本文整理汇总了C++中da函数的典型用法代码示例。如果您正苦于以下问题:C++ da函数的具体用法?C++ da怎么用?C++ da使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了da函数的20个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的C++代码示例。
示例1: newPolygon
Polygon* newPolygon(unsigned int nbVx, ...)
{
unsigned int i;
va_list ap;
Polygon* newPoly = (Polygon*) malloc(sizeof(Polygon));
/* Pas d'appel à polyInit possible à cause de la liste d'arguments...
Initialisation des Dynamic Arrays */
newPoly->Rigids = da();
newPoly->Vertices = da();
newPoly->InternalRigids = da();
daReserve(&newPoly->Rigids, nbVx);
daReserve(&newPoly->Vertices, nbVx);
newPoly->Center = NULL;
newPoly->Fixed = FALSE;
newPoly->GridPos.Valid = FALSE;
newPoly->Collided = FALSE;
va_start(ap, nbVx);
/* Ajoute les Vertices */
for(i = 0; i < nbVx; i++)
daAdd(&newPoly->Vertices, va_arg(ap, Vertex*));
va_end(ap);
/* Construit les limites, i.e. Créé un nouveau Rigid à partir de
deux Vertices de la liste et la distance les séparant, puis l'ajoute
à la liste */
for(i = 0; i < nbVx; i++)
daAdd(&newPoly->Rigids, newRigid((Vertex*)daGet(&newPoly->Vertices, i),
(Vertex*)daGet(&newPoly->Vertices, (i+1)%nbVx),
vec2Length(vec2Sub(vxGetPosition((Vertex*)daGet(&newPoly->Vertices, i)),
vxGetPosition((Vertex*)daGet(&newPoly->Vertices, (i+1)%nbVx))))));
return newPoly;
}
开发者ID:posva,项目名称:jump-n-run,代码行数:34,代码来源:Polygon.cpp
示例2: polyInit
void polyInit(Polygon* P, unsigned int nbVx, ...)
{
unsigned int i;
va_list ap;
/* Initialisation des Dynamic Arrays */
P->Rigids = da();
P->Vertices = da();
P->InternalRigids = da();
daReserve(&P->Rigids, nbVx);
daReserve(&P->Vertices, nbVx);
P->Center = NULL;
P->Fixed = FALSE;
P->GridPos.Valid = FALSE;
P->Collided = FALSE;
va_start(ap, nbVx);
/* Ajoute les Vertices */
for(i = 0; i < nbVx; i++)
daAdd(&P->Vertices, va_arg(ap, Vertex*));
va_end(ap);
/* Construit les limites, i.e. Créé un nouveau Rigid à partir de
deux Vertices de la liste et la distance les séparant, puis l'ajoute
à la liste */
for(i = 0; i < nbVx; i++)
daAdd(&P->Rigids, newRigid((Vertex*)daGet(&P->Vertices, i),
(Vertex*)daGet(&P->Vertices, (i+1)%nbVx),
vec2Length(vec2Sub(vxGetPosition((Vertex*)daGet(&P->Vertices, i)),
vxGetPosition((Vertex*)daGet(&P->Vertices, (i+1)%nbVx))))));
}
开发者ID:posva,项目名称:jump-n-run,代码行数:30,代码来源:Polygon.cpp
示例3: newPolygonL
Polygon* newPolygonL(List L)
{
unsigned int i = 0, nbVx = lstCount(&L);
Polygon* newPoly = (Polygon*) malloc(sizeof(Polygon));
Node* it = lstFirst(&L);
/*Initialisation des Dynamic Arrays */
newPoly->Rigids = da();
newPoly->Vertices = da();
newPoly->InternalRigids = da();
daReserve(&newPoly->Rigids, nbVx);
daReserve(&newPoly->Vertices, nbVx);
newPoly->Center = NULL;
newPoly->Fixed = FALSE;
newPoly->GridPos.Valid = FALSE;
newPoly->Collided = FALSE;
/* Ajoute les Vertices */
while(!nodeEnd(it))
{
daAdd(&newPoly->Vertices, (Vertex*) nodeGetData(it));
it = nodeGetNext(it);
}
/* Construit les limites, i.e. Créé un nouveau Rigid à partir de
deux Vertices de la liste et la distance les séparant, puis l'ajoute
à la liste */
for(i = 0; i < nbVx; i++)
daAdd(&newPoly->Rigids, newRigid((Vertex*)daGet(&newPoly->Vertices, i),
(Vertex*)daGet(&newPoly->Vertices, (i+1)%nbVx),
vec2Length(vec2Sub(vxGetPosition((Vertex*)daGet(&newPoly->Vertices, i)),
vxGetPosition((Vertex*)daGet(&newPoly->Vertices, (i+1)%nbVx))))));
return newPoly;
}
开发者ID:posva,项目名称:jump-n-run,代码行数:33,代码来源:Polygon.cpp
示例4: da
void WeightedDerivativesToRefined::apply_index(Model *m,
ParticleIndex pi) const {
// retrieving pis by ref if possible is cumbersome but is required for speed
ParticleIndexes pis_if_not_byref;
ParticleIndexes const* pPis;
if(refiner_->get_is_by_ref_supported()){
ParticleIndexes const& pis =
refiner_->get_refined_indexes_by_ref(m, pi);
pPis = &pis;
} else{
pis_if_not_byref = refiner_->get_refined_indexes(m, pi);
pPis = &pis_if_not_byref;
}
ParticleIndexes const& pis = *pPis;
// Prepare derivative accumulator to normalize by total weight
Float total_weight;
if(w_ != FloatKey()){
total_weight = m->get_attribute(w_, pi);
} else {
total_weight = pis.size();
}
DerivativeAccumulator da( 1.0 / total_weight);
// read K values for each key in keys_
Floats Ks(keys_.size());
for (unsigned int j = 0; j < Ks.size(); ++j){
Ks[j] = m->get_derivative(keys_[j], pi);
}
// store K reweighted per each particle, normalized with da
for (unsigned int i = 0; i < pis.size(); ++i) {
Float w = m->get_attribute(w_, pis[i]);
for (unsigned int j = 0; j < keys_.size(); ++j) {
m->add_to_derivative(keys_[j], pis[i], w * Ks[j], da);
}
}
}
开发者ID:AljGaber,项目名称:imp,代码行数:35,代码来源:WeightedDerivativesToRefined.cpp
示例5: main
int main()
{
int i,n,t,ans;
//scanf("%d",&t);
//while(t-->0)
{
while (scanf("%s",s)==1)
{
n=strlen(s);
for(i=0;i<n;i++) r[i]=s[i];
r[n]=0;
da(r,sa,n+1,128);
calheight(r,sa,n);
for (int i = 0;i < n;i++)
printf("%s\n",s+sa[i]);
puts("");
ans=n*(n+1)/2;
for(i=1;i<=n;i++) ans-=height[i];
printf("%d\n",ans);
}
}
return 0;
}
开发者ID:mzry1992,项目名称:workspace,代码行数:25,代码来源:I+-+Bacon’s+Cypher.cpp
示例6: main
int main() {
int n,k;
scanf("%d%d",&n,&k);
for(int i=0;i<n;i++) {scanf("%d",&r[i]);}
r[n]=0;
da(r,sa,rank,height,n,MAXM);
/*
printa(sa,n);
printa(rank,n);
printa(height,n);
*/
int low=1,high=n,mid;
while(low<high) {
mid=(low+high+1)/2;
//printf("%d %d %d\n",low,mid,high);
if(valid(mid,n,k)) {
low=mid;
} else {
high=mid-1;
}
}
//if(flag==0) printf("%d\n",)
printf("%d\n",low);
return 0;
}
开发者ID:Ycoronene,项目名称:OJ,代码行数:25,代码来源:poj-3261.cpp
示例7: main
int main(int, char*[])
{
/// leveraging our data abstraction
tut::data_abstraction da(10);
std::cout << tut::func(da) << std::endl;
std::cout << tut::func_ex(da) << std::endl;
/// we don't need std::bind for stand-alone functions - they're much easier to pass around than
/// member functions.
const auto fp(&tut::func);
std::cout << fp(da) << std::endl;
/// leveraging structural polymorphism
std::cout << tut::pow(da) << std::endl;
std::cout << tut::pow_ex(da) << std::endl;
/// leveraging static polymorphism
std::cout << tut::op(3, 5) << std::endl;
std::cout << tut::op(3.0f, 5.0f) << std::endl;
std::cout << tut::op('3', '5') << std::endl;
/// make a widget, and cast it back and forth
auto widget = std::make_unique<tut::widget>(12345, 101.0f, true);
auto castable = das::cast_unique<das::castable>(std::move(widget));
auto widget_again = das::cast_unique<tut::widget>(std::move(castable));
/// check its replacability :)
std::cout << tut::should_replace_with(*widget_again, 100.0f, 12345) << std::endl;
return 0;
}
开发者ID:bryanedds,项目名称:das,代码行数:30,代码来源:tut.cpp
示例8: main
int main()
{
int i,j=0,k,n;
int min,mid,max;
scanf("%d",&n);
while(n!=0)
{
n--;
scanf("%d",&j);
for(i=0;i<n;i++)
{
scanf("%d",&k);
r[i]=k-j+100;
j=k;
}
r[n]=0;
da(r,sa,n+1,200);
calheight(r,sa,n);
min=1;max=n/2;
while(min<=max)
{
mid=(min+max)/2;
if(check(sa,n,mid)) min=mid+1;
else max=mid-1;
}
if(max>=4) printf("%d\n",max+1);
else printf("0\n");
scanf("%d",&n);
}
return 0;
}
开发者ID:Voleking,项目名称:ICPC,代码行数:31,代码来源:pku1743_da.cpp
示例9: getPntrToVessel
vesselbase::StoreDataVessel* MultiColvarBase::buildDataStashes( const bool& allow_wcutoff, const double& wtol ){
// Check if vessels have already been setup
for(unsigned i=0;i<getNumberOfVessels();++i){
StoreColvarVessel* ssc=dynamic_cast<StoreColvarVessel*>( getPntrToVessel(i) );
if(ssc){
if( allow_wcutoff && !ssc->weightCutoffIsOn() ) error("Cannot have more than one data stash with different properties");
if( !allow_wcutoff && ssc->weightCutoffIsOn() ) error("Cannot have more than one data stash with different properties");
return ssc;
}
}
// Setup central atoms
vesselbase::VesselOptions da("","",0,"",this);
mycatoms=new StoreCentralAtomsVessel(da);
if( allow_wcutoff ) mycatoms->setHardCutoffOnWeight( wtol );
addVessel(mycatoms);
// Setup store values vessel
vesselbase::VesselOptions ta("","",0,"",this);
myvalues=new StoreColvarVessel(ta);
if( allow_wcutoff ) myvalues->setHardCutoffOnWeight( wtol );
addVessel(myvalues);
// Make sure resizing of vessels is done
resizeFunctions();
return myvalues;
}
开发者ID:whitead,项目名称:plumed2,代码行数:27,代码来源:MultiColvarBase.cpp
示例10: create_rand_dense_table
dense_table_t create_rand_dense_table(unsigned v0_id, unsigned v1_id, unsigned v2_id)
{
variable_t v0(v0_id, 4);
variable_t v1(v1_id, 3);
variable_t v2(v2_id, 2);
std::vector<variable_t> vars;
vars.push_back(v0);
vars.push_back(v1);
vars.push_back(v2);
domain_t domain(vars);
dense_table_t dt(domain);
assignment_t da(domain);
for(int i=0; i < domain.var(0).size(); ++i) {
da.set_asg(v0, i);
for(int j=0; j < domain.var(1).size(); ++j) {
da.set_asg(v1, j);
for(int k=0; k < domain.var(2).size(); ++k) {
da.set_asg(v2, k);
if(rand() % 100 <= 20) {
dt.set_logP(da, -1 * (rand() % 100));
}
}
}
}
return dt;
}
开发者ID:Alienfeel,项目名称:graphlab,代码行数:31,代码来源:test_dense_table.cpp
示例11: if
double bsmodel_4_vr::d(int pno, const double params[]) {
if (pno == 0) return(vega(params));
else if (pno == 1) return(rho(params));
else if (pno == 2) return(da(params));
else if (pno == 3) return(db(params));
else return(0.0);
}
开发者ID:bnsh,项目名称:fortrevor,代码行数:7,代码来源:bsmodel_4_vr.C
示例12: while
void GrVertexBatch::onDraw(GrBatchFlushState* state) {
int uploadCnt = fInlineUploads.count();
int currUpload = 0;
// Iterate of all the drawArrays. Before issuing the draws in each array, perform any inline
// uploads.
for (SkTLList<DrawArray>::Iter da(fDrawArrays); da.get(); da.next()) {
state->advanceLastFlushedToken();
while (currUpload < uploadCnt &&
fInlineUploads[currUpload]->lastUploadToken() <= state->lastFlushedToken()) {
fInlineUploads[currUpload++]->upload(state->uploader());
}
const GrVertexBatch::DrawArray& drawArray = *da.get();
GrProgramDesc desc;
const GrPipeline* pipeline = this->pipeline();
const GrPrimitiveProcessor* primProc = drawArray.fPrimitiveProcessor.get();
state->gpu()->buildProgramDesc(&desc, *primProc, *pipeline, fBatchTracker);
GrGpu::DrawArgs args(primProc, pipeline, &desc, &fBatchTracker);
int drawCount = drawArray.fDraws.count();
for (int i = 0; i < drawCount; i++) {
state->gpu()->draw(args, drawArray.fDraws[i]);
}
}
}
开发者ID:zhiqiang-li,项目名称:skia,代码行数:25,代码来源:GrVertexBatch.cpp
示例13: procedure
/*! \section example33 Example 33: Oscillating Search in very high-dimensional feature selection.
Very high-dimensional feature selection in text categorization, with
dimensionality in the order of 10000 or 100000.
The standard approach is BIF, yet we show here that a non-trivial
search procedure (OS) can be feasible. Here OS is applied in its
fastest form (delta=1), initialized by means of BIF. We use Multinomial
Bhattacharyya distance as the feature selection criterion (it has been
shown capable of overperforming traditional tools like Information
Gain etc., cf. Novovicova et al., LNCS 4109, 2006).
Randomly sampled 50% of data is used for multinomial
model parameter estimation to be used in the actual feature selection
process, another (disjunct) 40% of data is randomly sampled for testing.
The selected subset is eventually used for validation; multinomial Naive Bayes
classifier is trained on the training data on the selected subset
and classification accuracy is finally estimated on the test data.
*/
int main()
{
try{
typedef double RETURNTYPE; typedef double DATATYPE; typedef double REALTYPE;
typedef unsigned int IDXTYPE; typedef unsigned int DIMTYPE; typedef short BINTYPE;
typedef FST::Subset<BINTYPE, DIMTYPE> SUBSET;
typedef FST::Data_Intervaller<std::vector<FST::Data_Interval<IDXTYPE> >,IDXTYPE> INTERVALLER;
typedef boost::shared_ptr<FST::Data_Splitter<INTERVALLER,IDXTYPE> > PSPLITTER;
typedef FST::Data_Splitter_RandomRandom<INTERVALLER,IDXTYPE,BINTYPE> SPLITTERRR;
//typedef FST::Data_Accessor_Splitting_MemTRN<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for TRN data format
typedef FST::Data_Accessor_Splitting_MemARFF<DATATYPE,IDXTYPE,INTERVALLER> DATAACCESSOR; // uncomment for ARFF data format
typedef FST::Criterion_Multinomial_Bhattacharyya<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> BHATTMULTINOMIALDIST;
typedef FST::Classifier_Multinomial_NaiveBayes<RETURNTYPE,DATATYPE,REALTYPE,IDXTYPE,DIMTYPE,SUBSET,DATAACCESSOR> CLASSIFIERMULTINOMIAL;
typedef FST::Sequential_Step_Straight<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST> EVALUATOR;
std::cout << "Starting Example 33: Oscillating Search in very high-dimensional feature selection..." << std::endl;
// randomly sample 50% of data for training and randomly sample (disjunct) 40% for independent testing of final classification performance
PSPLITTER dsp_outer(new SPLITTERRR(1, 50, 40)); // (there will be one outer randomized split only)
// do not scale data
boost::shared_ptr<FST::Data_Scaler<DATATYPE> > dsc(new FST::Data_Scaler_void<DATATYPE>());
// set-up data access
boost::shared_ptr<std::vector<PSPLITTER> > splitters(new std::vector<PSPLITTER>); splitters->push_back(dsp_outer);
boost::shared_ptr<DATAACCESSOR> da(new DATAACCESSOR("data/reuters_apte.arff",splitters,dsc));
da->initialize();
// initiate access to split data parts
da->setSplittingDepth(0); if(!da->getFirstSplit()) throw FST::fst_error("50/40 random data split failed.");
// initiate the storage for subset to-be-selected
boost::shared_ptr<SUBSET> sub(new SUBSET(da->getNoOfFeatures()));
// set-up multinomial Bhattacharyya distance criterion
boost::shared_ptr<BHATTMULTINOMIALDIST> dmultinom(new BHATTMULTINOMIALDIST);
dmultinom->initialize(da); // (initialization = multinomial model parameter estimation on training data)
// set-up individual feature ranking to serve as OS initialization
FST::Search_BIF<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST> srch_bif;
// set-up the standard sequential search step object (option: hybrid, ensemble, etc.)
boost::shared_ptr<EVALUATOR> eval(new EVALUATOR);
// set-up the Oscillating Search procedure in its fastest setting
FST::Search_OS<RETURNTYPE,DIMTYPE,SUBSET,BHATTMULTINOMIALDIST,EVALUATOR> srch(eval);
srch.set_delta(1);
// target subset size must be set because a) Bhattacharyya is monotonous with respect to subset size,
// b) in very-high-dimensional problem d-optimizing search is not feasible due to search complexity
DIMTYPE target_subsize=500;
// run the search - first find the initial subset by means of BIF, then improve it by means of OS
std::cout << "Feature selection setup:" << std::endl << *da << std::endl << srch_bif << std::endl << srch << std::endl << *dmultinom << std::endl << std::endl;
RETURNTYPE critval_train, critval_test;
if(!srch_bif.search(target_subsize,critval_train,sub,dmultinom,std::cout)) throw FST::fst_error("Search (BIF) not finished.");
std::cout << std::endl << "Initialization result: " << std::endl << *sub << "Criterion value=" << critval_train << std::endl << std::endl;
if(!srch.search(target_subsize,critval_train,sub,dmultinom,std::cout)) throw FST::fst_error("Search (OS) not finished.");
std::cout << std::endl << "Search result: " << std::endl << *sub << "Criterion value=" << critval_train << std::endl;
// (optionally) validate result by estimating Naive Multinomial Bayes classifier accuracy on selected feature sub-space on independent test data
boost::shared_ptr<CLASSIFIERMULTINOMIAL> cmultinom(new CLASSIFIERMULTINOMIAL);
cmultinom->initialize(da);
cmultinom->train(da,sub);
cmultinom->test(critval_test,da);
std::cout << "Validated Multinomial NaiveBayes accuracy=" << critval_test << std::endl << std::endl;
}
catch(FST::fst_error &e) {std::cerr<<"FST ERROR: "<< e.what() << ", code=" << e.code() << std::endl;}
catch(std::exception &e) {std::cerr<<"non-FST ERROR: "<< e.what() << std::endl;}
return 0;
}
开发者ID:fgtlss,项目名称:sol,代码行数:76,代码来源:demo33.cpp
示例14: dump_code
void
dump_code(void)
{
fdump(); /* dumps all user functions */
if (begin_start) {
fprintf(stdout, "BEGIN\n");
da(begin_start, stdout);
}
if (end_start) {
fprintf(stdout, "END\n");
da(end_start, stdout);
}
if (main_start) {
fprintf(stdout, "MAIN\n");
da(main_start, stdout);
}
}
开发者ID:ThomasDickey,项目名称:mawk-20120627,代码行数:17,代码来源:code.c
示例15: da
BridgeVessel* ActionWithVessel::addBridgingVessel( ActionWithVessel* tome ){
VesselOptions da("","",0,"",this);
BridgeVessel* bv=new BridgeVessel(da);
bv->setOutputAction( tome );
functions.push_back( dynamic_cast<Vessel*>(bv) );
resizeFunctions();
return bv;
}
开发者ID:apoma,项目名称:plumed2,代码行数:8,代码来源:ActionWithVessel.cpp
示例16: StartTrace
void LDAPConnectionTest::ConnectionTest()
{
StartTrace(LDAPConnectionTest.ConnectionTest);
ROAnything cConfig;
AnyExtensions::Iterator<ROAnything> aEntryIterator(GetTestCaseConfig());
while ( aEntryIterator.Next(cConfig) ) {
for ( long l = 0; l < cConfig["NumberOfConnects"].AsLong(1); l++ ) {
Anything params;
params["Server"] = cConfig["LDAPServer"].AsString();
params["Port"] = cConfig["LDAPPort"].AsLong();
params["Timeout"] = cConfig["LDAPTimeout"].AsLong();
params["ConnectionTimeout"] = cConfig["LDAPConnectionTimeout"].AsLong(0);
params["BindName"] = cConfig["LDAPBindName"].AsString();
params["BindPW"] = cConfig["LDAPBindPW"].AsString();
params["PooledConnections"] = cConfig["LDAPPooledConnections"].AsLong(0L);
params["RebindTimeout"] = cConfig["LDAPRebindTimeout"].AsLong(3600L);
params["TryAutoRebind"] = cConfig["LDAPTryAutoRebind"].AsLong(0L);
params["MaxConnections"] = cConfig["LDAPMaxConnections"].AsLong(2L);
Context ctx;
ParameterMapper pm("ConnectionTestParameterMapper");
ResultMapper rm("ConnectionTestResultMapper");
pm.Initialize("ParameterMapper");
rm.Initialize("ResultMapper");
String da("DataAccess_");
da << aEntryIterator.Index();
LDAPErrorHandler eh(ctx, &pm, &rm, da);
eh.PutConnectionParams(params);
// connect
LDAPConnection lc(params);
LDAPConnection::EConnectState eConnectState = lc.DoConnect(params, eh);
String result(LDAPConnection::ConnectRetToString(eConnectState));
Trace("Connect result: " << result);
// check for errors
Anything error;
if ( !eh.GetError(error) ) {
Trace("No error reported.");
} else {
TraceAny(error, "Error description:");
}
// compare result and expected error
assertEqual(cConfig["ConnectRet"].AsString(), result);
bool ret = LDAPConnection::IsConnectOk(eConnectState);
assertEqual(cConfig["ConnectIsOk"].AsBool(1), ret);
if (!ret) {
String where;
aEntryIterator.SlotName(where);
assertAnyCompareEqual(cConfig["Error"], error, String(getConfigFileName()) << ":" << where, '.',':');
}
// now release sema and lock
lc.ReleaseHandleInfo();
}
}
}
开发者ID:chenbk85,项目名称:CuteTestForCoastTest,代码行数:57,代码来源:LDAPConnectionTest.cpp
示例17: da
void ActionWithVessel::addVessel( const std::string& name, const std::string& input, const int numlab ){
VesselOptions da(name,"",numlab,input,this);
Vessel* vv=vesselRegister().create(name,da);
FunctionVessel* fv=dynamic_cast<FunctionVessel*>(vv);
if( fv ){
std::string mylabel=Vessel::transformName( name );
plumed_massert( keywords.outputComponentExists(mylabel,false), "a description of the value calculated by vessel " + name + " has not been added to the manual");
}
addVessel(vv);
}
开发者ID:yongwangCPH,项目名称:plumed2,代码行数:10,代码来源:ActionWithVessel.cpp
示例18: da
void DTALikelihood::calculateStatistics(QVector<QVector3D> &points, LGraph& graph)
{
DistanceToAtom da(m_numberOfRandomVectors); // voxes_per_dimension
if (points.size()==0)
return;
da.compute(points, m_cutoff); // cutoff
QVector<QPointF> hist = da.histogram(m_histogramBins); // bins
graph.fromQVector(hist);
graph.normalizeArea();
}
开发者ID:leuat,项目名称:GeometryLibrary,代码行数:11,代码来源:dtalikelihood.cpp
示例19: TestExampleDA
void TestExampleDA(DAExposeFunc ef)
{
InitGtkmm();
ExampleDA da(ef);
Gtk::Window win;
win.set_default_size(400, 400);
win.add(da);
RunWindow(win);
}
开发者ID:cargabsj175,项目名称:bombono-dvd,代码行数:12,代码来源:test_text.cpp
示例20: main
int main(){
char str[maxn];
int i, m = 30, ans, len;
while(scanf("%s",str)!=EOF){
len = strlen(str);
for(i=0;i<=len;i++) num[i]=str[i]-'a'+1;
num[len]=0;
da(num, len + 1, m);
calHeight(num, len);
}
return 0;
}
开发者ID:toposort,项目名称:ACMICPCTemplate,代码行数:12,代码来源:后缀数组-倍增.cpp
注:本文中的da函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。 |
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