main(){
int a;
int b;
scanf("%d,%d",&a,&b);
int c =maxNum(a,b);
printf("max is %d\n",c);
}
int longestValidParentheses(char* s)
{
char stack[MAX]={0};
int maxCnt;
int i;
int top;
int cnt;
int maxLen;
int curLen;
if(NULL==s)
{
return 0;
}
i=0;
top=0;
curLen=0;
maxLen=0;
while(s[i]!='\0')
{
if(s[i]=='(')
{
stack[top++] = s[i++];
}
else
{
if(top>=0 && (stack[--top]=='(' && s[i]==')'))
{
if(top==0)
{
curLen = i+1;
}
else
{
curLen = i-(top+1);
}
maxLen = maxNum(maxLen,curLen);
i++;
}
else
{
top++;
i++;
}
}
}
return maxLen;
}
// 处理大数版本，字符串存储
int printMaxN(int n)
{
if (n <= 0)
{
return 0;
}
string maxNum(n, ' '); // 初始化为 0
maxNum[n] = '1'; // 第一个打印的是 1
while (maxNum[0] != '9')
{
cout << maxNum << endl;
maxNum = add1(maxNum);
}
cout << maxNum << endl;
return 0;
}
int maxNum(int max, int min)
{
int m=max;
int n=min;
if(max==1&&min==1)
return 0;
if(max==0 || min==0)
return 0;
if(max-2>min)
max=max-2;
else
{
int temp=max;
max=min;
min=temp-2;
}
//printf("max :%d ; min :%d\n",max,min);
return n+maxNum(max,min);
}
int main()
{
int m,n;
int max,min;
scanf("%d%d",&m,&n);
if(m>n)
{
max=m;
min=n;
}
else
{
max=n;
min=m;
}
printf("%d\n",maxNum(max,min));
return 0;
}
// add new numbers at the head of the list
main(){
void printList(NodePtr);
NodePtr makeNode(int);
int n;
NodePtr top, np;
top = NULL;
if (scanf("%d", &n) != 1) n = 0;
while (n != 0){
np = makeNode(n); // create a new node containing n
np -> next = top; // set link of new node to first node
top = np; // set top to point to new node
if (scanf("%d", &n) != 1) n = 0;
}
printf("Below is the linked list in reverse order\n");
printList(top);
printf("This is the end of the list\n");
printf("The max value of the list is: %d\n", maxNum(max));
}
float UCBVHaarSingleStumpLearner::run()
{
if ( UCBVHaarSingleStumpLearner::_numOfCalling == 0 ) {
init();
}
UCBVHaarSingleStumpLearner::_numOfCalling++;
//cout << "Num of iter:\t" << UCBVHaarSingleStumpLearner::_numOfCalling << " " << this->getTthSeriesElement( UCBVHaarSingleStumpLearner::_numOfCalling ) << flush << endl;
const int numClasses = _pTrainingData->getNumClasses();
// set the smoothing value to avoid numerical problem
// when theta=0.
setSmoothingVal( 1.0 / (float)_pTrainingData->getNumExamples() * 0.01 );
vector<sRates> mu(numClasses); // The class-wise rates. See BaseLearner::sRates for more info.
vector<float> tmpV(numClasses); // The class-wise votes/abstentions
float tmpThreshold;
float tmpAlpha;
float bestEnergy = numeric_limits<float>::max();
float tmpEnergy;
HaarData* pHaarData = static_cast<HaarData*>(_pTrainingData);
// get the whole data matrix
//const vector<int*>& intImages = pHaarData->getIntImageVector();
// The data matrix transformed into the feature's space
vector< pair<int, float> > processedHaarData(_pTrainingData->getNumExamples());
// I need to prepare both type of sampling
StumpAlgorithm<float> sAlgo(numClasses);
sAlgo.initSearchLoop(_pTrainingData);
float halfTheta;
if ( _abstention == ABST_REAL || _abstention == ABST_CLASSWISE )
halfTheta = _theta/2.0;
else
halfTheta = 0;
// The declared features types
vector<HaarFeature*>& loadedFeatures = pHaarData->getLoadedFeatures();
// for every feature type
vector<HaarFeature*>::iterator ftIt;
//vector<HaarFeature*>::iterator maxftIt;
vector<float> maxV( loadedFeatures.size() );
vector<int> maxKey( loadedFeatures.size() );
vector<int> maxNum( loadedFeatures.size() );
//claculate the Bk,s,t of the randomly chosen features
int key = getKeyOfMaximalElement();
int featureIdx = (int) (key / 10);
int featureType = (key % 10);
//for (i = 0, ftIt = loadedFeatures.begin(); ftIt != loadedFeatures.end(); i++ ++ftIt)
//*ftIt = loadedFeatures[ featureType ];
// just for readability
//HaarFeature* pCurrFeature = *ftIt;
HaarFeature* pCurrFeature = loadedFeatures[ featureType ];
if (_samplingType != ST_NO_SAMPLING)
pCurrFeature->setAccessType(AT_RANDOM_SAMPLING);
// Reset the iterator on the configurations. For random sampling
// this clear the visited list
pCurrFeature->loadConfigByNum( featureIdx );
if (_verbose > 1)
cout << "Learning type " << pCurrFeature->getName() << ".." << flush;
// transform the data from intImages to the feature's space
pCurrFeature->fillHaarData( _pTrainingData->getExamples(), processedHaarData );
//pCurrFeature->fillHaarData(intImages, processedHaarData);
// sort the examples in the new space by their coordinate
sort( processedHaarData.begin(), processedHaarData.end(),
nor_utils::comparePair<2, int, float, less<float> >() );
// find the optimal threshold
tmpThreshold = sAlgo.findSingleThresholdWithInit(processedHaarData.begin(),
processedHaarData.end(),
_pTrainingData, halfTheta, &mu, &tmpV);
tmpEnergy = getEnergy(mu, tmpAlpha, tmpV);
// Store it in the current weak hypothesis.
// note: I don't really like having so many temp variables
// but the alternative would be a structure, which would need
// to be inheritable to make things more consistent. But this would
// make it less flexible. Therefore, I am still undecided. This
// might change!
_alpha = tmpAlpha;
_v = tmpV;
// I need to save the configuration because it changes within the object
_selectedConfig = pCurrFeature->getCurrentConfig();
// I save the object because it contains the informations about the type,
// the name, etc..
_pSelectedFeature = pCurrFeature;
_threshold = tmpThreshold;
bestEnergy = tmpEnergy;
float edge = 0.0;
for( vector<sRates>::iterator itR = mu.begin(); itR != mu.end(); itR++ ) edge += ( itR->rPls - itR->rMin );
//need to set the X value
updateKeys( key, edge * edge );
if (!_pSelectedFeature)
{
cerr << "ERROR: No Haar Feature found. Something must be wrong!" << endl;
exit(1);
}
else
{
if (_verbose > 1)
cout << "Selected type: " << _pSelectedFeature->getName() << endl;
}
return bestEnergy;
}
