/usr/share/pyshared/mlpy/_canberra.py is in python-mlpy 2.2.0~dfsg1-2.
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## Canberra
## This code is written by Davide Albanese, <albanese@fbk.eu>.
## (C) 2009 Fondazione Bruno Kessler - Via Santa Croce 77, 38100 Trento, ITALY.
## This program is free software: you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation, either version 3 of the License, or
## (at your option) any later version.
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
## You should have received a copy of the GNU General Public License
## along with this program. If not, see <http://www.gnu.org/licenses/>.
__all__ = ['canberra', 'canberraq', 'normalizer']
from numpy import *
import canberracore
def mod(lists, modules):
"""Arrange 'lists'
"""
ret = lists.copy()
for m in modules:
tmp = sort(lists[:, m])
ret[:, m] = tmp
return ret
def canberra(lists, k, dist=False, modules=None):
"""Compute mean Canberra distance indicator on top-k sublists.
Input
* *lists* - [2D numpy array integer] position lists
Positions must be in [0, #elems-1]
* *k* - [integer] top-k sublists
* *modules* - [list] modules (list of group indicies)
* *dist* - [bool] return partial distances (True or False)
Output
* *cd* - [float] canberra distance
* *i1* - [1D numpy array integer] index 1 (if dist == True)
* *i2* - [1D numpy array integer] index 2 (if dist == True)
* *pd* - [1D numpy array float] partial distances for index1 and index2 (if dist == True)
>>> from numpy import *
>>> from mlpy import *
>>> lists = array([[2,4,1,3,0], # first positions list
... [3,4,1,2,0], # second positions list
... [2,4,3,0,1], # third positions list
... [0,1,4,2,3]]) # fourth positions list
>>> canberra(lists, 3)
1.0861983059292479
"""
if modules != None:
newlists = mod(lists, modules)
else:
newlists = lists
return canberracore.canberra(newlists, k, dist=dist)
def canberraq(lists, complete=True, normalize=False, dist=False):
"""Compute mean Canberra distance indicator on generic lists.
Input
* *lists* - [2D numpy array integer] position lists
Positions must be in [-1, #elems-1],
where -1 indicates features not present in the list
* *complete* - [bool] complete
* *normalize* - [bool] normalize
* *dist* - [bool] return partial distances (True or False)
Output
* *cd* - [float] canberra distance
* *i1* - [1D numpy array integer] index 1 (if dist == True)
* *i2* - [1D numpy array integer] index 2 (if dist == True)
* *pd* - [1D numpy array float] partial distances for index1 and index2 (if dist == True)
>>> from numpy import *
>>> from mlpy import *
>>> lists = array([[2,-1,1,-1,0], # first positions list
... [3,4,1,2,0], # second positions list
... [2,-1,3,0,1], # third positions list
... [0,1,4,2,3]]) # fourth positions list
>>> canberraq(lists)
1.0628570368721744
"""
return canberracore.canberraq(lists, complete, normalize, dist)
def normalizer(lists):
"""Compute the average length of the partial lists (nm) and the corresponding
normalizing factor (nf) given by 1 - a / b where a is the exact value computed
on the average length and b is the exact value computed on the whole set of
features.
Inputs
* *lists* - [2D numpy array integer] position lists
Positions must be in [-1, #elems-1],
where -1 indicates features not present in the list
Output
* *(nm, nf)* - (float, float)
"""
return canberracore.normalizer(lists)
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