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); }