Python implementation of the matrix information measurement examples from the Stats StackExchange answer for Measuring entropy/ information/ patterns of a 2d binary matrix which was originally written by William Huber using Mathematica at http://stats.stackexchange.com/a/17556/43909
Running the program:
python main.py
Output:
---------
Matrix #0
---------
Neighborhood size = 1
[[ 0. 1. 1. 1. 0.]
[ 1. 1. 0. 1. 1.]
[ 1. 0. 1. 0. 0.]
[ 0. 1. 1. 1. 1.]
[ 1. 0. 1. 0. 0.]]
Neighborhood size = 2
[[ 0.75 0.75 0.75 0.75]
[ 0.75 0.5 0.5 0.5 ]
[ 0.5 0.75 0.75 0.5 ]
[ 0.5 0.75 0.75 0.5 ]]
Neighborhood size = 3
[[ 0.66666667 0.66666667 0.55555556]
[ 0.66666667 0.66666667 0.66666667]
[ 0.66666667 0.55555556 0.55555556]]
Neighborhood size = 4
[[ 0.6875 0.6875]
[ 0.625 0.5625]]
Profile:
[0.9709505944546686, 0.9886994082884974, 0.9182958340544896, 1.5]
---------
Matrix #1
---------
Neighborhood size = 1
[[ 0. 0. 1. 1. 0.]
[ 0. 0. 1. 1. 0.]
[ 1. 1. 1. 1. 1.]
[ 0. 0. 1. 1. 0.]
[ 1. 0. 1. 0. 0.]]
Neighborhood size = 2
[[ 0. 0.5 1. 0.5 ]
[ 0.5 0.75 1. 0.75]
[ 0.5 0.75 1. 0.75]
[ 0.25 0.5 0.75 0.25]]
Neighborhood size = 3
[[ 0.55555556 0.77777778 0.77777778]
[ 0.55555556 0.77777778 0.77777778]
[ 0.66666667 0.66666667 0.66666667]]
Neighborhood size = 4
[[ 0.625 0.625 ]
[ 0.625 0.5625]]
Profile:
[0.9988455359952018, 2.126614471810182, 1.5304930567574824, 0.8112781244591328]
---------
Matrix #2
---------
Neighborhood size = 1
[[ 1. 1. 1. 1. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 0. 0. 0. 1.]
[ 1. 1. 1. 1. 1.]]
Neighborhood size = 2
[[ 0.75 0.5 0.5 0.75]
[ 0.5 0. 0. 0.5 ]
[ 0.5 0. 0. 0.5 ]
[ 0.75 0.5 0.5 0.75]]
Neighborhood size = 3
[[ 0.55555556 0.33333333 0.55555556]
[ 0.33333333 0. 0.33333333]
[ 0.55555556 0.33333333 0.55555556]]
Neighborhood size = 4
[[ 0.4375 0.4375]
[ 0.4375 0.4375]]
Profile:
[0.9426831892554922, 1.5, 1.3921472236645345, 0.0]
---------
Matrix #3
---------
Neighborhood size = 1
[[ 0. 1. 0. 1. 0.]
[ 1. 0. 1. 0. 1.]
[ 0. 1. 0. 1. 0.]
[ 1. 0. 1. 0. 1.]
[ 0. 1. 0. 1. 0.]]
Neighborhood size = 2
[[ 0.5 0.5 0.5 0.5]
[ 0.5 0.5 0.5 0.5]
[ 0.5 0.5 0.5 0.5]
[ 0.5 0.5 0.5 0.5]]
Neighborhood size = 3
[[ 0.44444444 0.55555556 0.44444444]
[ 0.55555556 0.44444444 0.55555556]
[ 0.44444444 0.55555556 0.44444444]]
Neighborhood size = 4
[[ 0.5 0.5]
[ 0.5 0.5]]
Profile:
[0.9988455359952018, 0.0, 0.9910760598382222, 0.0]
---------
Matrix #4
---------
Neighborhood size = 1
[[ 0. 0. 0. 0. 0.]
[ 1. 1. 1. 1. 1.]
[ 1. 1. 1. 1. 1.]
[ 1. 1. 1. 1. 1.]
[ 0. 0. 0. 0. 0.]]
Neighborhood size = 2
[[ 0.5 0.5 0.5 0.5]
[ 1. 1. 1. 1. ]
[ 1. 1. 1. 1. ]
[ 0.5 0.5 0.5 0.5]]
Neighborhood size = 3
[[ 0.66666667 0.66666667 0.66666667]
[ 1. 1. 1. ]
[ 0.66666667 0.66666667 0.66666667]]
Neighborhood size = 4
[[ 0.75 0.75]
[ 0.75 0.75]]
Profile:
[0.9709505944546686, 1.0, 0.9182958340544896, 0.0]
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