List of dotProduct() Examples

Examples of dotProduct()

cc.mallet.types.AugmentableFeatureVector.dotProduct()
cc.mallet.types.FeatureVector.dotProduct()
cc.mallet.types.HashedSparseVector.dotProduct()
cc.mallet.types.IndexedSparseVector.dotProduct()
cc.mallet.types.SparseVector.dotProduct()
VECTOR OPERATIONS
edu.stanford.nlp.sempre.FeatureVector.dotProduct()
gov.sandia.cognition.math.matrix.Vector.dotProduct()
mikera.vectorz.AVector.dotProduct()
Returns the dot product of this vector with another vector The vectors must have the same length: if not the result is undefined @param v @return
mikera.vectorz.Vector3.dotProduct()
net.minecraft.src.Vec3D.dotProduct()
net.wigis.graph.dnv.utilities.Vector2D.dotProduct()
Dot product. @param scalar the scalar @return the vector2 d
net.wigis.graph.dnv.utilities.Vector3D.dotProduct()
Dot product. @param scalar the scalar @return the vector3 d
org.apache.commons.math.linear.RealVector.dotProduct()
Compute the dot product. @param v vector with which dot product should be computed @return the scalar dot product between instance and v @throws org.apache.commons.math.exception.DimensionMismatchException if {@code v} is not the same size as this vector.
org.apache.commons.math3.linear.ArrayRealVector.dotProduct()
{@inheritDoc}
org.apache.commons.math3.linear.RealVector.dotProduct()
Compute the dot product of this vector with {@code v}. @param v Vector with which dot product should be computed @return the scalar dot product between this instance and {@code v}. @throws DimensionMismatchException if {@code v} is not the same size as{@code this} vector.
org.fnlp.ml.types.sv.ISparseVector.dotProduct()
点积 @param vector @return
org.jquantlib.math.matrixutilities.Array.dotProduct()
weka.core.pmml.VectorInstance.dotProduct()
Computes the dot product between this vector instance and the argument @param other the vector instance to perform the dot product with @return the dot product of this and the supplied vector instance @throws Exception if something goes wrong

Examples of cc.mallet.types.IndexedSparseVector.dotProduct()


  public void testEmptyLocations ()
  {
    IndexedSparseVector s = new IndexedSparseVector (new int[0], new double [0]);
    assertEquals (0.0, s.value (38), 1e-10);
    assertEquals (0.0, s.dotProduct (s1), 1e-10);
  }


  public void testSerializable () throws IOException, ClassNotFoundException
  {
    IndexedSparseVector s = (IndexedSparseVector) s1.cloneMatrix ();

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Examples of cc.mallet.types.SparseVector.dotProduct()

    }
  }


  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);

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Examples of cc.mallet.types.SparseVector.dotProduct()


    // 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); 
  }


  public void testIncrementValue ()
  {
    SparseVector s = (SparseVector) s1.cloneMatrix ();

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Examples of cc.mallet.types.SparseVector.dotProduct()

  }


  public void testDenseSparseVector ()
  {
    SparseVector svDense = new SparseVector (null, dbl3);
    double sdot = svDense.dotProduct (svDense);
    double ddot = d1.dotProduct (d1);
    assertEquals (sdot, ddot, 0.0001);


    svDense.plusEqualsSparse (s1);
    checkAnswer (svDense, new double[] { 2.0, 2.5, 3.0, 5.7, 3.5,

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Examples of cc.mallet.types.SparseVector.dotProduct()

                                             false, false, false);
    SparseVector binary2 = new SparseVector (idx2, null, idx2.length, idx2.length,
                                            false, false, false);


    assertEquals (3, binary1.dotProduct (binary2), 0.0001);
    assertEquals (3, binary2.dotProduct (binary1), 0.0001);


    assertEquals (15.0, binary1.dotProduct (s1), 0.0001);
    assertEquals (15.0, s1.dotProduct (binary1), 0.0001);


    assertEquals (9.0, binary2.dotProduct (s1), 0.0001);

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Examples of cc.mallet.types.SparseVector.dotProduct()

    assertEquals (3, binary2.dotProduct (binary1), 0.0001);


    assertEquals (15.0, binary1.dotProduct (s1), 0.0001);
    assertEquals (15.0, s1.dotProduct (binary1), 0.0001);


    assertEquals (9.0, binary2.dotProduct (s1), 0.0001);
    assertEquals (9.0, s1.dotProduct (binary2), 0.0001);


    SparseVector dblVec = (SparseVector) s1.cloneMatrix ();
    dblVec.plusEqualsSparse (binary1);
    checkAnswer (dblVec, new double[] { 2, 3, 4, 5, 6 });

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Examples of cc.mallet.types.SparseVector.dotProduct()

  public void testExtendedDotProduct () {
    SparseVector v1 = new SparseVector (null, dbl3);
    SparseVector vInf = new SparseVector (null, dbl4);
    double dp = v1.dotProduct (vInf);
    assertTrue (!Double.isNaN(dp));
    dp = vInf.dotProduct (v1);
    assertTrue (!Double.isNaN(dp));
  }
  
  public static Test suite ()
  {

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Examples of cc.mallet.types.SparseVector.dotProduct()

        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);

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Examples of edu.stanford.nlp.sempre.FeatureVector.dotProduct()

              fv = getFullMatrixFeatures(sourceTokenVec, targetTokenVec);
            else if(vsmSimilarityFunc==SimilarityFunc.DIAGNONAL)
              fv = getDiagonalMatrixFeatures(sourceTokenVec, targetTokenVec);
            else
              fv = getDotProductFeature(sourceTokenVec, targetTokenVec);
            double score = fv.dotProduct(params);
            scoreList.add(Pair.newPair(paraExample.sourceInfo.tokens.get(i)+","+paraExample.targetInfo.tokens.get(j), alpha*score));
          }
        }
      }
    }

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Examples of gov.sandia.cognition.math.matrix.Vector.dotProduct()


      /*
       * 1. Update the sigma2 sufficient stats.
       */
      final double newN = sigma2SS.getShape() + 1d;
      final double d = sigma2SS.getScale() + xHdiff.dotProduct(xHdiff);
      sigma2SS.setScale(d);
      sigma2SS.setShape(newN);
      
      /*
       * 2. Update psi sufficient stats. (i.e. offset and AR(1)).

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