# -*- coding: utf-8 -*- import pysmile import pysmile_license import numpy as np import cpd # This class creates the network, class CollaborationNetwork: OUTCOMES = ["Bajo", "Medio", "Alto"] def __init__(self): help(pysmile) net = pysmile.Network() cg = self.create_cpt_node(net, "Colaborar_G", "Colaborar en X con Otros", self.OUTCOMES) pg = self.create_cpt_node(net, "Proponer_G", "Proponer sobre X a Otros", self.OUTCOMES) ag = self.create_cpt_node(net, "Aportar_G", "Aportar a X con Otros", self.OUTCOMES) ce = self.create_cpt_node(net, "Colaborar_E", "Colaborar en Proyecto con Otros", self.OUTCOMES) pe = self.create_cpt_node(net, "Proponer_E", "Proponer sobre Proyecto a Otros", self.OUTCOMES) ae = self.create_cpt_node(net, "Aportar_E", "Aportar a Proyecto con Otros", self.OUTCOMES) ec = self.create_cpt_node(net, "e_colaborar", "Evidencia de Colaborar en Proyecto con Otros", self.OUTCOMES) ep = self.create_cpt_node(net, "e_proponer", "Evidencia de Proponer sobre Proyecto a Otros", self.OUTCOMES) ea = self.create_cpt_node(net, "e_aportar", "Evidencia de Aportar a Proyecto con Otros", self.OUTCOMES) net.add_arc(cg, pg) net.add_arc(cg, ag) net.add_arc(cg, ce) net.add_arc(pg, pe) net.add_arc(ce, pe) net.add_arc(ag, ae) net.add_arc(ce, ae) net.add_arc(ce, ec) net.add_arc(pe, ep) net.add_arc(ae, ea) net.add_temporal_arc(cg,cg,1) net.add_temporal_arc(pg,pg,1) net.add_temporal_arc(ag,ag,1) net.add_temporal_arc(ce,ce,1) net.add_temporal_arc(pe,pe,1) net.add_temporal_arc(ae,ae,1) # Non temporal conditionals # we can also use node identifiers when creating arcs # net.add_arc("Colaborar_G", "Proponer_G"); t = self.stream_table(cpd.flatDist(3)) net.set_node_definition(cg, t) t = self.stream_table(cpd.incsub(2)) net.set_node_definition(pg, t) net.set_node_definition(ag, t) t = self.stream_table(cpd.genesp()) net.set_node_definition(ce, t) t = self.stream_table(cpd.join(cpd.genesp(),cpd.incsub(2))) net.set_node_definition(pe, t) net.set_node_definition(ae, t) t = self.stream_table(cpd.compevi()) net.set_node_definition(ec, t) net.set_node_definition(ep, t) net.set_node_definition(ea, t) # Conditionals that include temporal relations tpp = cpd.paspre() t = self.stream_table(tpp) net.set_node_temporal_definition(cg, 1, t) t = self.stream_table(cpd.join(cpd.incsub(2),tpp)) net.set_node_temporal_definition(pg, 1, t) net.set_node_temporal_definition(ag, 1, t) t = self.stream_table(cpd.join(cpd.genesp(),tpp)) net.set_node_temporal_definition(ce, 1, t) t = self.stream_table(cpd.join(cpd.genesp(),cpd.incsub(2),tpp)) net.set_node_temporal_definition(pe, 1, t) net.set_node_temporal_definition(ae, 1, t) net.write_file("colaborar_din.xdsl") print("Network written to colaborar_din.xdsl") self._network = net def get_network (self): return self._network def create_cpt_node(self, net, id, name, outcomes): handle = net.add_node() net.set_node_temporal_type(handle, pysmile.NodeTemporalType.PLATE) net.set_node_id(handle,id) net.set_node_name(handle, name) initial_outcome_count = net.get_outcome_count(handle) for i in range(0, initial_outcome_count): net.set_outcome_id(handle, i, outcomes[i]) for i in range(initial_outcome_count, len(outcomes)): net.add_outcome(handle, outcomes[i]) return handle # We asume a is a numpy array of columns. def stream_table(self,a): return a.flatten().tolist() # Recreate the numpy array from the list, assuming n rows. def recreate_table (l, n): z = list() temp = list() m = len(l) for i in range(m): temp.append(l[i]) if (i % n) == (n-1): z.append(temp) temp = list() return np.array(z) class InferenceOnNetwork: def __init__(self, net): print("Starting inferencing...") """ net.update_beliefs() print("Posteriors with no evidence set:") self.print_all_posteriors(net) """ self.change_evidence_and_update(net, 'e_proponer', 1, 'Bajo') self.change_evidence_and_update(net, 'e_aportar', 2, 'Bajo') self.change_evidence_and_update(net, 'e_proponer', 4, 'Medio') self.change_evidence_and_update(net, 'e_aportar', 5, 'Medio') self.change_evidence_and_update(net, 'e_colaborar', 7, 'Medio') self.change_evidence_and_update(net, 'e_proponer', 10, 'Alto') self.change_evidence_and_update(net, 'e_aportar', 11, 'Medio') self.change_evidence_and_update(net, 'e_colaborar', 14, 'Medio') net.set_slice_count(16) net.update_beliefs() print("Posteriors with evidence set:") self.print_all_posteriors(net) net.write_file("colaborar_din2.xdsl") print("Network written to colaborar_din2.xdsl") def print_posteriors(self, net, node_handle): node_id = net.get_node_id(node_handle) if net.is_evidence(node_handle): print(node_id + " has evidence set (" + net.get_outcome_id(node_handle, net.get_evidence(node_handle)) + ")") else : posteriors = net.get_node_value(node_handle) n = net.get_outcome_count(node_handle) for i in range(0, len(posteriors)): value = value = net.get_outcome_id(node_handle, i%n) p = posteriors[i] print("P(" + node_id + " = " + value + ") = " + str(p)) def print_all_posteriors(self, net): handles = net.get_all_nodes() for h in handles: self.print_posteriors(net, h) def change_evidence_and_update(self, net, node_id, n_time, outcome_id): if outcome_id is not None: net.set_temporal_evidence(node_id, n_time, outcome_id) else: net.clear_temporal_evidence(node_id, n_time) cn = CollaborationNetwork() InferenceOnNetwork(cn.get_network())