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This code is not AFL - it is C code, and the header files are C headers
(with the exception of iostream, which is a C++ header). The only way
you will get this to work is by using a C compiler (Microsoft Visual C++
or GNU C are most common, both which can be had for free -
http://www.microsoft.com/express/vc/).
I have written a neural net plugin for AB based on the well regarded
FANN library which I was planning on atleast partially releasing, but
time constraints on me from my day job and having higher priorities on
my family have retarded the polishing and release process...
Regards,
Matt
Ton Sieverding wrote:
>
> Correct Prashanth. That's more or less where I left this AFL a year
> ago. Missing all kinds of .h includes to run it. I've tried to find
> the includes in my C++ stuff but failed ... Perhaps I should give it
> another try on the www ...
>
> Regards, Ton.
>
>
>
>
> > > #include <math.h>
> > > #include <stdlib.h>
> > > #include <iostream>
> > > double input_hidden_weights[5][6]=
> > > {
> > > {9.40985654649721e-001, 9.23136985641824e+000, -
> > > 2.86994950445746e+000, -1.26803931926022e+000, -
> > > 7.70008830456271e+000, 1.25421731000059e+000 },
> > > {3.08886279182467e+000, 5.78057919510794e+000,
> > > 1.10741139003555e+001, -3.65436515636221e+000, -
> > > 1.46817775035674e+001, 2.22520984659718e+001 },
> > > {1.69160825877441e-001, -9.91646811530742e+000, -
> > > 1.05635083282757e+001, 4.25517810226395e+000,
> 2.50631134577816e+000, -
> > > 2.91169885599088e+000 },
> > > {-6.47564626574024e-001, -6.79555863835188e+000, -
> > > 4.77495170169956e+000, 1.35029793165373e+000,
> 2.78079262918906e+000, -
> > > 1.90795645602409e+000 },
> > > {1.43573516935369e+000, 7.90014107622549e+000,
> > > 1.34639286162717e+000, -4.87454998380292e-001, -
> > > 5.75300672778634e+000, 2.44385407464775e+000 }
> > > };
> > > double hidden_bias[5]={ -1.72504436049218e+000, -
> > > 1.12353474266980e+001, 1.05782985606737e+001,
> 6.38618458656671e+000, -
> > > 4.76099367812151e+000 };
> > > double hidden_output_wts[1][5]=
> > > {
> > > {4.85655076789677e+000, 5.85365165791789e-001,
> > > 3.11879288899187e+000, -8.48772823147294e+000, -
> > > 9.35987517826960e+000 }
> > > };
> > > double output_bias[1]={ 5.51616000361064e+000 };
> > > double max_input[6]={ 3.02200000000000e-001,
> 6.05400000000000e-001,
> > > 1.81400000000000e-001, 1.59000000000000e-002,
> 6.06600000000000e-001,
> > > 2.43900000000000e-001 };
> > > double min_input[6]={ -2.82000000000000e-001,
> -8.46700000000000e-
> > > 001, -1.95700000000000e-001, -9.70000000000000e-003, -
> > > 5.52700000000000e-001, -2.45100000000000e-001 };
> > > double max_target[1]={ 1.35100000000000e-001 };
> > > double min_target[1]={ -1.50800000000000e-001 };
> > > double input[6];
> > > double hidden[5];
> > > double output[1];
> > > void FindMax(double* vec, double* max, long*
> maxIndex,int len)
> > > {
> > > long i;
> > > *max = vec[0];
> > > *maxIndex = 0;
> > > for(i=1; i<len; i++)
> > > {
> > > if(vec[i]>*max)
> > > {
> > > *max = vec[i];
> > > *maxIndex = i;
> > > }
> > > }
> > > }
> > > void FindMin(double* vec, double* min, long*
> minIndex,int len)
> > > {
> > > long i;
> > > *min = vec[0];
> > > *minIndex = 0;
> > > for(i=1; i<len; i++)
> > > {
> > > if(vec[i]<*min)
> > > {
> > > *min = vec[i];
> > > *minIndex = i;
> > > }
> > > }
> > > }
> > > void ScaleInputs(double* input, double min, double
> max, int size)
> > > {
> > > double delta;
> > > long i;
> > > for(i=0; i<size; i++)
> > > {
> > > delta = (max-min)/(max_input[i]-min_input[i]);
> > > input[i] = min - delta*min_input[i]+ delta*input[i];
> > > }
> > > }
> > > void UnscaleTargets(double* output, double min, double
> max, int size)
> > > {
> > > double delta;
> > > long i;
> > > for(i=0; i<size; i++)
> > > {
> > > delta = (max-min)/(max_target[i]-min_target[i]);
> > > output[i] = (output[i] - min + delta*min_target[i])/delta;
> > > }
> > > }
> > > double logistic(double x)
> > > {
> > > if(x > 100.0) x = 1.0;
> > > else if (x < -100.0) x = 0.0;
> > > else x = 1.0/(1.0+exp(-x));
> > > return x;
> > > }
> > > void ComputeFeedForwardSignals(double*
> MAT_INOUT,double* V_IN,double*
> > > V_OUT, double* V_BIAS,int size1,int size2,int layer)
> > > {
> > > int row,col;
> > > for(row=0;row < size2; row++)
> > > {
> > > V_OUT[row]=0.0;
> > >
> for(col=0;col<size1;col++)V_OUT[row]+=(*(MAT_INOUT+(row*size1)+col)
> > > *V_IN[col]);
> > > V_OUT[row]+=V_BIAS[row];
> > > if(layer==0) V_OUT[row] = logistic(V_OUT[row]);
> > > if(layer==1) V_OUT[row] = tanh(V_OUT[row]);
> > > }
> > > }
> > > void RunNeuralNet_Regression ()
> > > {
> > > ComputeFeedForwardSignals((double*)
> > > input_hidden_weights,input,hidden,hidden_bias,6, 5,0);
> > > ComputeFeedForwardSignals((double*)
> > > hidden_output_wts,hidden,output,output_bias,5, 1,1);
> > > }
>
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