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))
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()