Python Version of BRML toolbox for Bayesian Reasoning and Machine Learning
BRMLtoolbox in Python 3.7. My goal with this repository is to:
Variables
When initializing a potential we can pass variables as arguments in different ways: * We can pass a scalar value (int or float) when we only have 1 variable. (We could also pass a list or a numpy array with only one value)
import numpy as np
from brml.array import Array
pot = Array(variables=1)
pot2 = Array(variables=1.0)
pot3 = Array(variables=[1])
pot4 = Array(variables=np.array([1]))
pot5 = Array(variables=np.array([[1]]))
* We can pass a list or a numpy.array when we have multiple variables.
import numpy as np
from brml.array import Array
pot = Array(variables=[1, 2])
pot2 = Array(variables=np.array([1, 2]))
pot3 = Array(variables=np.array([[1, 2]]))
pot4 = Array(variables=np.array([[1], [2]]))
All of these calls are equivalent among them because they are all converted to
a 1D numpy.array (notice that in MATLAB, because 1D arrays don't exist, this
is different).
It might be good to discuss whether it is better to convert all of these to 1D
numpy.array or to a 2D array with shape (1, n) where n is the number of
variables.
It is worth mentioning that you don't need to pass variables on initialization.
You can initialize an array without variables and without table, and add the later.
For example:
import numpy as np
from brml.array import Array
pot = Array()
pot.set_variables(np.array([1, 2]))
Notice, however, that you CANNOT initialize variables before initializing the table.
That is, you cannot initialize an empty array pot = Array() and then call
pot.set_table(np.array([0.4, 0.6])) because this will throw an error. You must
first use set_variables method, and after use the set_table method. If, instead,
you decide to pass variables and table on initialization, then everything will be fine.
Go to check/potential_variables.py and you'll find a test to check
that the statements above are correct!
The BRMLtoolbox is a MATLAB toolbox written by David Barber, Reader for the Computational Statistics and Machine Learning MSc at UCL. The toolbox should be used together with the book Bayesian Reasoning and Machine Learning. It is a set of tools to help readers see how mathematical models translate into actual code.
I am currenlty trying to write an introduction to Probability here in my Proofwiki account, although it is just a draft for now.
Please feel free to contribute to this project! There are already a few incomplete implementations of the code, hopefully this version will be more up-to-date and efficient with your help!
As I said earlier BRMLtoolbox comes with Bayesian Reasoning and Machine Learning book.
@BOOK{barberBRML2012,
author = {Barber, D.},
title= {{Bayesian Reasoning and Machine Learning}},
publisher = {{Cambridge University Press}},
year = 2012}