Causal Graphical Models
A python library for building causal graphical models, closely following Daphne Koller's Coursera course on Probabilistic Graphical Models, and her 2009 book Probabilistic Graphical Models: Principles and Techniques. The source for this project is available here.
Installation
NumPy is the only dependency. Python version must be >= 3.7.
pip install cgm
Usage
import cgm
# Define all nodes
A = cgm.CG_Node('A', num_states=3)
B = cgm.CG_Node('B', 3)
C = cgm.CG_Node('C', 3)
D = cgm.CG_Node('D', 3)
# Specify all parents of nodes
cgm.CPD([B, A])
cgm.CPD([B, C])
cgm.CPD([D, A, B])
# Create causal graph
graph = cgm.CG([A, B, C, D])
print(graph)
# A ← []
# B ← [C]
# C ← []
# D ← [A, B]
Documentation
1 maintainer
Release files for cgm 0.0.10
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cgm-0.0.10-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 37.1 kB
Release files / cgm-0.0.10.tar.gz
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