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@ -5,6 +5,8 @@ from .trajectory import Trajectory |
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import numpy as np |
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import numpy as np |
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import pandas as pd |
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import pandas as pd |
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import re |
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import re |
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import os |
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import json |
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from numpy import random |
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from numpy import random |
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class TrajectoryGenerator(object): |
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class TrajectoryGenerator(object): |
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@ -32,6 +34,8 @@ class TrajectoryGenerator(object): |
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node_states_number = self._importer._df_variables.where(self._importer._df_variables["Name"] == v)["Value"], p_combs = p_combs, cims = v_cims) |
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node_states_number = self._importer._df_variables.where(self._importer._df_variables["Name"] == v)["Value"], p_combs = p_combs, cims = v_cims) |
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self._cims[v] = sof |
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self._cims[v] = sof |
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self._generated_trajectory = None |
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def CTBN_Sample(self, t_end = -1, max_tr = -1): |
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def CTBN_Sample(self, t_end = -1, max_tr = -1): |
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t = 0 |
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t = 0 |
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sigma = pd.DataFrame(columns = (["Time"] + self._vnames)) |
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sigma = pd.DataFrame(columns = (["Time"] + self._vnames)) |
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@ -40,10 +44,10 @@ class TrajectoryGenerator(object): |
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n_tr = 0 |
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n_tr = 0 |
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while True: |
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while True: |
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current_values = sigma.loc[len(sigma) - 1] |
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for i in range(0, time.size): |
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for i in range(0, time.size): |
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if np.isnan(time[i]): |
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if np.isnan(time[i]): |
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# Probability to transition from current state v_values[i] to (1 - v_values[i]) |
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current_values = sigma.loc[len(sigma) - 1] |
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cim = self._cims[self._vnames[i]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[i]]]), |
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cim = self._cims[self._vnames[i]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[i]]]), |
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[current_values.at[p] for p in self._parents[self._vnames[i]]])[0].cim |
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[current_values.at[p] for p in self._parents[self._vnames[i]]])[0].cim |
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param = -1 * cim[current_values.at[self._vnames[i]]][current_values.at[self._vnames[i]]] |
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param = -1 * cim[current_values.at[self._vnames[i]]][current_values.at[self._vnames[i]]] |
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@ -55,11 +59,11 @@ class TrajectoryGenerator(object): |
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t = time[next] |
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t = time[next] |
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if (max_tr != -1 and n_tr == max_tr) or (t_end != -1 and t >= t_end): |
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if (max_tr != -1 and n_tr == max_tr) or (t_end != -1 and t >= t_end): |
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""" columns = self._importer.build_list_of_samples_array(sigma) |
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self._generated_trajectory = sigma |
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columns[0] = pd.to_numeric(columns[0]) |
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return Trajectory(columns, len(self._vnames) + 1) """ |
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return sigma |
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return sigma |
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else: |
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else: |
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cim = self._cims[self._vnames[next]].filter_cims_with_mask(np.array([True for p in self._parents[self._vnames[next]]]), |
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[current_values.at[p] for p in self._parents[self._vnames[next]]])[0].cim |
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cim_row = np.array(cim[current_values.at[self._vnames[next]]]) |
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cim_row = np.array(cim[current_values.at[self._vnames[next]]]) |
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cim_row[current_values.at[self._vnames[next]]] = 0 |
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cim_row[current_values.at[self._vnames[next]]] = 0 |
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cim_row /= sum(cim_row) |
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cim_row /= sum(cim_row) |
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@ -74,3 +78,15 @@ class TrajectoryGenerator(object): |
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# undefine variable time |
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# undefine variable time |
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time[next] = np.NaN |
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time[next] = np.NaN |
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def out_json(self, filename): |
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data = { |
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"dyn.str": self._importer._raw_data[0]["dyn.str"], |
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"variables": self._importer._raw_data[0]["variables"], |
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"dyn.cims": self._importer._raw_data[0]["dyn.cims"], |
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"samples": [json.loads(self._generated_trajectory.to_json(orient="records"))] |
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} |
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path = os.getcwd() |
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with open(path + "/" + filename, "w") as json_file: |
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json.dump(data, json_file) |