//+==================================================================================== // // Method: ShapeChanged // // Synopsis: Hit the Layout for the size you should be and ask your adorner for it to give // you your position based on this // //------------------------------------------------------------------------------------ void CFocusAdorner::ShapeChanged() { Assert(_pView->IsInState(CView::VS_OPEN)); Assert(_pElement); delete _pShape; _pShape = NULL; _fTopLeftValid = FALSE; CDocument* pDoc = _pView->Doc(); CDocInfo dci(pDoc->_dci); CShape* pShape; if(_pElement->_etag==ETAG_DIV && DYNCAST(CDivElement, _pElement)->GetAAnofocusrect()) { pShape = NULL; } else { _pElement->GetFocusShape(_iDivision, &dci, &pShape); } if(pShape) { CRect rc; pShape->GetBoundingRect(&rc); if(rc.IsEmpty()) { delete pShape; } else { _pShape = pShape; } } EnsureDispNode(); if(_pDispNode) { CRect rc; Assert(_pShape); _pShape->GetBoundingRect(&rc); _pDispNode->SetSize(rc.Size(), TRUE); } }
void database_testDatacell() { std::string data("Text"); DBAbstraction::DataCell dc1(data.c_str()); DBAbstraction::DataCell dc2(""); dc2=dc1; ASSERT_EQUAL(data,dc2.getString()); ASSERT_THROWS(dc2.getInt(), dbexception ); ASSERT_THROWS(dc2.getBool(), dbexception ); DBAbstraction::DataCell dci(1); ASSERT_EQUAL(1,dci.getInt()); ASSERT_THROWS(dci.getString(), dbexception ); }
int Speculation_EnterJailMode(bool longRunningSpec) { AppProcess_t* process = GetMyProcess(); if (process != GetProcessByProcessorID(ProcessorID())) { Speculation_Restart(SPEC_GetSpeculationIDSelf_priv(), Kernel_SpecReturnCode_INVALID, &GetMyKThread()->Reg_State); return Kernel_SpecReturnCode_INVALID; } if(longRunningSpec) { uint64_t SpecPID; uint32_t ProcessOvercommit = 64 / GetMyAppState()->Active_Processes; if(ProcessOvercommit > 4) ProcessOvercommit = 4; vmm_getSpecPID(process->Tcoord, ProcessorThreadID() % ProcessOvercommit, &SpecPID); if(SpecPID) { mtspr(SPRN_PID, SpecPID); isync(); // A2 does not reliably notify A2 of DCI #if 0 volatile uint64_t* pf_sys_p=(volatile uint64_t*)(SPEC_GetL1PBase_priv()+L1P_CFG_PF_SYS-L1P_ESR); uint64_t pf_sys=*pf_sys_p; *pf_sys_p=pf_sys | L1P_CFG_PF_SYS_pf_invalidate_all; *pf_sys_p=pf_sys & ~L1P_CFG_PF_SYS_pf_invalidate_all; dci(); #else asm volatile ("dci 2"); #endif ppc_msync(); } else { Speculation_Restart(SPEC_GetSpeculationIDSelf_priv(), Kernel_SpecReturnCode_INVALID, &GetMyKThread()->Reg_State); return Kernel_SpecReturnCode_INVALID; } }
PClassifier TTreeSplitConstructor_ExhaustiveBinary::operator()( PStringList &descriptions, PDiscDistribution &subsetSizes, float &quality, int &spentAttribute, PExampleGenerator gen, const int &weightID , PDomainContingency dcont, PDistribution apriorClass, const vector<bool> &candidates, PClassifier ) { checkProperty(measure); measure->checkClassTypeExc(gen->domain->classVar->varType); PIntList bestMapping; int wins, bestAttr; PVariable bvar; if (measure->needs==TMeasureAttribute::Generator) { bool cse = candidates.size()==0; bool haveCandidates = false; vector<bool> myCandidates; myCandidates.reserve(gen->domain->attributes->size()); vector<bool>::const_iterator ci(candidates.begin()), ce(candidates.end()); TVarList::const_iterator vi, ve(gen->domain->attributes->end()); for(vi = gen->domain->attributes->begin(); vi != ve; vi++) { bool co = (*vi)->varType == TValue::INTVAR && (!cse || (ci!=ce) && *ci); myCandidates.push_back(co); haveCandidates = haveCandidates || co; } if (!haveCandidates) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); PDistribution thisSubsets; float thisQuality; wins = 0; int thisAttr = 0; int N = gen->numberOfExamples(); TSimpleRandomGenerator rgen(N); ci = myCandidates.begin(); for(vi = gen->domain->attributes->begin(); vi != ve; ci++, vi++, thisAttr++) { if (*ci) { thisSubsets = NULL; PIntList thisMapping = /*throughCont ? measure->bestBinarization(thisSubsets, thisQuality, *dci, dcont->classes, apriorClass, minSubset) : */measure->bestBinarization(thisSubsets, thisQuality, *vi, gen, apriorClass, weightID, minSubset); if (thisMapping && ( (!wins || (thisQuality>quality)) && ((wins=1)==1) || (thisQuality==quality) && rgen.randbool(++wins))) { bestAttr = thisAttr; quality = thisQuality; subsetSizes = thisSubsets; bestMapping = thisMapping; } } /*if (thoughCont) dci++; */ } if (!wins) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); if (quality<worstAcceptable) return returnNothing(descriptions, subsetSizes, spentAttribute); if (subsetSizes && subsetSizes->variable) bvar = subsetSizes->variable; else { TEnumVariable *evar = mlnew TEnumVariable(""); evar->addValue("0"); evar->addValue("1"); bvar = evar; } } else { bool cse = candidates.size()==0; if (!cse && noCandidates(candidates)) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); if (!dcont || dcont->classIsOuter) { dcont = PDomainContingency(mlnew TDomainContingency(gen, weightID)); // raiseWarningWho("TreeSplitConstructor_ExhaustiveBinary", "this class is not optimized for 'candidates' list and can be very slow"); } int N = gen ? gen->numberOfExamples() : -1; if (N<0) N = dcont->classes->cases; TSimpleRandomGenerator rgen(N); PDistribution classDistribution = dcont->classes; vector<bool>::const_iterator ci(candidates.begin()), ce(candidates.end()); TDiscDistribution *dis0, *dis1; TContDistribution *con0, *con1; int thisAttr = 0; bestAttr = -1; wins = 0; quality = 0.0; float leftExamples, rightExamples; TDomainContingency::iterator dci(dcont->begin()), dce(dcont->end()); for(; (cse || (ci!=ce)) && (dci!=dce); dci++, thisAttr++) { // We consider the attribute only if it is a candidate, discrete and has at least two values if ((cse || *(ci++)) && ((*dci)->outerVariable->varType==TValue::INTVAR) && ((*dci)->discrete->size()>=2)) { const TDistributionVector &distr = *(*dci)->discrete; if (distr.size()>16) raiseError("'%s' has more than 16 values, cannot exhaustively binarize", gen->domain->attributes->at(thisAttr)->get_name().c_str()); // If the attribute is binary, we check subsetSizes and assess the quality if they are OK if (distr.size()==2) { if ((distr.front()->abs<minSubset) || (distr.back()->abs<minSubset)) continue; // next attribute else { float thisMeas = measure->call(thisAttr, dcont, apriorClass); if ( ((!wins || (thisMeas>quality)) && ((wins=1)==1)) || ((thisMeas==quality) && rgen.randbool(++wins))) { bestAttr = thisAttr; quality = thisMeas; leftExamples = distr.front()->abs; rightExamples = distr.back()->abs; bestMapping = mlnew TIntList(2, 0); bestMapping->at(1) = 1; } continue; } } vector<int> valueIndices; int ind = 0; for(TDistributionVector::const_iterator dvi(distr.begin()), dve(distr.end()); (dvi!=dve); dvi++, ind++) if ((*dvi)->abs>0) valueIndices.push_back(ind); if (valueIndices.size()<2) continue; PContingency cont = prepareBinaryCheat(classDistribution, *dci, bvar, dis0, dis1, con0, con1); // A real job: go through all splits int binWins = 0; float binQuality = -1.0; float binLeftExamples = -1.0, binRightExamples = -1.0; // Selection: each element correspons to a value of the original attribute and is 1, if the value goes right // The first value always goes left (and has no corresponding bit in selection. TBoolCount selection(valueIndices.size()-1), bestSelection(0); // First for discrete classes if (dis0) { do { *dis0 = CAST_TO_DISCDISTRIBUTION(distr[valueIndices[0]]); *dis1 *= 0; vector<int>::const_iterator ii(valueIndices.begin()); ii++; for(TBoolCount::const_iterator bi(selection.begin()), be(selection.end()); bi!=be; bi++, ii++) *(*bi ? dis1 : dis0) += distr[*ii]; cont->outerDistribution->setint(0, dis0->abs); cont->outerDistribution->setint(1, dis1->abs); if ((dis0->abs < minSubset) || (dis1->abs < minSubset)) continue; // cannot split like that, to few examples in one of the branches float thisMeas = measure->operator()(cont, classDistribution, apriorClass); if ( ((!binWins) || (thisMeas>binQuality)) && ((binWins=1) ==1) || (thisMeas==binQuality) && rgen.randbool(++binWins)) { bestSelection = selection; binQuality = thisMeas; binLeftExamples = dis0->abs; binRightExamples = dis1->abs; } } while (selection.next()); } // And then exactly the same for continuous classes else { do { *con0 = CAST_TO_CONTDISTRIBUTION(distr[0]); *con1 = TContDistribution(); vector<int>::const_iterator ii(valueIndices.begin()); for(TBoolCount::const_iterator bi(selection.begin()), be(selection.end()); bi!=be; bi++, ii++) *(*bi ? con1 : con0) += distr[*ii]; if ((con0->abs<minSubset) || (con1->abs<minSubset)) continue; // cannot split like that, to few examples in one of the branches float thisMeas = measure->operator()(cont, classDistribution, apriorClass); if ( ((!binWins) || (thisMeas>binQuality)) && ((binWins=1) ==1) || (thisMeas==binQuality) && rgen.randbool(++binWins)) { bestSelection = selection; binQuality = thisMeas; binLeftExamples = con0->abs; binRightExamples = con1->abs; } } while (selection.next()); } if ( binWins && ( (!wins || (binQuality>quality)) && ((wins=1)==1) || (binQuality==quality) && rgen.randbool(++wins))) { bestAttr = thisAttr; quality = binQuality; leftExamples = binLeftExamples; rightExamples = binRightExamples; bestMapping = mlnew TIntList(distr.size(), -1); vector<int>::const_iterator ii = valueIndices.begin(); bestMapping->at(*(ii++)) = 0; ITERATE(TBoolCount, bi, bestSelection) bestMapping->at(*(ii++)) = *bi ? 1 : 0; } } } if (!wins) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); subsetSizes = mlnew TDiscDistribution(); subsetSizes->addint(0, leftExamples); subsetSizes->addint(1, rightExamples); } PVariable attribute = gen->domain->attributes->at(bestAttr); if (attribute->noOfValues() == 2) { spentAttribute = bestAttr; descriptions = mlnew TStringList(attribute.AS(TEnumVariable)->values.getReference()); TClassifierFromVarFD *cfv = mlnew TClassifierFromVarFD(attribute, gen->domain, bestAttr, subsetSizes); cfv->transformUnknowns = false; return cfv; } string s0, s1; int ns0 = 0, ns1 = 0; TValue ev; attribute->firstValue(ev); PITERATE(TIntList, mi, bestMapping) { string str; attribute->val2str(ev, str); if (*mi==1) { s1 += string(ns1 ? ", " : "") + str; ns1++; } else if (*mi==0) { s0 += string(ns0 ? ", " : "") + str; ns0++; } attribute->nextValue(ev); }
PClassifier TTreeSplitConstructor_Attribute::operator()( PStringList &descriptions, PDiscDistribution &subsetSizes, float &quality, int &spentAttribute, PExampleGenerator gen, const int &weightID, PDomainContingency dcont, PDistribution apriorClass, const vector<bool> &candidates, PClassifier nodeClassifier ) { checkProperty(measure); measure->checkClassTypeExc(gen->domain->classVar->varType); bool cse = candidates.size()==0; vector<bool>::const_iterator ci(candidates.begin()), ce(candidates.end()); if (!cse) { if (noCandidates(candidates)) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); ci = candidates.begin(); } int N = gen ? gen->numberOfExamples() : -1; if (N<0) N = dcont->classes->cases; TSimpleRandomGenerator rgen(N); int thisAttr = 0, bestAttr = -1, wins = 0; quality = 0.0; if (measure->needs == TMeasureAttribute::Contingency_Class) { vector<bool> myCandidates; if (cse) { myCandidates.reserve(gen->domain->attributes->size()); PITERATE(TVarList, vi, gen->domain->attributes) myCandidates.push_back((*vi)->varType == TValue::INTVAR); } else { myCandidates.reserve(candidates.size()); TVarList::const_iterator vi(gen->domain->attributes->begin()); for(; ci != ce; ci++, vi++) myCandidates.push_back(*ci && ((*vi)->varType == TValue::INTVAR)); } if (!dcont || dcont->classIsOuter) dcont = PDomainContingency(mlnew TDomainContingency(gen, weightID, myCandidates)); ci = myCandidates.begin(); ce = myCandidates.end(); TDomainContingency::iterator dci(dcont->begin()), dce(dcont->end()); for(; (ci != ce) && (dci!=dce); dci++, ci++, thisAttr++) if (*ci && checkDistribution((const TDiscDistribution &)((*dci)->outerDistribution.getReference()), minSubset)) { float thisMeas = measure->call(thisAttr, dcont, apriorClass); if ( ((!wins || (thisMeas>quality)) && ((wins=1)==1)) || ((thisMeas==quality) && rgen.randbool(++wins))) { quality = thisMeas; subsetSizes = (*dci)->outerDistribution; bestAttr = thisAttr; } } } else if (measure->needs == TMeasureAttribute::DomainContingency) { if (!dcont || dcont->classIsOuter) dcont = PDomainContingency(mlnew TDomainContingency(gen, weightID)); TDomainContingency::iterator dci(dcont->begin()), dce(dcont->end()); for(; (cse || (ci!=ce)) && (dci!=dce); dci++, thisAttr++) if ( (cse || *(ci++)) && ((*dci)->outerVariable->varType==TValue::INTVAR) && checkDistribution((const TDiscDistribution &)((*dci)->outerDistribution.getReference()), minSubset)) { float thisMeas = measure->call(thisAttr, dcont, apriorClass); if ( ((!wins || (thisMeas>quality)) && ((wins=1)==1)) || ((thisMeas==quality) && rgen.randbool(++wins))) { quality = thisMeas; subsetSizes = (*dci)->outerDistribution; bestAttr = thisAttr; } } } else { TDomainDistributions ddist(gen, weightID); TDomainDistributions::iterator ddi(ddist.begin()), dde(ddist.end()-1); for(; (cse || (ci!=ce)) && (ddi!=dde); ddi++, thisAttr++) if (cse || *(ci++)) { TDiscDistribution *discdist = (*ddi).AS(TDiscDistribution); if (discdist && checkDistribution(*discdist, minSubset)) { float thisMeas = measure->call(thisAttr, gen, apriorClass, weightID); if ( ((!wins || (thisMeas>quality)) && ((wins=1)==1)) || ((thisMeas==quality) && rgen.randbool(++wins))) { quality = thisMeas; subsetSizes = PDiscDistribution(*ddi); // not discdist - this would be double wrapping! bestAttr = thisAttr; } } } } if (!wins) return returnNothing(descriptions, subsetSizes, quality, spentAttribute); if (quality<worstAcceptable) return returnNothing(descriptions, subsetSizes, spentAttribute); PVariable attribute = gen->domain->attributes->at(bestAttr); TEnumVariable *evar = attribute.AS(TEnumVariable); if (evar) descriptions = mlnew TStringList(evar->values.getReference()); else descriptions = mlnew TStringList(subsetSizes->size(), ""); spentAttribute = bestAttr; TClassifierFromVarFD *cfv = mlnew TClassifierFromVarFD(attribute, gen->domain, bestAttr, subsetSizes); cfv->transformUnknowns = false; return cfv; }
void GraphicsImageCanvas::OnPaint (wxPaintEvent& event) { wxAlphaPixelData data (*image); if (!data) { wxLogError (wxT ("Failed to gain access to raw bitmap data")); event.Skip(); return; } wxAlphaPixelData::Iterator p (data); p.Offset (data, 0, 0); // no offset // Finding the maximum int max = 0; for (int ii = 0; ii < length; ii++) { if (max < imageData[ii]) { max = imageData[ii]; } } for (int y = 0; y < height; ++y) { wxAlphaPixelData::Iterator rowStart = p; //for (int x = 0; x < width; ++x, ++p) { for (int x = (width - 1); x >= 0; --x, ++p) { p.Red() = ( (this->imageData[ (y * width) + x]) /max) * 255; p.Green() = ( (this->imageData[ (y * width) + x]) /max) * 255; p.Blue() = ( (this->imageData[ (y * width) + x]) /max) * 255; } p = rowStart; p.OffsetY (data, 1); } wxBufferedPaintDC dci (this); // Find the image scale factor and center the image. double scaleW = ( (double) image->GetWidth() / (double) (GetClientSize()).GetWidth()); double scaleH = ( (double) image->GetHeight() / (double) (GetClientSize()).GetHeight()); double scaleImage = 1; if (scaleW > scaleH) { scaleImage = scaleW; } else { scaleImage = scaleH; } // The commented out code worked in xp but not osx //SetSize(width/scaleImage, height/scaleImage); //dci.DrawBitmap (*this->image, 0, 0, true); //dci.SetUserScale (scaleImage, scaleImage); //if (NULL != fullscreen){ // CentreOnParent(); //} wxImage displayImage = image->ConvertToImage(); displayImage = displayImage.Scale (displayImage.GetWidth() /scaleImage, displayImage.GetHeight() /scaleImage); int cw = (GetClientSize()).GetWidth(); int ch = (GetClientSize()).GetHeight(); int pw = ( (cw - displayImage.GetWidth()) /2); int ph = ( (ch - displayImage.GetHeight()) /2); displayImage = displayImage.Size (wxSize (cw, ch), wxPoint (pw, ph), 0, 0, 0); wxBitmap tmpBitmap = wxBitmap (displayImage); dci.DrawBitmap (tmpBitmap, 0, 0, true); }