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PyCTBN
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Transitions Improvement

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master
Pietro 4 years ago
parent 249c19aa63
commit df42c4f5c7
1 changed files with 8 additions and 3 deletions
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  1. 11
      PyCTBN/PyCTBN/structure_graph/trajectory_generator.py

11
PyCTBN/PyCTBN/structure_graph/trajectory_generator.py
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@ -25,7 +25,7 @@ class TrajectoryGenerator(object):
v_cims = [] v_cims = []
for comb in sampled_cims[v].keys(): for comb in sampled_cims[v].keys():
p_combs.append(np.array(re.findall(r"=(\d)", comb)).astype("int")) p_combs.append(np.array(re.findall(r"=(\d)", comb)).astype("int"))
cim = pd.DataFrame(sampled_cims[v][comb]).to_numpy() cim = pd.DataFrame(sampled_cims[v][comb]).to_numpy()
v_cims.append(ConditionalIntensityMatrix(cim = cim)) v_cims.append(ConditionalIntensityMatrix(cim = cim))
sof = SetOfCims(node_id = v, parents_states_number = [self._importer._df_variables.where(self._importer._df_variables["Name"] == p)["Value"] for p in self._parents[v]], sof = SetOfCims(node_id = v, parents_states_number = [self._importer._df_variables.where(self._importer._df_variables["Name"] == p)["Value"] for p in self._parents[v]],
@ -46,7 +46,7 @@ class TrajectoryGenerator(object):
current_values = sigma.loc[len(sigma) - 1] current_values = sigma.loc[len(sigma) - 1]
cim = self._cims[self._vnames[i]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[i]]]), cim = self._cims[self._vnames[i]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[i]]]),
[current_values.at[p] for p in self._parents[self._vnames[i]]])[0].cim [current_values.at[p] for p in self._parents[self._vnames[i]]])[0].cim
param = cim[current_values.at[self._vnames[i]]][1 - current_values.at[self._vnames[i]]] param = -1 * cim[current_values.at[self._vnames[i]]][current_values.at[self._vnames[i]]]
time[i] = t + random.exponential(scale = param) time[i] = t + random.exponential(scale = param)
@ -57,8 +57,13 @@ class TrajectoryGenerator(object):
if t >= t_end: if t >= t_end:
return sigma return sigma
else: else:
cim_row = np.array(cim[current_values.at[self._vnames[next]]])
cim_row[current_values.at[self._vnames[next]]] = 0
cim_row /= sum(cim_row)
rand_mult = np.random.multinomial(1, cim_row, size=1)
new_row = pd.DataFrame(sigma[-1:].values, columns = sigma.columns) new_row = pd.DataFrame(sigma[-1:].values, columns = sigma.columns)
new_row.loc[0].at[self._vnames[next]] = 1 - new_row.loc[0].at[self._vnames[next]] new_row.loc[0].at[self._vnames[next]] = np.where(rand_mult[0] == 1)[0][0]
new_row.loc[0].at["Time"] = round(t, 4) new_row.loc[0].at["Time"] = round(t, 4)
sigma = sigma.append(new_row, ignore_index = True) sigma = sigma.append(new_row, ignore_index = True)