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; }