private void run()
{
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), theta);
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), tau);
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
FPA oerror = PA.Subtract(doutput, ooutput);
FPA herror = PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 }));
herror = PA.InnerProduct(PA.Multiply(PA.Subtract(1.0f, t1), t1), herror);
FPA _owt = PA.Add(dowt, PA.Multiply(PA.InnerProduct(t1, oerror), betao));
FPA _iwt = PA.Multiply(PA.InnerProduct(herror, dinput), betah); //original dinput herror
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta); //orig herror
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
diwt = new DFPA(owt);
dowt = new DFPA(iwt);
}
private void run()
{
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), theta); // summation and theta
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f)); //applying sigmoid function
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), tau); // summation and tau
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
FPA oerror = PA.Subtract(doutput, ooutput); //numpat no
FPA herror = PA.Transpose(PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 })), new int[] { 1, 0 }); // doubtful transpose
herror = PA.Multiply(PA.Multiply(PA.Subtract(1.0f, t1), t1), herror);
FPA _owt = PA.Add(dowt, PA.Multiply(PA.InnerProduct(PA.Transpose(t1, new int[] { 1, 0 }), oerror), betao)); // orig no transpose
FPA _iwt = PA.Multiply(PA.InnerProduct(PA.Transpose(dinput, new int[] { 1, 0 }), herror), betah); //original dinput herror and no transpose
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta); //orig oerror
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
cleanup();
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
}
/*
* Function which performs all the GPU operations
*/
private void run()
{
/* Note : Inner product --- Matrix multiplication
* Multiply -- Element by element multiplication */
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), dtheta);
/* ohidden is the output of hidden layer
* Only Sigmoid function is used for timebeing */
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow(new FPA(2.71828f, new int[] { numpat, nh }), PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), dtau);
/* ooutput is the "actual" output of hidden layer
* Only Sigmoid function is used for timebeing */
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow(new FPA(2.71828f, new int[] { numpat, no }), PA.Negate(t2)), 1.0f));
/* Error between expected and actual */
FPA oerror = PA.Subtract(doutput, ooutput);
/* Checking if error has fallen below 1% if so terminatinf further cycles */
BoolParallelArray b = PA.All(PA.CompareGreater(derror, PA.Abs(oerror)), 1);
b = PA.All(b);
bool[] bt;
PA.ToArray(b, out bt);
if (bt[0] == true)
{
traincycles = 0;
}
/* herror is the error in the hidden layer */
FPA herror = PA.Transpose(PA.InnerProduct(dowt, PA.Transpose(oerror, new int[] { 1, 0 })), new int[] { 1, 0 });
herror = PA.Multiply(PA.Multiply(PA.Subtract(1.0f, ohidden), ohidden), herror);
/* Weights between hidden and output layer being updated */
FPA _owt = PA.Add(PA.Multiply(PA.InnerProduct(PA.Transpose(ohidden, new int[] { 1, 0 }), oerror), betao), dowt);
/* Weights between input and hidden layer being updated */
FPA _iwt = PA.Add(PA.Multiply(PA.InnerProduct(PA.Transpose(dinput, new int[] { 1, 0 }), herror), betah), diwt);
/*Updating threshold for output layer */
dtau = PA.Add(PA.Multiply(betao, oerror), dtau);
/*Updating threshold for hidden layer */
dtheta = PA.Add(PA.Multiply(betah, herror), dtheta);
/* Casting the Parallel arrays to normal arrays */
PA.ToArray(_owt, out owt);
PA.ToArray(_iwt, out iwt);
/* Rebuilding the disposable arrays from newly formed arrays */
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
}
public float[] Test(float[] iinput)
{
float[,] tinput = new float[1, ni];
for (int i = 0; i < ni; i++)
{
tinput[0, i] = iinput[i];
}
dinput = new DFPA(tinput);
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
dtheta = PA.Section(dtheta, new Slice(0, 1), new Slice(0, nh));
dtau = PA.Section(dtau, new Slice(0, 1), new Slice(0, no));
FPA t1 = PA.Add(PA.InnerProduct(dinput, diwt), dtheta);
FPA ohidden = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t1)), 1.0f));
FPA t2 = PA.Add(PA.InnerProduct(ohidden, dowt), dtau);
FPA ooutput = PA.Reciprocal(PA.Add(PA.Pow2(PA.Negate(t2)), 1.0f));
float[,] output;
float[] routput = new float[no];
PA.ToArray(ooutput, out output);
for (int i = 0; i < no; i++)
{
routput[i] = output[0, i];
}
/*Disposable Floating arrays need to be explicitly "disposed" */
dinput.Dispose();
diwt.Dispose();
dowt.Dispose();
doutput.Dispose();
/*Releasing all GPU Resources*/
PA.UnInit();
return(routput);
}
public void start()
{
init();
PA.InitGPU();
dinput = new DFPA(input);
doutput = new DFPA(output);
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
while (traincycles-- > 0)
{
run();
}
cleanup();
PA.UnInit();
}
/*
* Entry Function
*/
public void start()
{
/* Initialisation of all layers*/
init();
/*Normalisation of weights */
normali();
normalo();
/*Initialisation of GPU*/
PA.InitGPU();
/*Measurement starts*/
QueryPerformanceCounter(ref timbeg);
diwt = new DFPA(iwt);
dowt = new DFPA(owt);
dinput = new DFPA(input);
doutput = new DFPA(output);
/* Minimum permissible error */
derror = PA.Abs(PA.Multiply(doutput, 0.01f));
while (traincycles > 0)
{
traincycles--;
numcycles++;
run();
}
long freq = 0;
/*Measurement ends */
QueryPerformanceCounter(ref timend);
QueryPerformanceFrequency(ref freq);
_timtaken = (timend - timbeg) * 1.0 / freq;
}
private void InitMap()
{
// MAP VALUES AND SHAPE
Random RandomNumber = new Random();
weight_vals = new float[m_PatternLength,m_Width * m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
{
for(int k = 0; k < m_PatternLength; ++k )
weight_vals[k, i] = (float)(RandomNumber.NextDouble()*255.0f);
}
shape_vals = new float[2,m_Width*m_Height];
for( int i = 0; i < m_Height; i++ )
for (int j = 0; j < m_Width; j++)
{
shape_vals[0,m_Width*i + j] = j+1;
shape_vals[1,m_Width*i + j] = i+1;
}
m_Weights = new DisposableFloatParallelArray(weight_vals);
m_Shape = new DFPA(shape_vals);
//
//BMU INIT
m_bmucoord_vals = new float[2];
m_bmucodevector_vals = new float[m_PatternLength];
//ZEROS FOR FINDBMU
zero_vals = new float[2, m_Width * m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
{
zero_vals[0, i] = 0;
zero_vals[1, i] = 0;
}
zerocv_vals = new float[m_PatternLength, m_Width * m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
for (int k = 0; k < m_PatternLength; ++k)
{
zerocv_vals[k, i] = 0;
zerocv_vals[k, i] = 0;
}
zeroscv = new DFPA(zerocv_vals);
zeros = new DFPA(zero_vals);
//
//INIT TIME
//Don't use this
m_Time = new DFPA(new float[] { 1 });
//NEIGHBORHOOD VARIABLE INIT
m_maxtime_val = 100.0f;
m_theta_val = 100.0f / (float)Math.Log(25);
m_sigmainitial_val = 25;
m_epsiloninitial_val = 0.1f;
m_Theta = new DFPA(new float[] { m_theta_val });
m_SigmaInitial = new DFPA(new float[] { m_sigmainitial_val });
m_EpsilonInitial = new DFPA(new float[] { m_epsiloninitial_val });
m_MaxTime = new DFPA(new float[] { m_maxtime_val });
//LOG BASE
LogBase = new DFPA(new float[] { (float)(Math.E) });
}
private void InitMap()
{
// MAP VALUES AND SHAPE
Random RandomNumber = new Random();
weight_vals = new float[m_PatternLength, m_Width *m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
{
for (int k = 0; k < m_PatternLength; ++k)
{
weight_vals[k, i] = (float)(RandomNumber.NextDouble() * 255.0f);
}
}
shape_vals = new float[2, m_Width *m_Height];
for (int i = 0; i < m_Height; i++)
{
for (int j = 0; j < m_Width; j++)
{
shape_vals[0, m_Width *i + j] = j + 1;
shape_vals[1, m_Width *i + j] = i + 1;
}
}
m_Weights = new DisposableFloatParallelArray(weight_vals);
m_Shape = new DFPA(shape_vals);
//
//BMU INIT
m_bmucoord_vals = new float[2];
m_bmucodevector_vals = new float[m_PatternLength];
//ZEROS FOR FINDBMU
zero_vals = new float[2, m_Width *m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
{
zero_vals[0, i] = 0;
zero_vals[1, i] = 0;
}
zerocv_vals = new float[m_PatternLength, m_Width *m_Height];
for (int i = 0; i < m_Width * m_Height; ++i)
{
for (int k = 0; k < m_PatternLength; ++k)
{
zerocv_vals[k, i] = 0;
zerocv_vals[k, i] = 0;
}
}
zeroscv = new DFPA(zerocv_vals);
zeros = new DFPA(zero_vals);
//
//INIT TIME
//Don't use this
m_Time = new DFPA(new float[] { 1 });
//NEIGHBORHOOD VARIABLE INIT
m_maxtime_val = 100.0f;
m_theta_val = 100.0f / (float)Math.Log(25);
m_sigmainitial_val = 25;
m_epsiloninitial_val = 0.1f;
m_Theta = new DFPA(new float[] { m_theta_val });
m_SigmaInitial = new DFPA(new float[] { m_sigmainitial_val });
m_EpsilonInitial = new DFPA(new float[] { m_epsiloninitial_val });
m_MaxTime = new DFPA(new float[] { m_maxtime_val });
//LOG BASE
LogBase = new DFPA(new float[] { (float)(Math.E) });
}//Init