def experiment(alpha=alpha_default,
sd0=sd0_default,
sd=sd_default,
N=N_default,
distance=distance_default):
np.random.seed(1)
_model = WorldModel(CRP(alpha), Gauss(sigma_0=sd0))
_animal = Animal(worldModel=_model)
experiences = np.nan * np.ones((N * nPresentations * 2))
partition1 = np.array([])
partition2 = np.array([])
partitionN = np.array([])
lps = np.nan * np.empty((N, 3))
posts = np.nan * np.empty((N * nPresentations * 2, 3))
similarity = np.nan * np.empty((N, 1))
for i in range(N):
c1 = stats.norm(loc=np.array([-distance / 2]),
scale=sd * np.ones((1, K)))
c2 = stats.norm(loc=np.array([+distance / 2]),
scale=sd * np.ones((1, K)))
for _i in range(nPresentations):
experience1 = c1.rvs()
experiences[i * nPresentations * 2 + _i * 2] = experience1
post1 = _animal.experience(experience1)
if i + _i == 0:
partition1 = np.array([[0]])
partition2 = np.array([[0]])
partitionN = np.array([[0]])
else:
partition1 = np.vstack((partition1, 0))
partition2 = np.vstack((partition2, 0))
partitionN = np.vstack(
(partitionN, 2 * i * nPresentations + _i))
if i > 0:
posts[i * nPresentations * 2 + _i, :] = post1.squeeze()
_animal.clusterAssignments = partition2
for _i in range(nPresentations):
experience2 = c2.rvs()
experiences[i * nPresentations * 2 + _i * 2 + 1] = experience2
post2 = _animal.experience(experience2)
partition1 = np.vstack((partition1, 0))
partition2 = np.vstack((partition2, 1))
partitionN = np.vstack(
(partitionN, (2 * i + 1) * nPresentations + _i))
if i > 0:
posts[i * nPresentations * 2 + nPresentations +
_i, :] = post2.squeeze()
_animal.clusterAssignments = partition2
#TODO: look over this for what to do about partitions2activity
ret = partitions2activity(_animal, experience1,
[partition1, partition2],
[zeta0, zeta1A, zeta1B])
activity1 = ret[0]
tmp_lps = ret[1]
ret = partitions2activity(_animal, experience2,
[partition1, partition2],
[zeta0, zeta1A, zeta1B])
activity2 = ret[0]
tmp_lps_2 = ret[1]
np.testing.assert_array_equal(tmp_lps, tmp_lps_2)
lps[i, 0:2] = tmp_lps
lps[i, 0] = _animal.partition_prob(partition1)
lps[i, 1] = _animal.partition_prob(partition2)
lps[i, 2] = _animal.partition_prob(partitionN)
similarity[i] = np.corrcoef(activity1, activity2)[0, 1]
return similarity, lps, _animal, partition1, partition2, c1, c2, experiences, posts
#%%
np.random.seed(params['seed'])
sd = 0.2
c1 = stats.norm(loc=np.zeros((1, 4)), scale=sd * np.ones((1, 4)))
alpha = params['alpha']
worldModel = WorldModel(CRP(alpha), Gauss())
animal1 = Animal(worldModel=worldModel)
nExperiences = 20
for i in range(nExperiences):
animal1.experience(c1.rvs())
animal1.clusterAssignments = np.zeros((nExperiences, 1))
distance_default = 1 # distance between different features
a = 0
b = distance_default
post = np.vstack((animal1.belief(np.array([a, a, a, a])).transpose(),
animal1.belief(np.array([b, a, a, a])).transpose(),
animal1.belief(np.array([b, a, a, b])).transpose(),
animal1.belief(np.array([b, b, b, b])).transpose()))
#%%
figInd = 0
figInd += 1
f = plt.figure(figInd)
f.clear()
bar_width = 0.2