Examples of mikera.vectorz.AVector

mikera.vectorz.AVector
Main abstract base class for all types of vector Contains default implementations for most vector operations which can be overriden to achieve better performance in derived classes. @author Mike

      v.set(v2);
    }
  }
  
  public void timeJoinedVectorAddition(int runs) {
    AVector v=Vectorz.newVector(VECTOR_SIZE/2);
    v=v.join(Vectorz.newVector(VECTOR_SIZE-v.length()));


    Vector v2=new Vector(Vectorz.createUniformRandomVector(VECTOR_SIZE));
    for (int i=0; i<runs; i++) {
      v.add(v2);
    }
  }

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      v.add(v2);
    }
  }
  
  public void timeJoinedVectorAddMultiple(int runs) {
    AVector v=Vectorz.newVector(VECTOR_SIZE/2);
    v=v.join(Vectorz.newVector(VECTOR_SIZE-v.length()));


    Vector v2=new Vector(Vectorz.createUniformRandomVector(VECTOR_SIZE));
    for (int i=0; i<runs; i++) {
      v.addMultiple(v2,0.5);
    }
  }

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      v.addMultiple(v2,0.5);
    }
  }
  
  public void timeJoinedVectorAddProduct(int runs) {
    AVector v=Vectorz.newVector(VECTOR_SIZE/2);
    v=v.join(Vectorz.newVector(VECTOR_SIZE-v.length()));


    Vector v2=new Vector(Vectorz.createUniformRandomVector(VECTOR_SIZE));
    for (int i=0; i<runs; i++) {
      v.addProduct(v2,v2,0.001);
    }
  }

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//        System.out.println("-------------------------------------------------------------------------");
        int N = alg.getNumberOfEigenvalues();


        for( int i = 0; i < N; i++ ) {
            Vector2 c = alg.getEigenvalue(i);
            AVector v = alg.getEigenVector(i);


            if( Double.isInfinite(c.x) || Double.isNaN(c.x) ||
                    Double.isInfinite(c.y) || Double.isNaN(c.y))
                fail("Uncountable eigenvalue");


            if( Math.abs(c.y) > 1e-20 ) {
                assertTrue(v==null);
            } else {
                assertTrue(v != null );
//                if( MatrixFeatures.hasUncountable(v)) {
//                    throw new RuntimeException("Egads");
//                }
                assertFalse(v.hasUncountable());


//                CommonOps.mult(A,v,tempA);
                Matrix ta = Matrix.create(A.rowCount(), 1);
                ta.setColumn(0, (v));
                AMatrix tempA = Multiplications.multiply(A, ta);
//                CommonOps.scale(c.real,v,tempB);
                Matrix tb = Matrix.create(v.length(), 1);
                tb.setColumn(0, v);
                AMatrix tempB = tb.multiplyCopy(c.x);
//                double max = NormOps.normPInf(A);
                double max = normPInf(A);
                if( max == 0 ) max = 1;

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        // create a matrix out of the eigenvectors
        Matrix A = Matrix.create(N,N);


        int off = 0;
        for( int i = 0; i < N; i++ ) {
            AVector v = alg.getEigenVector(i);


            // it can only handle real eigenvectors
            if( v == null )
                off++;
            else {
                for( int j = 0; j < N; j++ ) {
                    A.set(i-off,j,v.get(j));
                }
            }
        }


        // see if there are any real eigenvectors

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  }
  
  @Test
  public void testAsVector() {
    Matrix m=Matrix.create(new double[][] {{1,2},{3,4}});
    AVector v=m.asVector();
    assertEquals(Vector.class,v.getClass());
    v.set(2,7);
    assertTrue(m.get(1,0)==7);
    
  }

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            if( Math.abs(c.x-valueReal) < 1e-4 && Math.abs(c.y-valueImg) < 1e-4) {


//                if( c.isReal() ) {
                if(Math.abs(c.y - 0) < 1e-8)
                    if( vector.length > 0 ) {
                        AVector v = alg.getEigenVector(i);
                        AMatrix e = Matrix.createFromRows(vector);
                        e = e.getTranspose();
                        
                        Matrix t = Matrix.create(v.length(), 1);
                        t.setColumn(0, v);
                        double error = diffNormF(e,t);
//                        CommonOps.changeSign(e);
                        e.multiply(-1);
                        double error2 = diffNormF(e,t);

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  @Test public void testVectorRejoin() {
    Vector v=Vector.createLength(10);
    Vectorz.fillGaussian(v);
    
    AVector rv=v.subVector(0, 3).join(v.subVector(3,5)).join(v.subVector(8,2));
    assertEquals(Vector.class,rv.getClass());
  }

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    AVector rv=v.subVector(0, 3).join(v.subVector(3,5)).join(v.subVector(8,2));
    assertEquals(Vector.class,rv.getClass());
  }
  
  @Test public void testMultiJoining() {
    AVector v=Vector0.INSTANCE;
    
    // initial join should replace Vector0
    v=v.join(AxisVector.create(1, 3));
    assertEquals(AxisVector.class,v.getClass());
    
    // second join should create JoinedVector
    v=v.join(Vector.of(1,2,3));
    assertEquals(JoinedVector.class,v.getClass());
    
    // third join should promote to JoinedMultiVector
    v=v.join(ZeroVector.create(3));
    assertEquals(JoinedMultiVector.class,v.getClass());
  }

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  }
  
  @Test public void testAddProduct() {
    SparseHashedVector sv=SparseHashedVector.create(10, Index.of(1,3,6), Vector.of(1.0,2.0,3.0));
    
    AVector vz=Vectorz.newVector(10);
    AVector vs=Vector.of(0,1,2,3,4,5,6,7,8,9);
    
    assertTrue(vz instanceof ADenseArrayVector);
    
    AVector v=vz.exactClone();
    v.addProduct(sv, vs);
    assertEquals(Vector.of(0,1,0,6,0,0,18,0,0,0),v);
    
    v.addProduct(sv, vs,2.0);
    assertEquals(Vector.of(0,3,0,18,0,0,54,0,0,0),v);


    AVector v2=Vectorz.newVector(20).subVector(5, 10);
    v2.addProduct(sv, vs);
    assertEquals(Vector.of(0,1,0,6,0,0,18,0,0,0),v2);
    
    v2.addProduct(sv, vs,2.0);
    assertEquals(Vector.of(0,3,0,18,0,0,54,0,0,0),v2);


    AVector v3=Vectorz.newVector(20).subVector(5, 10);
    
    v3.subVector(5,5).addProduct(sv,1, vs,1,1.0);
    assertEquals(Vector.of(0,0,0,0,0,1,0,6,0,0),v3);
    
    v3.subVector(5,5).addProduct(sv,1, vs,1,2.0);
    assertEquals(Vector.of(0,0,0,0,0,3,0,18,0,0),v3);


  }

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Related Classes of mikera.vectorz.AVector

example.SparseMatrix

mikera.arrayz.impl.AbstractArray

mikera.arrayz.impl.SliceArray

mikera.arrayz.INDArray

mikera.arrayz.NDArray

mikera.arrayz.TestArrays

mikera.arrayz.TestMiscArrayOps

mikera.indexz.Index

mikera.matrixx.algo.Rank

mikera.matrixx.algo.TestLinear

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