--- a/scripts/classify-objects.py	Tue Jan 22 09:57:26 2019 -0500
+++ b/scripts/classify-objects.py	Wed Jan 30 16:51:52 2019 -0500
@@ -44,17 +44,18 @@
 if args.plotSpeedDistribution:
     import matplotlib.pyplot as plt
     plt.figure()
+    speeds = np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)
     for k in speedProbabilities:
-        plt.plot(np.arange(0.1, args.maxSpeedDistributionPlot, 0.1), [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)], label = k)
+        plt.plot(speeds, [speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds], label = k) # the distribution parameters are in video intrinsic units, unit of distance per frame
     maxProb = -1.
     for k in speedProbabilities:
-        maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in np.arange(0.1, args.maxSpeedDistributionPlot, 0.1)]))
+        maxProb = max(maxProb, np.max([speedProbabilities[k](s/(3.6*params.videoFrameRate)) for s in speeds]))
     plt.plot([classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate]*2, [0., maxProb], 'k-')
     plt.text(classifierParams.minSpeedEquiprobable*3.6*params.videoFrameRate, maxProb, 'threshold for equiprobable class')
     plt.xlabel('Speed (km/h)')
-    plt.ylabel('Probability')
+    plt.ylabel('Probability density function')
     plt.legend()
-    plt.title('Probability Density Function')
+    #plt.title('Probability Density Function')
     plt.show()
     sys.exit()