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C++ UnSerialization::loadCascadeHypotheses方法代码示例

本文整理汇总了C++中UnSerialization::loadCascadeHypotheses方法的典型用法代码示例。如果您正苦于以下问题:C++ UnSerialization::loadCascadeHypotheses方法的具体用法?C++ UnSerialization::loadCascadeHypotheses怎么用?C++ UnSerialization::loadCascadeHypotheses使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在UnSerialization的用法示例。


在下文中一共展示了UnSerialization::loadCascadeHypotheses方法的2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,您的评价将有助于系统推荐出更棒的C++代码示例。

示例1: run

	void VJCascadeClassifier::run(const string& dataFileName, const string& shypFileName, 
								   int numIterations, const string& outResFileName )
	{
		// loading data
		InputData* pData = loadInputData(dataFileName, shypFileName);
		const int numOfExamples = pData->getNumExamples();
				
		//get the index of positive label		
		const NameMap& namemap = pData->getClassMap();
		_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );				
		
		if (_verbose > 0)
			cout << "Loading strong hypothesis..." << flush;
		
		
		
		// The class that loads the weak hypotheses
		UnSerialization us;
		
		// Where to put the weak hypotheses
		vector<vector<BaseLearner*> > weakHypotheses;
		
		// For stagewise thresholds 
		vector<AlphaReal> thresholds(0);
        
		// loads them
		//us.loadHypotheses(shypFileName, weakHypotheses, pData);
		us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
		

		// store result
		vector<CascadeOutputInformation> cascadeData(0);
		vector<CascadeOutputInformation>::iterator it;
		
		cascadeData.resize(numOfExamples);		
		for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
		{
			it->active=true;
		}										
		
		if (!_outputInfoFile.empty())
		{
			outputHeader();
		}
		
		for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
		{
			// for posteriors
			vector<AlphaReal> posteriors(0);		
			
			// calculate the posteriors after stage
			VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );			
			
			// update the data (posteriors, active element index etc.)
			updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
			
			if (!_outputInfoFile.empty())
			{
				_output << stagei + 1 << "\t";
				_output << weakHypotheses[stagei].size() << "\t";
				outputCascadeResult( pData, cascadeData );
			}
			
			int numberOfActiveInstance = 0;
			for( int i = 0; i < numOfExamples; ++i )
				if (cascadeData[i].active) numberOfActiveInstance++;
			
			if (_verbose > 0 )
				cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;									
		}
				
		vector<vector<int> > confMatrix(2);
		confMatrix[0].resize(2);
		fill( confMatrix[0].begin(), confMatrix[0].end(), 0 );
		confMatrix[1].resize(2);
		fill( confMatrix[1].begin(), confMatrix[1].end(), 0 );
		
	    // print accuracy
		for(int i=0; i<numOfExamples; ++i )
		{		
			vector<Label>& labels = pData->getLabels(i);
			if (labels[_positiveLabelIndex].y>0) // pos label				
				if (cascadeData[i].forecast==1)
					confMatrix[1][1]++;
				else
					confMatrix[1][0]++;
			else // negative label
				if (cascadeData[i].forecast==0)
					confMatrix[0][0]++;
				else
					confMatrix[0][1]++;
		}			
		
		double acc = 100.0 * (confMatrix[0][0] + confMatrix[1][1]) / ((double) numOfExamples);
		// output it
		cout << endl;
		cout << "Error Summary" << endl;
		cout << "=============" << endl;
		
		cout << "Accuracy: " << setprecision(4) << acc << endl;
//.........这里部分代码省略.........
开发者ID:busarobi,项目名称:MDDAG2,代码行数:101,代码来源:VJCascadeClassifier.cpp

示例2: savePosteriors

	void VJCascadeClassifier::savePosteriors(const string& dataFileName, const string& shypFileName, 
											  const string& outFileName, int numIterations)
	{
		// loading data
		InputData* pData = loadInputData(dataFileName, shypFileName);
		const int numOfExamples = pData->getNumExamples();
		
		//get the index of positive label		
		const NameMap& namemap = pData->getClassMap();
		_positiveLabelIndex = namemap.getIdxFromName( _positiveLabelName );
		
		
		if (_verbose > 0)
			cout << "Loading strong hypothesis..." << flush;
		
		
		// open outfile
		ofstream outRes(outFileName.c_str());
		if (!outRes.is_open())
		{
			cout << "Cannot open outfile!!! " << outFileName << endl;
		}
				
		
		// The class that loads the weak hypotheses
		UnSerialization us;
		
		// Where to put the weak hypotheses
		vector<vector<BaseLearner*> > weakHypotheses;
        		
		// For stagewise thresholds 
		vector<AlphaReal> thresholds(0);
		// loads them
		//us.loadHypotheses(shypFileName, weakHypotheses, pData);
		us.loadCascadeHypotheses(shypFileName, weakHypotheses, thresholds, pData);
		
		// output the number of stages
		outRes << "StageNum " << weakHypotheses.size() << endl;
		
		// output original labels
		outRes << "Labels";
		for(int i=0; i<numOfExamples; ++i )
		{		
			vector<Label>& labels = pData->getLabels(i);
			if (labels[_positiveLabelIndex].y>0) // pos label				
				outRes << " 1";
			else
				outRes << " 0";
		}				
		outRes << endl;
		
		// store result
		vector<CascadeOutputInformation> cascadeData(0);
		vector<CascadeOutputInformation>::iterator it;
		
		cascadeData.resize(numOfExamples);		
		for( it=cascadeData.begin(); it != cascadeData.end(); ++it )
		{
			it->active=true;
		}										
		
		for(int stagei=0; stagei < weakHypotheses.size(); ++stagei )
		{
			// for posteriors
			vector<AlphaReal> posteriors(0);		
			
			// calculate the posteriors after stage
			VJCascadeLearner::calculatePosteriors( pData, weakHypotheses[stagei], posteriors, _positiveLabelIndex );			
			
			// update the data (posteriors, active element index etc.)
			//VJCascadeLearner::forecastOverAllCascade( pData, posteriors, activeInstances, thresholds[stagei] );
			updateCascadeData(pData, weakHypotheses, stagei, posteriors, thresholds, _positiveLabelIndex, cascadeData);
			
			
			int numberOfActiveInstance = 0;
			for( int i = 0; i < numOfExamples; ++i )
				if (cascadeData[i].active) numberOfActiveInstance++;
			
			if (_verbose > 0 )
				cout << "Number of active instances: " << numberOfActiveInstance << "(" << numOfExamples << ")" << endl;									
			
			// output stats
			outRes << "Stage " << stagei << " " << weakHypotheses[stagei].size() << endl; 

			outRes << "Forecast";
			for(int i=0; i<numOfExamples; ++i )
			{	
				outRes << " " << cascadeData[i].forecast;
			}				
			outRes << endl;

			outRes << "Active";
			for(int i=0; i<numOfExamples; ++i )
			{	
				if( cascadeData[i].active)
					outRes << " 1";
				else
					outRes << " 0";
			}				
			outRes << endl;
//.........这里部分代码省略.........
开发者ID:busarobi,项目名称:MDDAG2,代码行数:101,代码来源:VJCascadeClassifier.cpp


注:本文中的UnSerialization::loadCascadeHypotheses方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,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。