giddy.markov.
kullback
(F)[source]¶Kullback information based test of Markov Homogeneity.
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Notes
Based on [KKK62]. Example below is taken from Table 9.2 .
Examples
>>> import numpy as np
>>> from giddy.markov import kullback
>>> s1 = np.array([
... [ 22, 11, 24, 2, 2, 7],
... [ 5, 23, 15, 3, 42, 6],
... [ 4, 21, 190, 25, 20, 34],
... [0, 2, 14, 56, 14, 28],
... [32, 15, 20, 10, 56, 14],
... [5, 22, 31, 18, 13, 134]
... ])
>>> s2 = np.array([
... [3, 6, 9, 3, 0, 8],
... [1, 9, 3, 12, 27, 5],
... [2, 9, 208, 32, 5, 18],
... [0, 14, 32, 108, 40, 40],
... [22, 14, 9, 26, 224, 14],
... [1, 5, 13, 53, 13, 116]
... ])
>>>
>>> F = np.array([s1, s2])
>>> res = kullback(F)
>>> "%8.3f"%res['Conditional homogeneity']
' 160.961'
>>> "%d"%res['Conditional homogeneity dof']
'30'
>>> "%3.1f"%res['Conditional homogeneity pvalue']
'0.0'