Mercurial Hosting > traffic-intelligence
changeset 963:2757efeabbb4
minor renaming
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
---|---|
date | Mon, 06 Nov 2017 23:04:03 -0500 |
parents | 64259b9885bf |
children | e3ec6caab984 |
files | scripts/train-object-classification.py |
diffstat | 1 files changed, 5 insertions(+), 5 deletions(-) [+] |
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--- a/scripts/train-object-classification.py Mon Nov 06 21:25:41 2017 -0500 +++ b/scripts/train-object-classification.py Mon Nov 06 23:04:03 2017 -0500 @@ -18,7 +18,7 @@ parser.add_argument('--nu', dest = 'nu', help = 'SVM nu', default = 0, type = int) parser.add_argument('--svmp', dest = 'svmP', help = 'SVM p', default = 0, type = int) parser.add_argument('--cfg', dest = 'configFilename', help = 'name of the classifier configuration file', required = True) -parser.add_argument('--compute-classifications', dest = 'computeClassifications', help = 'compute the confusion matrix on the training data', action = 'store_true') +parser.add_argument('--confusion-matrix', dest = 'computeConfusionMatrix', help = 'compute the confusion matrix on the training data', action = 'store_true') args = parser.parse_args() classifierParams = storage.ClassifierParameters(args.configFilename) @@ -51,27 +51,27 @@ print "Training Pedestrian-Cyclist-Vehicle Model" model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(trainingSamplesPBV.values()), np.concatenate(trainingLabelsPBV.values()), True) -if args.computeClassifications: +if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPBV.xml") print "Training Cyclist-Vehicle Model" model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(trainingSamplesBV.values()), np.concatenate(trainingLabelsBV.values()), True) -if args.computeClassifications: +if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelBV.xml") print "Training Pedestrian-Cyclist Model" model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(trainingSamplesPB.values()), np.concatenate(trainingLabelsPB.values()), True) -if args.computeClassifications: +if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPB.xml") print "Training Pedestrian-Vehicle Model" model = ml.SVM(args.svmType, args.kernelType, args.degree, args.gamma, args.coef0, args.cvalue, args.nu, args.svmP) classifications = model.train(np.concatenate(trainingSamplesPV.values()), np.concatenate(trainingLabelsPV.values()), True) -if args.computeClassifications: +if args.computeConfusionMatrix: print(classifications) model.save(args.directoryName + "/modelPV.xml")