bool KLUPD::HttpProtocol::HttpHeader::parseFirstLine(const std::string &httpCodeBuffer)
{
// Sample "HTTP/1.1 200 OK"
if(httpCodeBuffer.size() < strlen("HTTP/1.1 200"))
return false;
// additional check
if(_strnicmp(httpCodeBuffer.c_str(), "http/", strlen("http/")) != 0)
return false;
size_t codeOffset = httpCodeBuffer.find_first_not_of(' ', strlen("HTTP/1.1 "));
// skip blanks
while(httpCodeBuffer[codeOffset] == ' ')
++codeOffset;
// http code expected
if(httpCodeBuffer.size() < codeOffset + 3)
return false;
std::vector<char> codeBuffer(4, 0);
strncpy(&codeBuffer[0], httpCodeBuffer.c_str() + codeOffset, 3);
m_httpCode = atoi(&codeBuffer[0]);
return true;
}
_List* _LikelihoodFunction::RecoverAncestralSequencesMarginal (long index, _Matrix & supportValues, _List& expandedSiteMap, bool doLeaves)
// index: which part to process
// supportValues: for each internal node and site stores alphabetDimension values for the
// : relative support of each residue at a given site
// : linearized 3D matrix
// : 1st - node index (same order as flatTree)
// : 2nd - site index (only unique patterns are stored)
// : 3rd - the character
// doLeaves : compute support values leaves instead of internal nodes
{
_DataSetFilter* dsf = (_DataSetFilter*)dataSetFilterList (theDataFilters(index));
_TheTree *blockTree = (_TheTree*)LocateVar(theTrees.lData[index]);
long patternCount = dsf->NumberDistinctSites (),
alphabetDimension = dsf->GetDimension (),
unitLength = dsf->GetUnitLength (),
iNodeCount = blockTree->GetINodeCount (),
leafCount = blockTree->GetLeafCount (),
matrixSize = doLeaves?leafCount:iNodeCount,
siteCount = dsf->GetSiteCount (),
shiftForTheNode = patternCount * alphabetDimension;
_Parameter *siteLikelihoods = new _Parameter [2*patternCount],
*siteLikelihoodsSpecState = new _Parameter [2*patternCount];
_SimpleList scalersBaseline,
scalersSpecState,
branchValues,
postToIn;
blockTree->MapPostOrderToInOderTraversal (postToIn, doLeaves == false);
supportValues.Clear ();
CreateMatrix (&supportValues,matrixSize,shiftForTheNode,false,true,false);
ComputeSiteLikelihoodsForABlock (index, siteLikelihoods, scalersBaseline);
// establish a baseline likelihood for each site
if (doLeaves)
{
for (long currentChar = 0; currentChar < alphabetDimension; currentChar++)
{
branchValues.Populate (patternCount,currentChar,0);
for (long branchID = 0; branchID < leafCount; branchID ++)
{
blockTree->AddBranchToForcedRecomputeList (branchID);
long mappedBranchID = postToIn.lData[branchID];
ComputeSiteLikelihoodsForABlock (index, siteLikelihoodsSpecState, scalersSpecState,
branchID+iNodeCount, &branchValues);
for (long siteID = 0; siteID < patternCount; siteID++)
{
long scaleDiff = (scalersSpecState.lData[siteID]-scalersBaseline.lData[siteID]);
_Parameter ratio = siteLikelihoodsSpecState[siteID]/siteLikelihoods[siteID];
if (scaleDiff > 0)
ratio *= acquireScalerMultiplier(scaleDiff);
supportValues.theData[mappedBranchID*shiftForTheNode + siteID*alphabetDimension + currentChar] = ratio;
}
blockTree->AddBranchToForcedRecomputeList (branchID);
}
}
}
else
for (long currentChar = 0; currentChar < alphabetDimension-1; currentChar++)
// the prob for the last char is (1 - sum (probs other chars))
{
branchValues.Populate (patternCount,currentChar,0);
for (long branchID = 0; branchID < iNodeCount; branchID ++)
{
long mappedBranchID = postToIn.lData[branchID];
ComputeSiteLikelihoodsForABlock (index, siteLikelihoodsSpecState, scalersSpecState, branchID, &branchValues);
for (long siteID = 0; siteID < patternCount; siteID++)
{
long scaleDiff = (scalersSpecState.lData[siteID]-scalersBaseline.lData[siteID]);
_Parameter ratio = siteLikelihoodsSpecState[siteID]/siteLikelihoods[siteID];
if (scaleDiff > 0)
ratio *= acquireScalerMultiplier(scaleDiff);
supportValues.theData[mappedBranchID*shiftForTheNode + siteID*alphabetDimension + currentChar] = ratio;
}
blockTree->AddBranchToForcedRecomputeList (branchID+leafCount);
}
}
_SimpleList conversion;
_AVLListXL conversionAVL (&conversion);
_String codeBuffer (unitLength, false);
_List *result = new _List;
for (long k = 0; k < matrixSize; k++)
result->AppendNewInstance (new _String(siteCount*unitLength,false));
for (long siteID = 0; siteID < patternCount; siteID++)
{
_SimpleList* patternMap = (_SimpleList*) expandedSiteMap (siteID);
for (long nodeID = 0; nodeID < matrixSize ; nodeID++)
{
long mappedNodeID = postToIn.lData[nodeID];
_Parameter max_lik = 0.,
sum = 0.,
*scores = supportValues.theData + shiftForTheNode*mappedNodeID + siteID*alphabetDimension;
long max_idx = 0;
for (long charID = 0; charID < alphabetDimension-(!doLeaves); charID ++)
{
sum+=scores[charID];
if (scores[charID] > max_lik)
{
max_idx = charID; max_lik = scores[charID];
}
}
//if (fabs(scores[alphabetDimension-1]+sum-1.) > 0.1)
// WarnError (_String("Bad monkey!") & scores[alphabetDimension-1] & ":" & (1.-sum) );
if (doLeaves)
{
sum = 1./sum;
for (long charID = 0; charID < alphabetDimension; charID ++)
{
scores [charID] *= sum;
/*if (siteID == 16)
printf ("Site %ld Leaf %ld (%ld) Char %ld = %g\n", siteID, nodeID, mappedNodeID, charID,
supportValues.theData[mappedNodeID*shiftForTheNode + siteID*alphabetDimension + charID]);
*/
}
}
else
{
scores[alphabetDimension-1] = 1. - sum;
if (scores[alphabetDimension-1] > max_lik)
max_idx = alphabetDimension-1;
}
dsf->ConvertCodeToLettersBuffered (dsf->CorrectCode(max_idx), unitLength, codeBuffer.sData, &conversionAVL);
_String *sequence = (_String*) (*result)(mappedNodeID);
for (long site = 0; site < patternMap->lLength; site++)
{
//if (patternMap->lData[site] == 119)
// printf ("%ld\n",
// siteID);
char* storeHere = sequence->sData + patternMap->lData[site]*unitLength;
for (long charS = 0; charS < unitLength; charS ++)
storeHere[charS] = codeBuffer.sData[charS];
}
}
}
delete [] siteLikelihoods;
delete [] siteLikelihoodsSpecState;
return result;
}
