Improving the efficiency of c++ code calling Eigen to do the linear operation of the matrix












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I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.



#include <iostream>
#include <fstream>
#include <Eigen/Dense>
#include <time.h>
using namespace std;

int main()
{
// Weight coefficient matrix
Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
wi_1.resize(100,2);
wi_2.resize(100,100);
wi_3.resize(100,100);
wi_4.resize(5,100);
wi_1.setOnes();
wi_2.setOnes();
wi_3.setOnes();
wi_4.setOnes();

// Bias vector
Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
bias_1.resize(100);
bias_2.resize(100);
bias_3.resize(100);
bias_4.resize(5);
bias_1.setOnes();
bias_2.setOnes();
bias_3.setOnes();
bias_4.setOnes();
Eigen::Matrix<double,5,1> y_mean;
Eigen::Matrix<double,5,1> y_scale;
Eigen::Matrix<double,2,1> x_mean;
Eigen::Matrix<double,2,1> x_scale;

y_mean.setOnes();
y_scale.setOnes();
y_mean.setOnes();
x_scale.setOnes();

int n = 0;
int layer;
clock_t start,finish;
double totaltime;
start=clock();
while (n<10000)
{
Y.resize(2);
layer = 0;
Y << 0.185, 0.285;//inputx[1], x[0];
Y = (Y.array() - x_mean.array()) / x_scale.array();

//ANN forward
while (layer < 4)
{
layer++;
switch (layer) {
case 1:{
Y = wi_1 * Y + bias_1;
// Info << "ANN forward layer1" << endl;
break;
}
case 2:{
Y = wi_2 * Y + bias_2;
// Info << "ANN forward layer2" << endl;
break;
}
case 3:{
Y = wi_3 * Y + bias_3;
// Info << "ANN forward layer3" << endl;
break;
}
case 4:{
Y = wi_4 * Y + bias_4;
// Info << "ANN forward layer4" << endl;
break;
}
default:{
cout<<"error"<<endl;
break;
}
}

//Relu activation function
if (layer < 4)
{
for (int i = 0; i < Y.size(); i++)
{
Y(i) = ((Y(i) > 0) ? Y(i) : 0);
}
}
}
//inverse standardization
Y = Y.array() * y_scale.array() + y_mean.array();
n++;
}
finish=clock();
totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
cout<<"n Running time is "<<totaltime<<"ms!"<<endl;
}








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    I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.



    #include <iostream>
    #include <fstream>
    #include <Eigen/Dense>
    #include <time.h>
    using namespace std;

    int main()
    {
    // Weight coefficient matrix
    Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
    wi_1.resize(100,2);
    wi_2.resize(100,100);
    wi_3.resize(100,100);
    wi_4.resize(5,100);
    wi_1.setOnes();
    wi_2.setOnes();
    wi_3.setOnes();
    wi_4.setOnes();

    // Bias vector
    Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
    bias_1.resize(100);
    bias_2.resize(100);
    bias_3.resize(100);
    bias_4.resize(5);
    bias_1.setOnes();
    bias_2.setOnes();
    bias_3.setOnes();
    bias_4.setOnes();
    Eigen::Matrix<double,5,1> y_mean;
    Eigen::Matrix<double,5,1> y_scale;
    Eigen::Matrix<double,2,1> x_mean;
    Eigen::Matrix<double,2,1> x_scale;

    y_mean.setOnes();
    y_scale.setOnes();
    y_mean.setOnes();
    x_scale.setOnes();

    int n = 0;
    int layer;
    clock_t start,finish;
    double totaltime;
    start=clock();
    while (n<10000)
    {
    Y.resize(2);
    layer = 0;
    Y << 0.185, 0.285;//inputx[1], x[0];
    Y = (Y.array() - x_mean.array()) / x_scale.array();

    //ANN forward
    while (layer < 4)
    {
    layer++;
    switch (layer) {
    case 1:{
    Y = wi_1 * Y + bias_1;
    // Info << "ANN forward layer1" << endl;
    break;
    }
    case 2:{
    Y = wi_2 * Y + bias_2;
    // Info << "ANN forward layer2" << endl;
    break;
    }
    case 3:{
    Y = wi_3 * Y + bias_3;
    // Info << "ANN forward layer3" << endl;
    break;
    }
    case 4:{
    Y = wi_4 * Y + bias_4;
    // Info << "ANN forward layer4" << endl;
    break;
    }
    default:{
    cout<<"error"<<endl;
    break;
    }
    }

    //Relu activation function
    if (layer < 4)
    {
    for (int i = 0; i < Y.size(); i++)
    {
    Y(i) = ((Y(i) > 0) ? Y(i) : 0);
    }
    }
    }
    //inverse standardization
    Y = Y.array() * y_scale.array() + y_mean.array();
    n++;
    }
    finish=clock();
    totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
    cout<<"n Running time is "<<totaltime<<"ms!"<<endl;
    }








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      I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.



      #include <iostream>
      #include <fstream>
      #include <Eigen/Dense>
      #include <time.h>
      using namespace std;

      int main()
      {
      // Weight coefficient matrix
      Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
      wi_1.resize(100,2);
      wi_2.resize(100,100);
      wi_3.resize(100,100);
      wi_4.resize(5,100);
      wi_1.setOnes();
      wi_2.setOnes();
      wi_3.setOnes();
      wi_4.setOnes();

      // Bias vector
      Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
      bias_1.resize(100);
      bias_2.resize(100);
      bias_3.resize(100);
      bias_4.resize(5);
      bias_1.setOnes();
      bias_2.setOnes();
      bias_3.setOnes();
      bias_4.setOnes();
      Eigen::Matrix<double,5,1> y_mean;
      Eigen::Matrix<double,5,1> y_scale;
      Eigen::Matrix<double,2,1> x_mean;
      Eigen::Matrix<double,2,1> x_scale;

      y_mean.setOnes();
      y_scale.setOnes();
      y_mean.setOnes();
      x_scale.setOnes();

      int n = 0;
      int layer;
      clock_t start,finish;
      double totaltime;
      start=clock();
      while (n<10000)
      {
      Y.resize(2);
      layer = 0;
      Y << 0.185, 0.285;//inputx[1], x[0];
      Y = (Y.array() - x_mean.array()) / x_scale.array();

      //ANN forward
      while (layer < 4)
      {
      layer++;
      switch (layer) {
      case 1:{
      Y = wi_1 * Y + bias_1;
      // Info << "ANN forward layer1" << endl;
      break;
      }
      case 2:{
      Y = wi_2 * Y + bias_2;
      // Info << "ANN forward layer2" << endl;
      break;
      }
      case 3:{
      Y = wi_3 * Y + bias_3;
      // Info << "ANN forward layer3" << endl;
      break;
      }
      case 4:{
      Y = wi_4 * Y + bias_4;
      // Info << "ANN forward layer4" << endl;
      break;
      }
      default:{
      cout<<"error"<<endl;
      break;
      }
      }

      //Relu activation function
      if (layer < 4)
      {
      for (int i = 0; i < Y.size(); i++)
      {
      Y(i) = ((Y(i) > 0) ? Y(i) : 0);
      }
      }
      }
      //inverse standardization
      Y = Y.array() * y_scale.array() + y_mean.array();
      n++;
      }
      finish=clock();
      totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
      cout<<"n Running time is "<<totaltime<<"ms!"<<endl;
      }








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      Charryzzz is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      $endgroup$




      I am a rookie of coding and I have written a c++ procedure which calls the Eigen library to do the linear operation of the matrix. Please help me to improve the efficiency of the loop.



      #include <iostream>
      #include <fstream>
      #include <Eigen/Dense>
      #include <time.h>
      using namespace std;

      int main()
      {
      // Weight coefficient matrix
      Eigen::MatrixXd wi_1,wi_2,wi_3,wi_4;
      wi_1.resize(100,2);
      wi_2.resize(100,100);
      wi_3.resize(100,100);
      wi_4.resize(5,100);
      wi_1.setOnes();
      wi_2.setOnes();
      wi_3.setOnes();
      wi_4.setOnes();

      // Bias vector
      Eigen::VectorXd bias_1,bias_2,bias_3,bias_4,Y;
      bias_1.resize(100);
      bias_2.resize(100);
      bias_3.resize(100);
      bias_4.resize(5);
      bias_1.setOnes();
      bias_2.setOnes();
      bias_3.setOnes();
      bias_4.setOnes();
      Eigen::Matrix<double,5,1> y_mean;
      Eigen::Matrix<double,5,1> y_scale;
      Eigen::Matrix<double,2,1> x_mean;
      Eigen::Matrix<double,2,1> x_scale;

      y_mean.setOnes();
      y_scale.setOnes();
      y_mean.setOnes();
      x_scale.setOnes();

      int n = 0;
      int layer;
      clock_t start,finish;
      double totaltime;
      start=clock();
      while (n<10000)
      {
      Y.resize(2);
      layer = 0;
      Y << 0.185, 0.285;//inputx[1], x[0];
      Y = (Y.array() - x_mean.array()) / x_scale.array();

      //ANN forward
      while (layer < 4)
      {
      layer++;
      switch (layer) {
      case 1:{
      Y = wi_1 * Y + bias_1;
      // Info << "ANN forward layer1" << endl;
      break;
      }
      case 2:{
      Y = wi_2 * Y + bias_2;
      // Info << "ANN forward layer2" << endl;
      break;
      }
      case 3:{
      Y = wi_3 * Y + bias_3;
      // Info << "ANN forward layer3" << endl;
      break;
      }
      case 4:{
      Y = wi_4 * Y + bias_4;
      // Info << "ANN forward layer4" << endl;
      break;
      }
      default:{
      cout<<"error"<<endl;
      break;
      }
      }

      //Relu activation function
      if (layer < 4)
      {
      for (int i = 0; i < Y.size(); i++)
      {
      Y(i) = ((Y(i) > 0) ? Y(i) : 0);
      }
      }
      }
      //inverse standardization
      Y = Y.array() * y_scale.array() + y_mean.array();
      n++;
      }
      finish=clock();
      totaltime=(double)(finish-start)/CLOCKS_PER_SEC*1000;
      cout<<"n Running time is "<<totaltime<<"ms!"<<endl;
      }






      c++ eigen





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      asked 2 mins ago









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