├── README.md ├── gdb.cmd ├── doc ├── neural_network.ppt └── An empirical study of learning speed in back-propagation networks.pdf ├── test1.wts ├── test1.net ├── synth.net ├── pima.wts ├── test3.net ├── neural_network_test.c ├── tags ├── pima.tr ├── synth_tr_mass.csv ├── synth.tr ├── pima.te ├── pima_tr_trans.csv ├── pima.net ├── quickprop1.c ├── synth.te └── pima_te_trans.csv /README.md: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /gdb.cmd: -------------------------------------------------------------------------------- 1 | b 163 2 | r 3 | 4 | -------------------------------------------------------------------------------- /doc/neural_network.ppt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/roles/neural_network/master/doc/neural_network.ppt -------------------------------------------------------------------------------- /test1.wts: -------------------------------------------------------------------------------- 1 | 3 0 -0.225568 3 1 0.311642 3 2 -0.567805 4 0 0.018881 4 1 -0.018170 4 2 0.069086 4 3 1.317256 -1 -1 -1.000000 -------------------------------------------------------------------------------- /doc/An empirical study of learning speed in back-propagation networks.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/roles/neural_network/master/doc/An empirical study of learning speed in back-propagation networks.pdf -------------------------------------------------------------------------------- /test1.net: -------------------------------------------------------------------------------- 1 | Ninputs 2 Nhidden 1 Noutputs 1 2 | UnitType 1 3 | Connectcalls 2 4 | 1 2 3 4 5 | 3 3 4 4 6 | NTrainingPatterns 4 7 | 0.0 0.0 -0.5 8 | 0.0 1.0 0.5 9 | 1.0 0.0 0.5 10 | 1.0 1.0 -0.5 11 | NTestPatterns 4 12 | 0.0 0.0 -0.5 13 | 0.0 1.0 0.5 14 | 1.0 0.0 0.5 15 | 1.0 1.0 -0.5 16 | -------------------------------------------------------------------------------- /synth.net: -------------------------------------------------------------------------------- 1 | Ninputs 2 Nhidden 5 Noutputs 1 2 | UnitType 2 3 | Connectcalls 2 4 | 1 2 3 7 5 | 3 7 8 8 6 | NTrainingPatterns 20 7 | 0.33240813 0.27349865 0.0 8 | -0.80250753 0.66878999 0.0 9 | 0.32061253 0.33407547 0.0 10 | -0.36528923 0.45703265 1.0 11 | 0.09346162 0.67310494 1.0 12 | -0.69859164 0.38566851 0.0 13 | 0.31686848 0.39705856 0.0 14 | -0.82769016 0.36187460 0.0 15 | 0.07634798 0.56222204 1.0 16 | 0.02841337 0.75792620 1.0 17 | -0.21389978 1.09317811 1.0 18 | -0.30469807 0.86858292 1.0 19 | 0.18741315 0.29747132 0.0 20 | 0.21345680 0.43611756 0.0 21 | 0.58238323 0.22842741 0.0 22 | -0.07236203 0.33376524 0.0 23 | -0.62931544 0.63202159 0.0 24 | 0.68510504 0.78067440 1.0 25 | 0.37128253 0.70089181 1.0 26 | 0.46809108 0.87182416 1.0 27 | NTestPatterns 10 28 | -0.970990139 0.429424950 0.0 29 | -0.631997027 0.251952852 0.0 30 | -0.773605760 0.690750778 0.0 31 | -0.606211523 0.175677956 0.0 32 | -0.539409005 0.376744239 0.0 33 | 0.327675416 0.608013752 1.0 34 | 0.247589562 0.279270348 1.0 35 | 0.418514564 1.044157214 1.0 36 | 0.232314519 0.819642835 1.0 37 | 0.762040971 0.573218465 1.0 38 | -------------------------------------------------------------------------------- /pima.wts: -------------------------------------------------------------------------------- 1 | 8 0 -1.232724 8 1 -71.242531 8 2 6.154692 8 3 -37.255577 8 4 -1.082006 8 5 16.238241 8 6 22.848600 8 7 -0.406522 9 0 -7.012912 9 1 33.463943 9 2 -4.632163 9 3 -5.945941 9 4 5.079472 9 5 1.961388 9 6 -13.477055 9 7 -18.090960 10 0 5.868978 10 1 -45.618469 10 2 11.953819 10 3 -1.309897 10 4 13.489433 10 5 12.754076 10 6 44.526592 10 7 24.901770 11 0 10.150101 11 1 46.616364 11 2 -25.025705 11 3 0.224205 11 4 -4.565738 11 5 -25.977983 11 6 -54.698624 11 7 -20.846828 12 0 -1.360250 12 1 11.814714 12 2 -1.554524 12 3 -5.686502 12 4 -9.242708 12 5 0.980343 12 6 11.291533 12 7 10.541871 13 0 -0.758871 13 1 -18.848347 13 2 -2.178951 13 3 23.704260 13 4 25.294146 13 5 -0.777884 13 6 -17.343643 13 7 -5.130277 14 0 10.733139 14 1 2.604942 14 2 5.971740 14 3 3.659683 14 4 -9.793856 14 5 -19.227249 14 6 3.200071 14 7 -10.224911 15 0 6.348479 15 1 0.088999 15 2 -23.108562 15 3 7.720343 15 4 10.858939 15 5 -9.792147 15 6 -83.823715 15 7 17.255596 16 0 -13.215172 16 1 11.177195 16 2 16.254650 16 3 -14.044358 16 4 4.790176 16 5 -20.779537 16 6 23.069710 16 7 43.685009 17 0 1.872611 17 1 -11.212691 17 2 0.321174 17 3 -4.397352 17 4 18.140394 17 5 0.344840 17 6 -7.873133 17 7 -4.415442 18 0 -7.436044 18 1 11.135129 18 2 -6.326665 18 3 3.416167 18 4 -5.044317 18 5 -15.216316 18 6 -9.267368 18 7 -5.830709 18 8 -15.724033 18 9 9.519207 18 10 -8.340178 -1 -1 -1.000000 -------------------------------------------------------------------------------- /test3.net: -------------------------------------------------------------------------------- 1 | Ninputs 4 Nhidden 6 Noutputs 1 2 | UnitType 2 3 | Connectcalls 2 4 | 1 4 5 10 5 | 5 10 11 11 6 | NTrainingPatterns 36 7 | 0.33 0.33 0.33 0.33 1.0 8 | 0.67 0.33 0.33 0.33 0.0 9 | 1.00 0.33 0.33 0.33 0.0 10 | 0.33 0.33 0.67 0.33 0.0 11 | 0.67 0.33 0.67 0.33 1.0 12 | 1.00 0.33 0.67 0.33 0.0 13 | 0.33 0.33 1.00 0.33 0.0 14 | 0.67 0.33 1.00 0.33 0.0 15 | 1.00 0.33 1.00 0.33 1.0 16 | 17 | 0.33 0.33 0.33 0.67 1.0 18 | 0.67 0.33 0.33 0.67 1.0 19 | 1.00 0.33 0.33 0.67 0.0 20 | 0.33 0.33 0.67 0.67 0.0 21 | 0.67 0.33 0.67 0.67 1.0 22 | 1.00 0.33 0.67 0.67 1.0 23 | 24 | 0.33 0.33 0.33 1.00 1.0 25 | 0.67 0.33 0.33 1.00 1.0 26 | 1.00 0.33 0.33 1.00 1.0 27 | 28 | 0.33 0.67 0.33 0.33 0.0 29 | 0.67 0.67 0.33 0.33 0.0 30 | 0.33 0.67 0.67 0.33 0.0 31 | 0.67 0.67 0.67 0.33 0.0 32 | 0.33 0.67 1.00 0.33 0.0 33 | 0.67 0.67 1.00 0.33 0.0 34 | 35 | 0.33 0.67 0.33 0.67 1.0 36 | 0.67 0.67 0.33 0.67 0.0 37 | 0.33 0.67 0.67 0.67 0.0 38 | 0.67 0.67 0.67 0.67 1.0 39 | 40 | 0.33 0.67 0.33 1.00 1.0 41 | 0.67 0.67 0.33 1.00 1.0 42 | 43 | 0.33 1.00 0.33 0.33 0.0 44 | 0.33 1.00 0.67 0.33 0.0 45 | 0.33 1.00 1.00 0.33 0.0 46 | 47 | 0.33 1.00 0.33 0.67 0.0 48 | 0.33 1.00 0.67 0.67 0.0 49 | 50 | 0.33 1.00 0.33 1.00 1.0 51 | NTestPatterns 6 52 | 0.33 0.33 0.33 1.00 1.0 53 | 0.67 0.33 0.33 1.00 1.0 54 | 1.00 0.33 0.33 1.00 1.0 55 | 56 | 0.33 0.67 0.33 0.33 0.0 57 | 0.67 0.67 0.33 0.33 0.0 58 | 0.33 0.67 0.67 0.33 0.0 59 | -------------------------------------------------------------------------------- /neural_network_test.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include 4 | #include 5 | #include 6 | 7 | #define D 7 8 | #define N 200 9 | #define M 30 10 | #define K 1 11 | #define layer 2 12 | #define eta 0.1 13 | #define alpha 0.9 14 | 15 | #define rando() (rand() * 1.0 / RAND_MAX) 16 | 17 | double w[layer][M+1][M+1]; 18 | double x[N][D+1]; 19 | double a[M+1], z[M+1]; 20 | double y[N][K+1]; 21 | double t[N][K+1]; 22 | double err[layer][M+1]; 23 | double d[layer][M+1][M+1]; 24 | 25 | double tanh(double a){ 26 | return (exp(a) - exp(-a)) / (exp(a) + exp(-a)); 27 | } 28 | 29 | double equal(double a){ 30 | return a; 31 | } 32 | 33 | double sigmoid(double a){ 34 | return 1.0 / (1.0 + exp(-a)); 35 | } 36 | 37 | double feed_forward(double *in, int in_cnt, double *out, int out_cnt, double param[M+1][M+1], double (*trans_method)(double)){ 38 | int i, j; 39 | double a[M+1]; 40 | for(i = 1; i <= out_cnt; i++){ 41 | a[i] = param[i][0]; 42 | for(j = 1; j <= in_cnt; j++){ 43 | a[i] += param[i][j] * in[j]; 44 | } 45 | out[i] = trans_method(a[i]); 46 | } 47 | } 48 | 49 | void predict(double in[D+1], double out[K+1]){ 50 | double hidden_unit[M]; 51 | feed_forward(in, D, hidden_unit, M, w[0], sigmoid); 52 | feed_forward(hidden_unit, M, out, K, w[1], sigmoid); 53 | } 54 | 55 | double get_total_error(int n){ 56 | double ret = 0.0; 57 | int i, k; 58 | for(k = 0; k <= K; k++){ 59 | ret += 0.5 * (y[n][k] - t[n][k]) * (y[n][k] - t[n][k]); 60 | } 61 | return ret; 62 | } 63 | 64 | int main(){ 65 | int i, j, k, n; 66 | char res[5]; 67 | int d1, d2, d3, d4, d7; 68 | double d5, d6; 69 | int loop; 70 | double total_error; 71 | double init_weight[] = {0.5, 1.0 / 15, 1.0 / 200, 1.0 / 100, 1.0 / 50, 1.0 / 40, 1.0 / 100, 1.0 / 100}; 72 | 73 | double test[K+1]; 74 | double in1[8] = {1, 6, 148, 72, 35, 33.6, 0.627, 50}; //Yes 75 | double in2[8] = {1, 1, 85, 66, 29, 26.6, 0.351, 31}; //No 76 | 77 | freopen("pima.tr", "r", stdin); 78 | 79 | srand((int)time(0)); 80 | 81 | for(i = 0; i < N; i++){ 82 | scanf("%d%d%d%d%lf%lf%d%s", &d1, &d2, &d3, &d4, &d5, &d6, &d7, res); 83 | x[i][0] = 1.0; 84 | x[i][1] = 1.0 * d1; 85 | x[i][2] = 1.0 * d2; 86 | x[i][3] = 1.0 * d3; 87 | x[i][4] = 1.0 * d4; 88 | x[i][5] = 1.0 * d5; 89 | x[i][6] = 1.0 * d6; 90 | x[i][7] = 1.0 * d7; 91 | if(strcmp(res, "Yes") != -1){ 92 | t[i][1] = 1.0; 93 | }else{ 94 | t[i][1] = 0.0; 95 | } 96 | } 97 | 98 | for(k = 0; k < layer; k++) 99 | for(i = 0; i <= M; i++) 100 | for(j = 0; j <= M; j++){ 101 | d[k][i][j] = 0; 102 | if(k == 0){ 103 | w[k][i][j] = 2.0 * (rando() - 0.5) * init_weight[j]; 104 | }else{ 105 | w[k][i][j] = 2.0 * (rando() - 0.5) * 0.5; 106 | } 107 | } 108 | 109 | for(loop = 0; loop < 100; loop++){ 110 | total_error = 0; 111 | for(n = 0; n < N; n++){ 112 | feed_forward(x[n], D, z, M, w[0], sigmoid); 113 | feed_forward(z, M, y[n], K, w[1], sigmoid); 114 | total_error += get_total_error(n); 115 | for(i = 1; i <= K; i++){ 116 | err[1][i] = (t[n][i] - y[n][i]) * y[n][i] * (1.0 -y[n][i]); 117 | } 118 | for(i = 1; i <= M; i++){ 119 | err[0][i] = 0; 120 | for(j = 0; j <= K; j++){ 121 | err[0][i] += w[1][j][i] * err[1][j]; 122 | } 123 | err[0][i] *= z[i] * (1.0 - z[i]); 124 | } 125 | for(j = 1; j <= M; j++){ 126 | d[0][j][0] = alpha * d[0][j][0] + eta * err[0][j]; 127 | w[0][j][0] += d[0][j][0]; 128 | for(i = 0; i <= D; i++){ 129 | d[0][j][i] = alpha * d[0][j][i] + eta * err[0][j] * x[n][i]; 130 | w[0][j][i] += d[0][j][i]; 131 | } 132 | } 133 | for(k = 1; k <= K; k++){ 134 | d[1][k][0] = alpha * d[1][k][0] + eta * err[1][k]; 135 | w[1][k][0] += d[1][k][0]; 136 | for(j = 1; j <= M; j++){ 137 | d[1][k][j] = alpha * d[1][k][j] + eta * err[1][k] * z[j]; 138 | w[1][k][j] += d[1][k][j]; 139 | } 140 | } 141 | } 142 | } 143 | predict(in1, test); 144 | printf("%lf\n", test[1]); 145 | predict(in2, test); 146 | printf("%lf\n", test[1]); 147 | return 0; 148 | } 149 | -------------------------------------------------------------------------------- /tags: -------------------------------------------------------------------------------- 1 | !_TAG_FILE_FORMAT 2 /extended format; --format=1 will not append ;" to lines/ 2 | !_TAG_FILE_SORTED 1 /0=unsorted, 1=sorted, 2=foldcase/ 3 | !_TAG_PROGRAM_AUTHOR Darren Hiebert /dhiebert@users.sourceforge.net/ 4 | !_TAG_PROGRAM_NAME Exuberant Ctags // 5 | !_TAG_PROGRAM_URL http://ctags.sourceforge.net /official site/ 6 | !_TAG_PROGRAM_VERSION 5.9~svn20110310 // 7 | ACTIVATION quickprop1.c /^float ACTIVATION(sum)$/;" f 8 | ACTIVATION_PRIME quickprop1.c /^float ACTIVATION_PRIME(value)$/;" f 9 | ASYMSIGMOID quickprop1.c 37;" d file: 10 | BACKWARD_PASS quickprop1.c /^BACKWARD_PASS (goal)$/;" f 11 | BUILD_DATA_STRUCTURES quickprop1.c /^BUILD_DATA_STRUCTURES (ninputs, nhidden, noutputs)$/;" f 12 | CLEAR_SLOPES quickprop1.c /^CLEAR_SLOPES()$/;" f 13 | CONNECT_LAYERS quickprop1.c /^CONNECT_LAYERS (start1, end1, start2, end2)$/;" f 14 | Connections quickprop1.c /^int Connections[N][N]; \/* C[i][j] lists jth unit projecting to unit i *\/$/;" v 15 | DUMP_WEIGHTS quickprop1.c /^DUMP_WEIGHTS(fname)$/;" f 16 | Decay quickprop1.c /^float Decay; \/* Weight decay *\/$/;" v 17 | DeltaWeights quickprop1.c /^float DeltaWeights[N][N]; \/* Change between previous weight and current one *\/$/;" v 18 | DidGradient quickprop1.c /^int DidGradient; \/* Total # patterns that did gradient descent *\/$/;" v 19 | ERRFUN quickprop1.c /^float ERRFUN (desired, actual)$/;" f 20 | Epoch quickprop1.c /^int Epoch; \/* Current epoch number *\/$/;" v 21 | Epsilon quickprop1.c /^float Epsilon; \/* For grad descent if last step was (almost) 0 *\/$/;" v 22 | ErrorSums quickprop1.c /^float ErrorSums[N]; \/* Total error activation for each unit *\/$/;" v 23 | Errors quickprop1.c /^float Errors[N]; \/* Final error value for each unit *\/$/;" v 24 | FORWARD_PASS quickprop1.c /^FORWARD_PASS (input)$/;" f 25 | FirstHidden quickprop1.c /^int FirstHidden; \/* Index of 1st hidden unit *\/$/;" v 26 | FirstOutput quickprop1.c /^int FirstOutput; \/* Index of 1st output unit *\/$/;" v 27 | GET_NETWORK_CONFIGURATION quickprop1.c /^GET_NETWORK_CONFIGURATION(fname)$/;" f 28 | GET_WEIGHTS quickprop1.c /^GET_WEIGHTS(fname)$/;" f 29 | HyperErr quickprop1.c /^int HyperErr; \/* 1 => use atanh error function *\/$/;" v 30 | INITIALIZE_GLOBALS quickprop1.c /^INITIALIZE_GLOBALS()$/;" f 31 | INIT_WEIGHTS quickprop1.c /^INIT_WEIGHTS()$/;" f 32 | KeepScore quickprop1.c /^int KeepScore; \/* 1 => accumulate error score for each epoch *\/$/;" v 33 | MaxFactor quickprop1.c /^float MaxFactor; \/* Don't jump more than this times last step *\/$/;" v 34 | ModeSwitchThreshold quickprop1.c /^float ModeSwitchThreshold; \/* Inside thresh, do grad descent; outside, jump. *\/$/;" v 35 | Momentum quickprop1.c /^float Momentum; \/* Normal old momentum term *\/$/;" v 36 | N quickprop1.c 34;" d file: 37 | NTestPatterns quickprop1.c /^int NTestPatterns; \/* !! Not in Lisp version. Needed here. *\/$/;" v 38 | NTrainingPatterns quickprop1.c /^int NTrainingPatterns; \/* !! Not in Lisp version. Needed here. *\/$/;" v 39 | Nconnections quickprop1.c /^int Nconnections[N]; \/* # of INCOMING connections per unit *\/$/;" v 40 | Nhidden quickprop1.c /^int Nhidden; \/* Number of hidden units *\/$/;" v 41 | Ninputs quickprop1.c /^int Ninputs; \/* Number of input units *\/$/;" v 42 | Noutputs quickprop1.c /^int Noutputs; \/* Number of output units *\/$/;" v 43 | Nunits quickprop1.c /^int Nunits; \/* Total number of units in net *\/$/;" v 44 | Outputs quickprop1.c /^float Outputs[N]; \/* Final output value for each unit *\/$/;" v 45 | PrevSlopes quickprop1.c /^float PrevSlopes[N][N]; \/* Similarly, for the last position visited *\/$/;" v 46 | RANDOM_WEIGHT quickprop1.c /^float RANDOM_WEIGHT (range)$/;" f 47 | Restart quickprop1.c /^int Restart; \/* 1 => restart when max epochs reached *\/$/;" v 48 | SIGMOID quickprop1.c 36;" d file: 49 | ScoreThreshold quickprop1.c /^float ScoreThreshold; \/* This close to desired value => bit is correct *\/$/;" v 50 | SigmoidPrimeOffset quickprop1.c /^float SigmoidPrimeOffset; \/* Add to sigmoid-prime to kill flat spots *\/$/;" v 51 | SingleEpoch quickprop1.c /^int SingleEpoch; \/* 1 => Pause after each training epoch *\/$/;" v 52 | SinglePass quickprop1.c /^int SinglePass; \/* 1 => Pause after forward\/backward cycle *\/$/;" v 53 | Slopes quickprop1.c /^float Slopes[N][N]; \/* Accumulated slope value for each position *\/$/;" v 54 | SplitEpsilon quickprop1.c /^int SplitEpsilon; \/* 1 => divide epsilon by fan-in before use *\/$/;" v 55 | Step quickprop1.c /^int Step; \/* Turned to 1 after each pause, briefly *\/$/;" v 56 | TEST quickprop1.c /^TEST ()$/;" f 57 | TRAIN quickprop1.c /^TRAIN ( times )$/;" f 58 | TRAIN_ONE_EPOCH quickprop1.c /^TRAIN_ONE_EPOCH()$/;" f 59 | TestInputs quickprop1.c /^float TestInputs[200][N];$/;" v 60 | TestOutputs quickprop1.c /^float TestOutputs[200][N];$/;" v 61 | TotalError quickprop1.c /^float TotalError; \/* Total output error for one epoch *\/$/;" v 62 | TotalErrorBits quickprop1.c /^int TotalErrorBits; \/* Total # bits in epoch that were wrong *\/$/;" v 63 | TrainingInputs quickprop1.c /^float TrainingInputs[200][N];$/;" v 64 | TrainingOutputs quickprop1.c /^float TrainingOutputs[200][N];$/;" v 65 | UPDATE_WEIGHTS quickprop1.c /^UPDATE_WEIGHTS()$/;" f 66 | Unit_type quickprop1.c /^int Unit_type; \/* Type of hidden and Output Units: 1=> SIGMOID,and$/;" v 67 | WeightRange quickprop1.c /^float WeightRange; \/* Random-init weights in range [-WR,+WR] *\/$/;" v 68 | Weights quickprop1.c /^float Weights[N][N]; \/* W[i][j] holds weight of C[i][j] *\/$/;" v 69 | main quickprop1.c /^main ()$/;" f 70 | tinputs quickprop1.c /^float tinputs[N]; \/* Input vector to be tested. *\/$/;" v 71 | -------------------------------------------------------------------------------- /pima.tr: -------------------------------------------------------------------------------- 1 | 5 86 68 28 30.2 0.364 24 No 2 | 7 195 70 33 25.1 0.163 55 Yes 3 | 5 77 82 41 35.8 0.156 35 No 4 | 0 165 76 43 47.9 0.259 26 No 5 | 0 107 60 25 26.4 0.133 23 No 6 | 5 97 76 27 35.6 0.378 52 Yes 7 | 3 83 58 31 34.3 0.336 25 No 8 | 1 193 50 16 25.9 0.655 24 No 9 | 3 142 80 15 32.4 0.200 63 No 10 | 2 128 78 37 43.3 1.224 31 Yes 11 | 0 137 40 35 43.1 2.288 33 Yes 12 | 9 154 78 30 30.9 0.164 45 No 13 | 1 189 60 23 30.1 0.398 59 Yes 14 | 12 92 62 7 27.6 0.926 44 Yes 15 | 1 86 66 52 41.3 0.917 29 No 16 | 4 99 76 15 23.2 0.223 21 No 17 | 1 109 60 8 25.4 0.947 21 No 18 | 11 143 94 33 36.6 0.254 51 Yes 19 | 1 149 68 29 29.3 0.349 42 Yes 20 | 0 139 62 17 22.1 0.207 21 No 21 | 2 99 70 16 20.4 0.235 27 No 22 | 1 100 66 29 32.0 0.444 42 No 23 | 4 83 86 19 29.3 0.317 34 No 24 | 0 101 64 17 21.0 0.252 21 No 25 | 1 87 68 34 37.6 0.401 24 No 26 | 9 164 84 21 30.8 0.831 32 Yes 27 | 1 99 58 10 25.4 0.551 21 No 28 | 0 140 65 26 42.6 0.431 24 Yes 29 | 5 108 72 43 36.1 0.263 33 No 30 | 2 110 74 29 32.4 0.698 27 No 31 | 1 79 60 42 43.5 0.678 23 No 32 | 3 148 66 25 32.5 0.256 22 No 33 | 0 121 66 30 34.3 0.203 33 Yes 34 | 3 158 64 13 31.2 0.295 24 No 35 | 2 105 80 45 33.7 0.711 29 Yes 36 | 13 145 82 19 22.2 0.245 57 No 37 | 1 79 80 25 25.4 0.583 22 No 38 | 1 71 48 18 20.4 0.323 22 No 39 | 0 102 86 17 29.3 0.695 27 No 40 | 0 119 66 27 38.8 0.259 22 No 41 | 8 176 90 34 33.7 0.467 58 Yes 42 | 1 97 68 21 27.2 1.095 22 No 43 | 4 129 60 12 27.5 0.527 31 No 44 | 1 97 64 19 18.2 0.299 21 No 45 | 0 86 68 32 35.8 0.238 25 No 46 | 2 125 60 20 33.8 0.088 31 No 47 | 5 123 74 40 34.1 0.269 28 No 48 | 2 92 76 20 24.2 1.698 28 No 49 | 3 171 72 33 33.3 0.199 24 Yes 50 | 1 199 76 43 42.9 1.394 22 Yes 51 | 3 116 74 15 26.3 0.107 24 No 52 | 2 83 66 23 32.2 0.497 22 No 53 | 8 154 78 32 32.4 0.443 45 Yes 54 | 1 114 66 36 38.1 0.289 21 No 55 | 1 106 70 28 34.2 0.142 22 No 56 | 4 127 88 11 34.5 0.598 28 No 57 | 1 124 74 36 27.8 0.100 30 No 58 | 1 109 38 18 23.1 0.407 26 No 59 | 2 123 48 32 42.1 0.520 26 No 60 | 8 167 106 46 37.6 0.165 43 Yes 61 | 7 184 84 33 35.5 0.355 41 Yes 62 | 1 96 64 27 33.2 0.289 21 No 63 | 10 129 76 28 35.9 0.280 39 No 64 | 6 92 62 32 32.0 0.085 46 No 65 | 6 109 60 27 25.0 0.206 27 No 66 | 5 139 80 35 31.6 0.361 25 Yes 67 | 6 134 70 23 35.4 0.542 29 Yes 68 | 3 106 54 21 30.9 0.292 24 No 69 | 0 131 66 40 34.3 0.196 22 Yes 70 | 0 135 94 46 40.6 0.284 26 No 71 | 5 158 84 41 39.4 0.395 29 Yes 72 | 3 112 74 30 31.6 0.197 25 Yes 73 | 8 181 68 36 30.1 0.615 60 Yes 74 | 2 121 70 32 39.1 0.886 23 No 75 | 1 168 88 29 35.0 0.905 52 Yes 76 | 1 144 82 46 46.1 0.335 46 Yes 77 | 2 101 58 17 24.2 0.614 23 No 78 | 2 96 68 13 21.1 0.647 26 No 79 | 3 107 62 13 22.9 0.678 23 Yes 80 | 12 121 78 17 26.5 0.259 62 No 81 | 2 100 64 23 29.7 0.368 21 No 82 | 4 154 72 29 31.3 0.338 37 No 83 | 6 125 78 31 27.6 0.565 49 Yes 84 | 10 125 70 26 31.1 0.205 41 Yes 85 | 2 122 76 27 35.9 0.483 26 No 86 | 2 114 68 22 28.7 0.092 25 No 87 | 1 115 70 30 34.6 0.529 32 Yes 88 | 7 114 76 17 23.8 0.466 31 No 89 | 2 115 64 22 30.8 0.421 21 No 90 | 1 130 60 23 28.6 0.692 21 No 91 | 1 79 75 30 32.0 0.396 22 No 92 | 4 112 78 40 39.4 0.236 38 No 93 | 7 150 78 29 35.2 0.692 54 Yes 94 | 1 91 54 25 25.2 0.234 23 No 95 | 1 100 72 12 25.3 0.658 28 No 96 | 12 140 82 43 39.2 0.528 58 Yes 97 | 4 110 76 20 28.4 0.118 27 No 98 | 2 94 76 18 31.6 0.649 23 No 99 | 2 84 50 23 30.4 0.968 21 No 100 | 10 148 84 48 37.6 1.001 51 Yes 101 | 3 61 82 28 34.4 0.243 46 No 102 | 4 117 62 12 29.7 0.380 30 Yes 103 | 3 99 80 11 19.3 0.284 30 No 104 | 3 80 82 31 34.2 1.292 27 Yes 105 | 4 154 62 31 32.8 0.237 23 No 106 | 6 103 72 32 37.7 0.324 55 No 107 | 6 111 64 39 34.2 0.260 24 No 108 | 0 124 70 20 27.4 0.254 36 Yes 109 | 1 143 74 22 26.2 0.256 21 No 110 | 1 81 74 41 46.3 1.096 32 No 111 | 4 189 110 31 28.5 0.680 37 No 112 | 4 116 72 12 22.1 0.463 37 No 113 | 7 103 66 32 39.1 0.344 31 Yes 114 | 8 124 76 24 28.7 0.687 52 Yes 115 | 1 71 78 50 33.2 0.422 21 No 116 | 0 137 84 27 27.3 0.231 59 No 117 | 9 112 82 32 34.2 0.260 36 Yes 118 | 4 148 60 27 30.9 0.150 29 Yes 119 | 1 136 74 50 37.4 0.399 24 No 120 | 9 145 80 46 37.9 0.637 40 Yes 121 | 1 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0.39081936 1 161 | 0.66049881 0.89919213 1 162 | -0.28658801 0.73375946 1 163 | -0.32588080 0.39865509 1 164 | -0.25204565 0.67358326 1 165 | 0.37259022 0.49785904 1 166 | -0.29096564 1.04372060 1 167 | -0.30469807 0.86858292 1 168 | -0.21389978 1.09317811 1 169 | -0.36830015 0.75639546 1 170 | -0.46928218 0.88775091 1 171 | 0.39350146 0.77975197 1 172 | -0.45639966 0.80523454 1 173 | 0.51128242 0.76606136 1 174 | 0.22550468 0.46451215 1 175 | 0.01462984 0.40190926 1 176 | -0.19172785 0.80943313 1 177 | 0.38323479 0.75601744 1 178 | 0.49791612 0.61334375 1 179 | 0.35335230 0.77324337 1 180 | -0.34722575 0.70177856 1 181 | 0.58380468 0.76357539 1 182 | -0.13727764 0.71246351 1 183 | 0.38827268 0.44977123 1 184 | -0.53172709 0.61934293 1 185 | -0.11684624 0.87851210 1 186 | 0.54335864 0.41174865 1 187 | -0.45399302 0.66512988 1 188 | -0.21913200 0.83484947 1 189 | 0.30485742 0.98028760 1 190 | 0.65676798 0.75766017 1 191 | 0.61420447 0.75039019 1 192 | -0.45809964 0.77968606 1 193 | -0.21617465 0.88626305 1 194 | -0.26016108 0.81008591 1 195 | 0.31884531 0.84517725 1 196 | -0.23727415 0.80178784 1 197 | 0.58310323 0.77709806 1 198 | 0.02841337 0.75792620 1 199 | -0.41840136 0.68041440 1 200 | 0.67412880 0.60245461 1 201 | -0.25278281 0.70526103 1 202 | 0.51609843 0.62092390 1 203 | 0.20392294 0.91641482 1 204 | -0.17207124 1.00884096 1 205 | 0.27274507 0.29346977 1 206 | 0.07634798 0.56222204 1 207 | -0.36653499 0.64831007 1 208 | 0.44290673 0.80087721 1 209 | -0.19976385 0.54295162 1 210 | -0.54075738 0.65293033 1 211 | -0.07060266 1.00296912 1 212 | 0.50715054 0.35045758 1 213 | -0.06048611 0.62982713 1 214 | 0.21532928 0.60260249 1 215 | 0.46809108 0.87182416 1 216 | -0.29888511 0.73669866 1 217 | 0.86129620 0.47289330 1 218 | 0.70120877 0.74572893 1 219 | -0.11342797 0.60067099 1 220 | 0.31234354 0.90756345 1 221 | -0.12172541 0.84112851 1 222 | 0.36867857 0.37052586 1 223 | 0.57311489 0.40949740 1 224 | -0.25841225 0.67192335 1 225 | 0.30937186 0.50823318 1 226 | 0.43319338 0.77016967 1 227 | -0.30448035 0.57820106 1 228 | 0.44276338 0.58023403 1 229 | -0.19442057 0.89876808 1 230 | -0.06105237 0.74184946 1 231 | 0.07619347 0.35386246 1 232 | 0.85826993 0.95819523 1 233 | 0.37039200 0.72342401 1 234 | 0.51481515 0.76203996 1 235 | 0.43127521 0.54259166 1 236 | 0.42286091 0.65242185 1 237 | 0.29815001 0.93453682 1 238 | 0.37128253 0.70089181 1 239 | -0.51528729 0.76473490 1 240 | 0.38525783 0.65528189 1 241 | -0.34825368 0.50529981 1 242 | 0.68510504 0.78067440 1 243 | -0.36528923 0.45703265 1 244 | -0.40903577 0.74230433 1 245 | 0.43574387 0.44689789 1 246 | 0.26887846 0.44559230 1 247 | -0.49254862 1.01443372 1 248 | 0.07615960 0.63795180 1 249 | 0.49226224 0.46876241 1 250 | -0.40249641 0.71301084 1 251 | -------------------------------------------------------------------------------- /pima.te: -------------------------------------------------------------------------------- 1 | npreg glu bp skin bmi ped age type 2 | 6 148 72 35 33.6 0.627 50 Yes 3 | 1 85 66 29 26.6 0.351 31 No 4 | 1 89 66 23 28.1 0.167 21 No 5 | 3 78 50 32 31.0 0.248 26 Yes 6 | 2 197 70 45 30.5 0.158 53 Yes 7 | 5 166 72 19 25.8 0.587 51 Yes 8 | 0 118 84 47 45.8 0.551 31 Yes 9 | 1 103 30 38 43.3 0.183 33 No 10 | 3 126 88 41 39.3 0.704 27 No 11 | 9 119 80 35 29.0 0.263 29 Yes 12 | 1 97 66 15 23.2 0.487 22 No 13 | 5 109 75 26 36.0 0.546 60 No 14 | 3 88 58 11 24.8 0.267 22 No 15 | 10 122 78 31 27.6 0.512 45 No 16 | 4 103 60 33 24.0 0.966 33 No 17 | 9 102 76 37 32.9 0.665 46 Yes 18 | 2 90 68 42 38.2 0.503 27 Yes 19 | 4 111 72 47 37.1 1.390 56 Yes 20 | 3 180 64 25 34.0 0.271 26 No 21 | 7 106 92 18 22.7 0.235 48 No 22 | 9 171 110 24 45.4 0.721 54 Yes 23 | 0 180 66 39 42.0 1.893 25 Yes 24 | 2 71 70 27 28.0 0.586 22 No 25 | 1 103 80 11 19.4 0.491 22 No 26 | 1 101 50 15 24.2 0.526 26 No 27 | 5 88 66 21 24.4 0.342 30 No 28 | 7 150 66 42 34.7 0.718 42 No 29 | 1 73 50 10 23.0 0.248 21 No 30 | 0 105 64 41 41.5 0.173 22 No 31 | 5 99 74 27 29.0 0.203 32 No 32 | 0 109 88 30 32.5 0.855 38 Yes 33 | 1 95 66 13 19.6 0.334 25 No 34 | 4 146 85 27 28.9 0.189 27 No 35 | 2 100 66 20 32.9 0.867 28 Yes 36 | 4 129 86 20 35.1 0.231 23 No 37 | 5 95 72 33 37.7 0.370 27 No 38 | 2 112 66 22 25.0 0.307 24 No 39 | 3 113 44 13 22.4 0.140 22 No 40 | 7 83 78 26 29.3 0.767 36 No 41 | 0 101 65 28 24.6 0.237 22 No 42 | 13 106 72 54 36.6 0.178 45 No 43 | 2 100 68 25 38.5 0.324 26 No 44 | 15 136 70 32 37.1 0.153 43 Yes 45 | 4 123 80 15 32.0 0.443 34 No 46 | 7 81 78 40 46.7 0.261 42 No 47 | 2 92 62 28 31.6 0.130 24 No 48 | 6 93 50 30 28.7 0.356 23 No 49 | 1 122 90 51 49.7 0.325 31 Yes 50 | 1 81 72 18 26.6 0.283 24 No 51 | 1 126 56 29 28.7 0.801 21 No 52 | 4 144 58 28 29.5 0.287 37 No 53 | 1 89 76 34 31.2 0.192 23 No 54 | 7 160 54 32 30.5 0.588 39 Yes 55 | 4 97 60 23 28.2 0.443 22 No 56 | 0 162 76 56 53.2 0.759 25 Yes 57 | 2 107 74 30 33.6 0.404 23 No 58 | 1 88 30 42 55.0 0.496 26 Yes 59 | 1 117 88 24 34.5 0.403 40 Yes 60 | 4 173 70 14 29.7 0.361 33 Yes 61 | 3 170 64 37 34.5 0.356 30 Yes 62 | 8 84 74 31 38.3 0.457 39 No 63 | 0 100 70 26 30.8 0.597 21 No 64 | 0 93 60 25 28.7 0.532 22 No 65 | 5 106 82 30 39.5 0.286 38 No 66 | 2 108 52 26 32.5 0.318 22 No 67 | 2 106 64 35 30.5 1.400 34 No 68 | 2 90 70 17 27.3 0.085 22 No 69 | 9 156 86 28 34.3 1.189 42 Yes 70 | 1 153 82 42 40.6 0.687 23 No 71 | 7 152 88 44 50.0 0.337 36 Yes 72 | 2 88 74 19 29.0 0.229 22 No 73 | 17 163 72 41 40.9 0.817 47 Yes 74 | 4 151 90 38 29.7 0.294 36 No 75 | 7 102 74 40 37.2 0.204 45 No 76 | 0 114 80 34 44.2 0.167 27 No 77 | 6 104 74 18 29.9 0.722 41 Yes 78 | 2 75 64 24 29.7 0.370 33 No 79 | 8 179 72 42 32.7 0.719 36 Yes 80 | 0 129 110 46 67.1 0.319 26 Yes 81 | 1 128 98 41 32.0 1.321 33 Yes 82 | 8 109 76 39 27.9 0.640 31 Yes 83 | 4 109 64 44 34.8 0.905 26 Yes 84 | 0 113 80 16 31.0 0.874 21 No 85 | 0 108 68 20 27.3 0.787 32 No 86 | 5 111 72 28 23.9 0.407 27 No 87 | 8 196 76 29 37.5 0.605 57 Yes 88 | 2 81 60 22 27.7 0.290 25 No 89 | 0 147 85 54 42.8 0.375 24 No 90 | 5 109 62 41 35.8 0.514 25 Yes 91 | 6 125 68 30 30.0 0.464 32 No 92 | 5 85 74 22 29.0 1.224 32 Yes 93 | 7 142 60 33 28.8 0.687 61 No 94 | 1 100 66 15 23.6 0.666 26 No 95 | 1 87 78 27 34.6 0.101 22 No 96 | 3 162 52 38 37.2 0.652 24 Yes 97 | 4 197 70 39 36.7 2.329 31 No 98 | 0 117 80 31 45.2 0.089 24 No 99 | 6 134 80 37 46.2 0.238 46 Yes 100 | 3 74 68 28 29.7 0.293 23 No 101 | 7 181 84 21 35.9 0.586 51 Yes 102 | 0 179 90 27 44.1 0.686 23 Yes 103 | 1 91 64 24 29.2 0.192 21 No 104 | 4 91 70 32 33.1 0.446 22 No 105 | 6 119 50 22 27.1 1.318 33 Yes 106 | 2 146 76 35 38.2 0.329 29 No 107 | 9 184 85 15 30.0 1.213 49 Yes 108 | 0 165 90 33 52.3 0.427 23 No 109 | 9 124 70 33 35.4 0.282 34 No 110 | 1 111 86 19 30.1 0.143 23 No 111 | 2 90 80 14 24.4 0.249 24 No 112 | 1 113 64 35 33.6 0.543 21 Yes 113 | 3 111 56 39 30.1 0.557 30 No 114 | 11 155 76 28 33.3 1.353 51 Yes 115 | 4 95 70 32 32.1 0.612 24 No 116 | 5 96 74 18 33.6 0.997 43 No 117 | 2 128 64 42 40.0 1.101 24 No 118 | 10 101 86 37 45.6 1.136 38 Yes 119 | 2 108 62 32 25.2 0.128 21 No 120 | 2 100 70 52 40.5 0.677 25 No 121 | 7 106 60 24 26.5 0.296 29 Yes 122 | 0 104 64 23 27.8 0.454 23 No 123 | 2 108 62 10 25.3 0.881 22 No 124 | 7 133 88 15 32.4 0.262 37 No 125 | 7 136 74 26 26.0 0.647 51 No 126 | 1 119 86 39 45.6 0.808 29 Yes 127 | 4 96 56 17 20.8 0.340 26 No 128 | 0 78 88 29 36.9 0.434 21 No 129 | 0 107 62 30 36.6 0.757 25 Yes 130 | 6 151 62 31 35.5 0.692 28 No 131 | 2 146 70 38 28.0 0.337 29 Yes 132 | 0 126 84 29 30.7 0.520 24 No 133 | 2 144 58 33 31.6 0.422 25 Yes 134 | 2 120 76 37 39.7 0.215 29 No 135 | 10 161 68 23 25.5 0.326 47 Yes 136 | 0 128 68 19 30.5 1.391 25 Yes 137 | 2 124 68 28 32.9 0.875 30 Yes 138 | 2 155 74 17 26.6 0.433 27 Yes 139 | 3 113 50 10 29.5 0.626 25 No 140 | 7 109 80 31 35.9 1.127 43 Yes 141 | 3 115 66 39 38.1 0.150 28 No 142 | 13 152 90 33 26.8 0.731 43 Yes 143 | 2 112 75 32 35.7 0.148 21 No 144 | 1 157 72 21 25.6 0.123 24 No 145 | 1 122 64 32 35.1 0.692 30 Yes 146 | 2 102 86 36 45.5 0.127 23 Yes 147 | 6 105 70 32 30.8 0.122 37 No 148 | 8 118 72 19 23.1 1.476 46 No 149 | 2 87 58 16 32.7 0.166 25 No 150 | 1 95 60 18 23.9 0.260 22 No 151 | 1 130 70 13 25.9 0.472 22 No 152 | 1 95 74 21 25.9 0.673 36 No 153 | 8 126 88 36 38.5 0.349 49 No 154 | 1 139 46 19 28.7 0.654 22 No 155 | 3 99 62 19 21.8 0.279 26 No 156 | 1 125 50 40 33.3 0.962 28 Yes 157 | 1 196 76 36 36.5 0.875 29 Yes 158 | 5 189 64 33 31.2 0.583 29 Yes 159 | 5 103 108 37 39.2 0.305 65 No 160 | 4 147 74 25 34.9 0.385 30 No 161 | 5 99 54 28 34.0 0.499 30 No 162 | 3 81 86 16 27.5 0.306 22 No 163 | 3 173 82 48 38.4 2.137 25 Yes 164 | 0 84 64 22 35.8 0.545 21 No 165 | 0 98 82 15 25.2 0.299 22 No 166 | 1 87 60 37 37.2 0.509 22 No 167 | 0 93 100 39 43.4 1.021 35 No 168 | 0 105 68 22 20.0 0.236 22 No 169 | 1 90 62 18 25.1 1.268 25 No 170 | 1 125 70 24 24.3 0.221 25 No 171 | 1 119 54 13 22.3 0.205 24 No 172 | 5 116 74 29 32.3 0.660 35 Yes 173 | 8 105 100 36 43.3 0.239 45 Yes 174 | 3 100 68 23 31.6 0.949 28 No 175 | 1 131 64 14 23.7 0.389 21 No 176 | 2 127 58 24 27.7 1.600 25 No 177 | 3 96 56 34 24.7 0.944 39 No 178 | 3 193 70 31 34.9 0.241 25 Yes 179 | 5 136 84 41 35.0 0.286 35 Yes 180 | 9 72 78 25 31.6 0.280 38 No 181 | 1 172 68 49 42.4 0.702 28 Yes 182 | 6 102 90 39 35.7 0.674 28 No 183 | 1 112 72 30 34.4 0.528 25 No 184 | 1 143 84 23 42.4 1.076 22 No 185 | 3 173 84 33 35.7 0.258 22 Yes 186 | 4 144 82 32 38.5 0.554 37 Yes 187 | 3 129 64 29 26.4 0.219 28 Yes 188 | 1 119 88 41 45.3 0.507 26 No 189 | 2 94 68 18 26.0 0.561 21 No 190 | 0 102 64 46 40.6 0.496 21 No 191 | 8 151 78 32 42.9 0.516 36 Yes 192 | 1 181 64 30 34.1 0.328 38 Yes 193 | 1 95 82 25 35.0 0.233 43 Yes 194 | 3 89 74 16 30.4 0.551 38 No 195 | 1 80 74 11 30.0 0.527 22 No 196 | 1 90 68 8 24.5 1.138 36 No 197 | 0 189 104 25 34.3 0.435 41 Yes 198 | 4 117 64 27 33.2 0.230 24 No 199 | 0 180 78 63 59.4 2.420 25 Yes 200 | 0 104 64 37 33.6 0.510 22 Yes 201 | 0 120 74 18 30.5 0.285 26 No 202 | 1 82 64 13 21.2 0.415 23 No 203 | 0 91 68 32 39.9 0.381 25 No 204 | 9 134 74 33 25.9 0.460 81 No 205 | 9 120 72 22 20.8 0.733 48 No 206 | 8 74 70 40 35.3 0.705 39 No 207 | 5 88 78 30 27.6 0.258 37 No 208 | 0 124 56 13 21.8 0.452 21 No 209 | 0 97 64 36 36.8 0.600 25 No 210 | 1 144 82 40 41.3 0.607 28 No 211 | 0 137 70 38 33.2 0.170 22 No 212 | 4 132 86 31 28.0 0.419 63 No 213 | 3 158 70 30 35.5 0.344 35 Yes 214 | 0 123 88 37 35.2 0.197 29 No 215 | 0 84 82 31 38.2 0.233 23 No 216 | 0 135 68 42 42.3 0.365 24 Yes 217 | 1 139 62 41 40.7 0.536 21 No 218 | 0 173 78 32 46.5 1.159 58 No 219 | 2 83 65 28 36.8 0.629 24 No 220 | 2 89 90 30 33.5 0.292 42 No 221 | 4 99 68 38 32.8 0.145 33 No 222 | 4 125 70 18 28.9 1.144 45 Yes 223 | 2 81 72 15 30.1 0.547 25 No 224 | 6 154 74 32 29.3 0.839 39 No 225 | 2 117 90 19 25.2 0.313 21 No 226 | 3 84 72 32 37.2 0.267 28 No 227 | 7 94 64 25 33.3 0.738 41 No 228 | 3 96 78 39 37.3 0.238 40 No 229 | 12 84 72 31 29.7 0.297 46 Yes 230 | 3 99 54 19 25.6 0.154 24 No 231 | 3 163 70 18 31.6 0.268 28 Yes 232 | 9 145 88 34 30.3 0.771 53 Yes 233 | 6 129 90 7 19.6 0.582 60 No 234 | 2 68 70 32 25.0 0.187 25 No 235 | 3 87 60 18 21.8 0.444 21 No 236 | 2 122 60 18 29.8 0.717 22 No 237 | 1 77 56 30 33.3 1.251 24 No 238 | 0 127 80 37 36.3 0.804 23 No 239 | 3 128 72 25 32.4 0.549 27 Yes 240 | 10 90 85 32 34.9 0.825 56 Yes 241 | 4 84 90 23 39.5 0.159 25 No 242 | 1 88 78 29 32.0 0.365 29 No 243 | 8 186 90 35 34.5 0.423 37 Yes 244 | 5 187 76 27 43.6 1.034 53 Yes 245 | 4 131 68 21 33.1 0.160 28 No 246 | 1 116 70 28 27.4 0.204 21 No 247 | 3 84 68 30 31.9 0.591 25 No 248 | 1 88 62 24 29.9 0.422 23 No 249 | 1 84 64 23 36.9 0.471 28 No 250 | 11 103 68 40 46.2 0.126 42 No 251 | 6 99 60 19 26.9 0.497 32 No 252 | 1 99 72 30 38.6 0.412 21 No 253 | 3 111 58 31 29.5 0.430 22 No 254 | 2 98 60 17 34.7 0.198 22 No 255 | 1 143 86 30 30.1 0.892 23 No 256 | 1 119 44 47 35.5 0.280 25 No 257 | 6 108 44 20 24.0 0.813 35 No 258 | 3 176 86 27 33.3 1.154 52 Yes 259 | 11 111 84 40 46.8 0.925 45 Yes 260 | 2 112 78 50 39.4 0.175 24 No 261 | 2 82 52 22 28.5 1.699 25 No 262 | 6 123 72 45 33.6 0.733 34 No 263 | 1 89 24 19 27.8 0.559 21 No 264 | 1 108 88 19 27.1 0.400 24 No 265 | 1 124 60 32 35.8 0.514 21 No 266 | 1 181 78 42 40.0 1.258 22 Yes 267 | 1 92 62 25 19.5 0.482 25 No 268 | 0 152 82 39 41.5 0.270 27 No 269 | 3 174 58 22 32.9 0.593 36 Yes 270 | 6 105 80 28 32.5 0.878 26 No 271 | 11 138 74 26 36.1 0.557 50 Yes 272 | 2 68 62 13 20.1 0.257 23 No 273 | 9 112 82 24 28.2 1.282 50 Yes 274 | 0 94 70 27 43.5 0.347 21 No 275 | 4 90 88 47 37.7 0.362 29 No 276 | 4 94 65 22 24.7 0.148 21 No 277 | 0 102 78 40 34.5 0.238 24 No 278 | 1 128 82 17 27.5 0.115 22 No 279 | 7 97 76 32 40.9 0.871 32 Yes 280 | 1 100 74 12 19.5 0.149 28 No 281 | 3 103 72 30 27.6 0.730 27 No 282 | 0 179 50 36 37.8 0.455 22 Yes 283 | 11 136 84 35 28.3 0.260 42 Yes 284 | 1 117 60 23 33.8 0.466 27 No 285 | 2 155 52 27 38.7 0.240 25 Yes 286 | 2 101 58 35 21.8 0.155 22 No 287 | 1 112 80 45 34.8 0.217 24 No 288 | 4 145 82 18 32.5 0.235 70 Yes 289 | 10 111 70 27 27.5 0.141 40 Yes 290 | 6 98 58 33 34.0 0.430 43 No 291 | 6 165 68 26 33.6 0.631 49 No 292 | 10 68 106 23 35.5 0.285 47 No 293 | 3 123 100 35 57.3 0.880 22 No 294 | 0 162 76 36 49.6 0.364 26 Yes 295 | 0 95 64 39 44.6 0.366 22 No 296 | 2 129 74 26 33.2 0.591 25 No 297 | 1 107 50 19 28.3 0.181 29 No 298 | 7 142 90 24 30.4 0.128 43 Yes 299 | 3 169 74 19 29.9 0.268 31 Yes 300 | 6 80 80 36 39.8 0.177 28 No 301 | 2 127 46 21 34.4 0.176 22 No 302 | 2 93 64 32 38.0 0.674 23 Yes 303 | 5 126 78 27 29.6 0.439 40 No 304 | 10 129 62 36 41.2 0.441 38 Yes 305 | 0 134 58 20 26.4 0.352 21 No 306 | 7 187 50 33 33.9 0.826 34 Yes 307 | 3 173 78 39 33.8 0.970 31 Yes 308 | 10 94 72 18 23.1 0.595 56 No 309 | 1 108 60 46 35.5 0.415 24 No 310 | 5 117 86 30 39.1 0.251 42 No 311 | 1 116 78 29 36.1 0.496 25 No 312 | 0 141 84 26 32.4 0.433 22 No 313 | 2 174 88 37 44.5 0.646 24 Yes 314 | 2 106 56 27 29.0 0.426 22 No 315 | 0 126 86 27 27.4 0.515 21 No 316 | 8 65 72 23 32.0 0.600 42 No 317 | 2 99 60 17 36.6 0.453 21 No 318 | 11 120 80 37 42.3 0.785 48 Yes 319 | 3 102 44 20 30.8 0.400 26 No 320 | 1 109 58 18 28.5 0.219 22 No 321 | 13 153 88 37 40.6 1.174 39 No 322 | 12 100 84 33 30.0 0.488 46 No 323 | 1 147 94 41 49.3 0.358 27 Yes 324 | 3 187 70 22 36.4 0.408 36 Yes 325 | 1 121 78 39 39.0 0.261 28 No 326 | 3 108 62 24 26.0 0.223 25 No 327 | 0 181 88 44 43.3 0.222 26 Yes 328 | 1 128 88 39 36.5 1.057 37 Yes 329 | 2 88 58 26 28.4 0.766 22 No 330 | 9 170 74 31 44.0 0.403 43 Yes 331 | 10 101 76 48 32.9 0.171 63 No 332 | 5 121 72 23 26.2 0.245 30 No 333 | 1 93 70 31 30.4 0.315 23 No 334 | -------------------------------------------------------------------------------- /pima_tr_trans.csv: -------------------------------------------------------------------------------- 1 | 0.357142857142857 0.4321608040201 0.618181818181818 0.282828282828283 0.630480167014614 0.159090909090909 0.380952380952381 -0.5 2 | 0.5 0.979899497487437 0.636363636363636 0.333333333333333 0.524008350730689 0.0712412587412588 0.873015873015873 0.5 3 | 0.357142857142857 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0.171717171717172 0.488517745302714 0.195367132867133 0.523809523809524 0.5 148 | 0 0.904522613065327 0.818181818181818 0.262626262626263 0.762004175365344 0.137237762237762 0.555555555555556 0.5 149 | 0.142857142857143 0.613065326633166 0.636363636363636 0.272727272727273 0.768267223382046 0.148601398601399 0.428571428571429 -0.5 150 | 0.0714285714285714 0.452261306532663 0.563636363636364 0.121212121212121 0.567849686847599 0.253496503496503 0.380952380952381 -0.5 151 | 0.214285714285714 0.603015075376884 0.636363636363636 0.303030303030303 0.895615866388309 0.197552447552448 0.476190476190476 -0.5 152 | 0.428571428571429 0.773869346733668 0.709090909090909 0.414141414141414 0.962421711899791 0.249562937062937 0.428571428571429 -0.5 153 | 0.142857142857143 0.281407035175879 0.509090909090909 0.282828282828283 0.505219206680585 0.145104895104895 0.349206349206349 -0.5 154 | 0 0.889447236180904 0.545454545454545 0.292929292929293 0.722338204592902 0.468531468531469 0.333333333333333 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0.634920634920635 0.5 163 | 0.142857142857143 0.989949748743719 0.636363636363636 1 0.724425887265136 0.251311188811189 0.984126984126984 0.5 164 | 0.142857142857143 0.71356783919598 0.745454545454545 0.181818181818182 0.515657620041754 0.332604895104895 0.333333333333333 -0.5 165 | 0.571428571428571 0.633165829145729 0.672727272727273 0.383838383838384 0.54070981210856 0.0708041958041958 0.619047619047619 -0.5 166 | 0.214285714285714 0.793969849246231 0.690909090909091 0.363636363636364 0.659707724425887 0.371940559440559 0.444444444444444 0.5 167 | 0.214285714285714 0.653266331658292 0.709090909090909 0.232323232323232 0.592901878914405 0.141171328671329 0.53968253968254 0.5 168 | 0.142857142857143 0.50251256281407 0.490909090909091 0.282828282828283 0.789144050104384 0.217657342657343 0.380952380952381 -0.5 169 | 0.0714285714285714 0.824120603015075 0.745454545454545 0.434343434343434 0.684759916492693 0.149038461538462 0.793650793650794 -0.5 170 | 0.285714285714286 0.477386934673367 0.545454545454545 0.323232323232323 0.739039665970772 0.124125874125874 0.444444444444444 -0.5 171 | 0.142857142857143 0.613065326633166 0.472727272727273 0.434343434343434 0.755741127348643 0.356643356643357 0.444444444444444 -0.5 172 | 0.285714285714286 0.42713567839196 0.527272727272727 0.222222222222222 0.580375782881002 0.133741258741259 0.444444444444444 -0.5 173 | 0 0.758793969849246 0.818181818181818 0.464646464646465 0.878914405010438 0.16215034965035 0.333333333333333 0.5 174 | 0.428571428571429 0.723618090452261 0.654545454545455 0.272727272727273 0.707724425887265 0.111451048951049 0.634920634920635 -0.5 175 | 0.214285714285714 0.557788944723618 0.818181818181818 0.121212121212121 0.592901878914405 0.216346153846154 0.46031746031746 -0.5 176 | 0.0714285714285714 0.537688442211055 0.618181818181818 0.191919191919192 0.553235908141962 0.0721153846153846 0.380952380952381 -0.5 177 | 0.428571428571429 0.577889447236181 0.545454545454545 0.393939393939394 0.703549060542798 0.10708041958042 0.634920634920635 0.5 178 | 0.357142857142857 0.527638190954774 0.654545454545455 0.292929292929293 0.77035490605428 0.069493006993007 0.444444444444444 -0.5 179 | 0.5 0.974874371859296 0.618181818181818 0.282828282828283 0.749478079331942 0.325611888111888 0.650793650793651 0.5 180 | 0.285714285714286 0.924623115577889 0.709090909090909 0.393939393939394 0.772442588726514 0.115384615384615 0.492063492063492 0.5 181 | 0 0.477386934673367 0.772727272727273 0.252525252525253 0.780793319415449 0.107954545454545 0.380952380952381 0.5 182 | 0.5 0.623115577889447 0.636363636363636 0.333333333333333 0.532359081419624 0.0703671328671329 0.587301587301587 -0.5 183 | 0.0714285714285714 0.557788944723618 0.563636363636364 0.131313131313131 0.501043841336117 0.0603146853146853 0.365079365079365 -0.5 184 | 0.5 0.688442211055276 0.818181818181818 0.414141414141414 0.668058455114823 0.170891608391608 0.619047619047619 -0.5 185 | 0.642857142857143 0.28643216080402 0.727272727272727 0.373737373737374 0.684759916492693 0.041958041958042 0.650793650793651 -0.5 186 | 0.142857142857143 0.78894472361809 0.672727272727273 0.353535353535354 0.822546972860125 0.0585664335664336 0.476190476190476 -0.5 187 | 0.142857142857143 0.477386934673367 0.490909090909091 0.141414141414141 0.544885177453027 0.326923076923077 0.349206349206349 -0.5 188 | 0.857142857142857 0.703517587939699 0.772727272727273 0.333333333333333 0.780793319415449 0.106643356643357 0.650793650793651 -0.5 189 | 0 0.587939698492462 0.6 0.313131313131313 0.643006263048017 0.215472027972028 0.349206349206349 -0.5 190 | 0.571428571428571 0.50251256281407 0.672727272727273 0.404040404040404 0.822546972860125 0.288898601398601 0.682539682539683 0.5 191 | 0.642857142857143 0.618090452261307 0.636363636363636 0.444444444444444 0.691022964509395 0.163461538461538 0.634920634920635 -0.5 192 | 0 0.693467336683417 0.545454545454545 0.353535353535354 0.722338204592902 0.233391608391608 0.333333333333333 0.5 193 | 1 0.50251256281407 0.709090909090909 0.252525252525253 0.764091858037578 0.18006993006993 0.73015873015873 0.5 194 | 1 0.879396984924623 0.563636363636364 0.303030303030303 0.701461377870564 0.0926573426573427 0.603174603174603 0.5 195 | 0 0.371859296482412 0.472727272727273 0.101010101010101 0.580375782881002 0.11756993006993 0.349206349206349 -0.5 196 | 0.0714285714285714 0.668341708542714 0.927272727272727 0.282828282828283 0.684759916492693 0.102272727272727 0.714285714285714 0.5 197 | 0 0.597989949748744 0.581818181818182 0.181818181818182 0.728601252609603 0.316870629370629 0.365079365079365 -0.5 198 | 0.357142857142857 0.778894472361809 0.763636363636364 0.444444444444444 0.807933194154489 0.270541958041958 0.53968253968254 -0.5 199 | 0.0714285714285714 0.64321608040201 0.436363636363636 0.454545454545455 0.845511482254697 0.26791958041958 0.380952380952381 0.5 200 | 0.142857142857143 0.562814070351759 0.618181818181818 0.222222222222222 0.711899791231733 0.137674825174825 0.412698412698413 -0.5 201 | 0.0714285714285714 0.703517587939699 0.672727272727273 0.262626262626263 0.503131524008351 0.361888111888112 0.365079365079365 -0.5 202 | 0.142857142857143 0.708542713567839 0.527272727272727 0.343434343434343 0.53027139874739 0.305506993006993 0.380952380952381 -0.5 203 | 0.5 0.648241206030151 0.618181818181818 0.494949494949495 0.803757828810021 0.191870629370629 0.682539682539683 0.5 204 | 0 0.532663316582915 0.636363636363636 0.373737373737374 0.822546972860125 0.264423076923077 0.349206349206349 -0.5 205 | 0.0714285714285714 0.592964824120603 0.527272727272727 0.363636363636364 0.695198329853862 0.114073426573427 0.365079365079365 -0.5 206 | 0.571428571428571 0.778894472361809 0.563636363636364 0.262626262626263 0.709812108559499 0.237325174825175 0.73015873015873 0.5 207 | NTestPatterns 0 208 | -------------------------------------------------------------------------------- /quickprop1.c: -------------------------------------------------------------------------------- 1 | /* This is Scott Fahlman's quickprop program translated from Common Lisp 2 | * into C by Terry Regier at the University of California, Berkeley. 3 | * Netmail address: regier@cogsci.berkeley.edu 4 | * This version is Quickprop 1 from September, 1988. 5 | * 6 | * An example of network setup data is included at the end of this file. 7 | * 8 | * The algorithm and some test results are described in Fahlman's paper 9 | * "Faster-Learning Variations on Back-Propagation: An Empirical Study" 10 | * in Proceedings of 1988 Connectionist Models Summer School, published 11 | * by Morgan Kaufmann. 12 | * 13 | * Note: the parameter called "mu" in the paper is called "max-factor" here. 14 | * 15 | * Changes made to quickprop.c version 1 by N Karunanithi netmail: 16 | * . 17 | * 18 | * Connections can now be specified for multiple ranges of units. 19 | * For example if you had 3 layers of hidden units and wanted the 20 | * third layer to have connections to inputs and the second layer, 21 | * but not the first hidden layer. 22 | * 23 | * Bug fix in CONNECT_LAYERS by Richard Dale Romero 24 | * inserted into the code on September 18, 1991 25 | * 26 | * You may specify hidden and output units as sigmoids with ranges 27 | * of -0.5 to 0.5 (SIGMOIDAL) or from 0.0 to 1.0 (ASYMSIGMOIDAL) in 28 | * the input file. 29 | */ 30 | 31 | #include 32 | #include 33 | 34 | #define N 30 /* Max number of units allowed in net */ 35 | 36 | #define SIGMOID 1 /* Unit_type is SIGMOID with output = +0.5 to -0.5 */ 37 | #define ASYMSIGMOID 2 /* ASYMSIGMOID with output = 0.0 to 1.0 */ 38 | 39 | 40 | /* Global variables */ 41 | 42 | int Epoch; /* Current epoch number */ 43 | float WeightRange; /* Random-init weights in range [-WR,+WR] */ 44 | float SigmoidPrimeOffset; /* Add to sigmoid-prime to kill flat spots */ 45 | int HyperErr; /* 1 => use atanh error function */ 46 | int SplitEpsilon; /* 1 => divide epsilon by fan-in before use */ 47 | float Epsilon; /* For grad descent if last step was (almost) 0 */ 48 | float Momentum; /* Normal old momentum term */ 49 | float ModeSwitchThreshold; /* Inside thresh, do grad descent; outside, jump. */ 50 | float MaxFactor; /* Don't jump more than this times last step */ 51 | float Decay; /* Weight decay */ 52 | int SinglePass; /* 1 => Pause after forward/backward cycle */ 53 | int SingleEpoch; /* 1 => Pause after each training epoch */ 54 | int Step; /* Turned to 1 after each pause, briefly */ 55 | int Restart; /* 1 => restart when max epochs reached */ 56 | int KeepScore; /* 1 => accumulate error score for each epoch */ 57 | float TotalError; /* Total output error for one epoch */ 58 | float ScoreThreshold; /* This close to desired value => bit is correct */ 59 | int TotalErrorBits; /* Total # bits in epoch that were wrong */ 60 | int DidGradient; /* Total # patterns that did gradient descent */ 61 | 62 | int Nunits; /* Total number of units in net */ 63 | int Ninputs; /* Number of input units */ 64 | int FirstHidden; /* Index of 1st hidden unit */ 65 | int Nhidden; /* Number of hidden units */ 66 | int FirstOutput; /* Index of 1st output unit */ 67 | int Noutputs; /* Number of output units */ 68 | int Unit_type; /* Type of hidden and Output Units: 1=> SIGMOID,and 69 | 2 => ASYMSIGMOID */ 70 | 71 | float Outputs[N]; /* Final output value for each unit */ 72 | float ErrorSums[N]; /* Total error activation for each unit */ 73 | float Errors[N]; /* Final error value for each unit */ 74 | int Nconnections[N]; /* # of INCOMING connections per unit */ 75 | int Connections[N][N]; /* C[i][j] lists jth unit projecting to unit i */ 76 | float Weights[N][N]; /* W[i][j] holds weight of C[i][j] */ 77 | float DeltaWeights[N][N]; /* Change between previous weight and current one */ 78 | float Slopes[N][N]; /* Accumulated slope value for each position */ 79 | float PrevSlopes[N][N]; /* Similarly, for the last position visited */ 80 | 81 | int NTrainingPatterns; /* !! Not in Lisp version. Needed here. */ 82 | int NTestPatterns; /* !! Not in Lisp version. Needed here. */ 83 | float TrainingInputs[200][N]; 84 | float TrainingOutputs[200][N]; 85 | float TestInputs[200][N]; 86 | float TestOutputs[200][N]; 87 | 88 | float tinputs[N]; /* Input vector to be tested. */ 89 | 90 | 91 | main () 92 | { 93 | 94 | long seed; 95 | long lrand48(); 96 | 97 | int i, j, epox, response; 98 | float RANDOM_WEIGHT(); 99 | char fname[80]; 100 | 101 | 102 | /* Start up the random number generator */ 103 | printf ("Enter seed for random number generator: "); 104 | scanf ("%d", &seed); 105 | srand(time(0)); 106 | 107 | INITIALIZE_GLOBALS(); 108 | 109 | /* Get network */ 110 | printf ("Enter name of network: "); 111 | scanf ("%s", fname); 112 | 113 | GET_NETWORK_CONFIGURATION(fname); 114 | printf ("Want to retrieve old weights [0=>no, 1=>yes]? "); 115 | scanf ("%d", &response); 116 | if ( response ) 117 | GET_WEIGHTS(fname); 118 | 119 | /* Train the sucker. */ 120 | epox = 34; 121 | while ( epox != 0 ) 122 | { 123 | printf ("Enter number of epochs to train: "); 124 | scanf ("%d", &epox); 125 | if ( epox != 0 ) 126 | TRAIN ( epox ); 127 | } 128 | 129 | /* Test the sucker. */ 130 | printf ("Want to test [0=>no, 1=>yes]? "); 131 | scanf ("%d", &response); 132 | if ( response != 0 ) 133 | TEST(); 134 | 135 | printf ("Want to dump weights [0=>no, 1=>yes]? "); 136 | scanf ("%d", &response); 137 | if ( response ) 138 | DUMP_WEIGHTS ( fname ); 139 | } 140 | 141 | 142 | /* 143 | * Get and initialize a network. 144 | */ 145 | GET_NETWORK_CONFIGURATION(fname) 146 | char fname[]; 147 | { 148 | FILE *infile, *fopen(); 149 | char junk[5][80]; 150 | char stringjunk[80]; 151 | char realfname[80]; 152 | char c; 153 | int temp[10], i, j, connect; 154 | 155 | sprintf (realfname, "%s.net", fname); 156 | infile = fopen ( realfname, "r" ); 157 | 158 | c = 'c'; /* Discard leading comments */ 159 | //while (c != '#') 160 | // fscanf (infile, "%c", &c); 161 | 162 | /* Get numbers of inputs, hidden units, and output units */ 163 | fscanf (infile, "%s %d %s %d %s %d", 164 | junk[0], &temp[0], junk[1], &temp[1], junk[2], &temp[2]); 165 | BUILD_DATA_STRUCTURES( temp[0], temp[1], temp[2] ); 166 | 167 | /* Get the type units used in hidden and outpt layers. */ 168 | fscanf (infile, "%s %d ", junk[0], &temp[0]); 169 | if (temp[0] == 1) 170 | Unit_type = SIGMOID; 171 | else if (temp[0] == 2) 172 | Unit_type = ASYMSIGMOID; 173 | 174 | /* Connect layers. */ 175 | fscanf (infile, "%s %d", junk[0], &connect); 176 | 177 | for (i=0; i= 0 ) 252 | { 253 | fscanf (infile, "%d %d %f", &i, &j, &inweight); 254 | if ( i >= 0 ) 255 | Weights[i][j] = inweight; 256 | } 257 | 258 | fclose (infile); 259 | } 260 | 261 | 262 | INITIALIZE_GLOBALS() 263 | { 264 | Unit_type = SIGMOID; 265 | Epoch = 0; 266 | WeightRange = 0.7; 267 | SigmoidPrimeOffset = 0.1; 268 | HyperErr = 1; 269 | SplitEpsilon = 1; 270 | Epsilon = 0.55; /* 1.0 */ 271 | Momentum = 0.9; /* 0.0 */ 272 | ModeSwitchThreshold = 0.0; 273 | MaxFactor = 1.75; /* 1.75 */ 274 | Decay = -0.0001; /* -0.0001 */ 275 | SinglePass = SingleEpoch = 0; 276 | Step = KeepScore = 0; 277 | Restart = 1; 278 | TotalError = 0.0; 279 | ScoreThreshold = 0.35; 280 | TotalErrorBits = 0; 281 | } 282 | 283 | 284 | BUILD_DATA_STRUCTURES (ninputs, nhidden, noutputs) 285 | int ninputs, nhidden, noutputs; 286 | { 287 | int i; 288 | 289 | Nunits = 1 + ninputs + nhidden + noutputs; 290 | Ninputs = ninputs; 291 | FirstHidden = 1 + ninputs; 292 | Nhidden = nhidden; 293 | FirstOutput = 1 + ninputs + nhidden; 294 | Noutputs = noutputs; 295 | 296 | for (i=0; i<=Nunits; i++) Outputs[i] = 0.0; 297 | for (i=0; i<=Nunits; i++) ErrorSums[i] = 0.0; 298 | for (i=0; i<=Nunits; i++) Errors[i] = 0.0; 299 | for (i=0; i<=Nunits; i++) Nconnections[i] = 0; 300 | 301 | Outputs[0] = 1.0; /* The bias unit */ 302 | } 303 | 304 | 305 | /* 306 | * Return a float between -range and +range. 307 | */ 308 | float RANDOM_WEIGHT (range) 309 | float range; 310 | { 311 | return ( (float) (range * (rand()%1000 / 500.0)) - range ); 312 | } 313 | 314 | 315 | /* 316 | * Build a connection from every unit in range1 to every unit in range2. 317 | * Also add a connection from the bias unit (unit 0) to every unit in 318 | * range2. Set up random weights on links. 319 | */ 320 | CONNECT_LAYERS (start1, end1, start2, end2) 321 | int start1, end1, start2, end2; 322 | { 323 | 324 | int n, i, j, k; 325 | 326 | Epoch = 0; 327 | 328 | for (i=start2; i<=end2; i++) 329 | { 330 | if(Nconnections[i] == 0){ 331 | Nconnections[i] += 1; 332 | Connections[i][0] = 0; 333 | Weights[i][0] = RANDOM_WEIGHT(WeightRange); 334 | DeltaWeights[i][0] = 0.0; 335 | Slopes[i][0] = 0.0; 336 | PrevSlopes[i][0] = 0.0; 337 | k = 1; 338 | } 339 | else 340 | k = Nconnections[i]; 341 | /* k = start1; Bug found by 342 | Richard Dale Romero */ 343 | 344 | for (j=start1; j<=end1; j++){ 345 | Nconnections[i] += 1; 346 | Connections[i][k] = j; 347 | Weights[i][k] = RANDOM_WEIGHT(WeightRange); 348 | DeltaWeights[i][k] = 0.0; 349 | Slopes[i][k] = 0.0; 350 | PrevSlopes[i][k] = 0.0; 351 | k++; 352 | } 353 | } 354 | } 355 | 356 | 357 | /* 358 | * For each connection, select a random initial weight between WeightRange 359 | * and its negative. Clear delta and previous delta values. 360 | */ 361 | INIT_WEIGHTS() 362 | { 363 | int i, j; 364 | 365 | for (i=0; i 15.0) 407 | return(0.5); 408 | else 409 | return (1.0 /(1.0 + exp(-sum)) - 0.5); 410 | case ASYMSIGMOID: 411 | /* asymmetrical sigmoid function in range 0.0 to 1.0. */ 412 | if (sum < -15.0) 413 | return(0.0); 414 | else if (sum > 15.0) 415 | return(1.0); 416 | else 417 | return (1.0 /(1.0 + exp(-sum))); 418 | } 419 | } 420 | 421 | /* 422 | * Given the unit's activation value and sum of weighted inputs, compute 423 | * the derivative of the activation with respect to the sum. Defined unit 424 | * types are SIGMOID and ASYMSIGMOID. 425 | */ 426 | float ACTIVATION_PRIME(value) 427 | float value; 428 | { 429 | switch(Unit_type){ 430 | case SIGMOID: 431 | /* Symmetrical sigmoid function. */ 432 | return (SigmoidPrimeOffset + (0.25 - value*value)); 433 | case ASYMSIGMOID: 434 | /* asymmetrical sigmoid function in range 0.0 to 1.0. */ 435 | return (SigmoidPrimeOffset + (value * (1.0 - value))); 436 | } 437 | } 438 | 439 | /* 440 | * Compute the error for one output unit. 441 | * HyperErr==0 => use squared error. 442 | * HyperErr==1 => use atanh. 443 | */ 444 | float ERRFUN (desired, actual) 445 | float desired, actual; 446 | { 447 | float dif; 448 | 449 | dif = desired - actual; 450 | 451 | if ( KeepScore ) 452 | { 453 | TotalError += dif*dif; 454 | if ( fabs(dif) >= ScoreThreshold ) 455 | TotalErrorBits++; 456 | } 457 | 458 | if ( HyperErr == 0 ) /* Not using atanh for error */ 459 | { 460 | if ((-0.1 < dif) && (dif < 0.1)) 461 | return (0.0); 462 | else 463 | return (dif); 464 | } 465 | else /* Using atanh for error */ 466 | { 467 | if ( dif < -.9999999 ) 468 | return (-17.0); 469 | else if ( dif > .9999999 ) 470 | return (17.0); 471 | else 472 | return ( log ( (1.0+dif) / (1.0-dif) ) ); 473 | } 474 | } 475 | 476 | 477 | /* 478 | * This is it, ya Habaayib: the forward pass in BP. 479 | */ 480 | FORWARD_PASS (input) 481 | float input[]; 482 | { 483 | int i, j; 484 | float sum; 485 | 486 | /* Load in the input vector */ 487 | for (i=0; i=FirstHidden; j--) 520 | { 521 | Errors[j] = ACTIVATION_PRIME(Outputs[j]) * ErrorSums[j]; 522 | for (i=0; i ModeSwitchThreshold ) 549 | { /* Last step was signif. +ive..... */ 550 | if ( Slopes[j][i] > 0.0 ) /* Add in epsilon if +ive slope */ 551 | next_step += (SplitEpsilon ? 552 | ( (Epsilon * Slopes[j][i]) / Nconnections[j] ) : 553 | ( Epsilon * Slopes[j][i] )); 554 | /* If slope > (or close to) prev slope, take max size step. */ 555 | if ( Slopes[j][i] > (shrink_factor * PrevSlopes[j][i]) ) 556 | next_step += ( MaxFactor * DeltaWeights[j][i] ); 557 | else /* Use quadratic estimate */ 558 | next_step += ( (Slopes[j][i]/(PrevSlopes[j][i]-Slopes[j][i])) 559 | * DeltaWeights[j][i] ); 560 | } 561 | else if ( DeltaWeights[j][i] < -ModeSwitchThreshold ) 562 | { /* Last step was signif. -ive.... */ 563 | if ( Slopes[j][i] < 0.0 )/* Add in epsilon if -ive slope */ 564 | next_step += (SplitEpsilon ? 565 | ( (Epsilon * Slopes[j][i]) / Nconnections[j] ) : 566 | ( Epsilon * Slopes[j][i] )); 567 | /* If slope < (or close to) prev slope, take max size step. */ 568 | if ( Slopes[j][i] < (shrink_factor * PrevSlopes[j][i]) ) 569 | next_step += ( MaxFactor * DeltaWeights[j][i] ); 570 | else /* Use quadratic estimate */ 571 | next_step += ( (Slopes[j][i]/(PrevSlopes[j][i]-Slopes[j][i])) 572 | * DeltaWeights[j][i] ); 573 | } 574 | else /* Normal gradient descent, complete with momentum */ 575 | { 576 | DidGradient++; 577 | next_step += ((SplitEpsilon ? 578 | ( (Epsilon * Slopes[j][i]) / Nconnections[j] ) : 579 | ( Epsilon * Slopes[j][i] )) 580 | + (Momentum * DeltaWeights[j][i]) ); 581 | } 582 | 583 | /* Set delta weight, and adjust the weight itself. */ 584 | DeltaWeights[j][i] = next_step; 585 | Weights[j][i] += next_step; 586 | } 587 | } 588 | 589 | /* 590 | * Perform forward and back propagation once for each pattern in the 591 | * training set, collecting deltas. Then burn in the weights. 592 | */ 593 | TRAIN_ONE_EPOCH() 594 | { 595 | int i; 596 | 597 | CLEAR_SLOPES(); 598 | 599 | for (i=0; i= 0.0) 647 | { 648 | /* printf ("Enter the %d input values [first one less than 0.0 => quit]: ", 649 | Ninputs); 650 | for (i=0; i