示例#1
0
template<typename SparseMatrixType> void sparse_product()
{
  typedef typename SparseMatrixType::Index Index;
  Index n = 100;
  const Index rows  = internal::random<int>(1,n);
  const Index cols  = internal::random<int>(1,n);
  const Index depth = internal::random<int>(1,n);
  typedef typename SparseMatrixType::Scalar Scalar;
  enum { Flags = SparseMatrixType::Flags };

  double density = (std::max)(8./(rows*cols), 0.1);
  typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
  typedef Matrix<Scalar,Dynamic,1> DenseVector;
  typedef Matrix<Scalar,1,Dynamic> RowDenseVector;
  typedef SparseVector<Scalar,0,Index> ColSpVector;
  typedef SparseVector<Scalar,RowMajor,Index> RowSpVector;

  Scalar s1 = internal::random<Scalar>();
  Scalar s2 = internal::random<Scalar>();

  // test matrix-matrix product
  {
    DenseMatrix refMat2  = DenseMatrix::Zero(rows, depth);
    DenseMatrix refMat2t = DenseMatrix::Zero(depth, rows);
    DenseMatrix refMat3  = DenseMatrix::Zero(depth, cols);
    DenseMatrix refMat3t = DenseMatrix::Zero(cols, depth);
    DenseMatrix refMat4  = DenseMatrix::Zero(rows, cols);
    DenseMatrix refMat4t = DenseMatrix::Zero(cols, rows);
    DenseMatrix refMat5  = DenseMatrix::Random(depth, cols);
    DenseMatrix refMat6  = DenseMatrix::Random(rows, rows);
    DenseMatrix dm4 = DenseMatrix::Zero(rows, rows);
//     DenseVector dv1 = DenseVector::Random(rows);
    SparseMatrixType m2 (rows, depth);
    SparseMatrixType m2t(depth, rows);
    SparseMatrixType m3 (depth, cols);
    SparseMatrixType m3t(cols, depth);
    SparseMatrixType m4 (rows, cols);
    SparseMatrixType m4t(cols, rows);
    SparseMatrixType m6(rows, rows);
    initSparse(density, refMat2,  m2);
    initSparse(density, refMat2t, m2t);
    initSparse(density, refMat3,  m3);
    initSparse(density, refMat3t, m3t);
    initSparse(density, refMat4,  m4);
    initSparse(density, refMat4t, m4t);
    initSparse(density, refMat6, m6);

//     int c = internal::random<int>(0,depth-1);

    // sparse * sparse
    VERIFY_IS_APPROX(m4=m2*m3, refMat4=refMat2*refMat3);
    VERIFY_IS_APPROX(m4=m2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3);
    VERIFY_IS_APPROX(m4=m2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());
    VERIFY_IS_APPROX(m4=m2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose());

    VERIFY_IS_APPROX(m4 = m2*m3/s1, refMat4 = refMat2*refMat3/s1);
    VERIFY_IS_APPROX(m4 = m2*m3*s1, refMat4 = refMat2*refMat3*s1);
    VERIFY_IS_APPROX(m4 = s2*m2*m3*s1, refMat4 = s2*refMat2*refMat3*s1);

    VERIFY_IS_APPROX(m4=(m2*m3).pruned(0), refMat4=refMat2*refMat3);
    VERIFY_IS_APPROX(m4=(m2t.transpose()*m3).pruned(0), refMat4=refMat2t.transpose()*refMat3);
    VERIFY_IS_APPROX(m4=(m2t.transpose()*m3t.transpose()).pruned(0), refMat4=refMat2t.transpose()*refMat3t.transpose());
    VERIFY_IS_APPROX(m4=(m2*m3t.transpose()).pruned(0), refMat4=refMat2*refMat3t.transpose());

    // test aliasing
    m4 = m2; refMat4 = refMat2;
    VERIFY_IS_APPROX(m4=m4*m3, refMat4=refMat4*refMat3);

    // sparse * dense
    VERIFY_IS_APPROX(dm4=m2*refMat3, refMat4=refMat2*refMat3);
    VERIFY_IS_APPROX(dm4=m2*refMat3t.transpose(), refMat4=refMat2*refMat3t.transpose());
    VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3, refMat4=refMat2t.transpose()*refMat3);
    VERIFY_IS_APPROX(dm4=m2t.transpose()*refMat3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());

    VERIFY_IS_APPROX(dm4=m2*(refMat3+refMat3), refMat4=refMat2*(refMat3+refMat3));
    VERIFY_IS_APPROX(dm4=m2t.transpose()*(refMat3+refMat5)*0.5, refMat4=refMat2t.transpose()*(refMat3+refMat5)*0.5);

    // dense * sparse
    VERIFY_IS_APPROX(dm4=refMat2*m3, refMat4=refMat2*refMat3);
    VERIFY_IS_APPROX(dm4=refMat2*m3t.transpose(), refMat4=refMat2*refMat3t.transpose());
    VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3, refMat4=refMat2t.transpose()*refMat3);
    VERIFY_IS_APPROX(dm4=refMat2t.transpose()*m3t.transpose(), refMat4=refMat2t.transpose()*refMat3t.transpose());

    // sparse * dense and dense * sparse outer product
    test_outer<SparseMatrixType,DenseMatrix>::run(m2,m4,refMat2,refMat4);

    VERIFY_IS_APPROX(m6=m6*m6, refMat6=refMat6*refMat6);
    
    // sparse matrix * sparse vector
    ColSpVector cv0(cols), cv1;
    DenseVector dcv0(cols), dcv1;
    initSparse(2*density,dcv0, cv0);
    
    RowSpVector rv0(depth), rv1;
    RowDenseVector drv0(depth), drv1(rv1);
    initSparse(2*density,drv0, rv0);
    
    VERIFY_IS_APPROX(cv1=rv0*m3, dcv1=drv0*refMat3);
    VERIFY_IS_APPROX(rv1=rv0*m3, drv1=drv0*refMat3);
    VERIFY_IS_APPROX(cv1=m3*cv0, dcv1=refMat3*dcv0);
    VERIFY_IS_APPROX(cv1=m3t.adjoint()*cv0, dcv1=refMat3t.adjoint()*dcv0);
    VERIFY_IS_APPROX(rv1=m3*cv0, drv1=refMat3*dcv0);
  }
  
  // test matrix - diagonal product
  {
    DenseMatrix refM2 = DenseMatrix::Zero(rows, cols);
    DenseMatrix refM3 = DenseMatrix::Zero(rows, cols);
    DenseMatrix d3 = DenseMatrix::Zero(rows, cols);
    DiagonalMatrix<Scalar,Dynamic> d1(DenseVector::Random(cols));
    DiagonalMatrix<Scalar,Dynamic> d2(DenseVector::Random(rows));
    SparseMatrixType m2(rows, cols);
    SparseMatrixType m3(rows, cols);
    initSparse<Scalar>(density, refM2, m2);
    initSparse<Scalar>(density, refM3, m3);
    VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
    VERIFY_IS_APPROX(m3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
    VERIFY_IS_APPROX(m3=d2*m2, refM3=d2*refM2);
    VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1*refM2.transpose());
    
    // also check with a SparseWrapper:
    DenseVector v1 = DenseVector::Random(cols);
    DenseVector v2 = DenseVector::Random(rows);
    VERIFY_IS_APPROX(m3=m2*v1.asDiagonal(), refM3=refM2*v1.asDiagonal());
    VERIFY_IS_APPROX(m3=m2.transpose()*v2.asDiagonal(), refM3=refM2.transpose()*v2.asDiagonal());
    VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2, refM3=v2.asDiagonal()*refM2);
    VERIFY_IS_APPROX(m3=v1.asDiagonal()*m2.transpose(), refM3=v1.asDiagonal()*refM2.transpose());
    
    VERIFY_IS_APPROX(m3=v2.asDiagonal()*m2*v1.asDiagonal(), refM3=v2.asDiagonal()*refM2*v1.asDiagonal());
    
    // evaluate to a dense matrix to check the .row() and .col() iterator functions
    VERIFY_IS_APPROX(d3=m2*d1, refM3=refM2*d1);
    VERIFY_IS_APPROX(d3=m2.transpose()*d2, refM3=refM2.transpose()*d2);
    VERIFY_IS_APPROX(d3=d2*m2, refM3=d2*refM2);
    VERIFY_IS_APPROX(d3=d1*m2.transpose(), refM3=d1*refM2.transpose());
  }

  // test self adjoint products
  {
    DenseMatrix b = DenseMatrix::Random(rows, rows);
    DenseMatrix x = DenseMatrix::Random(rows, rows);
    DenseMatrix refX = DenseMatrix::Random(rows, rows);
    DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
    DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
    DenseMatrix refS = DenseMatrix::Zero(rows, rows);
    SparseMatrixType mUp(rows, rows);
    SparseMatrixType mLo(rows, rows);
    SparseMatrixType mS(rows, rows);
    do {
      initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
    } while (refUp.isZero());
    refLo = refUp.adjoint();
    mLo = mUp.adjoint();
    refS = refUp + refLo;
    refS.diagonal() *= 0.5;
    mS = mUp + mLo;
    // TODO be able to address the diagonal....
    for (int k=0; k<mS.outerSize(); ++k)
      for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
        if (it.index() == k)
          it.valueRef() *= 0.5;

    VERIFY_IS_APPROX(refS.adjoint(), refS);
    VERIFY_IS_APPROX(mS.adjoint(), mS);
    VERIFY_IS_APPROX(mS, refS);
    VERIFY_IS_APPROX(x=mS*b, refX=refS*b);

    VERIFY_IS_APPROX(x=mUp.template selfadjointView<Upper>()*b, refX=refS*b);
    VERIFY_IS_APPROX(x=mLo.template selfadjointView<Lower>()*b, refX=refS*b);
    VERIFY_IS_APPROX(x=mS.template selfadjointView<Upper|Lower>()*b, refX=refS*b);
    
    // sparse selfadjointView * sparse 
    SparseMatrixType mSres(rows,rows);
    VERIFY_IS_APPROX(mSres = mLo.template selfadjointView<Lower>()*mS,
                     refX = refLo.template selfadjointView<Lower>()*refS);
    // sparse * sparse selfadjointview
    VERIFY_IS_APPROX(mSres = mS * mLo.template selfadjointView<Lower>(),
                     refX = refS * refLo.template selfadjointView<Lower>());
  }
  
}
示例#2
0
文件: main.cpp 项目: samindaa/MLLib
void testStlDriver()
{
  const int numPatches = 200000; // 200000
  const int patchWidth = 9;

  const int numFeatures = 50;
  const double lambda = 0.0005f;
  const double epsilon = 1e-2;
  Config config;
  config.setValue("addBiasTerm", false);
  config.setValue("meanStddNormalize", false);
  config.setValue("configurePolicyTesting", false);
  config.setValue("trainingMeanAndStdd", false);
  updateMNISTConfig(config);

  if (false)
  {
    MNISTSamplePatchesUnlabeledDataFunction mnistUnlabeled(numPatches, patchWidth);
    SoftICACostFunction sfc(numFeatures, lambda, epsilon);

    LIBLBFGSOptimizer lbfgs(200); // 1000
    Driver drv1(&config, &mnistUnlabeled, &sfc, &lbfgs);
    const Vector_t optThetaRica = drv1.drive();

    Matrix_t Wrica(
        Eigen::Map<const Matrix_t>(optThetaRica.data(), numFeatures, pow(patchWidth, 2)));

    std::ofstream ofs_wrica("../W2.txt");
    ofs_wrica << Wrica << std::endl;
  }

  Matrix_t Wrica;
  // debug: read off the values
  std::ifstream in("/home/sam/School/online/stanford_dl_ex/W2.txt");
  if (in.is_open())
  {
    std::string str;
    int nbRows = 0;
    while (std::getline(in, str))
    {
      if (str.size() == 0)
        continue;
      std::istringstream iss(str);
      std::vector<double> tokens //
      { std::istream_iterator<double> { iss }, std::istream_iterator<double> { } };
      Wrica.conservativeResize(nbRows + 1, tokens.size());
      for (size_t i = 0; i < tokens.size(); ++i)
        Wrica(nbRows, i) = tokens[i];
      ++nbRows;
    }
  }
  else
  {
    std::cerr << "file W.txt failed" << std::endl;
    exit(EXIT_FAILURE);
  }

  const int imageDim = 28;
  Eigen::Vector2i imageConfig;
  imageConfig << imageDim, imageDim;

  const int numFilters = numFeatures;
  const int poolDim = 5;
  const int filterDim = patchWidth;
  const int convDim = (imageDim - filterDim + 1);
  assert(convDim % poolDim == 0);
  const int outputDim = (convDim / poolDim);

  StlFilterFunction stlFilterFunction(filterDim, Wrica);
  SigmoidFunction sigmoidFunction;
  ConvolutionFunction convolutionFunction(&stlFilterFunction, &sigmoidFunction);
  MeanPoolFunction meanPoolFunction(numFilters, outputDim);

  MNISTSamplePatchesLabeledDataFunction mnistLabeled(&convolutionFunction, &meanPoolFunction,
      imageConfig, numFilters, poolDim, outputDim);

  SoftmaxCostFunction mnistcf(0.01f);
  LIBLBFGSOptimizer lbfgs2(300);
  config.setValue("configurePolicyTesting", false);
  config.setValue("trainingMeanAndStdd", true);
  config.setValue("meanStddNormalize", true);
  config.setValue("addBiasTerm", true);

  config.setValue("numGrd", true);
  config.setValue("training_accuracy", true);
  config.setValue("testing_accuracy", true);
  //config.setValue("addBiasTerm", false);
  Driver drv2(&config, &mnistLabeled, &mnistcf, &lbfgs2);
  drv2.drive();

}