int
main ()
{
int n;
double *x, *f, *g;
printf ("\nEntreu les coordenades\n");
x = HGVM (&n);
printf ("\nEntreu els valors de la funcio\n");
f = GVM (n);
printf ("\nEntreu les derivades\n");
g = GVM (n);
/* Obte el polinomi desitjat */
difdivherm (n, &x, &f, &g);
/* Dibuixa el polinomi */
writeFile (2 * n, 1e-2, -2, 2, x, f);
FV (x, n);
FV (f, n);
return 0;
}
int main (void)
{
int n;
double tol = 1e-14;
double **A, *b;
for (n = 1; n <= 15; n++)
{
A = GMH (n);
b = GV1 (n, A);
gauss (n, A, b, tol);
trisup (n, A, b, tol);
printf ("%3d:\t%.30le\n", n, Norm (n, b)-1);
FM (A, n, n);
FV (b, n);
}
return 0;
}
void CLink::Load(CProfINIFile & SavePF)
{
if (1)
return;
CString Line, V;
Line=SavePF.RdStr("Links", m_sTag, "");
if (Line.GetLength()>0)
{
int iStart=0;
int lLink = SafeAtoL(Line.Tokenize(",", iStart));
//int lDbg = SafeAtoL(Line.Tokenize(",", iStart));
int lHold = SafeAtoL(Line.Tokenize(",", iStart));
V=Line.Tokenize("'", iStart);
//m_bSltDbgOn=lDbg!=0;
m_State.m_bHold=lHold!=0;
CFullValue FV(OPC_QUALITY_GOOD);
StringToVariant(Type(), V, FV, false);
gs_SlotMngr.AppendChange(eCSD_File, -1, eCSD_Link, m_lLink, -1/*gs_SlotMngr.GetTransactionID()*/, FV, NULL, true);
}
};
int main()
{
Bayes bn(4, 2); //constract a bayes network with 4 featuers and 2 classes
std::vector<double> FV(4);
//Class 0
FV[0] = 752; FV[1] = 265; FV[2] = 700; FV[3] = 271; bn.learn(FV, 0u);
FV[0] = 895; FV[1] = 355; FV[2] = 812; FV[3] = 288; bn.learn(FV, 0u);
FV[0] = 893; FV[1] = 352; FV[2] = 790; FV[3] = 298; bn.learn(FV, 0u);
FV[0] = 814; FV[1] = 326; FV[2] = 790; FV[3] = 296; bn.learn(FV, 0u);
FV[0] = 532; FV[1] = 405; FV[2] = 750; FV[3] = 401; bn.learn(FV, 0u);
FV[0] = 532; FV[1] = 405; FV[2] = 750; FV[3] = 401; bn.learn(FV, 0u);
FV[0] = 478; FV[1] = 385; FV[2] = 750; FV[3] = 394; bn.learn(FV, 0u);
FV[0] = 532; FV[1] = 405; FV[2] = 750; FV[3] = 401; bn.learn(FV, 0u);
FV[0] = 565; FV[1] = 47 ; FV[2] = 710; FV[3] = 142; bn.learn(FV, 0u);
FV[0] = 689; FV[1] = 127; FV[2] = 955; FV[3] = 162; bn.learn(FV, 0u);
//Class 1
FV[0] = 576; FV[1] = 726; FV[2] = 287; FV[3] =719; bn.learn(FV, 1);
FV[0] = 718; FV[1] = 783; FV[2] = 300; FV[3] =536; bn.learn(FV, 1);
FV[0] = 859; FV[1] = 724; FV[2] = 270; FV[3] =480; bn.learn(FV, 1);
FV[0] = 839; FV[1] = 512; FV[2] = 246; FV[3] =657; bn.learn(FV, 1);
FV[0] = 746; FV[1] = 343; FV[2] = 250; FV[3] =710; bn.learn(FV, 1);
FV[0] = 660; FV[1] = 527; FV[2] = 272; FV[3] =763; bn.learn(FV, 1);
FV[0] = 704; FV[1] = 621; FV[2] = 263; FV[3] =713; bn.learn(FV, 1);
FV[0] = 684; FV[1] = 836; FV[2] = 287; FV[3] =213; bn.learn(FV, 1);
FV[0] = 678; FV[1] = 800; FV[2] = 377; FV[3] =220; bn.learn(FV, 1);
FV[0] = 624; FV[1] = 697; FV[2] = 494; FV[3] =238; bn.learn(FV, 1);
LINFO("Class 0");
for(uint i=0; i<bn.getNumFeatures(); i++)
LINFO("Feature %i: mean %f, stddevSq %f", i, bn.getMean(0, i), bn.getStdevSq(0, i));
LINFO("Class 1");
for(uint i=0; i<bn.getNumFeatures(); i++)
LINFO("Feature %i: mean %f, stddevSq %f", i, bn.getMean(1, i), bn.getStdevSq(1, i));
LINFO("Class 0 frq %i prob %f", bn.getClassFreq(0), bn.getClassProb(0));
LINFO("Class 1 frq %i prob %f", bn.getClassFreq(1), bn.getClassProb(1));
//New FV to classify
FV[0] = 750; FV[1] = 269; FV[2] = 720; FV[3] = 291;
int cls = bn.classify(FV); //classify a given FV
LINFO("FV1 belongs to class %i", cls);
FV[0] = 458; FV[1] = 381; FV[2] = 350; FV[3] = 392;
cls = bn.classify(FV); //classify a given FV
LINFO("FV2 belongs to class %i", cls);
bn.save("Bayes.net");
bn.load("Bayes.net");
LINFO("Class 0");
for(uint i=0; i<bn.getNumFeatures(); i++)
LINFO("Feature %i: mean %f, stddevSq %f", i, bn.getMean(0, i), bn.getStdevSq(0, i));
LINFO("Class 1");
for(uint i=0; i<bn.getNumFeatures(); i++)
LINFO("Feature %i: mean %f, stddevSq %f", i, bn.getMean(1, i), bn.getStdevSq(1, i));
LINFO("Class 0 frq %i prob %f", bn.getClassFreq(0), bn.getClassProb(0));
LINFO("Class 1 frq %i prob %f", bn.getClassFreq(1), bn.getClassProb(1));
//New FV to classify
FV[0] = 750; FV[1] = 269; FV[2] = 720; FV[3] = 291;
cls = bn.classify(FV); //classify a given FV
LINFO("FV1 belongs to class %i", cls);
FV[0] = 458; FV[1] = 381; FV[2] = 350; FV[3] = 392;
cls = bn.classify(FV); //classify a given FV
LINFO("FV2 belongs to class %i", cls);
}
static int _find_middle_snake(const void *a,int aoff,int n,
const void *b,int boff,int m,
MatchContext *ctx,
MiddleSnake *ms)
{
int delta,odd,mid,d;
delta=n - m;
odd=delta & 1;
mid=(n+m) / 2;
mid += odd;
_setv(ctx,1,0,0);
_setv(ctx,delta - 1,1,n);
for(d=0; d <= mid; d++)
{
int k,x,y;
if((2 * d - 1) >= ctx->dmax) {
return ctx->dmax;
}
for(k=d; k >= -d; k -= 2) {
if(k==-d ||(k != d && FV(k - 1)<FV(k+1))) {
x=FV(k+1);
} else {
x=FV(k - 1)+1;
}
y=x - k;
ms->x=x;
ms->y=y;
const unsigned char *a0=(const unsigned char *)a+aoff;
const unsigned char *b0=(const unsigned char *)b+boff;
while(x<n && y<m && a0[x]==b0[y]) {
x++; y++;
}
_setv(ctx,k,0,x);
if(odd && k >=(delta -(d - 1)) && k <=(delta +(d - 1))) {
if(x >= RV(k)) {
ms->u=x;
ms->v=y;
return 2 * d - 1;
}
}
}
for(k=d; k >= -d; k -= 2) {
int kr=(n - m)+k;
if(k==d ||(k != -d && RV(kr - 1)<RV(kr+1))) {
x=RV(kr - 1);
} else {
x=RV(kr+1) - 1;
}
y=x - kr;
ms->u=x;
ms->v=y;
const unsigned char *a0=(const unsigned char *)a+aoff;
const unsigned char *b0=(const unsigned char *)b+boff;
while(x > 0 && y > 0 && a0[x - 1]==b0[y - 1]) {
x--; y--;
}
_setv(ctx,kr,1,x);
if(!odd && kr >= -d && kr <= d) {
if(x <= FV(kr)) {
ms->x=x;
ms->y=y;
return 2 * d;
}
}
}
}
errno=EFAULT;
return -1;
}
