cern.colt.matrix.linalg.Algebra.mult()方法的使用及代码示例

x33g5p2x  于2022-01-17 转载在 其他  
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本文整理了Java中cern.colt.matrix.linalg.Algebra.mult()方法的一些代码示例，展示了Algebra.mult()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Algebra.mult()方法的具体详情如下：
包路径：cern.colt.matrix.linalg.Algebra
类名称：Algebra
方法名：mult

Algebra.mult介绍

[英]Inner product of two vectors; Sum(x[i] * y[i]). Also known as dot product.
Equivalent to x.zDotProduct(y).
[中]两个向量的内积；和（x[i]*y[i]）。也称为点积。
相当于x.zdot积（y）。

代码示例

代码示例来源：origin: blazegraph/database

/**
 * Returns the two-norm (aka <i>euclidean norm</i>) of vector <tt>x</tt>; equivalent to <tt>mult(x,x)</tt>.
 */
public double norm2(DoubleMatrix1D x) {
  return mult(x,x);
}
/**

代码示例来源：origin: com.github.vincentk/joptimizer

@Override
public double[][] hessian(double[] X) {
  DoubleMatrix1D x = DoubleFactory1D.dense.make(X);
  DoubleMatrix1D g = ALG.mult(A, x).assign(b, Functions.plus).assign(Functions.exp);
  double den = g.zSum();
  DoubleMatrix1D r = DoubleFactory1D.dense.make(dim);
  for(int i=0; i<dim; i++){
    double d = 0d;
    for(int k=0; k<A.rows(); k++){
      d += g.get(k) * A.get(k, i);
    }
    r.set(i, d);
  }
  
  DoubleMatrix2D ret = DoubleFactory2D.dense.make(dim, dim);
  ret.assign(ALG.multOuter(r, r, null).assign(Mult.mult(-1d/Math.pow(den, 2))), Functions.plus);
  for(int k=0; k<A.rows(); k++){
    ret.assign(ALG.multOuter(A.viewRow(k), A.viewRow(k), null).assign(Mult.mult(g.get(k))).assign(Mult.mult(1d/den)), Functions.plus);
  }
  return ret.toArray();
}

代码示例来源：origin: com.blazegraph/colt

/**
 * Returns the two-norm (aka <i>euclidean norm</i>) of vector <tt>x</tt>; equivalent to <tt>mult(x,x)</tt>.
 */
public double norm2(DoubleMatrix1D x) {
  return mult(x,x);
}
/**

代码示例来源：origin: com.github.vincentk/joptimizer

/**
 * Surrogate duality gap.
 * 
 * @see "Convex Optimization, 11.59"
 */
private double getSurrogateDualityGap(DoubleMatrix1D fiX, DoubleMatrix1D L) {
  return -ALG.mult(fiX, L);
}

代码示例来源：origin: com.github.vincentk/joptimizer

/**
 * Surrogate duality gap.
 * 
 * @see "Convex Optimization, 11.59"
 */
private double getSurrogateDualityGap(DoubleMatrix1D fiX, DoubleMatrix1D L) {
  return -ALG.mult(fiX, L);
}

代码示例来源：origin: com.github.vincentk/joptimizer

@Override
public final double value(double[] X) {
  DoubleMatrix1D x = DoubleFactory1D.dense.make(X);
  double ret = r;
  if (P != null) {
    ret += 0.5 * ALG.mult(x, ALG.mult(P, x));
  }
  if (q != null) {
    ret += ALG.mult(q, x);
  }
  return ret;
}

代码示例来源：origin: blazegraph/database

/**
 * Linear algebraic matrix power; <tt>B = A<sup>k</sup> <==> B = A*A*...*A</tt>.
 * @param A the source matrix; must be square.
 * @param k the exponent, can be any number.
 * @return a new result matrix.
 * 
 * @throws IllegalArgumentException if <tt>!Testing.isSquare(A)</tt>.
 */
private DoubleMatrix2D xpowSlow(DoubleMatrix2D A, int k) {
  //cern.colt.Timer timer = new cern.colt.Timer().start();
  DoubleMatrix2D result = A.copy();
  for (int i=0; i<k-1; i++) {
    result = mult(result,A);
  }
  //timer.stop().display();
  return result;
}
}

代码示例来源：origin: com.blazegraph/colt

/**
 * Linear algebraic matrix power; <tt>B = A<sup>k</sup> <==> B = A*A*...*A</tt>.
 * @param A the source matrix; must be square.
 * @param k the exponent, can be any number.
 * @return a new result matrix.
 * 
 * @throws IllegalArgumentException if <tt>!Testing.isSquare(A)</tt>.
 */
private DoubleMatrix2D xpowSlow(DoubleMatrix2D A, int k) {
  //cern.colt.Timer timer = new cern.colt.Timer().start();
  DoubleMatrix2D result = A.copy();
  for (int i=0; i<k-1; i++) {
    result = mult(result,A);
  }
  //timer.stop().display();
  return result;
}
}

代码示例来源：origin: com.github.vincentk/joptimizer

/**
 * @TODO: check this!!!
 * @see "http://mathworld.wolfram.com/PositiveDefiniteMatrix.html"
 */
public static DoubleMatrix2D randomValuesPositiveMatrix(int rows, int cols, double min, double max, Long seed) {
  DoubleMatrix2D Q = Utils.randomValuesMatrix(rows, cols, min, max, seed);
  DoubleMatrix2D P = Algebra.DEFAULT.mult(Q, Algebra.DEFAULT.transpose(Q.copy()));
return Algebra.DEFAULT.mult(P, P);
}

代码示例来源：origin: blazegraph/database

QMatrix             = Algebra.DEFAULT.mult (transposeMatrix,patternMatrix);
inverseQMatrix      = Algebra.DEFAULT.inverse (QMatrix);
pseudoInverseMatrix = Algebra.DEFAULT.mult (inverseQMatrix,transposeMatrix);
weightMatrix        = Algebra.DEFAULT.mult (patternMatrix,pseudoInverseMatrix);
System.out.println("done.");

代码示例来源：origin: com.blazegraph/colt

QMatrix             = Algebra.DEFAULT.mult (transposeMatrix,patternMatrix);
inverseQMatrix      = Algebra.DEFAULT.inverse (QMatrix);
pseudoInverseMatrix = Algebra.DEFAULT.mult (inverseQMatrix,transposeMatrix);
weightMatrix        = Algebra.DEFAULT.mult (patternMatrix,pseudoInverseMatrix);
System.out.println("done.");

代码示例来源：origin: cmu-phil/tetrad

private double lik(DoubleMatrix2D K, DoubleMatrix2D S, int n, int k) {
  Algebra algebra = new Algebra();
  DoubleMatrix2D SK = algebra.mult(S, K);
  return (algebra.trace(SK) - Math.log(algebra.det(SK)) - k) * n;
}

代码示例来源：origin: com.github.vincentk/joptimizer

@Override
public double value(double[] X) {
  DoubleMatrix1D x = DoubleFactory1D.dense.make(X);
  DoubleMatrix1D g = ALG.mult(A, x).assign(b, Functions.plus).assign(Functions.exp);
  return Math.log(g.zSum());
}

代码示例来源：origin: lessthanoptimal/Java-Matrix-Benchmark

@Override
  public long process(BenchmarkMatrix[] inputs, BenchmarkMatrix[] outputs, long numTrials) {
    DenseDoubleMatrix2D matA = inputs[0].getOriginal();
    DenseDoubleMatrix2D matB = inputs[1].getOriginal();
    Algebra alg = new Algebra();
    DoubleMatrix2D result = null;
    long prev = System.nanoTime();
    for( long i = 0; i < numTrials; i++ ) {
      result = alg.mult(matA,matB);
    }
    long elapsed = System.nanoTime()-prev;
    if( outputs != null ) {
      outputs[0] = new ColtBenchmarkMatrix(result);
    }
    return elapsed;
  }
}

代码示例来源：origin: com.github.vincentk/joptimizer

@Override
public final double[] gradient(double[] X) {
  DoubleMatrix1D x = DoubleFactory1D.dense.make(X);
  DoubleMatrix1D ret = null;
  if(P!=null){
    if (q != null) {
      //P.x + q
      //ret = P.zMult(x, q.copy(), 1, 1, false);
      ret = ColtUtils.zMult(P, x, q, 1);
    } else {
      ret = ALG.mult(P, x);
    }
  }else{
    ret = q;
  }
  return ret.toArray();
  
}

代码示例来源：origin: com.github.vincentk/joptimizer

/**
 * Calculate the scaled residual 
 * <br> ||Ax-b||_oo/( ||A||_oo . ||x||_oo + ||b||_oo ), with
 * <br> ||x||_oo = max(||x[i]||)
 */
public static double calculateScaledResidual(DoubleMatrix2D A, DoubleMatrix2D X, DoubleMatrix2D B){
  double residual = -Double.MAX_VALUE;
  double niX = Algebra.DEFAULT.normInfinity(X);
  double niB = Algebra.DEFAULT.normInfinity(B);
  if(Double.compare(niX, 0.)==0 && Double.compare(niB, 0.)==0){
    return 0;
  }else{
    double num = Algebra.DEFAULT.normInfinity(Algebra.DEFAULT.mult(A, X).assign(B,	Functions.minus));
    double den = Algebra.DEFAULT.normInfinity(A) * niX + niB;
    residual =  num / den;
    //log.debug("scaled residual: " + residual);
    
    return residual;
  }
}

代码示例来源：origin: com.github.vincentk/joptimizer

@Override
public double[] gradient(double[] X) {
  DoubleMatrix1D x = DoubleFactory1D.dense.make(X);
  DoubleMatrix1D g = ALG.mult(A, x).assign(b, Functions.plus).assign(Functions.exp);
  double den = g.zSum();
  double[] R = new double[dim];
  for(int i=0; i<dim; i++){
    double d = 0d;
    for(int k=0; k<A.rows(); k++){
      d += g.get(k) * A.get(k, i);
    }
    R[i] = d/den;
  }
  return R;
}

代码示例来源：origin: blazegraph/database

/**
 */
public static void doubleTest14(int r1, int c, int r2) {
double[] values = {0, 1, 2, 3};
DoubleMatrix2D a = DoubleFactory2D.dense.ascending(r1,c);
DoubleMatrix2D b = Transform.mult(DoubleFactory2D.dense.ascending(c,r2), -1);

//System.out.println(a);
//System.out.println(b);
//System.out.println(Basic.product(a,b));
a.assign(0);
b.assign(0);

cern.colt.Timer timer = new cern.colt.Timer().start();
LinearAlgebra.mult(a,b);
timer.stop().display();
}
/**

代码示例来源：origin: com.blazegraph/colt

/**
 */
public static void doubleTest14(int r1, int c, int r2) {
double[] values = {0, 1, 2, 3};
DoubleMatrix2D a = DoubleFactory2D.dense.ascending(r1,c);
DoubleMatrix2D b = Transform.mult(DoubleFactory2D.dense.ascending(c,r2), -1);

//System.out.println(a);
//System.out.println(b);
//System.out.println(Basic.product(a,b));
a.assign(0);
b.assign(0);

cern.colt.Timer timer = new cern.colt.Timer().start();
LinearAlgebra.mult(a,b);
timer.stop().display();
}
/**

代码示例来源：origin: cmu-phil/tetrad

private double multiLL(DoubleMatrix2D coeffs, Node dep, List<Node> indep){
  DoubleMatrix2D indepData = factory2D.make(internalData.subsetColumns(indep).getDoubleData().toArray());
  List<Node> depList = new ArrayList<>();
  depList.add(dep);
  DoubleMatrix2D depData = factory2D.make(internalData.subsetColumns(depList).getDoubleData().toArray());
  int N = indepData.rows();
  DoubleMatrix2D probs = Algebra.DEFAULT.mult(factory2D.appendColumns(factory2D.make(N, 1, 1.0), indepData), coeffs);
  probs = factory2D.appendColumns(factory2D.make(indepData.rows(), 1, 1.0), probs).assign(Functions.exp);
  double ll = 0;
  for(int i = 0; i < N; i++){
    DoubleMatrix1D curRow = probs.viewRow(i);
    curRow.assign(Functions.div(curRow.zSum()));
    ll += Math.log(curRow.get((int)depData.get(i,0)));
  }
  return ll;
}

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