Examples of org.jblas.DoubleMatrix

org.jblas.DoubleMatrix

utes x = y + z. Even in-place methods return the result, such that you can easily chain in-place methods, for example: x.addi(y).addi(z) // computes x += y; x += z

Methods which operate element-wise only make sure that the length of the matrices is correct. Therefore, you can add a 3 * 3 matrix to a 1 * 9 matrix, for example.

Finally, there exist versions which take doubles instead of DoubleMatrix Objects as arguments. These then compute the operation with the same value as the right-hand-side. The same effect can be achieved by passing a DoubleMatrix with exactly one element.

Operation	Method	Comment
x + y	x.add(y)
x - y	x.sub(y), y.rsub(x)	rsub subtracts left from right hand side
x * y	x.mul(y)	element-wise multiplication
	x.mmul(y)	matrix-matrix multiplication
	x.dot(y)	scalar-product
x / y	x.div(y), y.rdiv(x)	rdiv divides right hand side by left hand side.
- x	x.neg()

There also exist operations which work on whole columns or rows.

Method	Description
x.addRowVector	adds a vector to each row (addiRowVector works in-place)
x.addColumnVector	adds a vector to each column
x.subRowVector	subtracts a vector from each row
x.subColumnVector	subtracts a vector from each column
x.mulRow	Multiplies a row by a scalar
x.mulColumn	multiplies a row by a column

In principle, you could achieve the same result by first calling getColumn(), adding, and then calling putColumn, but these methods are much faster.

The following comparison operations are available

Operation	Method
x < y	x.lt(y)
x <= y	x.le(y)
x > y	x.gt(y)
x >= y	x.ge(y)
x == y	x.eq(y)
x != y	x.ne(y)

Logical operations are also supported. For these operations, a value different from zero is treated as "true" and zero is treated as "false". All operations are carried out elementwise.

Operation	Method
x & y	x.and(y)
x \| y	x.or(y)
x ^ y	x.xor(y)
! x	x.not()

Finally, there are a few more methods to compute various things:

Method	Description
x.max()	Return maximal element
x.argmax()	Return index of largest element
x.min()	Return minimal element
x.argmin()	Return index of largest element
x.columnMins()	Return column-wise minima
x.columnArgmins()	Return column-wise index of minima
x.columnMaxs()	Return column-wise maxima
x.columnArgmaxs()	Return column-wise index of maxima

@author Mikio Braun, Johannes Schaback

        return ret;
    }
    
  @Override
  public void reconstruct(double[] x, double[] reconstruct_x) {
    DoubleMatrix x_m = new DoubleMatrix(x).transpose();
      DoubleMatrix ret = reconstruct(x_m);
      for(int i = 0; i < n_visible; i++) {
        reconstruct_x[i] = ret.get(0, i);
      }
  }

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      //w
      if((null != _w) && (null != _w[i])) {
        w[i] = _w[i].dup();
      }
      else {
        w[i] = new DoubleMatrix(curr_out, curr_in);
              for (int j = 0; j < curr_out; j++) {
                  for (int k = 0; k < curr_in; k++) {
                    w[i].put(j, k, rand.nextGaussian() * 0.01);
                  }
              }
      }
      
      //b
      if((null != _b) && (null != _b[i])) {
        b[i] = _b[i].dup();
      }
      else {
        b[i] = new DoubleMatrix(curr_out);
        for (int j = 0; j < curr_out; j++) {
          b[i].put(j, 0, rand.nextGaussian() * 0.01);
        }
      }
    }

View Full Code Here


        public RBMOptimizer(SGDTrainConfig config, DoubleMatrix samples, int n_visible, int n_hidden, DoubleMatrix w,
                DoubleMatrix hbias, DoubleMatrix vbias) {
            super(config, samples, n_visible, n_hidden, w, hbias, vbias);
            v1_sample = my_samples;
            h1_probability = new DoubleMatrix(nbr_sample, n_hidden);
            h1_sample = new DoubleMatrix(nbr_sample, n_hidden);
            v2_probability = new DoubleMatrix(nbr_sample, n_visible);
            v2_sample = new DoubleMatrix(nbr_sample, n_visible);
            h2_probability = new DoubleMatrix(nbr_sample, n_hidden);
            //h2_sample = new DoubleMatrix(nbr_sample, n_hidden);
        }

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          }
          else {
            sample_h_given_v(v2_sample, h2_probability, null, my_w, my_hbias);
          }
          
          DoubleMatrix delta_w = null;
          DoubleMatrix delta_hbias = null;
          DoubleMatrix delta_vbias = null;
          
          if(myConfig.isUseHintonCD1()) {
            delta_w = h1_probability.transpose().mmul(v1_sample).subi(h2_probability.transpose().mmul(v2_probability));
            delta_hbias = h1_probability.sub(h2_probability).columnSums().divi(nbr_sample);
            delta_vbias = v1_sample.sub(v2_probability).columnSums().divi(nbr_sample);
          }
          else {
            delta_w = h1_sample.transpose().mmul(v1_sample).subi(h2_probability.transpose().mmul(v2_sample));
            delta_hbias = h1_sample.sub(h2_probability).columnSums().divi(nbr_sample);
            delta_vbias = v1_sample.sub(v2_sample).columnSums().divi(nbr_sample);
          }


            if (myConfig.isUseRegularization()) {
        //only L2 for RBM
        if (0 != myConfig.getLamada2()) {
          delta_w.subi(my_w.mul(myConfig.getLamada2()));
                }
      }
            delta_w.divi(nbr_sample);
            
            int idx = 0;
            for (int i = 0; i < n_hidden; i++) {
                for (int j = 0; j < n_visible; j++) {
                    arg[idx++] = delta_w.get(i, j);
                }
            }
            for (int i = 0; i < n_hidden; i++) {
                arg[idx++] = delta_hbias.get(0, i);
            }
            for (int i = 0; i < n_visible; i++) {
                arg[idx++] = delta_vbias.get(0, i);
            }
        }

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    super(_n_in, _n_out, _w, _b);
  }


  @Override
  public DoubleMatrix reconstruct(DoubleMatrix input) {
    DoubleMatrix ret = input.mmul(hlparam.w.transpose()).addiRowVector(hlparam.hbias);
        MathUtil.sigmod(ret);
        ret = ret.mmul(hlparam.w).addiRowVector(hlparam.vbias);
        MathUtil.sigmod(ret);
        return ret;
  }

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   * Hidden layer output
   * @param input
   * @return
   */
  public final DoubleMatrix hidden_output(DoubleMatrix input) {
    DoubleMatrix ret = input.mmul(bpparam.w[0].transpose()).addiRowVector(bpparam.b[0]);
    MathUtil.sigmod(ret);
    return ret;
  }

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        return ret;
  }


  @Override
  public void reconstruct(double[] x, double[] reconstruct_x) {
    DoubleMatrix x_m = new DoubleMatrix(x).transpose();
      DoubleMatrix ret = reconstruct(x_m);
      for(int i = 0; i < n_visible; i++) {
        reconstruct_x[i] = ret.get(0, i);
      }
  }

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    MathUtil.sigmod(ret);
    return ret;
  }
  
  public void hidden_output(double[] x, double[] hidden_layer) {
    DoubleMatrix x_m = new DoubleMatrix(x).transpose();
      DoubleMatrix ret = hidden_output(x_m);
      for(int i = 0; i < n_hiddens[0]; i++) {
        hidden_layer[i] = ret.get(0, i);
      }
  }

View Full Code Here

  }


  @Override
  protected void gradientUpdateMiniBatch(SGDTrainConfig config, DoubleMatrix x_samples, DoubleMatrix y_samples, SGDParam curr_param) {
      int nbr_sample = x_samples.rows;
      DoubleMatrix curr_w = ((HiddenLayerParam)curr_param).w;
      DoubleMatrix curr_hbias = ((HiddenLayerParam)curr_param).hbias;
      DoubleMatrix curr_vbias = ((HiddenLayerParam)curr_param).vbias;
        
        /**
         * reconstruct
         */
      DoubleMatrix tilde_x = null;
        DoubleMatrix y = null;
        DoubleMatrix z = null;
      if (config.isDoCorruption()) {
            double p = 1 - config.getCorruption_level();
            tilde_x = get_corrupted_input(x_samples, p);
            y = tilde_x.mmul(curr_w.transpose()).addiRowVector(curr_hbias);
        }
      else {
        y = x_samples.mmul(curr_w.transpose()).addiRowVector(curr_hbias);
      }
      MathUtil.sigmod(y);
        z = y.mmul(curr_w).addiRowVector(curr_vbias);
        MathUtil.sigmod(z);
        
        /**
         * gradient update
         */
        DoubleMatrix L_vbias = x_samples.sub(z);
        DoubleMatrix L_hbias = L_vbias.mmul(curr_w.transpose()).muli(y).muli(y.neg().addi(1));
        DoubleMatrix delta_w = null;
        if (config.isDoCorruption()) {
            delta_w = L_hbias.transpose().mmul(tilde_x).addi(y.transpose().mmul(L_vbias));
        } else {
            delta_w = L_hbias.transpose().mmul(x_samples).addi(y.transpose().mmul(L_vbias));
        }
        if (config.isUseRegularization()) {
      //only L2 for autoencoder
      if (0 != config.getLamada2()) {
        delta_w.subi(curr_w.mul(config.getLamada2()));
            }
    }
        delta_w.divi(nbr_sample);
        DoubleMatrix delta_hbias = L_hbias.columnSums().divi(nbr_sample);
        DoubleMatrix delta_vbias = L_vbias.columnSums().divi(nbr_sample);
        
        curr_w.addi(delta_w.muli(config.getLearningRate()));
        curr_hbias.addi(delta_hbias.transpose().muli(config.getLearningRate()));
        curr_vbias.addi(delta_vbias.transpose().muli(config.getLearningRate()));
    
  }

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    return false;
  }
  
  @Override
  protected double loss(List<SampleVector> samples) {
    DoubleMatrix x_samples = MathUtil.convertX2Matrix(samples);
        DoubleMatrix reconstruct_x = reconstruct(x_samples);
    return MatrixFunctions.powi(reconstruct_x.sub(x_samples), 2).sum();
  }

View Full Code Here

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