WIP: optimisation algo.
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@ -353,10 +353,10 @@ class Conditions:
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if(child.tag == "condition"):
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self.__conditions.append(Condition(child))
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def get_condition(self, i):
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def get_one(self, i):
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return self.__conditions[i]
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def get_conditions_array(self):
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def get_all(self):
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return self.__conditions
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@ -397,10 +397,10 @@ class Transitions:
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if(child.tag == "transition"):
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self.__transitions.append(Transition(child))
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def get_transition(self, i):
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def get_one(self, i):
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return self.__transitions[i]
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def get_transitions_array(self):
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def get_all(self):
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return self.__transitions
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## Model
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@ -6,24 +6,6 @@ import model
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# 1) node_id <> pas de doublons
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# 2) tout node doit avoir un node_parent_id
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conditions_set = set()
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conditions_list = []
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transitions_set = set()
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for i in range(0, len(model.testmodel.get_conditions().get_conditions_array())):
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condition = model.testmodel.get_conditions().get_condition(i)
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conditions_set.add((condition.get_node_self_id(),
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condition.get_node_parent_id(),
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condition.get_coord("x"), # TODO y,z,...
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condition.get_site(),
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condition.get_weight()))
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for i in range(0,
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len(model.testmodel.get_transitions().get_transitions_array())):
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transition = model.testmodel.get_transitions().get_transition(i)
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transitions_set.add((transition.get_id(), transition.get_parent()))
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class CondTreeNode(NodeMixin):
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def __init__(self, id, x, site, weight, parent_node_id):
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@ -36,8 +18,8 @@ class CondTreeNode(NodeMixin):
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self.weight = weight
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def add_parent_node(self, parent_node):
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print('orphan node ', self.id, ' is adopted by node ', parent_node.id)
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self.parent = parent_node
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# print('orphan node ', self.id, ' is adopted by node ', parent_node.id)
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class TransTreeNode(NodeMixin):
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@ -52,28 +34,43 @@ class TransTreeNode(NodeMixin):
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self.parent = parent_node
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for i in conditions_set: # sorted(conditions_set, key=i.id):
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condition = CondTreeNode(i[0], i[2], i[3], i[4], i[1]) # TODO y,z,...
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conditions_list.append(condition)
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conditions_set = set()
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transitions_set = set()
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root = 0
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for i in conditions_list:
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if (i.parent is None):
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for j in conditions_list:
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if (i.parent_node_id == j.id):
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i.add_parent_node(j)
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else:
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root = j
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for i in range(0, len(model.testmodel.get_conditions().get_all())):
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condition = model.testmodel.get_conditions().get_one(i)
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conditions_set.add(CondTreeNode(
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condition.get_node_self_id(),
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condition.get_node_parent_id(),
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condition.get_coord("x"),
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condition.get_site(),
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condition.get_weight()))
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for i in range(0, len(model.testmodel.get_transitions().get_all())):
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transition = model.testmodel.get_transitions().get_one(i)
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transitions_set.add(TransTreeNode(
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transition.get_id(),
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transition.get_parent()))
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# 0 1 2 3 4
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# <condition site="1" weight="0" node_id="3" parent="2" x="2"/>
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for i in conditions_set:
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if (i.parent_node_id == 0):
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root = i
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for i in conditions_set:
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for j in conditions_set:
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if ((i != j) & (i.parent_node_id == j.id)):
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i.add_parent_node(j)
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for condition_node in LevelOrderIter(root):
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if condition_node.is_leaf:
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for i in transitions_set:
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transition_node = TransTreeNode(i[1], i[0])
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if (str(condition_node.id) == i[1]):
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for transition_node in transitions_set:
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if (str(condition_node.id) == transition_node.parent):
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transition_node.add_parent_leaf_node(condition_node)
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for pre, fill, node in RenderTree(root):
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treestr = u"%s%s" % (pre, node.id)
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if (type(node) == CondTreeNode):
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@ -84,4 +81,5 @@ for pre, fill, node in RenderTree(root):
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else:
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print('] > ', node.children[0].id, end='\n')
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print('\nNext step: single rule extraction from tree. 😃️')
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# print([node.id for node in LevelOrderIter(root) if node.is_leaf])
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