def __init__(self, filename):
"""Input: path and name of the file of a trace; how many filtering steps should be used for sliding window filtering"""
self.__id = int(os.path.basename(filename).split(".")[0])
x = []
y = []
with open(filename, "r") as trainfile:
trainfile.readline() # skip header
for line in trainfile:
items = line.split(",", 2)
x.append(float(items[0]))
y.append(float(items[1]))
triplength = distance(x[0], y[0], x[-1], y[-1])
self.triptime = len(x)
xvar, yvar = bettervariance(x, y)
overlaps = overlap(x, y)
self.featurelist = []
self.featurelist.append(triplength)
self.featurelist.append(self.triptime)
self.featurelist.append(xvar)
self.featurelist.append(yvar)
self.featurelist.append(overlaps)
v, distancecovered = velocities_and_distance_covered(x, y)
vfiltered = reject_outliers(v, 3)
maxspeed = max(vfiltered)
medianspeed = median(vfiltered)
meanspeed, varspeed = meanstdv(vfiltered)
angles, jumps = self.angles_and_jumps()
anglespeed = [speed * angle for (speed, angle) in zip(v, angles)]
anglespeedfiltered = reject_outliers(anglespeed, 3)
maxanglespeed = max(anglespeedfiltered)
mediananglespeed = median(anglespeedfiltered)
meananglespeed, varanglespeed = meanstdv(anglespeedfiltered)
totalangle = sum(angles)
maxangle = max(angles)
minangle = min(angles)
medianangle = max(angles)
acc, dec, stills = getratio(vfiltered, medianspeed)
if (acc + dec) == 0:
accratio = 0.5
else:
accratio = acc / (acc + dec)
self.featurelist.append(distancecovered)
self.featurelist.append(maxspeed)
self.featurelist.append(medianspeed)
self.featurelist.append(meanspeed)
self.featurelist.append(varspeed)
self.featurelist.append(maxanglespeed)
self.featurelist.append(mediananglespeed)
self.featurelist.append(meananglespeed)
self.featurelist.append(varanglespeed)
self.featurelist.append(totalangle)
self.featurelist.append(medianangle)
self.featurelist.append(maxangle)
self.featurelist.append(minangle)
self.featurelist.append(jumps)
self.featurelist.append(accratio)
self.featurelist.append(acc)
self.featurelist.append(dec)
self.featurelist.append(stills)
def __init__(self, filename, quantiles=[0.0, 0.25, 0.5, 0.75, 1.0]):
"""Input: path and name of the file of a trace; how many filtering steps should be used for sliding window filtering"""
self.__id = int(os.path.basename(filename).split(".")[0])
self._x = []
self._y = []
with open(filename, "r") as trainfile:
trainfile.readline() # skip header
for line in trainfile:
items = line.split(",", 2)
self._x.append(float(items[0]))
self._y.append(float(items[1]))
self.rawfeaturelist = []
self.rawfeaturelist.extend(self._x)
self.rawfeaturelist.extend(self._y)
triplength = distance(self._x[0], self._y[0], self._x[-1], self._y[-1])
self.triptime = len(self._x)
xvar, yvar = bettervariance(self._x, self._y)
overlaps = overlap(self._x, self._y)
self.featurelist = []
self.featurelist.append(triplength)
self.featurelist.append(self.triptime)
self.featurelist.append(xvar)
self.featurelist.append(yvar)
self.featurelist.append(overlaps)
v, distancecovered = velocities_and_distance_covered(self._x, self._y)
vfiltered = reject_outliers(v, 3)
self.rawfeaturelist.extend(vfiltered)
speed_qs = mquantiles(vfiltered, prob=quantiles)
angles, jumps = self.angles_and_jumps()
self.rawfeaturelist.extend(angles)
anglespeed = [speed * angle for (speed, angle) in zip(v, angles)]
anglespeedfiltered = reject_outliers(anglespeed, 3)
anglespeed_qs = mquantiles(anglespeedfiltered, prob=quantiles)
totalangle = sum(angles)
angle_qs = mquantiles(angles, prob=quantiles)
acc, dec, stills = getratio(vfiltered, anglespeed_qs[2])
self.featurelist.append(distancecovered)
self.featurelist.extend(speed_qs)
self.featurelist.extend(anglespeed_qs)
self.featurelist.append(totalangle)
self.featurelist.extend(angle_qs)
self.featurelist.append(acc)
self.featurelist.append(dec)
self.featurelist.append(stills)
def __init__(self, filename, quantiles=[0.0, 0.25, 0.5, 0.75, 1.0]):
"""Input: path and name of the file of a trace; how many filtering steps should be used for sliding window filtering"""
self.__id = int(os.path.basename(filename).split(".")[0])
self._x = []
self._y = []
with open(filename, "r") as trainfile:
trainfile.readline() # skip header
for line in trainfile:
items = line.split(",", 2)
self._x.append(float(items[0]))
self._y.append(float(items[1]))
self.rawfeaturelist = []
self.rawfeaturelist.extend(self._x)
self.rawfeaturelist.extend(self._y)
triplength = distance(self._x[0], self._y[0], self._x[-1], self._y[-1])
self.triptime = len(self._x)
xvar, yvar = bettervariance(self._x, self._y)
overlaps = overlap(self._x, self._y)
self.featurelist = []
self.featurelist.append(triplength)
self.featurelist.append(self.triptime)
self.featurelist.append(xvar)
self.featurelist.append(yvar)
self.featurelist.append(overlaps)
v, distancecovered = velocities_and_distance_covered(self._x, self._y)
vfiltered = reject_outliers(v, 3)
self.rawfeaturelist.extend(vfiltered)
speed_qs = mquantiles(vfiltered, prob=quantiles)
angles, jumps = self.angles_and_jumps()
self.rawfeaturelist.extend(angles)
anglespeed = [speed * angle for (speed, angle) in zip(v, angles)]
anglespeedfiltered = reject_outliers(anglespeed, 3)
anglespeed_qs = mquantiles(anglespeedfiltered, prob=quantiles)
totalangle = sum(angles)
angle_qs = mquantiles(angles, prob=quantiles)
acc, dec, stills = getratio(vfiltered, anglespeed_qs[2])
self.featurelist.append(distancecovered)
self.featurelist.extend(speed_qs)
self.featurelist.extend(anglespeed_qs)
self.featurelist.append(totalangle)
self.featurelist.extend(angle_qs)
self.featurelist.append(acc)
self.featurelist.append(dec)
self.featurelist.append(stills)