def getSeqO(seqX):
cnt = len(seqX)
nts = RandomNum.createNormal(0, 1, cnt)
ret = np.zeros(cnt)
for i in range(cnt):
ret[i] = 0.05 * math.pow(seqX[i], 2) + nts[i]
return ret
def samplingSIS(self, num):
weight_line = self.__getWeightLine()
random_seqs = RandomNum.createRandom(num)
n_pset = []
for i in range(num):
idx = self.__getIdxInLine(weight_line, random_seqs[i])
n_pset.append(Particle(self.pset[idx].x, 1.0 / num))
self.pset = n_pset
def getSeqX(init_val, cnt):
x = init_val
vts = RandomNum.createNormal(0, 1, cnt)
ret = np.zeros(cnt)
for i in range(cnt):
ret[i] = PFSim.getNextX(i, x, vts[i])
x = ret[i]
return ret
def getMeasureData(self, map, radarPos):
ret_pos = []
seq_nor = RandomNum.createNormal(0, self.sd, self.lines)
self.__traverseAllLines(self.__getLinePoint,
cur_pos=radarPos,
ret_pos=ret_pos,
map=map,
seq_nor=seq_nor)
return ret_pos
def samplingLVS(self, num):
weight_line = self.__getWeightLine()
r = RandomNum.createRandom(1) * 1.0 / num
n_pset = []
r_per = 1.0 / num
for i in range(num):
idx = self.__getIdxInLine(weight_line, r)
n_pset.append(Particle(self.pset[idx].x, 1.0 / num))
r += r_per
self.pset = n_pset
def predictSeq(self, t, vts):
vts = RandomNum.createNormal(0, 1, len(self.pset))
for i in range(len(self.pset)):
self.pset[i].x = PFSim.getNextX(t, self.pset[i].x, vts[i])
def genNormalRan(cnt, num):
ret = []
for i in range(cnt):
ret.append(RandomNum.createNormal(0, 1, num))
return ret
def genRandPos(x, y, w, h, num):
ran1 = RandomNum.createRandom(num) * w + x
ran2 = RandomNum.createRandom(num) * h + y
return ran1, ran2