int main (void)
{
int size, *vector, i;
size = 5;
vector = malloc (size * sizeof (int));
vector[0] = 0;
vector[1] = 1;
vector[2] = 2;
vector[3] = 3;
vector[4] = 4;
/*vector[5] = 2;
vector[6] = 2;*/
ShowVector (size, vector);
for (i = 0; i < 10; i++)
{
NextPermutation (size, vector);
ShowVector (size, vector);
}
printf ("Altre\n");
while (!NextPermutationV2 (size, vector))
ShowVector (size, vector);
printf ("Un cop acabat\n");
ShowVector (size, vector);
free (vector);
return 0;
}
/* Determine global mean and covariance of data in sequence s */
void CalcMeanCov(Sequence seq[], int s)
{
mean[s] = CreateVector(&dStack,swidth[s]);
SequenceMean(seq[s],mean[s]);
if (trace&T_MEAN) {
printf("Stream %d",s);
ShowVector(" global mean:\n",mean[s],20);
}
switch(ck){
case INVDIAGC:
case DIAGC:
cov[s].var = CreateVector(&dStack,swidth[s]);
SequenceCov(seq[s],ck,cov[s],mean[s]);
if(trace&T_MEAN) ShowVector("Global variance",cov[s].var,20);
break;
case FULLC:
cov[s].inv = CreateTriMat(&dStack,swidth[s]);
SequenceCov(seq[s],ck,cov[s],mean[s]);
if(trace&T_MEAN) ShowTriMat("Global covariance",cov[s].inv,20,20);
break;
case NULLC:
default:
cov[s].var = NULL;
if(trace&T_MEAN) printf("Using Euclidean distance\n");
break;
}
}
void TestInsert()
{
embDB::TBPVector<int32> vec;
embDB::TBPVector<int32> vec1;
embDB::TBPVector<int32> vec3;
size_t n = 20;
for (size_t i = 0; i < n; ++i)
{
vec.push_back(i);
vec1.push_back(i);
vec3.push_back(i);
}
ShowVector(vec);
std::cout << "movel 2 2 " << std::endl;
vec.movel(2, 2);
ShowVector(vec);
std::cout << "mover 0 3 " << std::endl;
vec1.mover(0, 3);
ShowVector(vec1);
std::cout << "mover 3 5 " << std::endl;
vec3.mover(3, 5);
ShowVector(vec3);
}
int main()
{
freopen("in.txt","r",stdin);
//freopen("out.txt","w",stdout);
vector *node_x, *node_y, *X4coe;
vector *coe_a, *coe_b;
/*--initialize the variables--*/
node_x = init_vector(node_x,1);
node_y = init_vector(node_y,1);
coe_a = init_vector(coe_a,0);
coe_b = init_vector(coe_b,0);
X4coe = change_nodex2X(node_x,X4coe);
/*--calculate the coefficients of polynomial--*/
calcu_coe_a(coe_a,X4coe,node_y);
printf("coe_a:\n");
ShowVector(coe_a);
calcu_coe_b(coe_b,X4coe,node_y);
printf("coe_b:\n");
ShowVector(coe_b);
/*--display the polynomial--*/
show_polynomial(coe_a,coe_b);
return 0;
}
int main()
{
//freopen("in.txt","r",stdin);
//freopen("test.txt","r",stdin);
//freopen("outin.txt","w",stdout);
vector *d, *h, *m, *u;
vector *Xnode, *Ynode;
double **S;
double ds_a = 0;
double ds_b = 98;
int i;
/*--initialize the vectors--*/
d = init_vector(d,0);
h = init_vector(h,0);
m = init_vector(m,0);
u = init_vector(m,0);
Xnode = init_vector(Xnode,1);
Ynode = init_vector(Ynode,1);
S = malloc((Xnode->N-1) * sizeof(double*));
/*--calculate every element of every vector--*/
calcu_h(h,Xnode);
calcu_d(d,Xnode,Ynode);
calcu_u(d,u,Xnode);
printf("\nh\n");
ShowVector(h);
printf("\nd\n");
ShowVector(d);
printf("\nm\n");
ShowVector(m);
printf("\nu\n");
printf("\nu\n");
ShowVector(u);
//ShowVector(X);
//ShowVector(Y);
/*--using Gauss-Seidel iteration to solve the equation HM=V--*/
calcu_m(m,d,u,h,Xnode,ds_a,ds_b);
*(m->value+0) = (3 / *(h->value)) * (*(d->value) - ds_a) - *(m->value+1) / 2;
*(m->value+Xnode->N - 1) = (3 / *(h->value+Xnode->N - 2)) * (ds_b - *(d->value+Xnode->N - 2)) - *(m->value+Xnode->N - 2) / 2;
printf("\nm:\n");
ShowVector(m);
/*--use what have calculated to calculate the coefficient of clamped cubic spline--*/
calcu_S(S,Ynode,d,h,m);
/*--show the value of coefficients--*/
for(i = 0; i < Ynode->N-1; i++){
printf("%lf&\t%lf&\t%lf&\t%lf\n",*(*(S+i)+0),*(*(S+i)+1),*(*(S+i)+2),*(*(S+i)+3));
}
return 0;
}
/* CalcCovs: calculate covariance of speech data */
void CalcCovs(void)
{
int x,y,s,V;
float meanx,meany,varxy,n;
Matrix fullMat;
if (totalCount<2)
HError(2021,"CalcCovs: Only %d speech frames accumulated",totalCount);
if (trace&T_TOP)
printf("%ld speech frames accumulated\n", totalCount);
n = (float)totalCount; /* to prevent rounding to integer below */
for (s=1; s<=hset.swidth[0]; s++){ /* For each stream */
V = hset.swidth[s];
for (x=1; x<=V; x++) /* For each coefficient ... */
accs[s].meanSum[x] /= n; /* ... calculate mean */
for (x=1;x<=V;x++) {
meanx = accs[s].meanSum[x]; /* ... and [co]variance */
if (fullcNeeded[s]) {
for (y=1; y<=x; y++) {
meany = accs[s].meanSum[y];
varxy = accs[s].squareSum.inv[x][y]/n - meanx*meany;
accs[s].squareSum.inv[x][y] =
(x != y || varxy > minVar) ? varxy : minVar;
}
}
else {
varxy = accs[s].squareSum.var[x]/n - meanx*meanx;
accs[s].fixed.var[x] = (varxy > minVar) ? varxy :minVar;
}
}
if (fullcNeeded[s]) { /* invert covariance matrix */
fullMat=CreateMatrix(&gstack,V,V);
ZeroMatrix(fullMat);
CovInvert(accs[s].squareSum.inv,fullMat);
Mat2Tri(fullMat,accs[s].fixed.inv);
FreeMatrix(&gstack,fullMat);
}
if (trace&T_COVS) {
printf("Stream %d\n",s);
if (meanUpdate)
ShowVector(" Mean Vector ", accs[s].meanSum,12);
if (fullcNeeded[s]) {
ShowTriMat(" Covariance Matrix ",accs[s].squareSum.inv,12,12);
} else
ShowVector(" Variance Vector ", accs[s].fixed.var,12);
}
}
}
/* PrintTree: Print each node as an entry */
static void PrintTree(VQNode n, CovKind ck)
{
if (n != NULL) {
printf("vqidx=%d ids=[%d %d %d]\n",
n->vqidx,n->nid,n->lid,n->rid);
ShowVector("Mean",n->mean,10);
switch(ck){
case NULLC:
break;
case INVDIAGC:
ShowVector("IVar",(Vector)n->cov.var,10);
break;
case FULLC:
ShowTriMat("ICov",(TriMat)n->cov.inv,10,10);
break;
}
printf("\n");
PrintTree(n->left,ck);
PrintTree(n->right,ck);
}
}
int main (void)
{
int s, *v;
s = 4;
v = malloc (s * sizeof (int));
if (!v)
{
printf ("Fatal error\n");
return 1;
}
v[0] = 0;
v[1] = 1;
v[2] = 1;
v[3] = 2;
ShowVector (s, v);
ParserPermutationDigitToVectorV2notCalc (s, v, 7, 12);
ShowVector (s, v);
return 0;
}
/* UpdateCounts: using frames in seg i and alignment in states/mixes */
void UpdateCounts(int segNum, int segLen, IntVec states,IntVec *mixes)
{
int M=0,i,j,k,s,m,state,last;
StreamElem *ste;
MixPDF *mp = NULL;
WtAcc *wa;
MuAcc *ma;
VaAcc *va;
TrAcc *ta;
Vector v;
Observation obs;
TMixRec *tmRec = NULL;
float x,y;
last = 1; /* last before 1st emitting state must be 1 */
ta = (TrAcc *)GetHook(hmmLink->transP);
for (i=1; i<=segLen; i++){
state = states[i];
if (trace&T_CNT)
printf(" Seg %d -> state %d\n",i,state);
if (uFlags&(UPMEANS|UPVARS|UPMIXES)){
obs = GetSegObs(segStore, segNum, i);
if (hset.hsKind == TIEDHS)
PrecomputeTMix(&hset, &obs, 50.0, 0);
ste = hmmLink->svec[state].info->pdf+1;
for (s=1; s<=nStreams; s++,ste++){
if (hset.hsKind==DISCRETEHS){
m = obs.vq[s]; v = NULL;
} else {
v = obs.fv[s]; m = mixes[s][i];
}
switch(hset.hsKind){
case TIEDHS:
tmRec = &(hset.tmRecs[s]);
M = tmRec->nMix;
break;
case PLAINHS:
case SHAREDHS:
case DISCRETEHS:
M = ste->nMix;
break;
}
if (m<1 || m > M)
HError(2170,"UpdateCounts: mix/vq idx out of range[%d]",m);
if (trace&T_CNT)
printf(" stream %d -> mix %d[%d]\n",s,m,M);
/* update mixture weight */
if (M>1 && (uFlags&UPMIXES)) {
wa = (WtAcc *)ste->hook;
wa->occ += 1.0; wa->c[m] += 1.0;
if (trace&T_CNT)
printf(" mix wt -> %.1f\n",wa->c[m]);
}
if (hset.hsKind==DISCRETEHS) continue;
/* update state/mixture component */
switch(hset.hsKind){
case PLAINHS:
case SHAREDHS:
mp = ste->spdf.cpdf[m].mpdf;
break;
case TIEDHS:
mp = tmRec->mixes[m];
break;
}
ma = (MuAcc *)GetHook(mp->mean);
va = (VaAcc *)GetHook(mp->cov.var);
ma->occ += 1.0; va->occ += 1.0;
for (j=1; j<=hset.swidth[s]; j++) {
x = v[j] - mp->mean[j];
ma->mu[j] += x;
if (uFlags&UPVARS)
switch(mp->ckind){
case DIAGC:
va->cov.var[j] += x*x;
break;
case FULLC:
for (k=1; k<=j; k++){
y = v[k]-mp->mean[k];
va->cov.inv[j][k] += x*y;
}
break;
default:
HError(2124,"UpdateCounts: bad cov kind %d\n",
mp->ckind);
}
}
if (trace&T_CNT) {
ShowVector(" mean ->",ma->mu,6);
if (uFlags&UPVARS) {
if (mp->ckind==DIAGC)
ShowVector(" var ->",va->cov.var,6);
else
ShowTriMat(" cov ->",va->cov.inv,6,6);
}
fflush(stdout);
}
}
}
/* update transition probs */
if (uFlags&UPTRANS){
ta->occ[last] += 1.0;
ta->tran[last][state] += 1.0;
last = state;
if (i==segLen){ /* remember final state */
ta->occ[state] += 1.0;
ta->tran[state][nStates] += 1.0;
}
if (trace&T_CNT) {
ShowMatrix(" tran ->",ta->tran,6,6);
fflush(stdout);
}
}
}
}
int main()
{
freopen("last_experience10.txt","r",stdin);
//freopen("last_experience_out10.txt","w",stdout);
//freopen("in.txt","r",stdin);
/************变量声明区*********************************/
//h 代表自变量之间的间距。 h_n=x_(n+1)-x(n)
//d 表示相邻两点之之间的斜率。d_n=(y_(n+1)-y_n)/h_n
//m 代表在某点处的二次导数值
//u 代表解m的方程组时等号右边的数。u_n=6(d_(n+1)-d_n)
vector *d, *h, *m, *u;
//Xnode, Ynode代表已知点的横、纵坐标值。
vector *Xnode, *Ynode;
//S 为二重指针，存储多项式系数。
double **S;
//ds_a、ds_b 分别表示左、右端点出的一次导数值
double ds_a = 0;//-7;
double ds_b = 0.6274;//0.5;
//i 控制循环次数
int i;
/***----------------initialize the vectors-----------------***/
d = init_vector(d,0);
h = init_vector(h,0);
m = init_vector(m,0);
u = init_vector(m,0);
Xnode = init_vector(Xnode,1);
Ynode = init_vector(Ynode,1);
ShowVector(Xnode);
ShowVector(Ynode);
S = malloc((Xnode->N-1) * sizeof(double*));
/**------calculate every element of every vector-----**/
calcu_h(h,Xnode);
calcu_d(d,Xnode,Ynode);
calcu_u(d,u,Xnode);
//输出计算得到的各个数值
printf("\nh\n");
ShowVector(h);
printf("\nd\n");
ShowVector(d);
printf("\nm\n");
ShowVector(m);
printf("\nu\n");
printf("\nu\n");
ShowVector(u);
/*--using Gauss-Seidel iteration to solve the equation HM=V--*/
//计算m的数值（除了端点处的之外）
calcu_m(m,d,u,h,Xnode,ds_a,ds_b);
//计算第一个和最后一个m值
*(m->value+0) = (3 / *(h->value)) * (*(d->value) - ds_a) - *(m->value+1) / 2;
*(m->value+Xnode->N - 1) = (3 / *(h->value+Xnode->N - 2)) * (ds_b - *(d->value+Xnode->N - 2)) - *(m->value+Xnode->N - 2) / 2;
//输出m数列
printf("\nm:\n");
ShowVector(m);
/*--use what have calculated to calculate the coefficient of clamped cubic spline--*/
calcu_S(S,Ynode,d,h,m);
/*--show the value of coefficients--*/
for(i = 0; i < Ynode->N-1; i++) {
printf("%lf&\t%lf&\t%lf&\t%lf\n",*(*(S+i)+0),*(*(S+i)+1),*(*(S+i)+2),*(*(S+i)+3));
}
return 0;
}
void main()
{
double e = 0;
int n; //初始节点个数
double * pd_Result = new double [1]; //u(xi)的结果
//复化梯形法,初始n = 32
n = 32 ;
do
{
n = n*2;
if ( n > MaxN)
{
n = n/2;
break;
}
delete []pd_Result ;
pd_Result = FormulaTrapezia(n,e);
}
while( e >= ee);
OutPutResult(pd_Result,n);
cout<<"The number of FormulaTrapezia is "<<n<<"!"<<endl;
cout<<"The error of FormulaTrapezia is:";
cout<<setiosflags(ios::scientific)<<setprecision(11)<<e<<endl;
//复Simpson法,初始n = 32
n = 32 ;
do
{
n = n*2;
if ( n > MaxN)
{
n = n/2;
break;
}
delete []pd_Result ;
pd_Result = FormulaSimpson(n,e);
}
while( e >= ee);
OutPutResult(pd_Result,n);
cout<<"The number of FormulaSimpson is "<<n<<"!"<<endl;
cout<<"The error of FormulaSimpson is:";
cout<<setiosflags(ios::scientific)<<setprecision(11)<<e<<endl;
//Gauss积分法
n = 7;
pd_Result = FormulaGauss(n,e);
ShowVector(pd_Result,n);
cout<<"The error of FormulaGauss is:";
cout<<setiosflags(ios::scientific)<<setprecision(11)<<e<<endl;
}
