def inference(self, x):
"""
x: 2d numpy array
"""
num = x.shape[0]
latentVars = self._model.getInternalVariables()
datacase = ContinuousDatacase.create(self.varNames)
datacase.synchronize(self._model)
ctp = NaturalCliqueTreePropagation(self._model)
out = [None]*len(latentVars)
datacase.putValues(x)
evidence = datacase.getEvidence()
ctp.use(evidence)
ctp.propagate()
for j in range(len(latentVars)):
latent = np.exp(ctp.getMarginal(latentVars[j]).logprob)
out[j] = latent
return out
datacase = ContinuousDatacase.create(varNames)
datacase.synchronize(net)
ctp = NaturalCliqueTreePropagation(net)
targetVar = None
for v in net._variables.keys():
if v.name == "z":
targetVar = v
break
print(targetVar)
y_pred = []
datacase.putValues(X)
evidence = datacase.getEvidence()
ctp.use(evidence)
ctp.propagate()
latent = np.exp(ctp.getMarginal(targetVar).logprob)
y_pred = np.argmax(latent, axis=1)
print(y_pred)
colors = np.array(
list(
islice(
cycle([
'#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628',
'#984ea3', '#999999', '#e41a1c', '#dede00'
]), int(max(y_pred) + 1))))
plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])
plt.show()
# set up evidence
datacase = ContinuousDatacase.create(varNames)
datacase.synchronize(net)
ctp = NaturalCliqueTreePropagation(net)
targetVar = None
for v in net._variables.keys():
if v.name == "z":
targetVar = v
break
print(targetVar)
y_pred = []
for i in range(len(X)):
datacase.putValues(X[i])
evidence = datacase.getEvidence()
ctp.use(evidence)
ctp.propagate()
latent = ctp.getMarginal(targetVar).parameter.prob
y_pred.append(np.argmax(latent))
print(y_pred)
colors = np.array(
list(
islice(
cycle([
'#377eb8', '#ff7f00', '#4daf4a', '#f781bf', '#a65628',
'#984ea3', '#999999', '#e41a1c', '#dede00'
]), int(max(y_pred) + 1))))
plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])
plt.show()