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comparison scripts/classify-objects.py @ 687:de278c5e65f6 dev

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minor comments for lognormal parameters (numpy and usual names differ)
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Mon, 22 Jun 2015 15:27:33 +0200
parents cdee6a3a47b4
children 5b970a5bc233
comparison
equal deleted inserted replaced
686:cdee6a3a47b4 687:de278c5e65f6
50 bikeCarSVM.load(params.bikeCarSVMFilename) 50 bikeCarSVM.load(params.bikeCarSVMFilename)
51 51
52 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.) 52 # log logistic for ped and bik otherwise ((pedBeta/pedAlfa)*((sMean/pedAlfa)**(pedBeta-1)))/((1+(sMean/pedAlfa)**pedBeta)**2.)
53 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s), 53 speedProbabilities = {'car': lambda s: norm(params.meanVehicleSpeed, params.stdVehicleSpeed).pdf(s),
54 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s), 54 'pedestrian': lambda s: norm(params.meanPedestrianSpeed, params.stdPedestrianSpeed).pdf(s),
55 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # lognorm shape, loc, scale 55 'bicycle': lambda s: lognorm(params.scaleCyclistSpeed, loc = 0., scale = np.exp(params.locationCyclistSpeed)).pdf(s)} # numpy lognorm shape, loc, scale: shape for numpy is scale (std of the normal) and scale for numpy is location (mean of the normal)
56 56
57 if args.plotSpeedDistribution: 57 if args.plotSpeedDistribution:
58 import matplotlib.pyplot as plt 58 import matplotlib.pyplot as plt
59 plt.figure() 59 plt.figure()
60 for k in speedProbabilities: 60 for k in speedProbabilities: