Mercurial Hosting > traffic-intelligence

changeset 963:2757efeabbb4

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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(-) [+]
line wrap: on
line diff
--- 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")