def testInvertedSoftmaxModels():
b = GM()
b.addG(Gaussian([2, 2], [[1, 0], [0, 1]], 1))
b.addG(Gaussian([4, 2], [[1, 0], [0, 1]], 1))
b.addG(Gaussian([2, 4], [[1, 0], [0, 1]], 1))
b.addG(Gaussian([3, 3], [[1, 0], [0, 1]], 1))
b.normalizeWeights()
b.plot2D()
pz = Softmax()
pz.buildOrientedRecModel([2, 2], 0, 1, 1, 5)
#pz.plot2D();
startTime = time.clock()
b2 = GM()
for i in range(1, 5):
b2.addGM(pz.runVBND(b, i))
print(time.clock() - startTime)
b2.plot2D()
startTime = time.clock()
b3 = GM()
b3.addGM(b)
tmpB = pz.runVBND(b, 0)
tmpB.normalizeWeights()
tmpB.scalerMultiply(-1)
b3.addGM(tmpB)
tmpBWeights = b3.getWeights()
mi = min(b3.getWeights())
#print(mi);
for g in b3.Gs:
g.weight = g.weight - mi
b3.normalizeWeights()
print(time.clock() - startTime)
#b3.display();
b3.plot2D()
def measurementUpdate(meas, sm, s, curs, goals):
origLen = len(s)
s = np.array(s)
curs = np.array(curs)
goals = np.array(goals)
sm = Softmax()
# sm.buildRectangleModel([[1,5],[3,7]],steepness=7);
sm.buildOrientedRecModel([2000, -2000], 0, 1, 1, steepness=7)
weights = [sm.pointEvalND(meas, s[i]) for i in range(0, len(s))]
weights /= np.sum(weights)
csum = np.cumsum(weights)
csum[-1] = 1
indexes = np.searchsorted(csum, np.random.random(len(s)))
s[:] = s[indexes]
curs[:] = curs[indexes]
goals[:] = goals[indexes]
return s, curs, goals
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestSoftmax'
#Problem specific
def buildTransition(self):
#self.bounds = [[0,10],[0,5],[0,10],[0,5]];
#Hallway
#self.bounds = [[-9.5,4],[-1,1.4],[-9.5,4],[-1,1.4]];
#Dining Room
#self.bounds = [[-9.5,-7],[-3.33,-1],[-9.5,-7],[-3.33,-1]]
#Study
#self.bounds = [[-7,-2],[-3.33,-1],[-7,-2],[-3.33,-1]];
#Library
#self.bounds = [[-2,4],[-3.33,-1],[-2,4],[-3.33,-1]];
#Billiard
#self.bounds = [[0,4],[1.4,3.68],[0,4],[1.4,3.68]];
#Kitchen
self.bounds = [[-9.5, 0], [1.4, 3.68], [-9.5, 0], [1.4, 3.68]]
self.delAVar = (np.identity(4) * 1).tolist()
self.delAVar[0][0] = 0.00001
self.delAVar[1][1] = 0.00001
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1
self.delA = [[-delta, 0, 0, 0], [delta, 0, 0, 0], [0, delta, 0, 0],
[0, -delta, 0, 0], [0, 0, 0, 0]]
self.discount = 0.95
def buildObs(self, gen=True):
if (gen):
self.pz = Softmax()
#Vern
#self.pz.buildOrientedRecModel([-2.475,1.06],270,0.5,0.5,5);
#Desk
#self.pz.buildOrientedRecModel([-5.5,-2.0],0,0.61,0.99,5);
#bookcase
#self.pz.buildOrientedRecModel([0,-1.1662],270,0.38,0.18,5);
#checkers
#self.pz.buildOrientedRecModel([2.04,2.16],270,0.5,0.5,5);
#Fridge
self.pz.buildOrientedRecModel([-9.1, 3.07], 315, 0.46, 0.46, 5)
for i in range(0, len(self.pz.weights)):
self.pz.weights[i] = [
0, 0, self.pz.weights[i][0], self.pz.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open(self.fileNamePrefix + "OBS.npy", "w")
np.save(f, self.pz)
self.pz2 = Softmax()
#filing cabinet
#self.pz2.buildOrientedRecModel([-3.8638,-1.3262],270,0.5,.37,5);
#chair
#self.pz2.buildOrientedRecModel([2.975,-2.435],90,0.46,0.41,5);
#cassini
#self.pz2.buildOrientedRecModel([1.38,3.475],270,0.05,0.56,5);
#mars
self.pz2.buildOrientedRecModel([-4.38, 3.475], 270, 0.05, 0.84, 5)
for i in range(0, len(self.pz2.weights)):
self.pz2.weights[i] = [
0, 0, self.pz2.weights[i][0], self.pz2.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open(self.fileNamePrefix + "OBS2.npy", "w")
np.save(f, self.pz2)
else:
self.pz = np.load(self.fileNamePrefix + "OBS.npy").tolist()
self.pz2 = np.load(self.fileNamePrefix + "OBS2.npy").tolist()
#Problem Specific
def buildReward(self, gen=True):
if (gen):
self.r = [0] * len(self.delA)
for i in range(0, len(self.r)):
self.r[i] = GM()
var = (np.identity(4) * 5).tolist()
#Need gaussians along the borders for positive and negative rewards
for i in range(0, len(self.r)):
for x1 in range(
int(np.floor(self.bounds[0][0])) - 1,
int(np.ceil(self.bounds[0][1])) + 1):
for y1 in range(
int(np.floor(self.bounds[1][0])) - 1,
int(np.ceil(self.bounds[1][1])) + 1):
for x2 in range(
int(np.floor(self.bounds[2][0])) - 1,
int(np.ceil(self.bounds[2][1])) + 1):
for y2 in range(
int(np.floor(self.bounds[3][0])) - 1,
int(np.ceil(self.bounds[3][1])) + 1):
if (np.sqrt((x1 - x2)**2 + (y1 - y2)**2) < 1):
self.r[i].addG(
Gaussian(
np.array(([x1, y1, x2, y2]) -
np.array(self.delA[i])).
tolist(), var, 10))
for r in self.r:
r.display()
f = open(self.fileNamePrefix + "REW.npy", "w")
np.save(f, self.r)
else:
self.r = np.load(self.fileNamePrefix + "REW.npy").tolist()
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestBilliardSoftmax'
#Problem specific
def buildTransition(self):
#Billiard
self.bounds = [[0, 4], [1.4, 3.68], [0, 4], [1.4, 3.68]]
self.delAVar = (np.identity(4) * 1).tolist()
self.delAVar[0][0] = 0.001
self.delAVar[1][1] = 0.001
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1
self.delA = [[-delta, 0, 0, 0], [delta, 0, 0, 0], [0, delta, 0, 0],
[0, -delta, 0, 0], [0, 0, 0, 0]]
self.discount = 0.95
def buildObs(self, gen=True):
if (gen):
self.pz = Softmax()
#checkers
self.pz.buildOrientedRecModel([2.04, 2.16], 270, 0.5, 0.5, 5)
for i in range(0, len(self.pz.weights)):
self.pz.weights[i] = [
0, 0, self.pz.weights[i][0], self.pz.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS.npy", "w")
np.save(f, self.pz)
self.pz2 = Softmax()
#cassini
self.pz2.buildOrientedRecModel([1.38, 3.475], 270, 0.05, 0.56, 5)
for i in range(0, len(self.pz2.weights)):
self.pz2.weights[i] = [
0, 0, self.pz2.weights[i][0], self.pz2.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS2.npy", "w")
np.save(f, self.pz2)
else:
self.pz = np.load("../models/" + self.fileNamePrefix +
"OBS.npy").tolist()
self.pz2 = np.load("../models/" + self.fileNamePrefix +
"OBS2.npy").tolist()
#Problem Specific
def buildReward(self, gen=True):
if (gen):
self.r = [0] * len(self.delA)
for i in range(0, len(self.r)):
self.r[i] = GM()
var = (np.identity(4) * 5).tolist()
cutFactor = 3
for i in range(0, len(self.r)):
for x1 in range(
int(np.floor(self.bounds[0][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[0][1] / cutFactor)) + 1):
for y1 in range(
int(np.floor(self.bounds[1][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[1][1] / cutFactor)) + 1):
for x2 in range(
int(np.floor(self.bounds[2][0] / cutFactor)) -
1,
int(np.ceil(self.bounds[2][1] / cutFactor)) +
1):
for y2 in range(
int(np.floor(
self.bounds[3][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[3][1] / cutFactor))
+ 1):
if (np.sqrt((x1 - x2)**2 + (y1 - y2)**2) < 1):
self.r[i].addG(
Gaussian(
np.array(([
x1 * cutFactor, y1 *
cutFactor, x2 * cutFactor, y2 *
cutFactor
]) - np.array(self.delA[i])).
tolist(), var, 100))
for r in self.r:
r.display()
f = open("../models/" + self.fileNamePrefix + "REW.npy", "w")
np.save(f, self.r)
else:
self.r = np.load("../models/" + self.fileNamePrefix +
"REW.npy").tolist()
def obs2models(self, obs, pose):
"""Map received observation to the appropriate softmax model and class.
Observation may be a str type with a pushed observation or a list with
question and answer.
"""
print(obs)
sign = None
model = None
model_name = None
room_num = None
class_idx = None
# check if observation is statement (str) or question (list)
if type(obs) is str:
# obs = obs.split()
if 'not' in obs:
sign = False
else:
sign = True
else:
sign = obs[1]
obs = obs[0]
# find map object mentioned in statement
for obj in self.map_.objects:
if re.search(obj, obs.lower()):
model = self.map_.objects[obj].softmax
model_name = self.map_.objects[obj].name
for i, room in enumerate(self.map_.rooms):
if obj in self.map_.rooms[room][
'objects']: # potential for matching issues if obj is 'the <obj>', as only '<obj>' will be found in room['objects']
room_num = i + 1
print self.map_.rooms[room]['objects']
print room_num
break
# if observation is relative to the cop
if re.search('cop', obs.lower()):
model = Softmax()
model.buildOrientedRecModel((pose[0], pose[1]),
pose[2] * 180 / np.pi,
0.5,
0.5,
steepness=2)
room_num = 1
for i in range(0, len(model.weights)):
model.weights[i] = [
0, 0, model.weights[i][0], model.weights[i][1]
]
# if no model is found, try looking for room mentioned in observation
if model is None:
for room in self.map_.rooms:
if re.search(room, obs.lower()):
model = self.map_.rooms[room]['softmax']
room_num = 0
break
# find softmax class index
if re.search('inside', obs.lower()) or re.search('in', obs.lower()):
class_idx = 0
if re.search('front', obs.lower()):
class_idx = 1
elif re.search('right', obs.lower()):
class_idx = 2
elif re.search('behind', obs.lower()):
class_idx = 3
elif re.search('left', obs.lower()):
class_idx = 4
# elif 'near' in obs:
# class_idx = 5
print(room_num, model, model_name, class_idx, sign)
return room_num, model, class_idx, sign
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestDiningSoftmax';
self.yamlFile = yaml.load(open('../../../models/mapA.yaml'));
#Problem specific
def buildTransition(self):
#Dining Room
#self.bounds = [[-9.5,-7],[-3.33,-1],[-9.5,-7],[-3.33,-1]]
r = self.yamlFile['info']['rooms']['dining room'];
self.bounds = [[r['min_x'],r['max_x']],[r['min_y'],r['max_y']],[r['min_x'],r['max_x']],[r['min_y'],r['max_y']]];
self.delAVar = (np.identity(4)*1).tolist();
self.delAVar[0][0] = 0.001;
self.delAVar[1][1] = 0.001;
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1;
self.delA = [[-delta,0,0,0],[delta,0,0,0],[0,delta,0,0],[0,-delta,0,0],[0,0,0,0]];
self.discount = 0.95;
def buildObs(self,gen=True):
if(gen):
self.pz = Softmax();
#dining table
#self.pz.buildOrientedRecModel([-8.5,-2.3],90,1.17,0.69,5);
dining = self.yamlFile['dining table'];
self.pz.buildOrientedRecModel([dining['centroid_x'],dining['centroid_y']],dining['orientation']+90,dining['x_len'],dining['y_len'],5);
for i in range(0,len(self.pz.weights)):
self.pz.weights[i] = [0,0,self.pz.weights[i][0],self.pz.weights[i][1]];
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/"+self.fileNamePrefix + "OBS.npy","w");
np.save(f,self.pz);
self.pz2 = Softmax();
f = open("../models/"+self.fileNamePrefix + "OBS2.npy","w");
np.save(f,self.pz2);
else:
self.pz = np.load("../models/"+self.fileNamePrefix + "OBS.npy").tolist();
self.pz2 = np.load("../models/"+self.fileNamePrefix+ "OBS2.npy").tolist();
#Problem Specific
def buildReward(self,gen = True):
if(gen):
self.r = [0]*len(self.delA);
for i in range(0,len(self.r)):
self.r[i] = GM();
var = (np.identity(4)*5).tolist();
cutFactor = 3;
for i in range(0,len(self.r)):
for x1 in range(int(np.floor(self.bounds[0][0]/cutFactor))-1,int(np.ceil(self.bounds[0][1]/cutFactor))+1):
for y1 in range(int(np.floor(self.bounds[1][0]/cutFactor))-1,int(np.ceil(self.bounds[1][1]/cutFactor))+1):
for x2 in range(int(np.floor(self.bounds[2][0]/cutFactor))-1,int(np.ceil(self.bounds[2][1]/cutFactor))+1):
for y2 in range(int(np.floor(self.bounds[3][0]/cutFactor))-1,int(np.ceil(self.bounds[3][1]/cutFactor))+1):
if(np.sqrt((x1-x2)**2 + (y1-y2)**2) < 1):
self.r[i].addG(Gaussian(np.array(([x1*cutFactor,y1*cutFactor,x2*cutFactor,y2*cutFactor])-np.array(self.delA[i])).tolist(),var,100));
# for r in self.r:
# r.display();
f = open("../models/"+self.fileNamePrefix + "REW.npy","w");
np.save(f,self.r);
else:
self.r = np.load("../models/"+self.fileNamePrefix + "REW.npy").tolist();
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestHallwaySoftmax';
#Problem specific
def buildTransition(self):
#Hallway
self.bounds = [[-9.5,4],[-1,1.4],[-9.5,4],[-1,1.4]];
self.delAVar = (np.identity(4)*1).tolist();
self.delAVar[0][0] = 0.001;
self.delAVar[1][1] = 0.001;
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1;
self.delA = [[-delta,0,0,0],[delta,0,0,0],[0,delta,0,0],[0,-delta,0,0],[0,0,0,0]];
self.discount = 0.95;
def buildObs(self,gen=True):
if(gen):
self.pz = Softmax();
#Vern
self.pz.buildOrientedRecModel([-2.475,1.06],270,0.5,0.5,5);
for i in range(0,len(self.pz.weights)):
self.pz.weights[i] = [0,0,self.pz.weights[i][0],self.pz.weights[i][1]];
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/"+self.fileNamePrefix + "OBS.npy","w");
np.save(f,self.pz);
self.pz2 = Softmax();
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/"+self.fileNamePrefix + "OBS2.npy","w");
np.save(f,self.pz2);
else:
self.pz = np.load("../models/"+self.fileNamePrefix + "OBS.npy").tolist();
self.pz2 = np.load("../models/"+self.fileNamePrefix+ "OBS2.npy").tolist();
#Problem Specific
def buildReward(self,gen = True):
if(gen):
self.r = [0]*len(self.delA);
for i in range(0,len(self.r)):
self.r[i] = GM();
var = (np.identity(4)*5).tolist();
cutFactor = 3;
for i in range(0,len(self.r)):
for x1 in range(int(np.floor(self.bounds[0][0]/cutFactor))-1,int(np.ceil(self.bounds[0][1]/cutFactor))+1):
for y1 in range(int(np.floor(self.bounds[1][0]/cutFactor))-1,int(np.ceil(self.bounds[1][1]/cutFactor))+1):
for x2 in range(int(np.floor(self.bounds[2][0]/cutFactor))-1,int(np.ceil(self.bounds[2][1]/cutFactor))+1):
for y2 in range(int(np.floor(self.bounds[3][0]/cutFactor))-1,int(np.ceil(self.bounds[3][1]/cutFactor))+1):
if(np.sqrt((x1-x2)**2 + (y1-y2)**2) < 1):
self.r[i].addG(Gaussian(np.array(([x1*cutFactor,y1*cutFactor,x2*cutFactor,y2*cutFactor])-np.array(self.delA[i])).tolist(),var,100));
for r in self.r:
r.display();
f = open("../models/"+self.fileNamePrefix + "REW.npy","w");
np.save(f,self.r);
else:
self.r = np.load("../models/"+self.fileNamePrefix + "REW.npy").tolist();
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestLibrarySoftmax'
self.yamlFile = yaml.load(open('../../../models/mapA.yaml'))
#Problem specific
def buildTransition(self):
#Library
#self.bounds = [[-2,4],[-3.33,-1],[-2,4],[-3.33,-1]];
r = self.yamlFile['info']['rooms']['library']
self.bounds = [[r['min_x'], r['max_x']], [r['min_y'], r['max_y']],
[r['min_x'], r['max_x']], [r['min_y'], r['max_y']]]
self.delAVar = (np.identity(4) * 1).tolist()
self.delAVar[0][0] = 0.001
self.delAVar[1][1] = 0.001
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1
self.delA = [[-delta, 0, 0, 0], [delta, 0, 0, 0], [0, delta, 0, 0],
[0, -delta, 0, 0], [0, 0, 0, 0]]
self.discount = 0.95
def buildObs(self, gen=True):
if (gen):
self.pz = Softmax()
#bookcase
#self.pz.buildOrientedRecModel([0,-1.1662],270,0.38,0.18,5);
bookcase = self.yamlFile['bookcase']
self.pz.buildOrientedRecModel(
[bookcase['centroid_x'], bookcase['centroid_y']],
bookcase['orientation'] + 90, bookcase['x_len'],
bookcase['y_len'], 5)
for i in range(0, len(self.pz.weights)):
self.pz.weights[i] = [
0, 0, self.pz.weights[i][0], self.pz.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS.npy", "w")
np.save(f, self.pz)
self.pz2 = Softmax()
#chair
#self.pz2.buildOrientedRecModel([2.975,-2.435],90,0.46,0.41,5);
chair = self.yamlFile['chair']
self.pz2.buildOrientedRecModel(
[chair['centroid_x'], chair['centroid_y']],
chair['orientation'] + 90, chair['x_len'], chair['y_len'], 5)
for i in range(0, len(self.pz2.weights)):
self.pz2.weights[i] = [
0, 0, self.pz2.weights[i][0], self.pz2.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS2.npy", "w")
np.save(f, self.pz2)
else:
self.pz = np.load("../models/" + self.fileNamePrefix +
"OBS.npy").tolist()
self.pz2 = np.load("../models/" + self.fileNamePrefix +
"OBS2.npy").tolist()
#Problem Specific
def buildReward(self, gen=True):
if (gen):
self.r = [0] * len(self.delA)
for i in range(0, len(self.r)):
self.r[i] = GM()
var = (np.identity(4) * 5).tolist()
cutFactor = 3
for i in range(0, len(self.r)):
for x1 in range(
int(np.floor(self.bounds[0][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[0][1] / cutFactor)) + 1):
for y1 in range(
int(np.floor(self.bounds[1][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[1][1] / cutFactor)) + 1):
for x2 in range(
int(np.floor(self.bounds[2][0] / cutFactor)) -
1,
int(np.ceil(self.bounds[2][1] / cutFactor)) +
1):
for y2 in range(
int(np.floor(
self.bounds[3][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[3][1] / cutFactor))
+ 1):
if (np.sqrt((x1 - x2)**2 + (y1 - y2)**2) < 1):
self.r[i].addG(
Gaussian(
np.array(([
x1 * cutFactor, y1 *
cutFactor, x2 * cutFactor, y2 *
cutFactor
]) - np.array(self.delA[i])).
tolist(), var, 100))
for r in self.r:
r.display()
f = open("../models/" + self.fileNamePrefix + "REW.npy", "w")
np.save(f, self.r)
else:
self.r = np.load("../models/" + self.fileNamePrefix +
"REW.npy").tolist()
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestKitchenSoftmax'
self.yamlFile = yaml.load(open('../../../models/mapA.yaml'))
#Problem specific
def buildTransition(self):
#Kitchen
#self.bounds = [[-9.5,0],[1.4,3.68],[-9.5,0],[1.4,3.68]];
r = self.yamlFile['info']['rooms']['kitchen']
self.bounds = [[r['min_x'], r['max_x']], [r['min_y'], r['max_y']],
[r['min_x'], r['max_x']], [r['min_y'], r['max_y']]]
self.delAVar = (np.identity(4) * 1).tolist()
self.delAVar[0][0] = 0.001
self.delAVar[1][1] = 0.001
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1
self.delA = [[-delta, 0, 0, 0], [delta, 0, 0, 0], [0, delta, 0, 0],
[0, -delta, 0, 0], [0, 0, 0, 0]]
self.discount = 0.95
def buildObs(self, gen=True):
if (gen):
self.pz = Softmax()
#Fridge
#self.pz.buildOrientedRecModel([-9.1,3.07],315,0.46,0.46,5);
fridge = self.yamlFile['fridge']
self.pz.buildOrientedRecModel(
[fridge['centroid_x'], fridge['centroid_y']],
fridge['orientation'] + 90, fridge['x_len'], fridge['y_len'],
5)
for i in range(0, len(self.pz.weights)):
self.pz.weights[i] = [
0, 0, self.pz.weights[i][0], self.pz.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS.npy", "w")
np.save(f, self.pz)
self.pz2 = Softmax()
#mars
#self.pz2.buildOrientedRecModel([-4.38,3.475],270,0.05,0.84,5);
mars = self.yamlFile['mars poster']
self.pz2.buildOrientedRecModel(
[mars['centroid_x'], mars['centroid_y']],
mars['orientation'] + 90, mars['x_len'], mars['y_len'], 5)
for i in range(0, len(self.pz2.weights)):
self.pz2.weights[i] = [
0, 0, self.pz2.weights[i][0], self.pz2.weights[i][1]
]
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open("../models/" + self.fileNamePrefix + "OBS2.npy", "w")
np.save(f, self.pz2)
else:
self.pz = np.load("../models/" + self.fileNamePrefix +
"OBS.npy").tolist()
self.pz2 = np.load("../models/" + self.fileNamePrefix +
"OBS2.npy").tolist()
#Problem Specific
def buildReward(self, gen=True):
if (gen):
self.r = [0] * len(self.delA)
for i in range(0, len(self.r)):
self.r[i] = GM()
var = (np.identity(4) * 5).tolist()
cutFactor = 3
for i in range(0, len(self.r)):
for x1 in range(
int(np.floor(self.bounds[0][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[0][1] / cutFactor)) + 1):
for y1 in range(
int(np.floor(self.bounds[1][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[1][1] / cutFactor)) + 1):
for x2 in range(
int(np.floor(self.bounds[2][0] / cutFactor)) -
1,
int(np.ceil(self.bounds[2][1] / cutFactor)) +
1):
for y2 in range(
int(np.floor(
self.bounds[3][0] / cutFactor)) - 1,
int(np.ceil(self.bounds[3][1] / cutFactor))
+ 1):
if (np.sqrt((x1 - x2)**2 + (y1 - y2)**2) < 1):
self.r[i].addG(
Gaussian(
np.array(([
x1 * cutFactor, y1 *
cutFactor, x2 * cutFactor, y2 *
cutFactor
]) - np.array(self.delA[i])).
tolist(), var, 100))
for r in self.r:
r.display()
f = open("../models/" + self.fileNamePrefix + "REW.npy", "w")
np.save(f, self.r)
else:
self.r = np.load("../models/" + self.fileNamePrefix +
"REW.npy").tolist()
class ModelSpec:
def __init__(self):
self.fileNamePrefix = 'D4QuestSoftmax';
#Problem specific
def buildTransition(self):
self.bounds = [[0,10],[0,5],[0,10],[0,5]];
self.delAVar = (np.identity(4)*4).tolist();
self.delAVar[0][0] = 0.00001;
self.delAVar[1][1] = 0.00001;
#self.delA = [[-0.5,0],[0.5,0],[0,-0.5],[0,0.5],[0,0],[-0.5,-0.5],[0.5,-0.5],[-0.5,0.5],[0.5,0.5]];
delta = 1;
self.delA = [[-delta,0,0,0],[delta,0,0,0],[0,delta,0,0],[0,-delta,0,0],[0,0,0,0]];
self.discount = 0.95;
def buildObs(self,gen=True):
#cardinal + 1 model
#left,right,up,down,near
if(gen):
self.pz = Softmax();
self.pz.buildOrientedRecModel([4,4.75],270,.5,2,5);
for i in range(0,len(self.pz.weights)):
self.pz.weights[i] = [0,0,self.pz.weights[i][0],self.pz.weights[i][1]];
#print('Plotting Observation Model');
#self.pz.plot2D(low=[0,0],high=[10,5],vis=True);
f = open(self.fileNamePrefix + "OBS.npy","w");
np.save(f,self.pz);
else:
self.pz = np.load(self.fileNamePrefix + "OBS.npy").tolist();
#Problem Specific
def buildReward(self,gen = True):
if(gen):
self.r = [0]*len(self.delA);
for i in range(0,len(self.r)):
self.r[i] = GM();
var = (np.identity(4)*1).tolist();
#Need gaussians along the borders for positive and negative rewards
for i in range(0,len(self.r)):
for x1 in range(self.bounds[0][0],self.bounds[0][1]):
for y1 in range(self.bounds[1][0],self.bounds[1][1]):
for x2 in range(self.bounds[2][0],self.bounds[2][1]):
for y2 in range(self.bounds[3][0],self.bounds[3][1]):
if(math.sqrt((x1-x2)**2 + (y1-y2)**2) < 0.5):
self.r[i].addG(Gaussian([x1,y1,x2,y2],var,1));
for r in self.r:
r.display();
f = open(self.fileNamePrefix + "REW.npy","w");
np.save(f,self.r);
else:
self.r = np.load(self.fileNamePrefix + "REW.npy").tolist();