如何在Matlab / octave和C ++之间正确比较结果

Question

I'm writing some piece of code available in Matlab/Octave into C++ code. I only have octave so I will just say octave from now on. I want to properly compare the results between the octave code and the C++ code. The algorithms I'm writing take as input a 2D matrix, and output another 2D matrix.

To compare the results, I write the input matrix A from octave using the function save A.mat A , with default options. This creates an ascii file A.mat which starts like

# Created by Octave 3.8.1, Tue May 27 12:12:53 2014 CEST <remi@desktop>
# name: values
# type: matrix
# rows: 25
# columns: 5 
43.0656 6.752420000000001 68.39323 35.75617 98.85446
...

I run the algorithm using octave and save the output matrix B similarly.

In my C++ code, I load the matrices A and B using the following piece of code:

// I opened the file A.mat with std::ifstream infile(filename);
// and read the first lines starting by # and loaded the matrix dimensions
std::string buffer;
double* matBuffer = new double[rows*cols];
while (std::getline(infile, buffer)) {
  std::istringstream iss(buffer);
  while (iss >> *matBuffer) {
    matBuffer++;
  }
}

Then I run the C++ code with the values read from A.mat, and compare the results with the values read from B.mat by computing the mean squared error(MSE) on the coeff of B read vs B computed.

However, with such a design, can I expect that the MSE be 0 between the C++ and octave code? Of course I do the computation on octave and C++ with the same machine. But what about the loss in precision due to writing/reading the matrices in files? Also, I assume that coefficients of octave matrices are stored in double by default, is this correct?

Answer 1

can I expect that the MSE be 0 between the C++ and octave code?

I don't think so, because of the many levels of conversion, a precision loss is hard to avoid.

Also, I assume that coefficients of octave matrices are stored in double by default, is this correct?

Octave uses double precision for internal representation of the values, but again there can be a loss in precision when storing the values in ASCII.

I'd suggest you try to use the binary format for storing the values, which will exclude any problems with precision. You can go with the HDF5 format by using

save -hdf5 A.mat A

You can then use the HDF5 API to read the values in your CPP application.