def test_observed(self):
"""
Test observed categorical nodes
"""
# Single observation
X = Categorical([0.7, 0.2, 0.1])
X.observe(2)
u = X._message_to_child()
self.assertAllClose(u[0], [0, 0, 1])
# One plate axis
X = Categorical([0.7, 0.2, 0.1], plates=(2, ))
X.observe([2, 1])
u = X._message_to_child()
self.assertAllClose(u[0], [[0, 0, 1], [0, 1, 0]])
# Several plate axes
X = Categorical([0.7, 0.1, 0.1, 0.1], plates=(
2,
3,
))
X.observe([[2, 1, 1], [0, 2, 3]])
u = X._message_to_child()
self.assertAllClose(u[0], [[[0, 0, 1, 0], [0, 1, 0, 0], [0, 1, 0, 0]],
[[1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]])
# Check invalid observations
X = Categorical([0.7, 0.2, 0.1])
self.assertRaises(ValueError, X.observe, -1)
self.assertRaises(ValueError, X.observe, 3)
self.assertRaises(ValueError, X.observe, 1.5)
pass
def test_observed(self):
"""
Test observed categorical nodes
"""
# Single observation
X = Categorical([0.7,0.2,0.1])
X.observe(2)
u = X._message_to_child()
self.assertAllClose(u[0],
[0,0,1])
# One plate axis
X = Categorical([0.7,0.2,0.1], plates=(2,))
X.observe([2,1])
u = X._message_to_child()
self.assertAllClose(u[0],
[[0,0,1],
[0,1,0]])
# Several plate axes
X = Categorical([0.7,0.1,0.1,0.1], plates=(2,3,))
X.observe([[2,1,1],
[0,2,3]])
u = X._message_to_child()
self.assertAllClose(u[0],
[ [[0,0,1,0],
[0,1,0,0],
[0,1,0,0]],
[[1,0,0,0],
[0,0,1,0],
[0,0,0,1]] ])
# Check invalid observations
X = Categorical([0.7,0.2,0.1])
self.assertRaises(ValueError,
X.observe,
-1)
self.assertRaises(ValueError,
X.observe,
3)
self.assertRaises(ValueError,
X.observe,
1.5)
pass
lung = Mixture(smoking, Categorical, [[0.98, 0.02], [0.25, 0.75]])
bronchitis = Mixture(smoking, Categorical, [[0.97, 0.03], [0.08, 0.92]])
xray = Mixture(tuberculosis, Mixture, lung, Categorical,
_or([0.96, 0.04], [0.115, 0.885]))
dyspnea = Mixture(
bronchitis, Mixture, tuberculosis, Mixture, lung, Categorical,
[_or([0.6, 0.4], [0.18, 0.82]),
_or([0.11, 0.89], [0.04, 0.96])])
# Mark observations
tuberculosis.observe(TRUE)
smoking.observe(FALSE)
bronchitis.observe(
TRUE) # not a "chance" observation as in the original example
# Run inference
Q = VB(dyspnea, xray, bronchitis, lung, smoking, tuberculosis, asia)
Q.update(repeat=100)
# Show results
print("P(asia):", asia.get_moments()[0][TRUE])
print("P(tuberculosis):", tuberculosis.get_moments()[0][TRUE])
print("P(smoking):", smoking.get_moments()[0][TRUE])
print("P(lung):", lung.get_moments()[0][TRUE])
print("P(bronchitis):", bronchitis.get_moments()[0][TRUE])
print("P(xray):", xray.get_moments()[0][TRUE])
print("P(dyspnea):", dyspnea.get_moments()[0][TRUE])
])
"""
data-->[[0, 0, 0, 1, 3, 0, 0], [0, 1, 0, 1, 3, 0, 0], [1, 0, 1, 0, 2, 1, 0],
[4, 0, 0, 1, 3, 2, 1],[3, 1, 0, 0, 0, 2, 1], [2, 0, 0, 1, 1, 0, 0], [4, 0, 0, 0, 2, 0, 0],
[0, 0, 0, 1, 3, 0, 0],[3, 1, 0, 0, 0, 2, 1], [1, 1, 1, 0, 0, 2, 0], [4, 1, 1, 1, 2, 0, 0]]
"""
data = np.array(data)
N = len(data)
print(N)
p_age = Dirichlet(
1.0 *
np.ones(5)) #used to classify text in a document to a particular topic.
age = Categorical(
p_age, plates=(N, )) #a sequence of unique values and no missing values
age.observe(data[:, 0])
p_gender = Dirichlet(1.0 * np.ones(2))
gender = Categorical(p_gender, plates=(N, ))
gender.observe(data[:, 1])
p_familyhistory = Dirichlet(1.0 * np.ones(2))
familyhistory = Categorical(p_familyhistory, plates=(N, ))
familyhistory.observe(data[:, 2])
p_diet = Dirichlet(1.0 * np.ones(3))
diet = Categorical(p_diet, plates=(N, ))
diet.observe(data[:, 3])
p_lifestyle = Dirichlet(1.0 * np.ones(4))
lifestyle = Categorical(p_lifestyle, plates=(N, ))
smoking = Categorical([0.5, 0.5])
lung = Mixture(smoking, Categorical, [[0.98, 0.02], [0.25, 0.75]])
bronchitis = Mixture(smoking, Categorical, [[0.97, 0.03], [0.08, 0.92]])
xray = Mixture(tuberculosis, Mixture, lung, Categorical,
_or([0.96, 0.04], [0.115, 0.885]))
dyspnea = Mixture(bronchitis, Mixture, tuberculosis, Mixture, lung, Categorical,
[_or([0.6, 0.4], [0.18, 0.82]),
_or([0.11, 0.89], [0.04, 0.96])])
# Mark observations
tuberculosis.observe(TRUE)
smoking.observe(FALSE)
bronchitis.observe(TRUE) # not a "chance" observation as in the original example
# Run inference
Q = VB(dyspnea, xray, bronchitis, lung, smoking, tuberculosis, asia)
Q.update(repeat=100)
# Show results
print("P(asia):", asia.get_moments()[0][TRUE])
print("P(tuberculosis):", tuberculosis.get_moments()[0][TRUE])
print("P(smoking):", smoking.get_moments()[0][TRUE])
print("P(lung):", lung.get_moments()[0][TRUE])
print("P(bronchitis):", bronchitis.get_moments()[0][TRUE])
print("P(xray):", xray.get_moments()[0][TRUE])
print("P(dyspnea):", dyspnea.get_moments()[0][TRUE])
A = Categorical([0.5, 0.5])
T = Mixture(A, Categorical, [[0.99, 0.01], [0.8, 0.2]])
S = Categorical([0.5, 0.5])
L = Mixture(S, Categorical, [[0.98, 0.02], [0.75, 0.25]])
B = Mixture(S, Categorical, [[0.97, 0.03], [0.70, 0.30]])
X = Mixture(T, Mixture, L, Categorical, _or([0.96, 0.04], [0.115, 0.885]))
D = Mixture(B, Mixture, X, Categorical, _or([0.115, 0.885], [0.04, 0.96]))
T.observe(TRUE)
S.observe(FALSE)
B.observe(TRUE)
Q = VB(A, T, S, L, B, X, D)
Q.update(repeat=100)
print("P(asia): ", A.get_moments()[0][TRUE])
print("P(tuberculosis): ", T.get_moments()[0][TRUE])
print("P(smoking): ", S.get_moments()[0][TRUE])
print("P(lung): ", L.get_moments()[0][TRUE])
print("P(bronchitis): ", B.get_moments()[0][TRUE])
print("P(xray): ", X.get_moments()[0][TRUE])
print("P(dyspnea): ", D.get_moments()[0][TRUE])
O_n = Gaussian(mu, lambda_, name=f"O_{n}")
obs_nodes[n] = O_n
X.observe(o_n)
for action in actions:
trial, agent, a_n, n = action
if a_n < 0: #action reset
continue
if n in action_nodes:
A = action_nodes[n]
else:
category_prob = Dirichlet(1e-3 * np.ones(A_D),
name='category_prob') #FIXME: Unconfirmed!
A = Categorical(category_prob)
action_nodes[n] = A
A.observe(a_n)
# In[139]:
action_nodes[0].__dict__
# In[ ]:
Dirichlet(1e-3 * np.ones(A_D))
# In[120]:
np.prod(env.action_space.shape)
# In[102]:
