad [gidx++] = constraint - expectation - priorScale * (weight / gaussianPriorVariance);
        }
      }


      // Now do other weights
      for (int tidx = 0; tidx < templates.length; tidx++) {
        ACRF.Template tmpl = templates [tidx];
        SparseVector[] weights = tmpl.getWeights ();
        for (int i = 0; i < weights.length; i++) {
          SparseVector thisWeightVec = weights [i];
          SparseVector thisConstraintVec = constraints [tidx][i];
          SparseVector thisExpectationVec = expectations [tidx][i];


          for (int j = 0; j < thisWeightVec.numLocations(); j++) {
            double w = thisWeightVec.valueAtLocation (j);
            double gradient;  // Computed below


            double constraint = thisConstraintVec.valueAtLocation(j);
            double expectation = thisExpectationVec.valueAtLocation(j);


            /* A parameter may be set to -infinity by an external user.
             * We set gradient to 0 because the parameter's value can
             * never change anyway and it will mess up future calculations
             * on the matrix. */

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Examples of cc.mallet.types.SparseVector

    return new TestSuite (TestAugmentableFeatureVector.class);
  }


  public void testDotProductBinaryToSV ()
  {
    SparseVector v = makeSparseVectorToN (5);
    AugmentableFeatureVector afv = makeAfv (new int[] { 1, 3 }, true);
    double dp = afv.dotProduct (v);
    assertEquals (4.0, dp, 1e-5);
    new AugmentableFeatureVector (new Alphabet(), true);
  }

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Examples of cc.mallet.types.SparseVector

    logger.info ("CRF using dense weights, num input features = "+max);
    for (int i = 0; i < parameters.weights.length; i++) {
      int nfeatures;
      if (featureSelections[i] == null) {
        nfeatures = max;
        newWeights [i] = new SparseVector (null, new double [max],
            max, max, false, false, false);
      } else {
        // Respect the featureSelection
        FeatureSelection fs = featureSelections[i];
        nfeatures = fs.getBitSet ().cardinality ();

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Examples of cc.mallet.types.SparseVector

    new AugmentableFeatureVector (new Alphabet(), true);
  }


  public void testDotProductSparseASVToSV ()
  {
    SparseVector v = makeSparseVectorToN (7);
    AugmentableFeatureVector afv = makeAfv (new int[] { 1, 3 }, false);
    double dp = afv.dotProduct (v);
    assertEquals (4.0, dp, 1e-5);


    afv = makeAfv (new int[] { 2, 5 }, false);

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Examples of cc.mallet.types.SparseVector

  {
    double[] vals = new double [N];
    for (int i = 0; i < N; i++) {
      vals [i] = i;
    }
    return new SparseVector (vals);
  }

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Examples of cc.mallet.types.SparseVector

    else {    
      out.println ("\n\n*** CRF WEIGHTS ***");
      for (int widx = 0; widx < parameters.weights.length; widx++) {
        out.println ("WEIGHTS NAME = " + parameters.weightAlphabet.lookupObject (widx));
        out.print (": <DEFAULT_FEATURE> = "); out.print (parameters.defaultWeights[widx]); out.print ('\n');
        SparseVector transitionWeights = parameters.weights[widx];
        if (transitionWeights.numLocations () == 0)
          continue;
        RankedFeatureVector rfv = new RankedFeatureVector (inputAlphabet, transitionWeights);
        for (int m = 0; m < rfv.numLocations (); m++) {
          double v = rfv.getValueAtRank (m);
          //int index = rfv.indexAtLocation (rfv.getIndexAtRank (m));  // This doesn't make any sense.  How did this ever work?  -akm 12/2007

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Examples of cc.mallet.types.SparseVector

  }
  
  public void testTimesEquals ()
  {
    double[] d1 = new double[] {1, 2, 3, 4, 5};
    SparseVector m1 = new SparseVector (d1);
    SparseVector m2 = new SparseVector (d1);
    m2.timesEqualsSparse(m1);
    m2.print();
  }

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Examples of cc.mallet.types.SparseVector


      try {
        int[] theseWeights = source.weightsIndices[index];
        for (int i = 0; i < theseWeights.length; i++) {
          int wi = theseWeights[i];
          SparseVector w = crf.parameters.weights[wi];


          buf.append ("WEIGHTS <br />\n" + crf.parameters.weightAlphabet.lookupObject (wi) + "<br />\n");
          buf.append ("  d.p. = "+f.format (w.dotProduct (input))+"<br />\n");


          double[] vals = new double[input.numLocations ()];
          double[] absVals = new double[input.numLocations ()];
          for (int k = 0; k < vals.length; k++) {
            int index = input.indexAtLocation (k);
            vals[k] = w.value (index) * input.value (index);
            absVals[k] = Math.abs (vals[k]);
          }


          buf.append ("DEFAULT " + f.format (crf.parameters.defaultWeights[wi]) + "<br />\n");
          RankedFeatureVector rfv = new RankedFeatureVector (crf.inputAlphabet, input.getIndices (), absVals);

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Examples of ch.akuhn.hapax.linalg.SparseVector

  
  double BIG_DELTA = 0.0000001;


    @Test
    public SparseVector empty() {
        SparseVector v = new SparseVector(10, 3);
        assertEquals(10, v.size());
        assertEquals(0, v.used());
        assertEquals(0, v.get(0), Double.MIN_VALUE);
        assertEquals(0, v.get(1), Double.MIN_VALUE);
        assertEquals(0, v.get(2), Double.MIN_VALUE);
        assertEquals(0, v.get(3), Double.MIN_VALUE);
        assertEquals(0, v.get(4), Double.MIN_VALUE);
        assertEquals(0, v.get(5), Double.MIN_VALUE);
        assertEquals(0, v.get(6), Double.MIN_VALUE);
        assertEquals(0, v.get(7), Double.MIN_VALUE);
        assertEquals(0, v.get(8), Double.MIN_VALUE);
        assertEquals(0, v.get(9), Double.MIN_VALUE);
        return v;
    }

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