class ProbDriver(Driver):
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
def data_process(self, filePath):
ListT, ListX, ListY, ListP = self.pre_process(filePath)
ListR = self.svmProcessor.radius(ListX, ListY)
ListVX = self.svmProcessor.velocity_of_x(ListX, ListT)
ListVY = self.svmProcessor.velocity_of_y(ListY, ListT)
ListAX = self.svmProcessor.acc_of_vx(ListVX, ListT)
ListAY = self.svmProcessor.acc_of_vy(ListVY, ListT)
return ListT, ListR, ListVX, ListVY, ListAX, ListAY
def generate_features(self, filePath):
ListT, ListR, ListVX, ListVY, ListAX, ListAY = self.data_process(
filePath)
self.probFeatureExtractor.generate_features(ListT, ListR, ListVX,
ListVY, ListAX, ListAY)
return self.probFeatureExtractor.features()
def feature_clear(self):
self.probFeatureExtractor.clear()
def PS(self, F):
P = []
for i in range(len(F)):
p = numpy.exp(-1 * pow(
(F[i] - self.meanF[i]), 2) / 2 / self.varF[i]) / numpy.sqrt(
2 * self.meanF[i] * self.varF[i])
P.append(p)
# print [round(p, 11) for p in P]
# print numpy.isnan(P)
PArray = numpy.asarray(P)
PArray[numpy.isnan(PArray)] = 0
return PArray.sum()
def ps_temp(self, ListF):
self.meanF = []
self.varF = []
Features = []
for F in ListF:
for i in range(len(F)):
if i >= len(Features):
Features.append([])
Features[i].append(F[i])
for F in Features:
self.meanF.append(numpy.mean(F))
self.varF.append(numpy.var(F))
class ProbDriver(Driver):
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
def data_process(self, filePath):
ListT, ListX, ListY, ListP = self.pre_process(filePath)
ListR = self.svmProcessor.radius(ListX, ListY)
ListVX = self.svmProcessor.velocity_of_x(ListX, ListT)
ListVY = self.svmProcessor.velocity_of_y(ListY, ListT)
ListAX = self.svmProcessor.acc_of_vx(ListVX, ListT)
ListAY = self.svmProcessor.acc_of_vy(ListVY, ListT)
return ListT, ListR, ListVX, ListVY, ListAX, ListAY
def generate_features(self, filePath):
ListT, ListR, ListVX, ListVY, ListAX, ListAY = self.data_process(filePath)
self.probFeatureExtractor.generate_features(ListT, ListR, ListVX, ListVY, ListAX, ListAY)
return self.probFeatureExtractor.features()
def feature_clear(self):
self.probFeatureExtractor.clear()
def PS(self, F):
P = []
for i in range(len(F)):
p = numpy.exp(-1 * pow((F[i] - self.meanF[i]), 2) / 2 / self.varF[i]) / numpy.sqrt(
2 * self.meanF[i] * self.varF[i]
)
P.append(p)
# print [round(p, 11) for p in P]
# print numpy.isnan(P)
PArray = numpy.asarray(P)
PArray[numpy.isnan(PArray)] = 0
return PArray.sum()
def ps_temp(self, ListF):
self.meanF = []
self.varF = []
Features = []
for F in ListF:
for i in range(len(F)):
if i >= len(Features):
Features.append([])
Features[i].append(F[i])
for F in Features:
self.meanF.append(numpy.mean(F))
self.varF.append(numpy.var(F))
class SVMDriver(Driver):
def __init__(self):
self.preProcessor = PreProcessor()
self.svmProcessor = SVMProcessor()
self.svmFeatureExtractor = SVMFeatureExtractor()
def data_process(self, fileName):
ListT, ListX, ListY, ListP = self.pre_process(fileName)
ListR = self.svmProcessor.radius(ListX, ListY)
ListVX = self.svmProcessor.velocity_of_x(ListX, ListT)
ListVY = self.svmProcessor.velocity_of_y(ListY, ListT)
ListVR = self.svmProcessor.velocity_of_r(ListR, ListT)
ListVP = self.svmProcessor.velocity_of_p(ListP, ListT)
ListAX = self.svmProcessor.acc_of_vx(ListVX, ListT)
ListAY = self.svmProcessor.acc_of_vy(ListVY, ListT)
ListAR = self.svmProcessor.acc_of_vr(ListVR, ListT)
return ListT, ListX, ListY, ListP, ListR, ListVX, ListVY, ListVR, ListVP, ListAX, ListAY, ListAR
def generate_features(self, fileName):
ListT, ListX, ListY, ListR, ListP, ListVX, ListVY, ListVR, ListVP, ListAX, ListAY, ListAR = self.data_process(
fileName
)
self.svmFeatureExtractor.generate_features(
ListT, ListX, ListY, ListR, ListP, ListVX, ListVY, ListVR, ListVP, ListAX, ListAY, ListAR
)
return self.svmFeatureExtractor.features()
def feature_clear(self):
self.svmFeatureExtractor.clear()
class SVMDriver(Driver):
def __init__(self):
self.preProcessor = PreProcessor()
self.svmProcessor = SVMProcessor()
self.svmFeatureExtractor = SVMFeatureExtractor()
def data_process(self, fileName):
ListT, ListX, ListY, ListP = self.pre_process(fileName)
ListR = self.svmProcessor.radius(ListX, ListY)
ListVX = self.svmProcessor.velocity_of_x(ListX, ListT)
ListVY = self.svmProcessor.velocity_of_y(ListY, ListT)
ListVR = self.svmProcessor.velocity_of_r(ListR, ListT)
ListVP = self.svmProcessor.velocity_of_p(ListP, ListT)
ListAX = self.svmProcessor.acc_of_vx(ListVX, ListT)
ListAY = self.svmProcessor.acc_of_vy(ListVY, ListT)
ListAR = self.svmProcessor.acc_of_vr(ListVR, ListT)
return ListT, ListX, ListY, ListP, ListR, ListVX, ListVY, ListVR, \
ListVP, ListAX, ListAY, ListAR
def generate_features(self, fileName):
ListT, ListX, ListY, ListR, ListP, ListVX, ListVY, ListVR, ListVP, \
ListAX, ListAY, ListAR = self.data_process(fileName)
self.svmFeatureExtractor.generate_features(ListT, ListX, ListY, ListR,
ListP, ListVX, ListVY,
ListVR, ListVP, ListAX,
ListAY, ListAR)
return self.svmFeatureExtractor.features()
def feature_clear(self):
self.svmFeatureExtractor.clear()
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
def features_of_acceleration(self, ListAR, ListAX, ListAY):
"""
acceleration related features
"""
maxAR = self._max(ListAR)
maxAX = self._max(ListAX)
maxAY = self._max(ListAY)
minAR = self._min(ListAR)
minAX = self._min(ListAX)
minAY = self._min(ListAY)
avgAR = self._avg(ListAR)
avgAX = self._avg(ListAX)
avgAY = self._avg(ListAY)
stdAX = self._std(ListAX)
stdAY = self._std(ListAY)
avgPosAX = numpy.mean(self._select_positive(ListAX))
avgPosAY = numpy.mean(self._select_positive(ListAY))
avgNegAX = numpy.mean(self._select_negative(ListAX))
avgNegAY = numpy.mean(self._select_negative(ListAY))
ListAbsAX = SVMProcessor.abs_acc_of_x(SVMProcessor(), ListAX)
ListAbsAY = SVMProcessor.abs_acc_of_y(SVMProcessor(), ListAY)
avgAbsAX = self._avg(ListAbsAX)
avgAbsAY = self._avg(ListAbsAY)
maxAbsAX = self._max(ListAbsAX)
maxAbsAY = self._max(ListAbsAY)
stdAbsAX = self._std(ListAbsAX)
stdAbsAY = self._std(ListAbsAY)
pointsCntGtZeroAX = self._points_gt_zero(ListAX)
pointsCntLtZeroAX = self._points_lt_zero(ListAX)
pointsCntGtZeroAY = self._points_gt_zero(ListAY)
pointsCntLtZeroAY = self._points_lt_zero(ListAY)
self.svmFeature.append(maxAR) # 88
self.svmFeature.append(avgAR) # 89
self.svmFeature.append(0) # 90 ####TODO####
self.svmFeature.append(0) # 91 ####TODO####
self.svmFeature.append(stdAX) # 92
self.svmFeature.append(stdAY) # 93
self.svmFeature.append(avgAbsAX / maxAbsAX) # 94
self.svmFeature.append(avgAbsAY / maxAbsAY) # 95
self.svmFeature.append(avgAX / self.avgPosVX) # 96
self.svmFeature.append(avgAY / self.avgPosVY) # 97
self.svmFeature.append(avgAR / self.maxVR) # 98
self.svmFeature.append(minAR / self.avgVR) # 99
self.svmFeature.append(minAY / avgAY) # 100
self.svmFeature.append(avgPosAX / avgNegAX) # 101
self.svmFeature.append(maxAR / self.avgPosVX) # 102
self.svmFeature.append(maxAR / self.avgPosVY) # 103
self.svmFeature.append(maxAR / self.avgNegVX) # 104
self.svmFeature.append(maxAR / self.avgNegVY) # 105
self.svmFeature.append(minAR / self.avgPosVX) # 106
self.svmFeature.append(minAR / self.avgPosVY) # 107
self.svmFeature.append(minAR / self.avgNegVX) # 108
self.svmFeature.append(minAR / self.avgNegVY) # 109
self.svmFeature.append(avgNegAY / avgPosAY) # 110
self.svmFeature.append(avgAR / self.avgNegVX) # 111
self.svmFeature.append(avgAR / self.avgNegVY) # 112
self.svmFeature.append(avgAbsAX) # 113
self.svmFeature.append(avgAbsAY) # 114
self.svmFeature.append(stdAbsAX) # 115
self.svmFeature.append(stdAbsAY) # 116
self.svmFeature.append(maxAX) # 117
self.svmFeature.append(minAX) # 118
self.svmFeature.append(maxAY) # 119
self.svmFeature.append(minAY) # 120
self.svmFeature.append(avgAX) # 121
self.svmFeature.append(avgAY) # 122
self.svmFeature.append(pointsCntGtZeroAX) # 123
self.svmFeature.append(pointsCntLtZeroAX) # 124
self.svmFeature.append(pointsCntGtZeroAY) # 125
self.svmFeature.append(pointsCntLtZeroAY) # 126
def features_of_velocity(self, ListVX, ListVY, ListVR):
"""
velocity related features
"""
# 将ListVX, ListVY, ListVR保存
self.ListVX = ListVX
self.ListVY = ListVY
self.ListVR = ListVR
self.avgVX = self._avg(ListVX)
self.avgVY = self._avg(ListVY)
self.avgVR = self._avg(ListVR)
self.maxVX = self._max(ListVX)
self.maxVY = self._max(ListVY)
self.maxVR = self._max(ListVR)
self.minVX = self._min(ListVX)
self.minVY = self._min(ListVY)
self.minVR = self._min(ListVR)
posVX = self._select_positive(ListVX)
posVY = self._select_positive(ListVY)
negVX = self._select_negative(ListVX)
negVY = self._select_negative(ListVY)
self.avgPosVX = numpy.mean(posVX)
self.avgPosVY = numpy.mean(posVY)
self.maxPosVX = max(posVX)
self.maxPosVY = max(posVY)
self.minPosVX = min(posVX)
self.minPosVY = min(posVY)
self.avgNegVX = numpy.mean(negVX)
self.avgNegVY = numpy.mean(negVY)
self.maxNegVX = max(negVX)
self.maxNegVY = max(negVY)
self.minNegVX = min(negVX)
self.minNegVY = min(negVY)
self.stdVX = self._std(ListVX)
self.stdVY = self._std(ListVY)
ListAbsVX = SVMProcessor.abs_velocity_of_x(SVMProcessor(), ListVX)
ListAbsVY = SVMProcessor.abs_velocity_of_y(SVMProcessor(), ListVY)
self.avgAbsVX = self._avg(ListAbsVX)
self.avgAbsVY = self._avg(ListAbsVY)
self.stdAbsVX = self._std(ListAbsVX)
self.stdAbsVY = self._std(ListAbsVY)
self.svmFeature.append(self.avgVX) # 1
self.svmFeature.append(self.avgVY) # 2
self.svmFeature.append(self.stdVX) # 3
self.svmFeature.append(self.stdVY) # 4
self.svmFeature.append(self.avgVR) # 5
self.svmFeature.append(self.maxVX/self.maxVY) # 6
self.svmFeature.append(self.avgPosVX/self.maxPosVX) # 7
self.svmFeature.append(self.avgPosVY/self.maxPosVY) # 8
self.svmFeature.append(self.avgNegVX/self.maxNegVX) # 9
self.svmFeature.append(self.avgNegVY/self.maxNegVY) # 10
self.svmFeature.append(self.avgVR/self.maxVR) # 11
self.svmFeature.append(self.minVX/self.maxVX) # 12
self.svmFeature.append(self.minVY/self.maxVY) # 13
self.svmFeature.append(self.minVX/self.avgVX) # 14
self.svmFeature.append(self.minVY/self.avgVY) # 15
self.svmFeature.append(self.minVR/self.maxVR) # 16
self.svmFeature.append(self.avgPosVX/self.avgNegVX) # 17
self.svmFeature.append(self.avgPosVY/self.avgNegVY) # 18
self.svmFeature.append(self.avgPosVX/self.avgPosVY) # 19
self.svmFeature.append(self.avgPosVX/self.avgNegVY) # 20
self.svmFeature.append(self.avgNegVX/self.avgPosVY) # 21
self.svmFeature.append(self.avgNegVX/self.avgNegVY) # 22
self.svmFeature.append(self.avgPosVX/self.maxNegVX) # 23
self.svmFeature.append(self.avgPosVY/self.maxNegVY) # 24
self.svmFeature.append(self.minVX/self.avgVX) # 25
self.svmFeature.append(self.minVY/self.avgVY) # 26
self.svmFeature.append(self.avgVX/self.maxVX) # 27
self.svmFeature.append(self.avgVY/self.maxVY) # 28
self.svmFeature.append(self.avgPosVX) # 29
self.svmFeature.append(self.avgPosVY) # 30
self.svmFeature.append(self.avgNegVX) # 31
self.svmFeature.append(self.avgNegVY) # 32
self.svmFeature.append(self.maxVX) # 33
self.svmFeature.append(self.maxVY) # 34
self.svmFeature.append(self.avgVX) # 35
self.svmFeature.append(self.avgVY) # 36
self.svmFeature.append(self.avgAbsVX) # 37
self.svmFeature.append(self.avgAbsVY) # 38
self.svmFeature.append(self.stdAbsVX) # 39
self.svmFeature.append(self.stdAbsVY) # 40
self.svmFeature.append(self._points_lt_zero(self.ListVX)) # 41
self.svmFeature.append(self._points_lt_zero(self.ListVY)) # 42
self.svmFeature.append(self._points_gt_zero(self.ListVX)) # 43
self.svmFeature.append(self._points_gt_zero(self.ListVY)) # 44
self.svmFeature.append(self.maxNegVX) # 45
self.svmFeature.append(self.maxNegVY) # 46
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
