Module pyFINGER

Functions

[hide private]

_FingerIter(instr)

Finger(instr)

ABC(f1, f2)

tversky(f1, f2, alpha=1, beta=1)

FPabc(fp1, fp2)
Alternative implementation, unsure what the other one is actually calculating.

tverskyX(fp1, fp2, alpha=1.0, beta=1.0)
Alternative tversky implementation, this one seems consistent with what I've read.

Variables

[hide private]

_FingerTable = ['-', '0', '1', '2', '3', '4', '5', '6', '7', '...

_rFingerTable = [99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 9...

_BitsSetTable256 = [0, 1, 1, 2, 1, 2, 2, 3, 1, 2, 2, 3, 2, 3, ...

Function Details

[hide private]

FPabc(fp1, fp2)

Alternative implementation, unsure what the other one is actually calculating. Given two fingerprints, assumed to be bit vectors of equal size, based on definitions:

a = # of bits set in fp1 not set in fp2 b = # of bits set in fp2 not set in fp1 c = # of bits set in both fp1 and fp2

tverskyX(fp1, fp2, alpha=1.0, beta=1.0)

Alternative tversky implementation, this one seems consistent with what I've read. http://www.daylight.com/dayhtml/doc/theory/theory.finger.html#RTFToC87

Variables Details

[hide private]

_FingerTable

Value:

['-',
 '0',
 '1',
 '2',
 '3',
 '4',
 '5',
 '6',
...

_rFingerTable

Value:

[99,
 99,
 99,
 99,
 99,
 99,
 99,
 99,
...

_BitsSetTable256

Value:

[0,
 1,
 1,
 2,
 1,
 2,
 2,
 3,
...