#include <ros/package.h>
#include <boost/filesystem/path.hpp>
#include <boost/date_time/posix_time/posix_time.hpp>
#include <ISM/utility/TableHelper.hpp>
#include "learner/SceneLearningEngine.h"
#include "inference/model/SceneModelDescription.h"
#include <sys/time.h>
#include <string>
#include <chrono>
#include <vector>
#include <random>
#include <algorithm>
Go to the source code of this file.
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| double | calculateStandardDeviation (const std::vector< double > &values, const double &average) |
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| void | createHistogram (const std::vector< double > &values, const unsigned int intervals, const std::string &fileName) |
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| PerformanceEvaluationResult | evaluate (const std::vector< ObjectSetPtr > &ts, bool valid, const std::string &patternName, const std::string &trainedScenePath) |
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| void | initRandomScenes () |
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| void | initTestSets () |
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| std::vector< ObjectSetPtr > | loadTestSetsFromDB (std::string fileName, std::string patternName) |
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| int | main (int argc, char *argv[]) |
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| void | runOptimization (std::ostringstream &os) |
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| void | setDefaultParameters () |
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| void | testPerformance () |
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| void | trainScenes (unsigned int run) |
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| void | writeFile (const std::string &directoryPath, const std::string &filenName, std::ostringstream &content) |
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| double calculateStandardDeviation |
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const std::vector< double > & |
values, |
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const double & |
average |
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| void createHistogram |
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const std::vector< double > & |
values, |
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const unsigned int |
intervals, |
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const std::string & |
fileName |
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| PerformanceEvaluationResult evaluate |
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const std::vector< ObjectSetPtr > & |
ts, |
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bool |
valid, |
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const std::string & |
patternName, |
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const std::string & |
trainedScenePath |
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| void initRandomScenes |
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| std::vector<ObjectSetPtr> loadTestSetsFromDB |
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std::string |
fileName, |
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std::string |
patternName |
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| int main |
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int |
argc, |
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char * |
argv[] |
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This program serves to test training and recognition performance of asr_psm. Note that it does not generate test sets (for learning) and validation sets (for recognition) on its own. Generate them using the standard combinatorial optimization launch file with quit_after_test_set_generation set to true and the same parameters as below. Write the databases to asr_psm/data/test/performanceTestRessources/[ValidationSets/]demoRecording_X_Y00.sqlite with X from objectCounts, Y from timestepCounts below. Also note that training will, unless stopped earlier, perform 1000 runs while cycling through the different algorithms, so the comparably long tests can be run mostly automated. Errors are tolerated and marked in the results, again to allow it to run without needing constant supervision. The recognition tests have to be run one each, but they are also usually much quicker.
Definition at line 518 of file performanceTester.cpp.
| void runOptimization |
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std::ostringstream & |
os | ) |
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| void setDefaultParameters |
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| void trainScenes |
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unsigned int |
run | ) |
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| void writeFile |
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const std::string & |
directoryPath, |
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const std::string & |
filenName, |
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std::ostringstream & |
content |
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| std::vector<std::string> demoRecordings |
| std::vector<std::string> demoRecordingsNames |
| std::string inferenceAlgorithm = "maximum" |
| std::string invalidTestSetPref = "invalidValidationSets_" |
| std::vector<unsigned> objectCounts |
| std::string optimizationAlgorithm = "RecordHunt" |
| double recognitionThreshold = 1.0e-04 |
| std::string testSetFolder = "ValidationSets" |
| std::string testSetFolderNoRefs = "TestSetsNoRefs" |
| std::vector<std::string> testSets |
| std::vector<std::string> testSetsNoRefs |
| std::vector<unsigned> timestepCounts |
| std::string treeAlgorithm = "fullymeshed" |
| std::string validTestSetPref = "validValidationSets_" |
asr_psm
Author(s): Braun Kai, Gehrung Joachim, Heizmann Heinrich, Meißner Pascal
autogenerated on Fri Nov 15 2019 04:00:08
