public static void loadTestPattern(int[] ProjectedPattern)
{
int noPatternsTest = 1;
int sizVctTest = netSizeVCT;
int noClassesTest = 2;
double[,] PMainTest = new double[MAXPATS, MAXNEURONSIN];
int[] PClassMainTest = new int[MAXPATS];
//{k}
//{u}
//{u}
//{k}
//{u}
/* These were for the sample patterns. These are the pa
* PMainTest = new double[,]{{-23.822354, 5.568767,3.366701 ,-5.918748 ,-0.149950 ,-1.964589},
*
* {-27.851184 , -2.204501,5.308936 ,-1.622889 ,2.583660,2.661420 },
*
* { -22.297961 ,-11.698899 , 3.169333, -3.255060 ,3.125650,2.841822},
*
* { -26.272963, 10.087778, 1.886150 ,0.830147 ,-4.468987 , 1.192726},
*
* { -27.851184 , -2.204501,5.308936 , -1.622889 ,2.583660, 2.661420}
*
* };
*
* PClassMainTest = new int[] { 0, 0, 1, 1, 1 };
*/
PMainTest = new double[1, netSizeVCT];
//for (int m = 0; m < ProjectedPattern.GetLength(0); m++)
for (int m = 0; m < netSizeVCT; m++)
{
PMainTest[0, m] = Convert.ToDouble(ProjectedPattern[m]);
}
PClassMainTest = new int[] { 0 };
//-----------------------------------------------
InPat2.GetPatterns(noPatternsTest, sizVctTest, noClassesTest, PMainTest, PClassMainTest);
net.SetPattern(InPat2);
}
public static void loadNetwork()
{
//-----------------------------------------------
double[,] PMain = new double[MAXPATS, MAXNEURONSIN];
int[] PClassMain = new int[MAXPATS];
//int totalNoOfPatterns = 15;
int sizVct = netSizeVCT;
int noClasses = 2;
//First 10 patterns are for known face and later 5 are for unknown faces
/*
* PMain = new double[,]{{-34.343893,8.094488 , 3.968100, 3.045380, -7.269270, 1.443817 },
*
* {-31.059496,7.242294 , 5.421005,4.110571 ,-3.988898 , 2.907260},
*
* {-31.561112,8.548724 ,8.567595 , 6.621193,-1.306165 ,2.271074 },
*
* {-34.689996,5.689937 ,5.254726 ,5.033862 , -3.955592, 1.439173},
*
* { -26.738242,7.655457 ,-1.150949 ,0.125031 ,-1.154293 ,0.457124},
*
* { -28.391232,5.648331 ,-6.961994 ,3.455556 , -0.605938,-2.826946},
*
* { -29.122389, 8.408293, -0.345115, -5.241427 ,-0.884164 ,-0.797609},
*
* {-29.617417 , 5.759380,-2.683569 , -0.284353, -2.470025,-3.619862},
*
* {-27.496802 ,-2.412461 , -2.511118, -3.616672, -1.554588 ,1.038868},
*
* { -28.702483, 9.889178, -12.715754,4.955846 , -6.532904, 3.333166},
*
* { -29.797715,-9.787343 ,-0.562956 , -5.747552,-0.005083 ,-0.763473},
*
* {-30.848025 , -5.985676,5.408539 , -3.647051, 2.881709 ,1.137439},
*
* { -29.797715, -9.787343, -0.562956, -5.747552,-0.005083 ,-0.763473},
*
* { -29.496174, 0.114694, -4.809610, -6.867701, 3.702615,-4.079926},
*
* { -30.848025,-5.985676 , 5.408539,-3.647051 ,2.881709 ,1.137439}};
*
* PClassMain = new int[] { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1 };
*/
PMain = net.readFeatureXML();
PClassMain = new int[totalNoOfPatterns];
for (int i = 0; i < noOfKnownPatterns; i++)
{
PClassMain[i] = 0;
}
for (int i = 0; i < noOfUnknownPatterns; i++)
{
PClassMain[i + noOfKnownPatterns] = 1;
}
//if (PMain == null)
//Console.Out.WriteLine("null");
//MessageBox.Show("no of patterns:" + totalNoOfPatterns);
InPat.GetPatterns(totalNoOfPatterns, sizVct, noClasses, PMain, PClassMain);
net.SetPattern(InPat);
//net.SetParms(2, 0.2500);
net.SetParms(2, 0.100);
}
