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test.c
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test.c
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#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <math.h>
#include <malloc.h>
#include "popc.h"
#include "cell.h"
#include "ann.h"
#include "mlp.h"
#include "mlpLayer.h"
#include "training.h"
#include "test.h"
int trainAndTest (double lr, double lmr) {
mlpSingleton ptr nwp;
mlp ptr nw;
nwp = mlpSingletonNew (); // get singleton instance of networkI
nw = nwp -> mlpNew ();
nwp -> addLayer (nw, 1, ltInput, ctInput, aftLogisticSigmoid, lftActualError, oftGradientDescent);
//nwp -> addLayer (nw, 2, ltHidden, ctHidden, aftLogisticSigmoid, lftActualError, oftGradientDescent);
nwp -> addLayer (nw, 1, ltOutput, ctOutput, aftLogisticSigmoid, lftActualError, oftGradientDescent);
double ptr dataIn, ptr dataOut;
for (int r = 0; r < 1; r ++) {
double x = annRandomizer ();
dataIn = addr x;
dataOut = addr x;
nwp -> trainTuple (nw, ttOnlineTraining, (double ptr) dataIn, (double ptr) dataOut, lr, lmr);
}
nwp -> predictTuple (nw, dataIn);
return 0;
}
int trainAndPredictXorTable (int epoch, int tStart, int tEnd, int start, int end, double lr, double lmr) {
int ret = 0;
//int trainDataRows = 10;
//int testDataRows = 10;
int epochMax = epoch;
//double score = 0;
//double ptr3d input1d = NULL, ptr3d target1d = NULL;
int dataCount = tEnd; //12;
double dataIn [12][2] = {
{0, 1},
{1, 0},
{1, 1},
{0, 0},
{0, 1},
{1, 0},
{1, 1},
{0, 0},
{0, 1},
{1, 0},
{1, 1},
{0, 0},
};
double dataOut [12][1] = {
{1},
{1},
{0},
{0},
{1},
{1},
{0},
{0},
{1},
{1},
{0},
{0},
};
mlpSingleton ptr nwp;
mlp ptr nw;
nwp = mlpSingletonNew (); // get singleton instance of networkI
nw = nwp -> mlpNew ();
nwp -> addLayer (nw, 2, ltInput, ctInput, aftLogisticSigmoid, lftActualError, oftGradientDescent);
nwp -> addLayer (nw, 200, ltHidden, ctHidden, aftLogisticSigmoid, lftActualError, oftGradientDescent);
nwp -> addLayer (nw, 1, ltOutput, ctOutput, aftLogisticSigmoid, lftActualError, oftGradientDescent);
//networkSetEpoch (nn, epoch);
printf ("==TRAININIG==\n");
for (int j = 0; j < epochMax; j ++) {
int toggle = 0;
if (j % 1000 == 0) {
toggle = 1 - toggle;
}
//if (j == epochMax -1) {
// toggle = 1;
//}
//toggle = 1;
//#pragma omp parallel for
for (int r = tStart; r < dataCount; r ++) {
nwp -> trainTuple (nw, ttOnlineTraining, (double ptr) dataIn [r], (double ptr) dataOut [r], lr, lmr);
if (toggle == 1) {
//printf ("%f XOR %f = %f. Predicted: %f\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], ((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [0] -> axon -> output );
printf ("[TRAIN] %f XOR %f = %f. Target: %f; Predicted: %f.\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], nw -> layer2d [nw -> totalLayerCount - 1] -> target1d [0], nw -> layer2d [nw -> totalLayerCount - 1] -> output1d [0] );
//printf ("%d\n", r);
}
}
printf("Epoch: %0d\n", j+1);
}
printf ("==TRIAL==\n");
//#pragma omp parallel for
for (int r = start; r < end; r ++) {
nwp -> predictTuple (nw, dataIn [r]);
//printf ("%f XOR %f = %f. Predicted: %f\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], ((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [0] -> axon -> output );
printf ("[TRIAL] %f XOR %f = %f. Target: %f; Predicted: %f.\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], dataOut [r][0], nw -> layer2d [nw -> totalLayerCount - 1] -> output1d [0] );
}
printf ("HW[0,0]=%lf, HW[0,1]=%lf, HW[1,0]=%lf, HW[1,1]=%lf\n", nw -> layer2d [1] -> weight2d [0][0], nw -> layer2d [1] -> weight2d [0][1], nw -> layer2d [1] -> weight2d [1][0], nw -> layer2d [1] -> weight2d [1][1]);
printf ("HW[0,0]=%lf, HB[0,1]=%lf\n\n", nw -> layer2d [2] -> weight2d [0][0], nw -> layer2d [2] -> weight2d [0][1]);
printf ("HB[0]=%lf, HB[1]=%lf\n", nw -> layer2d [1] -> bias1d [0], nw -> layer2d [1] -> bias1d [1]);
printf ("OB[0]=%lf\n", nw -> layer2d [2] -> bias1d [0]);
return ret;
}
int trainAndPredictMultiplicationTable (int start, int end) {
int ret = 0;
//int trainDataRows = 10;
//int testDataRows = 10;
int epochMax = 5;
//double score = 0;
//double ptr3d input1d = NULL, ptr3d target1d = NULL;
mlpSingleton ptr nwp;
mlp ptr nw;
int dataCount = 110;
double dataIn [110][2] = {
// 0/
{ 0, 1},
{ 0, 2},
{ 0, 3},
{ 0, 4},
{ 0, 5},
{ 0, 6},
{ 0, 7},
{ 0, 8},
{ 0, 9},
{ 0, 10},
// 1/
{ 1, 1},
{ 1, 2},
{ 1, 3},
{ 1, 4},
{ 1, 5},
{ 1, 6},
{ 1, 7},
{ 1, 8},
{ 1, 9},
{ 1, 10},
// 2/
{ 2, 1},
{ 2, 2},
{ 2, 3},
{ 2, 4},
{ 2, 5},
{ 2, 6},
{ 2, 7},
{ 2, 8},
{ 2, 9},
{ 2, 10},
// 3/
{ 3, 1},
{ 3, 2},
{ 3, 3},
{ 3, 4},
{ 3, 5},
{ 3, 6},
{ 3, 7},
{ 3, 8},
{ 3, 9},
{ 3, 10},
// 4/
{ 4, 1},
{ 4, 2},
{ 4, 3},
{ 4, 4},
{ 4, 5},
{ 4, 6},
{ 4, 7},
{ 4, 8},
{ 4, 9},
{ 4, 10},
// 5/
{ 5, 1},
{ 5, 2},
{ 5, 3},
{ 5, 4},
{ 5, 5},
{ 5, 6},
{ 5, 7},
{ 5, 8},
{ 5, 9},
{ 5, 10},
// 6/
{ 6, 1},
{ 6, 2},
{ 6, 3},
{ 6, 4},
{ 6, 5},
{ 6, 6},
{ 6, 7},
{ 6, 8},
{ 6, 9},
{ 6, 10},
// 7/
{ 7, 1},
{ 7, 2},
{ 7, 3},
{ 7, 4},
{ 7, 5},
{ 7, 6},
{ 7, 7},
{ 7, 8},
{ 7, 9},
{ 7, 10},
// 8/
{ 8, 1},
{ 8, 2},
{ 8, 3},
{ 8, 4},
{ 8, 5},
{ 8, 6},
{ 8, 7},
{ 8, 8},
{ 8, 9},
{ 8, 10},
// 9/
{ 9, 1},
{ 9, 2},
{ 9, 3},
{ 9, 4},
{ 9, 5},
{ 9, 6},
{ 9, 7},
{ 9, 8},
{ 9, 9},
{ 9, 10},
// 10/
{10, 1},
{10, 2},
{10, 3},
{10, 4},
{10, 5},
{10, 6},
{10, 7},
{10, 8},
{10, 9},
{10, 10},
};
double dataOut [110][101] = {
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
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{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,},
{0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,},
};
nwp = mlpSingletonNew (); // get singleton instance of networkI
nw = nwp -> mlpNew ();
nwp -> addLayer (nw, 2, ltInput, ctInput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 110, ltHidden, ctHidden, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 101, ltOutput, ctOutput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
//networkSetEpoch (nn, epoch);
printf ("==TRAININIG==\n");
//#pragma omp parallel for
for (int j = 0; j < epochMax; j ++) {
printf ("Epoch: %d\n", j);
int toggle = 0;
if (j % 1000 == 0) {
//printf ("Epoch: %0d\n", j+1);
toggle = 1 - toggle;
}
//if (j == epochMax -1) {
// toggle = 1;
//}
toggle = 1;
//#pragma omp parallel for
for (int r = start; r < dataCount; r ++) {
nwp -> trainTuple (nw, ttOnlineTraining, (double ptr) dataIn [r], (double ptr) dataOut [r], 0.05, 1);
if (toggle == 1) {
//printf ("%f MUL %f = %f. Predicted: %f\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], ((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [0] -> axon -> output );
}
}
}
printf ("==TRIAL==\n");
//#pragma omp parallel for
for (int r = start; r < end; r ++) {
nwp -> predictTuple (nw, dataIn [r]);
/*
printf (
"%f MUL %f = %f. Teached: %f, Predicted: %f\n",
dataIn [r][0],
dataIn [r][1],
(float) dataIn [r][0] * dataIn [r][1],
1.0,
((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [(int) (dataIn [r][0] * dataIn [r][1])] -> axon -> output
);
*/
}
return ret;
}
double dataIn [42000][784], dataOut [42000][1];
int trainAndPredictMultiplicationTableV3 (int start, int end) {
FILE* stream = fopen("./train.csv", "r");
if (stream == NULL) {
printf ("FIle Not found.");
return 1;
}
int now = -1;
char line [10240];
while (fgets (line, 10240, stream))
{
if(now < 0) {
now ++;
continue; // to avoid label
}
char* tmp = strdup (line);
int index = 0;
double value = 0.0;
dataOut [now][0] = tmp [0]-'0';
for (size_t i = 2; i<strlen (tmp); i ++) {
if(tmp [i]== ',') {
dataIn [now][index ++] = value;
//printf("%d, %d : %lf\n", now, index, value);
value = 0;
}
else value = (value) * 10 + (tmp [i]-'0') ;
//printf("%lf\n", value);
}
dataIn [now][index ++] = value;
// NOTE strtok clobbers tmp
free (tmp);
now ++;
}
int ret = 0;
//int trainDataRows = 10;
//int testDataRows = 10;
int epochMax = 1;
//double score = 0;
//double ptr3d input1d = NULL, ptr3d target1d = NULL;
mlpSingleton ptr nwp;
mlp ptr nw;
int dataCount = 42000;
nwp = mlpSingletonNew (); // get singleton instance of networkI
nw = nwp -> mlpNew ();
nwp -> addLayer (nw, 784, ltInput, ctInput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 100, ltHidden, ctHidden, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 1, ltOutput, ctOutput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
//networkSetEpoch (nn, epoch);
//#pragma omp parallel for
for (int i = 0; i < 42000; i ++) {
dataIn [i][0] = (double) (dataIn [i][0]/255.0);
dataIn [i][1] = (double) (dataIn [i][1]/255.0);
dataOut [i][0] = (double) (dataOut [i][0]/10);
}
//for (int i = 0; i < 110; i ++) printf("%lf, %lf : %lf\n", dataIn [i][0], dataIn [i][1], dataOut [i][0] );
printf ("==TRAININIG==\n");
#pragma omp parallel for
for (int j = 0; j < epochMax; j ++) {
//printf ("Epoch: %d\n", j);
int toggle = 0;
if (j % 100 == 0) {
printf ("Epoch: %0d\n", j + 1);
toggle = 1 - toggle;
}
//if (j == epochMax -1) {
// toggle = 1;
//}
toggle = 1;
#pragma omp parallel for
for (int r = start; r < dataCount; r ++) {
// if(r % 100 == 0) {
printf("Epoch : %d , Row : %d\n", j, r);
// }
time_t b;
b = clock ();
nwp -> trainTuple (nw, ttOnlineTraining, (double ptr) (dataIn [r]), (double ptr) (dataOut [r]), 0.05, 1);
printf ("Row: %d, Time: %ld\n", r, (clock() - b));
if (toggle == 1) {
//printf ("%f MUL %f = %f. Predicted: %f\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], ((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [0] -> axon -> output );
}
}
}
printf ("==TRIAL==\n");
//#pragma omp parallel for
for (int r = start; r < end; r ++) {
nwp -> predictTuple (nw, dataIn [r]);
/*
printf (
"Actual : %lf, Predicted : %lf\n", dataOut [r],
((layer ptr) nw -> layerLinkedList -> tail -> obj) -> neuron1d [0] -> axon -> output
);
*/
}
return ret;
}
void annTest () {
}
int testAnother () {
int v [101]; // = {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
for (int r = 0; r <= 10; r ++) {
for (int c = 1; c <= 10; c ++) {
//printf ("[%d, %d] ={", r, c);
printf ("{");
for (int x = 0; x < 101; x ++) {
if (x == r * c) {
if (r * c > 0) {
v [r * c] = 1;
}
else {
v [r * c] = 0;
}
}
else {
v [r * c] = 0;
}
printf ("%d,", v [r * c]);
}
printf ("},\n");
}
}
return 0;
}
int trainAndPredictMultiplicationTableV2 (int start, int end) {
int ret = end;
//int trainDataRows = 10;
//int testDataRows = 10;
int epochMax = 5000;
//double score = 0;
//double ptr3d inputMatrix = NULL, ptr3d outputTargetMatrix = NULL;
mlpSingleton ptr nwp;
mlp ptr nw;
int dataCount = 100;
double dataIn [100][2] = {
// 0/
{ 0, 1},
{ 0, 2},
{ 0, 3},
{ 0, 4},
{ 0, 5},
{ 0, 6},
{ 0, 7},
{ 0, 8},
{ 0, 9},
{ 0, 10},
// 1/
/*{1, 1},
{ 1, 2},
{ 1, 3},
{ 1, 4},
{ 1, 5},
{ 1, 6},
{ 1, 7},
{ 1, 8},
{ 1, 9},
{ 1, 10},*/
// 2/
{ 2, 1},
{ 2, 2},
{ 2, 3},
{ 2, 4},
{ 2, 5},
{ 2, 6},
{ 2, 7},
{ 2, 8},
{ 2, 9},
{ 2, 10},
// 3/
{ 3, 1},
{ 3, 2},
{ 3, 3},
{ 3, 4},
{ 3, 5},
{ 3, 6},
{ 3, 7},
{ 3, 8},
{ 3, 9},
{ 3, 10},
// 4/
{ 4, 1},
{ 4, 2},
{ 4, 3},
{ 4, 4},
{ 4, 5},
{ 4, 6},
{ 4, 7},
{ 4, 8},
{ 4, 9},
{ 4, 10},
// 5/
{ 5, 1},
{ 5, 2},
{ 5, 3},
{ 5, 4},
{ 5, 5},
{ 5, 6},
{ 5, 7},
{ 5, 8},
{ 5, 9},
{ 5, 10},
// 6/
{ 6, 1},
{ 6, 2},
{ 6, 3},
{ 6, 4},
{ 6, 5},
{ 6, 6},
{ 6, 7},
{ 6, 8},
{ 6, 9},
{ 6, 10},
// 7/
{ 7, 1},
{ 7, 2},
{ 7, 3},
{ 7, 4},
{ 7, 5},
{ 7, 6},
{ 7, 7},
{ 7, 8},
{ 7, 9},
{ 7, 10},
// 8/
{ 8, 1},
{ 8, 2},
{ 8, 3},
{ 8, 4},
{ 8, 5},
{ 8, 6},
{ 8, 7},
{ 8, 8},
{ 8, 9},
{ 8, 10},
// 9/
{ 9, 1},
{ 9, 2},
{ 9, 3},
{ 9, 4},
{ 9, 5},
{ 9, 6},
{ 9, 7},
{ 9, 8},
{ 9, 9},
{ 9, 10},
// 10/
{10, 1},
{10, 2},
{10, 3},
{10, 4},
{10, 5},
{10, 6},
{10, 7},
{10, 8},
{10, 9},
{10, 10},
};
double dataOut [100][1] = {
{0},
{0},
{0},
{0},
{0},
{0},
{0},
{0},
{0},
{0},
/*{1},
{2},
{3},
{4},
{5},
{6},
{7},
{8},
{9},
{10},*/
{2},
{4},
{6},
{8},
{10},
{12},
{14},
{16},
{18},
{20},
{3},
{6},
{9},
{12},
{15},
{18},
{21},
{24},
{27},
{30},
{4},
{8},
{12},
{16},
{20},
{24},
{28},
{32},
{36},
{40},
{5},
{10},
{15},
{20},
{25},
{30},
{35},
{40},
{45},
{50},
{6},
{12},
{18},
{24},
{30},
{36},
{42},
{48},
{54},
{60},
{7},
{14},
{21},
{28},
{35},
{42},
{49},
{56},
{63},
{70},
{8},
{16},
{24},
{32},
{40},
{48},
{56},
{64},
{72},
{80},
{9},
{18},
{27},
{36},
{45},
{54},
{63},
{72},
{81},
{90},
{10},
{20},
{30},
{40},
{50},
{60},
{70},
{80},
{90},
{100},
};
double dataIn2 [10][2] = {
// 0/
{ 1, 1},
{ 1, 2},
{ 1, 3},
{ 1, 4},
{ 1, 5},
{ 1, 6},
{ 1, 7},
{ 1, 8},
{ 1, 9},
{ 1, 10}
};
nwp = mlpSingletonNew (); // get singleton instance of annProto
nw = nwp -> mlpNew ();
nwp -> addLayer (nw, 2, ltInput, ctInput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 100, ltHidden, ctHidden, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
nwp -> addLayer (nw, 1, ltOutput, ctOutput, aftLogisticSigmoid, lftMeanAbsoluteError, oftStochasticGradientDescent);
//networkSetEpoch (nn, epoch);
for (int i = 0; i < 100; i ++) {
dataIn [i][0] = (double) (dataIn [i][0]/10.0);
dataIn [i][1] = (double) (dataIn [i][1]/10.0);
dataOut [i][0] = (double) (dataOut [i][0]/100.0);
}
for (int i = 0; i < 10; i ++) {
dataIn2 [i][0] = (double) (dataIn2 [i][0]/10.0);
dataIn2 [i][1] = (double) (dataIn2 [i][1]/10.0);
}
//for (int i = 0; i < 110; i ++) printf("%lf, %lf : %lf\n", dataIn [i][0], dataIn [i][1], dataOut [i][0] );
clock_t begin = clock();
/* here, do your time-consuming job */
printf ("==TRAININIG==\n");
#pragma omp parallel for
for (int j = 0; j < epochMax; j ++) {
//printf ("Epoch: %d\n", j);
int toggle = 0;
if (j % 100 == 0) {
printf ("Epoch: %0d\n", j + 1);
toggle = 1 - toggle;
}
//if (j == epochMax -1) {
// toggle = 1;
//}
toggle = 1;
for (int r = start; r < dataCount; r ++) {
nwp -> trainTuple (nw, ttOnlineTraining, (double ptr) (dataIn [r]), (double ptr) (dataOut [r]), 0.05, 1);
if (toggle == 1) {
//printf ("%f MUL %f = %f. Predicted: %f\n", dataIn [r][0], dataIn [r][1], dataOut [r][0], ((layer ptr) nw -> layerLinkedList -> tail -> object) -> neuronVector [0] -> axon -> output );
}
}
}
clock_t c_end = clock();
double time_spent = (double)(c_end - begin) / (CLOCKS_PER_SEC*100.0);
printf("Training Time : %lf seconds \n", time_spent);
printf ("==TRIAL==\n");
for (int r = 0; r < 10; r ++) {
nwp -> predictTuple (nw, dataIn2 [r]);
/*
printf (
"%lf MUL %lf = %lf. Teached: %lf, Predicted: %lf\n",
(double) dataIn2 [r][0],
(double) dataIn2 [r][1],
(double) (dataIn2 [r][0] * dataIn2 [r][1]),
1.0,
((layer ptr) nw -> layerLinkedList -> tail -> object) -> neuronVector [0] -> axon -> output
);
*/
}
return ret;
}
// <testmain/> /////////////////////////////////////////////////////////////////////////////////////////////////////////
int testMain () {
//this line works/ trainAndPredictXorTable (100000, 0, 4, 0, 4, 0.05, 1.0);
//trainAndPredictMultiplicationTableV3 (0, 42000);
//trainAndTest (0.05, 0.01);
trainAndPredictXorTable (100000, 0, 4, 0, 4, 0.06, 0.01);
return 0;
}