void transform(int i, float rot_offset, float rot_speed, float size,
float height, float radius, int wobble) {
float angle = (glutGet(GLUT_ELAPSED_TIME) / 1000.0) * (180/M_PI);
float RotationMatrixAnim[16];
float ScaleMatrix[16];
float InitialTransform[16];
float scale = 0.001;
float maxHeight = height + 0.5;
float minHeight = height - 0.5;
static int up[OBJECTS];
static int time[OBJECTS];
if(wobble == 1) {
if(up[i] == 0) height = maxHeight - (time[i]++) * scale;
else height = minHeight + (time[i]++) * scale;
if(time[i] >= 1000) {
up[i] = (up[i] == 0) ? 1 : 0;
time[i] = 0;
}
}
SetScaling(size, size, size, ScaleMatrix);
SetRotationY(rot_speed*angle + rot_offset, RotationMatrixAnim);
SetTranslation(radius, height, 0, InitialTransform);
MultiplyMatrix(InitialTransform, ScaleMatrix, ModelMatrix[i]);
MultiplyMatrix(RotationMatrixAnim, ModelMatrix[i], ModelMatrix[i]);
}
void keyboard2(unsigned char key, int x, int y) {
float rotation[16];
float translation[16];
float camera_disp = -10.0;
if ( key == 'w' || key == 'a' || key == 's' || key == 'd' || key == ' ') {
SetTranslation(0.0, 0.0, camera_disp, translation);
MultiplyMatrix(translation, ViewMatrix, ViewMatrix);
} else {
return;
}
switch(key) {
case 'w' : SetRotationX(5, rotation);
break;
case 'a' : SetRotationY(5, rotation);
break;
case 's' : SetRotationX(-5, rotation);
break;
case 'd' : SetRotationY(-5, rotation);
break;
case ' ' : SetTranslation(0.0, 0.0, camera_disp, ViewMatrix);
return;
default : return;
}
MultiplyMatrix(ViewMatrix, rotation, ViewMatrix);
SetTranslation(0.0, 0.0,camera_disp * -1, translation);
MultiplyMatrix(translation, ViewMatrix, ViewMatrix);
}
void OnIdle()
{
float angle = (glutGet(GLUT_ELAPSED_TIME) / 1000.0) * (180.0/M_PI);
float RotationMatrixAnim[16];
/* Time dependent rotation */
SetRotationY(angle, RotationMatrixAnim);
/* Apply model rotation; finally move cube down */
int i = 0;
for(; i < OBJECTS; i++) {
MultiplyMatrix(RotationMatrixAnim, InitialTransform, ModelMatrix[i]);
MultiplyMatrix(TranslateDown, ModelMatrix[i], ModelMatrix[i]);
}
// transform karusell
transform(0, 0.0, 0.3, 1.0, -2.5, 0.0, 0);
transform(1, 0.0, 0.3, 1.0, -2.5, 0.0, 0);
transform(2, 0.0, 0.3, 1.0, -2.5, 0.0, 0);
// transform cube
transform(3, 0.0, 0.3, 0.25, -1.0, 2.0, 1);
transform(4, 90.0, 0.3, 0.5 , -1.0, 2.0, 1);
transform(5, 180.0, 0.3, 0.25, -1.0, 2.0, 1);
transform(6, 270.0, 0.3, 0.5 , -1.0, 2.0, 1);
/* Request redrawing forof window content */
glutPostRedisplay();
}
void ColorRenderMatrixFunctions(void)
{
MultiplyMatrix(modelViewMatrix, viewMatrix, modelWorldMatrix);
//------------------------------------------------------------
LoadIdentity(tempMatrix_A);
MultiplyMatrix(tempMatrix_A, viewMatrix, viewRotateMatrix);
//---------------------------------------------------------------------
MultiplyMatrix(moveSet_x_modelWorldMatrix, moveSetMatrix, modelWorldMatrix);
//---------------------------------------------------------------------
LoadIdentity(tempMatrix_B);
MultiplyMatrix(tempMatrix_B, shadowTextureMatrix, moveSet_x_modelWorldMatrix);
//---------------------------------------------------------------------
LoadIdentity(tempMatrix_C);
MultiplyMatrix(tempMatrix_C, projectionMatrix, tempMatrix_A);
//---------------------------------------------------------------------
LoadIdentity(tempMatrix_D);
MultiplyMatrix(tempMatrix_D, tempMatrix_C, moveSet_x_modelWorldMatrix);
//----------------------------------------------------------------------------------------------------------
LoadIdentity(shadowTextureMatrix);
MultiplyMatrix(shadowTextureMatrix, shadowBiasMatrix, shadowProjectionMatrix);
MultiplyMatrix(shadowTextureMatrix, shadowTextureMatrix, shadowViewMatrix); //_MULTIPLY THESE TO A TEMP MATRIX TO AVOID ROUND OFF ERRORS //USES MORE CODE WITH INCREASED PRECISION
//----------------------------------------------------------------------------------------------------------
LoadIdentity(tempMatrix_E);
MultiplyMatrix(tempMatrix_E, viewRotateMatrix, modelRotationMatrix);
//----------------------------------------------------------------------------------------------------------
LoadIdentity(modelRotationINVmatrix);
InvertMatrix(modelRotationINVmatrix, tempMatrix_E);
//----------------------------------------------------------------------------------------------------------
}
/** Rotates the current rendering position
*
* @param angle The angle by which to rotate
* @param x The x value (1 or 0)
* @param y The y value (1 or 0)
* @param z The z value (1 or 0)
*
* This method specifies a type of Angle/Axis rotation,
* you specify an angle to rotate, then give an axis
* to rotate around, therefore x/y/z must be 1 or 0.
* Any other value will result in error
*/
void OGLGraphics::Rotate(float angle, float x, float y, float z)
{
m_quat.LoadIdentity();
m_quat.Rotate(angle,x,y,z);
MultiplyMatrix(m_quat.GetMatrix());
}
/** Rotates the current rendering position
*
* @param r The Roll component
* @param p The pitch component
* @param y The Yaw component
*/
void OGLGraphics::Rotate(float r, float p, float y)
{
m_quat.LoadIdentity();
m_quat.Rotate(r,p,y);
MultiplyMatrix(m_quat.GetMatrix());
}
string TestDoubleMatrix()
{
double* a, * b, * x;
a = GenerateMatrix(MatrixSize);
b = GenerateMatrix(MatrixSize);
x = MultiplyMatrix(a, b, MatrixSize, MatrixSize, MatrixSize);
double result = x[MatrixSize / 2 * MatrixSize + MatrixSize / 2];
delete[] a;
delete[] b;
delete[] x;
return printstring("%f", result);
}
void mouse(int button, int state, int x, int y){
float rotation[16];
float translation[16];
float camera_disp = -10.0;
if(button != 0) {return;}
/* mouse click*/
if (state == 0) {
mouse_x = x;
mouse_y = y;
return;
/* mouse release*/
} else if ( state == 1) {
SetTranslation(0.0, 0.0, camera_disp, translation);
MultiplyMatrix(translation, ViewMatrix, ViewMatrix);
int diffX = mouse_x - x;
int diffY = mouse_y - y;
/* right to left or reverse*/
if (fabs(diffX) > fabs(diffY)) {
if(diffX < 0) {
SetRotationY(-5, rotation);
} else {
SetRotationY(5, rotation);
}
} else {
if (diffY < 0) {
SetRotationX(-5, rotation);
} else {
SetRotationX(5, rotation);
}
}
}
MultiplyMatrix(ViewMatrix, rotation, ViewMatrix);
SetTranslation(0.0, 0.0,camera_disp * -1, translation);
MultiplyMatrix(translation, ViewMatrix, ViewMatrix);
}
int calcDiffusionTensorAtPoint(floatMatrixType *Tensor, int x, int y, densityDataType *densityData, int scanningRange)
{
/*
//Diagonal
//Debug
(*Tensor).a11=0.0; (*Tensor).a12=1.0;
(*Tensor).a21=1.0; (*Tensor).a22=0.0;
return 0;
*/
intVectorType point;
point.x=x;
point.y=y;
momentOfInertiaType Inertia;
floatMatrixType BT, B, Diag, Help;//, Diffusion;
float a, structure;
evaluateDirectionAndStructure(&a, &structure ,&Inertia, *densityData, point, scanningRange);
//BTransposed
float magV1=FLOATVECNORM((Inertia).v1)+EPS;
float magV2=FLOATVECNORM((Inertia).v2)+EPS;
BT.a11=(Inertia).v1.x/magV1; BT.a12=(Inertia).v1.y/magV1;
BT.a21=(Inertia).v2.x/magV2; BT.a22=(Inertia).v2.y/magV2;
//Diagonal
Diag.a11=1.0; Diag.a12=0.0;
Diag.a21=0.0; Diag.a22=EPS;
//B
B.a11=BT.a11; B.a12=BT.a21;
B.a21=BT.a12; B.a22=BT.a22;
//Create Diffusion Matrix
MultiplyMatrix(&Help, &Diag, &BT);
MultiplyMatrix(Tensor, &B, &Help);
return 0;
}
void ComputeInvModelMatrix(float* matrix, float* dest)
{
float* invTranslation;
float* invRotation;
invTranslation[0] = 1 ; invTranslation[4] = 0 ; invTranslation[8] = 0 ; invTranslation[12] = -matrix[12] ;
invTranslation[1] = 0 ; invTranslation[5] = 1 ; invTranslation[9] = 0 ; invTranslation[13] = -matrix[13] ;
invTranslation[2] = 0 ; invTranslation[6] = 0 ; invTranslation[10] = 1 ; invTranslation[14] = -matrix[14] ;
invTranslation[3] = 0 ; invTranslation[7] = 0 ; invTranslation[11] = 0 ; invTranslation[15] = 1 ;
invRotation[0] = matrix[0] ; invRotation[4] = matrix[1] ; invRotation[8] = matrix[2] ; invRotation[12] = 0 ;
invRotation[1] = matrix[4] ; invRotation[5] = matrix[5] ; invRotation[9] = matrix[6] ; invRotation[13] = 0 ;
invRotation[2] = matrix[8] ; invRotation[6] = matrix[9] ; invRotation[10] = matrix[10] ; invRotation[14] = 0 ;
invRotation[3] = 0 ; invRotation[7] = 0 ; invRotation[11] = 0 ; invRotation[15] = 1 ;
MultiplyMatrix(invRotation, invTranslation, dest);
}
int main(int argc,char **argv){
int i,j,k = 0;
double MSE = 0,TrainingAccuracy;
float **input,**weight,*biase,**tranpI,**tempH,**H;
float **tranpw;
float **train_set;
float **T,**Y;
float **out;
train_set = (float **)calloc(DATASET,sizeof(float *));
tranpI = (float **)calloc(INPUT_NEURONS,sizeof(float *));
input = (float **)calloc(DATASET,sizeof(float *)); /*datasize * INPUT_NEURONS*/
weight = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * INPUT_NEURONS*/
biase = (float *)calloc(HIDDEN_NEURONS,sizeof(float)); /*HIDDEN_NEURONS*/
tempH = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * datasize*/
tranpw = (float **)calloc(INPUT_NEURONS,sizeof(float *));
H = (float **)calloc(DATASET,sizeof(float *));
T = (float **)calloc(DATASET,sizeof(float *));
Y = (float **)calloc(DATASET,sizeof(float *));
out = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
for(i=0;i<DATASET;i++){
train_set[i] = (float *)calloc(NUMROWS,sizeof(float));
input[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
H[i] = (float *)calloc(HIDDEN_NEURONS,sizeof(float));
}
for(i=0;i<DATASET;i++)
{
T[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
Y[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
for(i=0;i<INPUT_NEURONS;i++){
tranpI[i] = (float *)calloc(DATASET,sizeof(float));
tranpw[i] = (float *)calloc(HIDDEN_NEURONS,sizeof(float));
}
for(i=0;i<HIDDEN_NEURONS;i++)
{
weight[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
tempH[i] = (float *)calloc(DATASET,sizeof(float));
out[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
printf("begin to regression test...\n");
printf("begin to random weight and biase...\n");
/*得到随机的偏置和权重*/
//RandomWeight(weight,HIDDEN_NEURONS,INPUT_NEURONS);
//RandomBiase(biase,HIDDEN_NEURONS);
if(LoadMatrix(tranpw,"../result/weight",INPUT_NEURONS,HIDDEN_NEURONS) == 0){
printf("load input file error!!!\n");
return 0;
}
TranspositionMatrix(tranpw,weight,INPUT_NEURONS,HIDDEN_NEURONS);
if(LoadMatrix_s(biase,"../result/bias",1,HIDDEN_NEURONS) == 0){
printf("load input file error!!!\n");
return 0;
}
/*加载数据集到内存*/
printf("begin to load input from the file...\n");
if(LoadMatrix(train_set,"../sample/ppp",DATASET,NUMROWS) == 0){
printf("load input file error!!!\n");
return 0;
}
/*将数据集划分成输入和输出*/
for(i = 0;i < DATASET ;i++)
{
T[k++][0] = train_set[i][0];
for(j = 1;j <= INPUT_NEURONS;j++)
{
input[i][j-1] = train_set[i][j];
}
}
/*ELM*/
printf("begin to compute...\n");
TranspositionMatrix(input,tranpI,DATASET,INPUT_NEURONS);
printf("begin to compute step 1...\n");
MultiplyMatrix(weight,HIDDEN_NEURONS,INPUT_NEURONS, tranpI,INPUT_NEURONS,DATASET,tempH);
printf("begin to compute setp 2...\n");
AddMatrix_bais(tempH,biase,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 3...\n");
SigmoidHandle(tempH,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 4...\n");
TranspositionMatrix(tempH,H,HIDDEN_NEURONS,DATASET);
printf("begin to load input from the file...\n");
if(LoadMatrix(out,"../result/result",HIDDEN_NEURONS,OUTPUT_NEURONS) == 0){
printf("load input file error!!!\n");
return 0;
}
//检测准确率
MultiplyMatrix(H,DATASET,HIDDEN_NEURONS,out,HIDDEN_NEURONS,OUTPUT_NEURONS,Y);
for(i = 0;i< DATASET;i++)
{
MSE += (Y[i][0] - T[i][0])*(Y[i][0] - T[i][0]);
}
TrainingAccuracy = sqrt(MSE/DATASET);
printf("trainning accuracy :%f\n",TrainingAccuracy);
printf("test complete...\n");
//print(PIMatrix,DATASET,HIDDEN_NEURONS);
return 0;
}
/**
* Construct an ELM
* @param
* elm_type - 0 for regression; 1 for (both binary and multi-classes) classification
*/
void ELMTrain(int elm_type)
{
double starttime,endtime;
double TrainingAccuracy;
int i,j,k = 0;
float **input,**weight,*biase,**tranpI,**tempH,**H;
float **PIMatrix;
float **train_set;
float **T,**Y;
float **out;
train_set = (float **)calloc(DATASET,sizeof(float *));
tranpI = (float **)calloc(INPUT_NEURONS,sizeof(float *));
input = (float **)calloc(DATASET,sizeof(float *)); /*datasize * INPUT_NEURONS*/
weight = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * INPUT_NEURONS*/
biase = (float *)calloc(HIDDEN_NEURONS,sizeof(float)); /*HIDDEN_NEURONS*/
tempH = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * datasize*/
PIMatrix = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
H = (float **)calloc(DATASET,sizeof(float *));
T = (float **)calloc(DATASET,sizeof(float *));
Y = (float **)calloc(DATASET,sizeof(float *));
out = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
for(i=0;i<DATASET;i++){
train_set[i] = (float *)calloc(NUMROWS,sizeof(float));
input[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
H[i] = (float *)calloc(HIDDEN_NEURONS,sizeof(float));
}
for(i=0;i<DATASET;i++)
{
T[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
Y[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
for(i=0;i<INPUT_NEURONS;i++)
tranpI[i] = (float *)calloc(DATASET,sizeof(float));
for(i=0;i<HIDDEN_NEURONS;i++)
{
weight[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
tempH[i] = (float *)calloc(DATASET,sizeof(float));
out[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
PIMatrix[i] = (float *)calloc(DATASET,sizeof(float));
}
printf("begin to random weight and biase...\n");
/*得到随机的偏置和权重*/
RandomWeight(weight,HIDDEN_NEURONS,INPUT_NEURONS);
RandomBiase(biase,HIDDEN_NEURONS);
SaveMatrix(weight,"./result/weight",HIDDEN_NEURONS,INPUT_NEURONS);
SaveMatrix_s(biase,"./result/biase",1,HIDDEN_NEURONS);
/*加载数据集到内存*/
printf("begin to load input from the file...\n");
if(LoadMatrix(train_set,"./sample/frieman",DATASET,NUMROWS) == 0){
printf("load input file error!!!\n");
return;
}
/*将数据集划分成输入和输出*/
if(elm_type == 0){ //regression
/*
the first column is the output of the dataset
*/
for(i = 0;i < DATASET ;i++)
{
T[k++][0] = train_set[i][0];
for(j = 1;j <= INPUT_NEURONS;j++)
{
input[i][j-1] = train_set[i][j];
}
}
}else{ //classification
/*
the last column is the lable of class of the dataset
*/
InitMatrix(T,DATASET,OUTPUT_NEURONS,-1);
for(i = 0;i < DATASET ;i++)
{
//get the last column
T[k++][0] = train_set[i][0] - 1;//class label starts from 0,so minus one
for(j = 1;j <= INPUT_NEURONS;j++)
{
input[i][j-1] = train_set[i][j];
}
}
for(i = 0;i < DATASET ;i++)
{
for(j = 0;j < OUTPUT_NEURONS;j++)
{
k = T[i][0];
if(k < OUTPUT_NEURONS && k >= 0){
T[i][k] = 1;
}
if(k != 0){
T[i][0] = -1;
}
}
}
}
/*ELM*/
printf("begin to compute...\n");
starttime = omp_get_wtime();
TranspositionMatrix(input,tranpI,DATASET,INPUT_NEURONS);
printf("begin to compute step 1...\n");
MultiplyMatrix(weight,HIDDEN_NEURONS,INPUT_NEURONS, tranpI,INPUT_NEURONS,DATASET,tempH);
printf("begin to compute setp 2...\n");
AddMatrix_bais(tempH,biase,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 3...\n");
SigmoidHandle(tempH,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 4...\n");
TranspositionMatrix(tempH,H,HIDDEN_NEURONS,DATASET);
PseudoInverseMatrix(H,DATASET,HIDDEN_NEURONS,PIMatrix);
MultiplyMatrix(PIMatrix,HIDDEN_NEURONS,DATASET,T,DATASET,OUTPUT_NEURONS,out);
//SaveMatrix(H,"./result/H",DATASET,HIDDEN_NEURONS);
//SaveMatrix(PIMatrix,"./result/PIMatrix",HIDDEN_NEURONS,DATASET);
printf("begin to compute step 5...\n");
endtime = omp_get_wtime();
//保存输出权值
SaveMatrix(out,"./result/result",HIDDEN_NEURONS,OUTPUT_NEURONS);
MultiplyMatrix(H,DATASET,HIDDEN_NEURONS,out,HIDDEN_NEURONS,OUTPUT_NEURONS,Y);
printf("use time :%f\n",endtime - starttime);
printf("train complete...\n");
if(elm_type == 0){
//检测准确率
double MSE = 0;
for(i = 0;i< DATASET;i++)
{
MSE += (Y[i][0] - T[i][0])*(Y[i][0] - T[i][0]);
}
TrainingAccuracy = sqrt(MSE/DATASET);
printf("Regression/trainning accuracy :%f\n",TrainingAccuracy);
}else{
float MissClassificationRate_Training=0;
double maxtag1,maxtag2;
int tag1 = 0,tag2 = 0;
for (i = 0; i < DATASET; i++) {
maxtag1 = Y[i][0];
tag1 = 0;
maxtag2 = T[i][0];
tag2 = 0;
for (j = 1; j < OUTPUT_NEURONS; j++) {
if(Y[i][j] > maxtag1){
maxtag1 = Y[i][j];
tag1 = j;
}
if(T[i][j] > maxtag2){
maxtag2 = T[i][j];
tag2 = j;
}
}
if(tag1 != tag2)
MissClassificationRate_Training ++;
}
TrainingAccuracy = 1 - MissClassificationRate_Training*1.0f/DATASET;
printf("Classification/training accuracy :%f\n",TrainingAccuracy);
}
FreeMatrix(train_set,DATASET);
FreeMatrix(tranpI,INPUT_NEURONS);
FreeMatrix(input,DATASET);
FreeMatrix(weight,HIDDEN_NEURONS);
free(biase);
FreeMatrix(tempH,HIDDEN_NEURONS);
FreeMatrix(PIMatrix,HIDDEN_NEURONS);
FreeMatrix(H,DATASET);
FreeMatrix(T,DATASET);
FreeMatrix(Y,DATASET);
FreeMatrix(out,HIDDEN_NEURONS);
}
void Colours_YUV2RGB(double y, double u, double v, double *r, double *g, double *b)
{
MultiplyMatrix(y, u, v, r, g, b, YUV2RGB_matrix);
}
void Colours_RGB2YUV(double r, double g, double b, double *y, double *u, double *v)
{
MultiplyMatrix(r, g, b, y, u, v, RGB2YUV_matrix);
}
float divDGradU(intVectorType point ,densityDataType *densityData, int scanningRange)
{
momentOfInertiaType Inertia;
floatMatrixType BT, B, Diag, Help, Diffusion;
float a, structure;
//evaluateDirectionAndStructure(&a, &structure ,&Inertia, *densityData, point, scanningRange);
/*
(Inertia).v1.x=0.0;
(Inertia).v1.y=1.0;
(Inertia).v2.x=1.0;
(Inertia).v2.y=0.0;
*/
//BTransposed
float magV1=FLOATVECNORM((Inertia).v1)+EPS;
float magV2=FLOATVECNORM((Inertia).v2)+EPS;
/*
printf("magV1=%f\n",magV1);
printf("magV2=%f\n",magV2);
*/
BT.a11=(Inertia).v1.x/magV1; BT.a12=(Inertia).v1.y/magV1;
BT.a21=(Inertia).v2.x/magV2; BT.a22=(Inertia).v2.y/magV2;
//Diagonal
Diag.a11=1.0; Diag.a12=0.0;
Diag.a21=0.0; Diag.a22=1.0;
//B
B.a11=(Inertia).v1.x/magV1; B.a12=(Inertia).v2.x/magV2;
B.a21=(Inertia).v1.y/magV1; B.a22=(Inertia).v2.y/magV2;
/*
printf("BT:\n");
SHOWMATRIX(BT);
printf("Diag:\n");
SHOWMATRIX(Diag);
*/
//Create Diffusion Matrix
MultiplyMatrix(&Help, &Diag, &BT);
/*
printf("DiagBT:\n");
SHOWMATRIX(Help);
*/
/*
printf("B:\n");
SHOWMATRIX(B);
*/
MultiplyMatrix(&Diffusion, &B, &Help);
//printf("Diffusion\n");
//SHOWMATRIX(Diffusion);
return Divergence(point.x , point.y, &Diag , densityData);
//return Divergence(point.x , point.y, &Diffusion , densityData);
};
//回归
void ELMTrain()
{
double starttime,endtime;
int i,j,k = 0;
double MSE = 0,TrainingAccuracy;
float **input,**weight,*biase,**tranpI,**tempH,**H;
float **PIMatrix;
float **train_set;
float **T,**Y;
float **out;
train_set = (float **)calloc(DATASET,sizeof(float *));
tranpI = (float **)calloc(INPUT_NEURONS,sizeof(float *));
input = (float **)calloc(DATASET,sizeof(float *)); /*datasize * INPUT_NEURONS*/
weight = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * INPUT_NEURONS*/
biase = (float *)calloc(HIDDEN_NEURONS,sizeof(float)); /*HIDDEN_NEURONS*/
tempH = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * datasize*/
PIMatrix = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
H = (float **)calloc(DATASET,sizeof(float *));
T = (float **)calloc(DATASET,sizeof(float *));
Y = (float **)calloc(DATASET,sizeof(float *));
out = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
for(i=0;i<DATASET;i++){
train_set[i] = (float *)calloc(NUMROWS,sizeof(float));
input[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
H[i] = (float *)calloc(HIDDEN_NEURONS,sizeof(float));
}
for(i=0;i<DATASET;i++)
{
T[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
Y[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
for(i=0;i<INPUT_NEURONS;i++)
tranpI[i] = (float *)calloc(DATASET,sizeof(float));
for(i=0;i<HIDDEN_NEURONS;i++)
{
weight[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
tempH[i] = (float *)calloc(DATASET,sizeof(float));
out[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
PIMatrix[i] = (float *)calloc(DATASET,sizeof(float));
}
printf("begin to random weight and biase...\n");
/*得到随机的偏置和权重*/
RandomWeight(weight,HIDDEN_NEURONS,INPUT_NEURONS);
RandomBiase(biase,HIDDEN_NEURONS);
/*加载数据集到内存*/
printf("begin to load input from the file...\n");
if(LoadMatrix(train_set,"../TrainingDataSet/covtype",DATASET,NUMROWS,1) == 0){
printf("load input file error!!!\n");
return;
}
/*将数据集划分成输入和输出*/
for(i = 0;i < DATASET ;i++)
{
T[k++][0] = train_set[i][0];
for(j = 1;j <= INPUT_NEURONS;j++)
{
input[i][j-1] = train_set[i][j];
}
}
SaveMatrix(input,"./result/input",DATASET,INPUT_NEURONS);
/*ELM*/
printf("begin to compute...\n");
starttime = omp_get_wtime();
TranspositionMatrix(input,tranpI,DATASET,INPUT_NEURONS);
printf("begin to compute step 1...\n");
MultiplyMatrix(weight,HIDDEN_NEURONS,INPUT_NEURONS, tranpI,INPUT_NEURONS,DATASET,tempH);
printf("begin to compute setp 2...\n");
AddMatrix_bais(tempH,biase,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 3...\n");
SigmoidHandle(tempH,HIDDEN_NEURONS,DATASET);
printf("begin to compute step 4...\n");
TranspositionMatrix(tempH,H,HIDDEN_NEURONS,DATASET);
PseudoInverseMatrix(H,DATASET,HIDDEN_NEURONS,PIMatrix);
MultiplyMatrix(PIMatrix,HIDDEN_NEURONS,DATASET,T,DATASET,OUTPUT_NEURONS,out);
//SaveMatrix(H,"./result/H",DATASET,HIDDEN_NEURONS);
//SaveMatrix(PIMatrix,"./result/PIMatrix",HIDDEN_NEURONS,DATASET);
printf("begin to compute step 5...\n");
endtime = omp_get_wtime();
//保存输出权值
SaveMatrix(out,"./result/result",HIDDEN_NEURONS,OUTPUT_NEURONS);
//检测准确率
MultiplyMatrix(H,DATASET,HIDDEN_NEURONS,out,HIDDEN_NEURONS,OUTPUT_NEURONS,Y);
for(i = 0;i< DATASET;i++)
{
MSE += (Y[i][0] - T[i][0])*(Y[i][0] - T[i][0]);
}
SaveMatrix(T,"./result/t",DATASET,OUTPUT_NEURONS);
SaveMatrix(Y,"./result/y",DATASET,OUTPUT_NEURONS);
TrainingAccuracy = sqrt(MSE/DATASET);
printf("use time :%f\n",endtime - starttime);
printf("trainning accuracy :%f\n",TrainingAccuracy);
printf("train complete...\n");
//print(PIMatrix,DATASET,HIDDEN_NEURONS);
}
bool Run()
{
int result = ::DxLib::SetGraphMode(
static_cast<int>(m_width),
static_cast<int>(m_height),
32);
::DxLib::ChangeWindowMode(TRUE); // ウインドウモードに変更
if (::DxLib::DxLib_Init() == -1)
{
MessageBox(nullptr, "Initializing error", "Error!", MB_ICONERROR | MB_TASKMODAL | MB_TOPMOST);
return false;
}
// 描画先画面を裏画面にセット
SetDrawScreen(DX_SCREEN_BACK);
float nearZ = 1.f, farZ = 100.f;
SetCameraNearFar(nearZ, farZ);
VECTOR3 cameraPos(0.f, 0.5f, -10.f);
VECTOR3 targetPos(0.f, 0.f, 0.f);
DxLib::SetCameraPositionAndTarget_UpVecY(cameraPos, targetPos);
Cube cube(1.f);
int keys[] = { KEY_INPUT_UP, KEY_INPUT_DOWN, KEY_INPUT_LEFT, KEY_INPUT_RIGHT };
float val[] = { +0.1f, -0.1f, -0.1f, +0.1f };
float* pos[][2] = {
{ &cameraPos.y, &targetPos.y },
{ &cameraPos.y, &targetPos.y },
{ &cameraPos.x, &targetPos.x },
{ &cameraPos.x, &targetPos.x },
};
while (_LoopProc() && CheckHitKey(KEY_INPUT_ESCAPE) == 0)
{
const std::vector<Face>& faces = cube.GetFaces();
for (const Face& face : faces)
{
int vertexNum = static_cast<int>(face.GetVertexNum());
for (int i = 0; i < vertexNum - 1; ++i)
{
int j = (i + 1) % vertexNum;
// 頂点の取得
VECTOR4 from(face.GetVertexAt(i));
VECTOR4 to(face.GetVertexAt(j));
VECTOR3 from3d(from);
VECTOR3 to3d(to);
//------------------------------
// 座標変換
//------------------------------
float dist = targetPos.z - cameraPos.z;
MATRIX matT;
CreateTranslationMatrix(matT, from.x, from.y, from.z);
MATRIX matP;
CreatePerspectiveLH(matP, m_width, m_height, nearZ, farZ);
MATRIX mat;
MultiplyMatrix(mat, matT, matP);
Vector4Transform(from, from, mat);
CreateTranslationMatrix(matT, to.x, to.y, to.z);
MultiplyMatrix(mat, matT, matP);
Vector4Transform(to, to, mat);
/*
D3DXMATRIX * D3DXMatrixPerspectiveLH(
D3DXMATRIX * pOut,
FLOAT w, 近くのビュー プレーンでのビュー ボリュームの幅
FLOAT h, 近くのビュー プレーンでのビュー ボリュームの高さ
FLOAT zn, 近くのビュー プレーンの z 値
FLOAT zf 遠くのビュー プレーンの z 値
);
2*zn/w 0 0 0
0 2*zn/h 0 0
0 0 zf/(zf-zn) 1
0 0 zn*zf/(zn-zf) 0
D3DXMATRIX * D3DXMatrixPerspectiveRH(
D3DXMATRIX * pOut,
FLOAT w, FLOAT h,
FLOAT zn, FLOAT zf);
2*zn/w 0 0 0
0 2*zn/h 0 0
0 0 zf/(zn-zf) -1
0 0 zn*zf/(zn-zf) 0
*/
/*
D3DXMATRIX* D3DXMatrixPerspectiveFovLH(
D3DXMATRIX * pOut,
FLOAT fovy, y 方向の視野角 (ラジアン単位)
FLOAT Aspect, アスペクト比 (ビュー空間の幅を高さで除算して定義(width/height))
FLOAT zn, 近くのビュー プレーンの z 値
FLOAT zf 遠くのビュー プレーンの z 値
);
where:
yScale = cot(fovY/2)
xScale = yScale / aspect ratio
xScale 0 0 0
0 yScale 0 0
0 0 zf/(zf-zn) 1
0 0 -zn*zf/(zf-zn) 0
D3DXMATRIX * D3DXMatrixPerspectiveFovRH(
D3DXMATRIX * pOut,
FLOAT fovy, FLOAT Aspect,
FLOAT zn, FLOAT zf);
xScale 0 0 0
0 yScale 0 0
0 0 zf/(zn-zf) -1
0 0 zn*zf/(zn-zf) 0
*/
/*
D3DXMATRIX * D3DXMatrixLookAtLH(
D3DXMATRIX * pOut, 処理の結果を表す D3DXMATRIX 構造体へのポインター
CONST D3DXVECTOR3 * pEye, 視点を定義する D3DXVECTOR3 構造体へのポインターです。この値は変換に使用されます。
CONST D3DXVECTOR3 * pAt, カメラの注視対象を定義する D3DXVECTOR3 構造体へのポインターです。
CONST D3DXVECTOR3 * pUp 現在のワールド座標における上方向を定義する D3DXVECTOR3 構造体へのポインター。この構造体の値は通常 [0, 1, 0] です。
);
zaxis = normal(At - Eye)
xaxis = normal(cross(Up, zaxis))
yaxis = cross(zaxis, xaxis)
xaxis.x yaxis.x zaxis.x 0
xaxis.y yaxis.y zaxis.y 0
xaxis.z yaxis.z zaxis.z 0
-dot(xaxis, eye) -dot(yaxis, eye) -dot(zaxis, eye) l
D3DXMATRIX * D3DXMatrixLookAtRH(
D3DXMATRIX * pOut,
CONST D3DXVECTOR3 * pEye,
CONST D3DXVECTOR3 * pAt,
CONST D3DXVECTOR3 * pUp
);
zaxis = normal(Eye - At)
xaxis = normal(cross(Up, zaxis))
yaxis = cross(zaxis, xaxis)
xaxis.x yaxis.x zaxis.x 0
xaxis.y yaxis.y zaxis.y 0
xaxis.z yaxis.z zaxis.z 0
-dot(xaxis, eye) -dot(yaxis, eye) -dot(zaxis, eye) l
*/
//------------------------------
// 描画
DxLib::DrawLine(from.x, from.y,
to.x, to.y,
GetColor(250, 0, 0));
printf("from(%.1f, %.1f, %.1f) ", from.x, from.y, from.z);
printf("to(%.1f, %.1f, %.1f)\n", to.x, to.y, to.z);
DxLib::DrawLine3D(from3d, to3d, -1);
}
}
{
int i = 0;
for (int key : keys)
{
if (CheckHitKey(key) != 0)
{
*pos[i][0] += val[i];
*pos[i][1] += val[i];
}
++i;
}
DxLib::SetCameraPositionAndTarget_UpVecY(cameraPos, targetPos);
DrawExtendFormatString(0, 0, 0.75, 0.75, -1, "camera(%.1f, %.1f, %.1f) target(%.1f, %.1f, %.1f)", cameraPos.x, cameraPos.y, cameraPos.z, targetPos.x, targetPos.y, targetPos.z);
}
}
return true;
}
void Keyboard(unsigned char key, int x, int y){
switch( key ) { // NEW: Manual camera mode: allways on -> use WASDRC to translate cam position and IJKL for cam rotation
case 'w' :
camera_disp += 0.1f;
break;
case 's' :
camera_disp -= 0.1f;
break;
case 'd' :
xx -= 0.1;
break;
case 'a' :
xx += 0.1;
break;
case 'i' :
angle2 -= 0.3;
break;
case 'k' :
angle2 += 0.3;
break;
case 'l' :
angle3 += 0.3;
break;
case 'j' :
angle3 -= 0.3;
break;
case 'r' :
yy -= 0.1f;
break;
case 'c' :
yy += 0.1f;
break;
case 'm' : // set automatic camera mode and reset camera position an rotation (angle)
anim_cam = GL_TRUE;
camera_disp = -10.0;
angle=0;
angle1=0;
angle2=0;
angle3=0;
xx=0;
yy=0;
SetTranslation(xx, yy, camera_disp, ViewMatrix); // camera_disp == z coordinate of camera
return;
break;
case 'n' : // unset automatic camera mode and reset camera position an rotation (angle)
anim_cam = GL_FALSE;
camera_disp = -10.0;
angle=0;
angle1=0;
angle2=0;
angle3=0;
xx=0;
yy=0;
SetTranslation(xx, yy, camera_disp, ViewMatrix); // camera_disp == z coordinate of camera
return;
break;
}
SetIdentityMatrix(RotationMatrixCam);
SetIdentityMatrix(RotationMatrixCam2);
SetIdentityMatrix(RotationMatrixCam3);
SetIdentityMatrix(TranslationMatrixCam);
// set camera movement
SetTranslation(xx, yy, camera_disp, TranslationMatrixCam);
SetRotationX(angle2, RotationMatrixCam);
SetRotationY(angle3, RotationMatrixCam2);
// apply movement to viewMatrix (camera matrix)
MultiplyMatrix(RotationMatrixCam, RotationMatrixCam2, RotationMatrixCam3);
MultiplyMatrix(RotationMatrixCam3, TranslationMatrixCam, ViewMatrix);
glutPostRedisplay();
}
void OnIdle(){
/* Determine delta time between two frames to ensure constant animation */
int newTime = glutGet(GLUT_ELAPSED_TIME);
int delta = newTime - oldTime;
oldTime = newTime;
SetIdentityMatrix(IdentityMatrixAnim);
SetIdentityMatrix(TranslationMatrixAnim);
/* automatic camera mode: press 'm' to start, and 'n' to reset */
if(anim_cam){
if(camera_disp > -50){ // zoom out of world (until camera position on z == -50) and rotate on x axis
camera_disp -= 0.1;
}
if(camMov2 < 500){
angle1 += 0.1;
}
else if(camMov2 < 800){
angle1 -= 0.1;
}
else{
camMov2 = 200;
}
++camMov2;
if(camMov1 < 10 && camMov1 > -1){ // move camera to left and right with some pause of movement
// Pause movement
}
else if(camMov1 < 80){
angle -= 0.1;
}
else if(camMov1 < 100){
// Pause movement
}
else if(camMov1 < 240){
angle += 0.1;
}
else if(camMov1 < 250){
// Pause movement
}
else if(camMov1 < 320){
angle -= 0.1;
}
else{
// Pause for some time
if(camMov1 > 1000){
camMov1 = 0;
}
}
++camMov1;
SetIdentityMatrix(RotationMatrixCam);
SetIdentityMatrix(RotationMatrixCam2);
SetTranslation(0.0, 0.0, camera_disp, TranslationMatrixAnim); // translate cameras z position
SetRotationY(angle, RotationMatrixCam); // set rotation of camera
SetRotationX(angle1, RotationMatrixCam2);
MultiplyMatrix(RotationMatrixCam, TranslationMatrixAnim, RotationMatrixCam3);
MultiplyMatrix(RotationMatrixCam3, RotationMatrixCam2, ViewMatrix);
}
/* If animation is set to true, set new rotation angles */
if(anim){
/* Increment rotation angles and update matrix */
if(axis == YaxisStop){
// angleY = 0;
SetRotationY(angleY, RotationMatrixAnimY);
}
else if(axis == Yaxis){
angleY = fmod(angleY + delta/20.0, 360.0);
SetRotationY(angleY, RotationMatrixAnimY);
}
}
else { /* else: do not apply rotation below */
return;
}
// Set Translation for Center-Imported Objects (ball_01, ball_02 and ring) // NEW
SetIdentityMatrix(IdentityMatrixAnim);
SetIdentityMatrix(TranslationMatrixAnim);
SetIdentityMatrix(TranslationMatrixAnim2);
SetIdentityMatrix(TranslationMatrixAnim3);
SetIdentityMatrix(TranslationMatrixAnim4);
SetIdentityMatrix(TranslationMatrixAnim5);
SetTranslation(-1.8, 1, -1.8, TranslationMatrixAnim); // ball_01 -1,8/1,8/1
SetTranslation(1.8, 1, 1.8, TranslationMatrixAnim3); // ball_02 1,8/-1,8/1
SetTranslation(-0.96, -1.73403, -0.66, TranslationMatrixAnim5); // elliptic_ring -0,96/0,66/-1,73403
/* Rotation of models */
int k;
for(k=1; k<13; ++k){ // do not rotate top ring (index 0) and fixed background structures (index 13 and 14)
// Set main rotaiton
/* Update of transformation matrices
* Note order of transformations and rotation of reference axes */
SetIdentityMatrix(IdentityMatrixAnim);
MultiplyMatrix(IdentityMatrixAnim, RotationMatrixAnimY, RotationMatrixAnim);
// rotate top bar in different direction and double the rotation speed
if(k==1 || k==6 || k == 7){
angleY2 = -fmod(angleY + delta/20.0, 360.0);
SetRotationY(angleY2, RotationMatrixAnimY2);
// in daddition: rotate models ball_01 and ball_02 around their own center
if(k==6){
MultiplyMatrix(RotationMatrixAnimY2, RotationMatrixAnimY2, RotationMatrixAnimY3);
MultiplyMatrix(RotationMatrixAnimY3, TranslationMatrixAnim, TranslationMatrixAnim2);
MultiplyMatrix(TranslationMatrixAnim2, RotationMatrixAnimY2, ModelMatrix[k]);
continue;
}
else if(k==7){
MultiplyMatrix(RotationMatrixAnimY2, RotationMatrixAnimY2, RotationMatrixAnimY3);
MultiplyMatrix(RotationMatrixAnimY3, TranslationMatrixAnim3, TranslationMatrixAnim2);
MultiplyMatrix(TranslationMatrixAnim2, RotationMatrixAnimY2, ModelMatrix[k]);
continue;
}
MultiplyMatrix(RotationMatrixAnimY2, IdentityMatrixAnim, ModelMatrix[k]);
MultiplyMatrix(RotationMatrixAnimY2,RotationMatrixAnimY2,ModelMatrix[k]);
}
else {
// additional rotation of ring with double speed oround its center
if(k==11){
MultiplyMatrix(RotationMatrixAnimY, TranslationMatrixAnim5, RotationMatrixAnimY3);
MultiplyMatrix(RotationMatrixAnimY3, RotationMatrixAnimY, TranslationMatrixAnim4);
MultiplyMatrix(TranslationMatrixAnim4, RotationMatrixAnimY, ModelMatrix[k]);
continue;
}
MultiplyMatrix(RotationMatrixAnim, IdentityMatrixAnim, ModelMatrix[k]);
}
}
/* Issue display refresh */
glutPostRedisplay();
}
void Initialize(void)
{
/* Set background (clear) color to dark blue */
glClearColor(0.0, 0.0, 0.4, 0.0);
/* Enable depth testing */
glEnable(GL_DEPTH_TEST);
glDepthFunc(GL_LESS);
/* Transform matrix for the bar in the middle*/
/*copy objects into buffer*/
memcpy(vertex_buffer_data1, vertex_octagon, sizeof(vertex_octagon));
memcpy(vertex_buffer_data2, vertex_pyramid, sizeof(vertex_pyramid));
memcpy(vertex_buffer_data3, vertex_mainBar, sizeof(vertex_mainBar));
memcpy(vertex_buffer_data4, vertex_cube, sizeof(vertex_cube));
memcpy(vertex_buffer_data5, vertex_cube, sizeof(vertex_cube));
memcpy(vertex_buffer_data6, vertex_cube, sizeof(vertex_cube));
memcpy(vertex_buffer_data7, vertex_cube, sizeof(vertex_cube));
/*
memcpy(color_buffer_data1, color_octagon, sizeof(color_octagon));
memcpy(color_buffer_data2, color_pyramid, sizeof(color_pyramid));
memcpy(color_buffer_data3, color_mainBar, sizeof(color_mainBar));
memcpy(color_buffer_data4, color_cube, sizeof(color_cube));
memcpy(color_buffer_data5, color_cube, sizeof(color_cube));
memcpy(color_buffer_data6, color_cube, sizeof(color_cube));
memcpy(color_buffer_data7, color_cube, sizeof(color_cube));
*/
memcpy(index_buffer_data1, index_octagon, sizeof(index_octagon));
memcpy(index_buffer_data2, index_pyramid, sizeof(index_pyramid));
memcpy(index_buffer_data3, index_mainBar, sizeof(index_mainBar));
memcpy(index_buffer_data4, index_cube, sizeof(index_cube));
memcpy(index_buffer_data5, index_cube, sizeof(index_cube));
memcpy(index_buffer_data6, index_cube, sizeof(index_cube));
memcpy(index_buffer_data7, index_cube, sizeof(index_cube));
/* Setup vertex, color, and index buffer objects */
SetupDataBuffers();
/* Setup shaders and shader program */
CreateShaderProgram();
/* Initialize matrices */
SetIdentityMatrix(ProjectionMatrix);
SetIdentityMatrix(ViewMatrix);
int i = 0;
for(;i<OBJECTS;i++) {
SetIdentityMatrix(ModelMatrix[i]);
}
printf("DEBUG: adding light-source ...\n");
/* Add lighting source to the code */
glLightModeli(GL_LIGHT_MODEL_LOCAL_VIEWER, GL_TRUE);
glEnable(GL_LIGHTING);
glEnable(GL_COLOR_MATERIAL);
glEnable(GL_LIGHT0); // adds first light-source to the scene
GLfloat ambientLight[] = {0.2, 0.2, 0.2, 1.0};
GLfloat diffuseLight[] = {0.8, 0.8, 0.8, 1.0};
GLfloat specularLight[]= {1.0, 1.0, 1.0, 1.0};
GLfloat positionLight[]= {5.0, 0.0, 0.0, 0.0};
//glShadeModel(GL_SMOOTH);
glLightfv(GL_LIGHT0, GL_AMBIENT, ambientLight);
glLightfv(GL_LIGHT0, GL_DIFFUSE, diffuseLight);
glLightfv(GL_LIGHT0, GL_SPECULAR, specularLight);
glLightfv(GL_LIGHT0, GL_POSITION, positionLight);
GLfloat black[] = {0.0, 0.0, 0.0, 1.0};
GLfloat green[] = {0.0, 1.0, 0.0, 1.0};
GLfloat white[] = {1.0, 1.0, 1.0, 1.0};
glMaterialfv(GL_FRONT, GL_AMBIENT, green);
glMaterialfv(GL_FRONT, GL_DIFFUSE, green);
glMaterialfv(GL_FRONT, GL_SPECULAR, white);
glMaterialf(GL_FRONT, GL_SHININESS, 60.0);
printf("DEBUG: finished adding light-source\n");
/* Set projection transform */
float fovy = 45.0;
float aspect = 1.0;
float nearPlane = 1.0;
float farPlane = 50.0;
SetPerspectiveMatrix(fovy, aspect, nearPlane, farPlane, ProjectionMatrix);
/* Set viewing transform */
float camera_disp = -10.0;
SetTranslation(0.0, 0.0, camera_disp, ViewMatrix);
/* Translate and rotate cube */
SetTranslation(0, 0, 0, TranslateOrigin);
SetRotationX(0.0, RotateX);
SetRotationZ(0.0, RotateZ);
/* Translate down */
SetTranslation(0, -sqrtf(sqrtf(2.0) * 1.0), 0, TranslateDown);
/* Initial transformation matrix */
MultiplyMatrix(RotateX, TranslateOrigin, InitialTransform);
MultiplyMatrix(RotateZ, InitialTransform, InitialTransform);
}
int main(int argc,char **argv){
int i,j,k = 0;
double TrainingAccuracy;
float **input,**weight,*biase,**tranpI,**tempH,**H;
float **train_set;
float **T,**Y;
float **out;
train_set = (float **)calloc(TESTSET,sizeof(float *));
tranpI = (float **)calloc(INPUT_NEURONS,sizeof(float *));
input = (float **)calloc(TESTSET,sizeof(float *)); /*datasize * INPUT_NEURONS*/
weight = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * INPUT_NEURONS*/
biase = (float *)calloc(HIDDEN_NEURONS,sizeof(float)); /*HIDDEN_NEURONS*/
tempH = (float **)calloc(HIDDEN_NEURONS,sizeof(float *)); /*HIDDEN_NEURONS * datasize*/
H = (float **)calloc(TESTSET,sizeof(float *));
T = (float **)calloc(TESTSET,sizeof(float *));
Y = (float **)calloc(TESTSET,sizeof(float *));
out = (float **)calloc(HIDDEN_NEURONS,sizeof(float *));
for(i=0;i<TESTSET;i++){
train_set[i] = (float *)calloc(NUMROWS,sizeof(float));
input[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
H[i] = (float *)calloc(HIDDEN_NEURONS,sizeof(float));
}
for(i=0;i<TESTSET;i++)
{
T[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
Y[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
for(i=0;i<INPUT_NEURONS;i++){
tranpI[i] = (float *)calloc(TESTSET,sizeof(float));
}
for(i=0;i<HIDDEN_NEURONS;i++)
{
weight[i] = (float *)calloc(INPUT_NEURONS,sizeof(float));
tempH[i] = (float *)calloc(TESTSET,sizeof(float));
out[i] = (float *)calloc(OUTPUT_NEURONS,sizeof(float));
}
printf("begin to classification test...\n");
/*得到随机的偏置和权重*/
//RandomWeight(weight,HIDDEN_NEURONS,INPUT_NEURONS);
//RandomBiase(biase,HIDDEN_NEURONS);
printf("begin to load weight...\n");
if(LoadMatrix(weight,"../result/weight",HIDDEN_NEURONS,INPUT_NEURONS) == 0){
printf("load weight file error!!!\n");
return 0;
}
printf("begin to load bias...\n");
if(LoadMatrix_s(biase,"../result/biase",1,HIDDEN_NEURONS) == 0){
printf("load bias file error!!!\n");
return 0;
}
/*加载数据集到内存*/
printf("begin to load test file...\n");
if(LoadMatrix(train_set,"../sample/covtype_test",TESTSET,NUMROWS) == 0){
printf("load input file error!!!\n");
return 0;
}
InitMatrix(T,TESTSET,OUTPUT_NEURONS,-1);
/*将数据集划分成输入和输出*/
for(i = 0;i < TESTSET ;i++)
{
//get the last column
T[k++][0] = train_set[i][0] - 1;//class label starts from 0,so minus one
for(j = 1;j <= INPUT_NEURONS;j++)
{
input[i][j-1] = train_set[i][j];
}
}
for(i = 0;i < TESTSET ;i++)
{
for(j = 0;j < OUTPUT_NEURONS;j++)
{
k = T[i][0];
if(k < OUTPUT_NEURONS && k >= 0){
T[i][k] = 1;
}
if(k != 0){
T[i][0] = -1;
}
}
}
/*ELM*/
printf("begin to compute...\n");
TranspositionMatrix(input,tranpI,TESTSET,INPUT_NEURONS);
printf("begin to compute step 1...\n");
MultiplyMatrix(weight,HIDDEN_NEURONS,INPUT_NEURONS, tranpI,INPUT_NEURONS,TESTSET,tempH);
printf("begin to compute setp 2...\n");
AddMatrix_bais(tempH,biase,HIDDEN_NEURONS,TESTSET);
printf("begin to compute step 3...\n");
SigmoidHandle(tempH,HIDDEN_NEURONS,TESTSET);
printf("begin to compute step 4...\n");
TranspositionMatrix(tempH,H,HIDDEN_NEURONS,TESTSET);
printf("begin to load hidden output matrix from the file...\n");
if(LoadMatrix(out,"../result/result",HIDDEN_NEURONS,OUTPUT_NEURONS) == 0){
printf("load input file error!!!\n");
return 0;
}
MultiplyMatrix(H,TESTSET,HIDDEN_NEURONS,out,HIDDEN_NEURONS,OUTPUT_NEURONS,Y);
float MissClassificationRate_Training=0;
double maxtag1,maxtag2;
int tag1 = 0,tag2 = 0;
for (i = 0; i < TESTSET; i++) {
maxtag1 = Y[i][0];
tag1 = 0;
maxtag2 = T[i][0];
tag2 = 0;
for (j = 1; j < OUTPUT_NEURONS; j++) {
if(Y[i][j] > maxtag1){
maxtag1 = Y[i][j];
tag1 = j;
}
if(T[i][j] > maxtag2){
maxtag2 = T[i][j];
tag2 = j;
}
}
if(tag1 != tag2)
MissClassificationRate_Training ++;
}
TrainingAccuracy = 1 - MissClassificationRate_Training*1.0f/TESTSET;
printf("trainning accuracy :%f\n",TrainingAccuracy);
printf("test complete...\n");
//print(PIMatrix,TESTSET,HIDDEN_NEURONS);
return 0;
}
