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 ariba.util.core.SparseVector

        {
            if (!_hasUnnavigableErrors) {
                return _orderedUndisplayedErrors;
            }


            SparseVector /*AWErrorBucket*/ sortedErrors =
                new SparseVector(_orderedUndisplayedErrors.size(), false);
            allocEmptySlots(sortedErrors, _orderedUndisplayedErrors.size());
            int bottomCounter = _orderedUndisplayedErrors.size() - 1;
            Map<Integer, AWErrorBucket> unnavigableErrors = MapUtil.map();
            int highestUnnavNumber = 0;
            for (int i = _orderedUndisplayedErrors.size() - 1; i >= 0; i--) {
                AWErrorBucket bucket = _orderedUndisplayedErrors.get(i);
                if (bucket.getUnnavigableDisplayOrder() != AWErrorInfo.NotDisplayed) {
                    // pick out the unnavigable errors
                    Integer key = Constants.getInteger(bucket.getUnnavigableDisplayOrder());
                    unnavigableErrors.put(key, bucket);
                    highestUnnavNumber = Math.max(bucket.getUnnavigableDisplayOrder(), highestUnnavNumber);
                }
                else {
                    sortedErrors.add(bottomCounter, bucket);
                    bottomCounter--;
                }
            }


            int topCounter = 0;
            int start = (positioningError.getUnnavigableDisplayOrder() != AWErrorInfo.NotDisplayed)
                ? positioningError.getUnnavigableDisplayOrder() + 1
                : 0;
            for (int i = start; i <= highestUnnavNumber; i++) {
                Integer key = Constants.getInteger(i);
                AWErrorBucket bucket = unnavigableErrors.get(key);
                if (bucket != null) {
                    sortedErrors.add(topCounter, bucket);
                    topCounter++;
                }
            }
            for (int i = 0; i < start; i++) {
                Integer key = Constants.getInteger(i);
                AWErrorBucket bucket = unnavigableErrors.get(key);
                if (bucket != null) {
                    sortedErrors.add(topCounter, bucket);
                    topCounter++;
                }
            }


            return sortedErrors;

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Examples of ca.uwo.csd.ai.nlp.common.SparseVector

    @Override
    public double evaluate(svm_node x, svm_node y) {                        
        if (!(x.data instanceof SparseVector) || !(y.data instanceof SparseVector)) {
            throw new RuntimeException("Could not find sparse vectors in svm_nodes");
        }        
        SparseVector v1 = (SparseVector) x.data;
        SparseVector v2 = (SparseVector) y.data;
                        
        return v1.dot(v2);
    }

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Examples of cc.mallet.types.SparseVector

    {
      // Do the defaults first
      defaultConstraints = new SparseVector [templates.length];
      defaultExpectations = new SparseVector [templates.length];
      for (int tidx = 0; tidx < templates.length; tidx++) {
        SparseVector defaults = templates[tidx].getDefaultWeights();
        defaultConstraints[tidx] = (SparseVector) defaults.cloneMatrixZeroed ();
        defaultExpectations[tidx] = (SparseVector) defaults.cloneMatrixZeroed ();
      }


      // And now the others
      constraints = new SparseVector [templates.length][];
      expectations = new SparseVector [templates.length][];

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Examples of cc.mallet.types.SparseVector

        throw new IllegalArgumentException("Argument is not of the " +
                                           " correct dimensions");
      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 (values, 0, buf, idx, values.length);
        idx += values.length;
      }


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

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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 - (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

    }
  }
  
  public void testPlusEquals ()
  {
    SparseVector s = (SparseVector) s1.cloneMatrix ();
    s.plusEqualsSparse (s2, 2.0);
    checkAnswer (s, new double[] { 3, 5, 7, 6, 7 }); 


    SparseVector s2p = new SparseVector
                       (new int[] { 13 },
                        new double[] { 0.8 });
    s.plusEqualsSparse (s2p, 1.0);
    checkAnswer (s, new double[] { 3, 5, 7, 6.8, 7 }); 


    SparseVector s3p = new SparseVector
                       (new int[] { 14 },
                        new double[] { 0.8 });
    s.plusEqualsSparse (s3p, 1.0);
    checkAnswer (s, new double[] { 3, 5, 7, 6.8, 7 });     // verify s unchanged


    SparseVector s4 = new SparseVector
                      (new int[] { 7, 14, 15 },
                       new double[] { 0.2, 0.8, 1.2 });
    s.plusEqualsSparse (s4, 1.0);
    checkAnswer (s, new double[] { 3, 5, 7.2, 6.8, 8.2 });  


    SparseVector s5 = new SparseVector (new int[] { 7 }, new double[] { 0.2 });
    s5.plusEqualsSparse (s1);
    for (int i = 0; i < s5.numLocations(); i++) {
      assertEquals (7, s5.indexAtLocation (i));
      assertEquals (3.2, s5.valueAtLocation (i), 0.0);
    }


    SparseVector s6 = new SparseVector (new int[] { 7 }, new double[] { 0.2 });
    s6.plusEqualsSparse (s1, 3.5);
    for (int i = 0; i < s6.numLocations(); i++) {
      assertEquals (7, s6.indexAtLocation (i));
      assertEquals (10.7, s6.valueAtLocation (i), 0.0);
    }
  }

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Examples of cc.mallet.types.SparseVector

    // Now add -infinite weight onto a transition, and make sure that it's
    // honored.
    CRF.State state = crf.getState("notstart");
    int widx = crf.getWeightsIndex("BadBad");
    int numFeatures = crf.getInputAlphabet().size();
    SparseVector w = new SparseVector(new double[numFeatures]);
    w.setAll(Double.NEGATIVE_INFINITY);
    crf.setWeights(widx, w);


    state.addWeight(0, "BadBad");
    state.addWeight(1, "BadBad");

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Examples of cc.mallet.types.SparseVector

      assertEquals (10.7, s6.valueAtLocation (i), 0.0);
    }
  }


  public void testDotProduct () {
    SparseVector t1 = new SparseVector (new int[] { 7 }, new double[] { 0.2 });
    assertEquals (0.6, t1.dotProduct (s1), 0.00001);
    assertEquals (0.6, s1.dotProduct (t1), 0.00001);
    
    assertEquals (19.0, s1.dotProduct (s2), 0.00001);
    assertEquals (19.0, s2.dotProduct (s1), 0.00001);


    assertEquals (11.9, s1.dotProduct (d1), 0.00001);
    assertEquals (10.1, s2.dotProduct (d1), 0.00001);


    // test dotproduct when vector with more locations has a lower
    //   max-index than short vector
    SparseVector t2 = new SparseVector (new int[] { 3, 30 }, new double[] { 0.2, 3.5 });
    SparseVector t3 = new SparseVector (null, new double[] { 1, 1, 1, 1, });
    assertEquals (0.2, t3.dotProduct (t2), 0.00001); 
  }

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Examples of cc.mallet.types.SparseVector

    assertEquals (0.2, t3.dotProduct (t2), 0.00001); 
  }


  public void testIncrementValue ()
  {
    SparseVector s = (SparseVector) s1.cloneMatrix ();
    s.incrementValue (5, 0.75);


    double[] ans = new double[] {1, 2.75, 3, 4, 5};
    for (int i = 0; i < s.numLocations(); i++) {
      assertTrue (s.valueAtLocation (i) == ans[i]);
    }
  }

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