def sample():
while True:
p = AbsolutePoint.sample()
r = 1 if NIPSPRIMITIVES() else sampleRadius()
c = Circle(p, r)
if c.inbounds():
return c
def randomLineOfCode():
k = choice(range(5))
if k == 0: return None
if k == 1: return Circle.sample()
if k == 2: return Line.sample()
if k == 3: return Rectangle.sample()
if NIPSPRIMITIVES(): return randomLineOfCode()
if k == 4: return Label.sample()
assert False
def sample():
while True:
c = AbsolutePoint.sample()
r = 1 if NIPSPRIMITIVES() else sampleRadius()
# r = choice(range(5)) + 1 if SNAPTOGRID else (1 + random()*5)
ang = 120 * random()
tri = Triangle(c, r, ang)
if tri.onplane():
return tri
def mutate(self):
if self.radius < 3 and random() < 0.15:
return Rectangle.absolute(self.center.x - self.radius,
self.center.y - self.radius,
self.center.x + self.radius,
self.center.y + self.radius)
while True:
if random() < 0.5 or NIPSPRIMITIVES():
c = Circle(self.center.mutate(), self.radius)
else:
if self.radius < 2: r = self.radius + 1
else: r = self.radius + randomRadiusPerturbation()
c = Circle(self.center, r)
if c.inbounds():
return c
def sampler():
while True:
n = choice(range(maximumNumberOfObjects)) + 1
if NIPSPRIMITIVES():
shapeIdentities = [choice(range(3)) for _ in range(n)]
else:
shapeIdentities = [choice(range(4)) for _ in range(n)]
numberOfLabels = len([i for i in shapeIdentities if i == 3])
# make it so that there are not too many labels
if numberOfLabels > n / 2: continue
return multipleObjects(
rectangles=len([x for x in shapeIdentities if x == 0]),
lines=len([x for x in shapeIdentities if x == 1]),
circles=len([x for x in shapeIdentities if x == 2]),
labels=len([x for x in shapeIdentities if x == 3]))()
def mutate(self):
dx = self.p2.x - self.p1.x
dy = self.p2.y - self.p1.y
if dx == dy and dx < 8 and dx % 2 == 0 and random() < 0.5 and (
dx == 2 or (not NIPSPRIMITIVES())):
return Circle(
AbsolutePoint(self.p1.x + dx / 2, self.p1.y + dy / 2), dx / 2)
while True:
p1 = self.p1
p2 = self.p2
if random() > 0.5:
p1 = p1.mutate()
else:
p2 = p2.mutate()
if p1.x < p2.x and p1.y < p2.y:
return Triangle(p1, p2)
def sampleRadius():
if NIPSPRIMITIVES(): return 1
return choice(range(5)) + 1 if SNAPTOGRID else (1 + random() * 5)
(n, startingPoint, k) = arguments
# IMPORTANT!
# You *do not* want directories with an enormous number of files in them
# pack it all up into a directory that we will tar together later
os.system('mkdir %s/%d' % (outputName, startingPoint))
makeSyntheticData("%s/%d/%s" % (outputName, startingPoint, n),
generators[n],
k=k,
offset=startingPoint)
print "Generated %d training sequences into %s/%d" % (k, outputName,
startingPoint)
if __name__ == '__main__':
if not NIPSPRIMITIVES():
loadCharacters()
if 'continuous' in sys.argv:
setSnapToGrid(False)
outputName = 'syntheticContinuousTrainingData'
else:
# models imperfect grid alignment
setCoordinateNoise(0.2)
setRadiusNoise(0.1)
outputName = 'syntheticTrainingData'
if len(sys.argv) > 1:
totalNumberOfExamples = int(sys.argv[1])
else:
totalNumberOfExamples = 100000
