/*++
* @method: twitCurl::performPost
*
* @description: method to send http POST request. this is an internal method.
* twitcurl users should not use this method.
*
* @input: postUrl - url,
* dataStr - data to be posted
*
* @output: none
*
* @remarks: internal method
*
*--*/
bool twitCurl::performPost( const std::string& postUrl, std::string dataStr )
{
std::string oAuthHttpHeader( "" );
struct curl_slist* pOAuthHeaderList = NULL;
/* Prepare standard params */
prepareStandardParams();
/* urlencode data */
if( dataStr.length() > 0 )
{
/* Data should already be urlencoded once */
std::string dataKey( "" );
std::string dataValue( "" );
size_t nPos = dataStr.find_first_of( "=" );
if( std::string::npos != nPos )
{
dataKey = dataStr.substr( 0, nPos );
dataValue = dataStr.substr( nPos + 1 );
dataValue = urlencode( dataValue );
dataStr.assign( dataKey );
dataStr.append( "=" );
dataStr.append( dataValue );
}
}
/* Set OAuth header */
m_oAuth.getOAuthHeader( eOAuthHttpPost, postUrl, dataStr, oAuthHttpHeader );
if( oAuthHttpHeader.length() > 0 )
{
pOAuthHeaderList = curl_slist_append( pOAuthHeaderList, oAuthHttpHeader.c_str() );
if( pOAuthHeaderList )
{
curl_easy_setopt( m_curlHandle, CURLOPT_HTTPHEADER, pOAuthHeaderList );
}
}
/* Set http request, url and data */
curl_easy_setopt( m_curlHandle, CURLOPT_POST, 1 );
curl_easy_setopt( m_curlHandle, CURLOPT_URL, postUrl.c_str() );
if( dataStr.length() )
{
curl_easy_setopt( m_curlHandle, CURLOPT_COPYPOSTFIELDS, dataStr.c_str() );
}
/* Send http request */
if( CURLE_OK == curl_easy_perform( m_curlHandle ) )
{
if( pOAuthHeaderList )
{
curl_slist_free_all( pOAuthHeaderList );
}
return true;
}
if( pOAuthHeaderList )
{
curl_slist_free_all( pOAuthHeaderList );
}
return false;
}
void cDialogueList::LoadScript()
{
CMHFile file;
file.Init(
"Data/Script/Npc/Npc_Msg.bin",
"rb");
DWORD messageIndex = 0;
WORD wLineIdx = 0;
while(FALSE == file.IsEOF())
{
// 091207 LUJ, 1K 이하로 선언하면 메모리 오류가 발생한다... 그런데 파싱 코드가 겁나게 복잡하다.
TCHAR buffer[1024] = {0};
file.GetLine(
buffer,
sizeof(buffer) / sizeof(*buffer));
TCHAR textLine[1024] = {0};
SafeStrCpy(
textLine,
buffer,
sizeof(textLine) / sizeof(*textLine));
LPCTSTR seperator = " \t#";
LPCTSTR token = _tcstok(buffer, seperator);
LPCTSTR comment = "@";
LPCTSTR openMark = "{";
LPCTSTR closeMark = "}";
if(0 == token)
{
continue;
}
else if(0 == _tcsnicmp(comment, token, _tcslen(comment)))
{
continue;
}
else if(0 == _tcsnicmp(openMark, token, _tcslen(openMark)))
{
continue;
}
else if(0 == _tcsnicmp(closeMark, token, _tcslen(closeMark)))
{
continue;
}
else if(0 == _tcsicmp("msg", token))
{
LPCTSTR textMessageIndex = _tcstok(0, seperator);
messageIndex = _ttoi(textMessageIndex ? textMessageIndex : "");
wLineIdx = 0;
continue;
}
// 091216 ShinJS --- Npc_Msg.bin의 Data를 저장(인덱스,라인,메세지)
NpcMsgDataKey dataKey( messageIndex, wLineIdx );
m_NpcMsgData.insert( std::make_pair( dataKey, textLine ) );
++wLineIdx;
}
}
Status
Wallet::nameSet(const std::string &name)
{
std::lock_guard<std::mutex> lock(mutex_);
name_ = name;
NameJson json;
ABC_CHECK(json.nameSet(name));
ABC_CHECK(json.save(syncDir() + WALLET_NAME_FILENAME, dataKey()));
return Status();
}
Status
Wallet::currencySet(int currency)
{
std::lock_guard<std::mutex> lock(mutex_);
currency_ = currency;
CurrencyJson currencyJson;
ABC_CHECK(currencyJson.currencySet(currency));
ABC_CHECK(currencyJson.save(syncDir() + WALLET_CURRENCY_FILENAME, dataKey()));
return Status();
}
Status
Wallet::loadSync()
{
ABC_CHECK(fileEnsureDir(gContext->paths.walletsDir()));
ABC_CHECK(fileEnsureDir(dir()));
ABC_CHECK(syncEnsureRepo(syncDir(), dir() + "tmp/", syncKey_));
// Load the currency:
CurrencyJson currencyJson;
currencyJson.load(syncDir() + WALLET_CURRENCY_FILENAME, dataKey());
ABC_CHECK(currencyJson.currencyOk());
currency_ = currencyJson.currency();
// Load the name (failure is acceptable):
NameJson json;
json.load(syncDir() + WALLET_NAME_FILENAME, dataKey());
name_ = json.name();
// Load the databases:
ABC_CHECK(addresses.load());
ABC_CHECK(txs.load());
return Status();
}
void ResourceManagerTest::manualPolicy() {
ResourceManager rm;
ResourceKey dataKey("data");
/* Manual free */
{
rm.set(dataKey, new Data, ResourceDataState::Mutable, ResourcePolicy::Manual);
Resource<Data> data = rm.get<Data>(dataKey);
rm.free();
}
CORRADE_COMPARE(rm.count<Data>(), 1);
CORRADE_COMPARE(Data::count, 1);
rm.free();
CORRADE_COMPARE(rm.count<Data>(), 0);
CORRADE_COMPARE(Data::count, 0);
rm.set(dataKey, new Data, ResourceDataState::Mutable, ResourcePolicy::Manual);
CORRADE_COMPARE(rm.count<Data>(), 1);
CORRADE_COMPARE(Data::count, 1);
}
DIALOGUE* cDialogueList::GetDialogue( DWORD dwMsgId, WORD wLine ) // 다이얼로그 정보를 반환하는 함수.
{
MessageMap::iterator messageIterator = mMessageMap.find(dwMsgId);
if(mMessageMap.end() == messageIterator)
{
// 091216 ShinJS --- Pasring 되어 있지 않은 경우 저장된 NpcMsgData를 Parsing 한다.
NpcMsgDataKey dataKey( dwMsgId, 0 );
NpcMsgData::const_iterator iter = m_NpcMsgData.find( dataKey );
while( iter != m_NpcMsgData.end() )
{
const std::string &npcMsg = iter->second;
ParsingLine(
dwMsgId,
npcMsg.c_str());
dataKey.second = dataKey.second + 1;
iter = m_NpcMsgData.find( dataKey );
}
messageIterator = mMessageMap.find( dwMsgId );
if(mMessageMap.end() == messageIterator)
return 0;
}
DialogMap& dialogMap = messageIterator->second;
DialogMap::iterator dialogIteartor = dialogMap.find(wLine);
if(dialogMap.end() == dialogIteartor)
{
return 0;
}
return &(dialogIteartor->second);
}
/*****************************************************************************************
* vector< int > K_MeansPredict::Train( const vector< vector< float > >& Data, const float stopDist, const int stopIter, const int fast )
*
* Purpose: Train predictor
* input:
* Data: vector of data
* stopDist: Distance stopping criteria
* stopIter: Max Iteration stopping criteria
*
* return:
* vector of cluster membership
*
* 01.07.2006 djh added stoping criterion parameters
* stopDist minimum euclidean distance
* stopIter maximum iterations
* extra error output
* 03.06.2006 djh replaced _totalUpper/_totalLowerConfBound with _totalBoundStub
*
******************************************************************************************/
vector< int > K_MeansPredict::Train( const vector< vector< float > >& Data, const float stopDist, const int stopIter, const int fast ){
// create vector of example coordinates
vector< Coord< float > > coordData( Data.size() );
// create vector of example key values
vector< float > dataKey( Data.size() );
//
for( int i=0; i<Data.size(); i++)
{
vector< float > tempCoords( Data[i].size()-1 );
dataKey[i]=Data[i][0];
for( int j=1; j<Data[i].size(); j++ )
{
tempCoords[j-1] = Data[i][j];
}
coordData[i] = Coord< float >( tempCoords );
}
// calculate clusters
float dist;
int numIter;
vector<int> clusterMap = CreateClusters( coordData, stopDist, stopIter, dist, numIter );
if( fast == 1 ){
return( clusterMap );
}
cout << "# Training:\n";
cout << "# Training required " << numIter << " rounds, the max Euclid. Dist. is: " << dist << endl;
// calculate cluster stats
vector< float > sum_x( _k, 0. );
vector< float > sum_x2( _k, 0. );
_key_supports = vector< int >( _k, 0);
// find n and sums
for( int i=0; i<Data.size(); i++ ){
_key_supports[ clusterMap[i] ]++;
sum_x[ clusterMap[i] ] += dataKey[ clusterMap[i] ];
sum_x2[ clusterMap[i] ] += pow( dataKey[ clusterMap[i] ], 2);
}
// compute mean and variance
_key_means = vector< float >(_k,0.);
_key_variances = vector< float >(_k,0.);
for( int i=0; i<_k; i++ ){
_key_means[i]=sum_x[i]/_key_supports[i];
_key_variances[i] = ( sum_x2[i] - (sum_x[i]/float(_key_supports[i])) )/float( _key_supports[i]-1 );
}
//
// Calc error means and variances
sum_x = vector<float>( _k, 0.);
sum_x2 = vector<float>( _k, 0.);
float tot_sum_x = 0.0;
float tot_sum_x2 = 0.0;
for( int i=0; i<coordData.size(); i++ ){
int clusterIdx = FindClusterIdx( coordData[i] );
float err = _key_means[ clusterIdx ] - dataKey[i];
sum_x[ clusterIdx ] += err;
sum_x2[ clusterIdx ] += pow( err, 2 );
tot_sum_x += err;
tot_sum_x2 += pow( err, 2 );
}
_errMean = vector< float >( _k );
_lowerConfBound = vector< float >( _k );
_upperConfBound = vector< float >( _k );
for( int i=0; i< _k; i++ ){
_errMean[i] = sum_x[i]/( float( _key_supports[i] ) );
float errVar = ( sum_x2[i] - (sum_x[i]/float(_key_supports[i])) )/float( _key_supports[i]-1 );
float t_val = TDist( _key_supports[i] );
_lowerConfBound[i] = _errMean[i] - t_val*sqrt( errVar * (1.0+( 1.0/float(_key_supports[i]) )) );
_upperConfBound[i] = _errMean[i] + t_val*sqrt( errVar * (1.0+( 1.0/float(_key_supports[i]) )) );
}
//
_totalErrMean = tot_sum_x / coordData.size();
float totalErrVar = ( tot_sum_x2 - (tot_sum_x/float(coordData.size())) )/float( coordData.size()-1 );
_totalBoundStub = sqrt( totalErrVar * (1.0+( 1.0/float(coordData.size()) )) );
//_totalLowerConfBound = _totalErrMean - TDist( coordData.size() )*sqrt( totalErrVar * (1.0+( 1.0/float(coordData.size()) )) );
//_totalUpperConfBound = _totalErrMean + TDist( coordData.size() )*sqrt( totalErrVar * (1.0+( 1.0/float(coordData.size()) )) );
// return labels
cout << "# Error:\n";
cout << "# Mean Squared Error (MSE) is: " << tot_sum_x2/float(coordData.size() ) << endl;
cout << "# Error Mean is : " << _totalErrMean << endl;
cout << "# Error Variance is : " << totalErrVar << endl;
//
return( clusterMap );
}
