BDT
===
This is a simple library to implement Binary decision Trees.
In computer science, a binary decision diagram (BDD) or branching
program is a data structure that is used to represent a Boolean
function. On a more abstract level, BDDs can be considered as a
compressed representation of sets or relations. Unlike other compressed
representations, operations are performed directly on the compressed
representation, i.e. without decompression.
A simple example of this would be the following tree: |alt text|
Each node has a pair of childs, one child is the one associated to its
father when the function inside the father returns a true value, and the
other is associated when the fathers function returns false.
Use
---
You can create the nodes manually as python objects like is shown in
this example:
.. code:: python
from bdt.tree import BDT
from bdt.node import Node
true_node = Node(
'True Node'
)
false_node = Node(
'False Node'
)
head_node = Node(
'Head Node',
lambda var: var < 10,
true_child=true_node,
false_child=false_node
)
tree = BDT(
head_node
)
Other option is to create the tree by passing a python dictionary:
.. code:: python
from bdt.tools import tree_from_dict
tree_dict = {
'head': 'Head Node',
'variables': [
'withd',
'height'
],
'nodes': [
{
'name': 'Head Node',
'function': 'withd * height < 50',
'true_child': 'True Node',
'false_child': 'False Node'
},
{
'name': 'True Node',
'function': 'withd * height < 25',
'true_child': 'True True Node',
'false_child': 'True False Node'
},
{
'name': 'False Node',
'function': 'withd * height < 100',
'true_child': 'False True Node',
'false_child': 'False False Node'
},
{
'name': 'True True Node',
'function': 'None',
'true_child': 'None',
'false_child': 'None'
},
{
'name': 'True False Node',
'function': 'None',
'true_child': 'None',
'false_child': 'None',
},
{
'name': 'False True Node',
'function': 'None',
'true_child': 'None',
'false_child': 'None'
},
{
'name': 'False False Node',
'function': 'None',
'true_child': 'None',
'false_child': 'None'
},
]
}
tree = tree_from_dict(tree_dict)
And the final form would be to load it from a JSON file the matches the
previous dictionary and load it by using:
.. code:: python
import json
from bdt.tools import tree_from_json
json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)
And the final form would be to load it from a JSON file the matches the
previous dictionary and load it by using:
.. code:: python
import json
from bdt.tools import tree_from_json
json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)
To traverse the created tree yo can makeit like this:
.. code:: python
import json
from bdt.tools import tree_from_json
json_data = open('{PATH_TO_FILE/file.json}', 'r')
tree = tree_from_json(json_data)
tree.set_parameters({
'withd': 25,
'height': 25
})
for node in tree:
print node.name
The set\_parameters function let you initialize the values needed to run
the boolean functions inside each node.
.. |alt text| image:: https://upload.wikimedia.org/wikipedia/commons/9/91/BDD.png
