How to use trained neural network as function using MATLAB coder?

I've trained a simple neural network that just multiply 4 numbers and gives 1 number as an output.

( output(x0) = in1(x0)*in2(x0)*in3(x0) *in4(x0)).

or description:

My neural net has 4 inputs, and 1 output, [10 10] is the hidden layer. I used 'genFunction' to generate a .m file out of network.then I used MATLAB coder to generate C++ function. I generate the code with following input types: input types:

My problem is when I test the C++ code it only gives the 2 first samples for the output.

I store my entries in a std:: vector which has size of 400 (each input size is 100)

I've done the following so far (no desirable output though):

std::vector<double> Multiplier(std::vector<double>& input)

{

double* X_data = new double[input.size()];

X_data = vec2ar(input);

int X_size[2];

X_size[0] = 4;

X_size[1] = 100;

double* Y_data = new double[input.size()];

int Y_size[100];

While the original was:

void multiplier(const double X_data[], const int X_size[2], double Y_data[], int Y_size[2])

{

double Xp1_data[800];

int Xp1_size[2];

int j;

double a1_data[2000];

int coffset;

int a1_size[2];

int boffset;

double tmp_data[2000];

int k;

double b_a1_data[2000];

And for getting the output:

std::vector<double> output;

output = ar2vec(Y_data);

return output;

All I want is we have 4 vectors and give them to this function simply just multiply corresponding samples as shown in 'description' (in my case we have 4 vectors of size 100, and we expect an output with size 100).

And for ar2vec and vec2ar functions:

std::vector<double> ar2vec(double arr[])

{std::vector<double> vec;

copy(&arr[0], &arr[100], std::back_inserter(vec));

return vec;

}

double* vec2ar(std::vector<double> vec){

double * arr = new double[vec.size()];

copy(vec.begin(), vec.end(), arr);

return arr;

}

Could you please help me to fix my problem?

Regards.