#! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# graph_tool -- a general graph manipulation python module
#
# Copyright (C) 2006-2023 Tiago de Paula Peixoto <tiago@skewed.de>
#
# This program is free software; you can redistribute it and/or modify it under
# the terms of the GNU Lesser 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 Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
import scipy.special
from numpy import *
from .. import PropertyMap
from .. dl_import import dl_import
dl_import("from . import libgraph_tool_inference as libinference")
class DictState(dict):
"""Dictionary with (key,value) pairs accessible via attributes."""
def __init__(self, d):
self.update(d)
def __getattr__(self, attr):
return self[attr]
def __setattr__(self, attr, val):
self[attr] = val
def dmask(d, ks):
"""Copy dictionary ``d`` and remove key list ``ks``."""
d = d.copy()
for k in ks:
if k in d:
del d[k]
return d
def check_verbose(verbose):
if isinstance(verbose, tuple):
return verbose[0] != False
return verbose != False
def verbose_pad(verbose):
if isinstance(verbose, tuple):
return verbose_pad(verbose[1])
if verbose == True:
return ""
return verbose
def verbose_push(verbose, push):
if check_verbose(verbose):
if isinstance(verbose, tuple):
return (verbose[0] - 1, verbose_push(verbose[1], push))
if isinstance(verbose, bool):
return push
return verbose + push
return False
def lbinom(n, k):
"""Return log of binom(n, k)."""
return (scipy.special.gammaln(float(n + 1)) -
scipy.special.gammaln(float(n - k + 1)) -
scipy.special.gammaln(float(k + 1)))
def lbinom_careful(n, k):
return libinference.lbinom_careful(n, k)
def lbinom_fast(n, k):
return libinference.lbinom_fast(n, k)
def partition_entropy(B, N, nr=None):
if nr is None:
S = N * log(B) + log1p(-(1 - 1./B) ** N)
else:
S = lbinom(N - 1, B - 1)
S += (scipy.special.gammaln(N + 1) -
scipy.special.gammaln(nr + 1).sum())
return S
def pmap(prop, value_map):
"""Maps all the values of `prop` to the values given by `value_map` in-place
according to: ``prop[i] = value_map[prop[i]]``."""
if isinstance(prop, PropertyMap):
a = prop.fa
else:
a = prop
if len(a) == 0:
return
if isinstance(value_map, PropertyMap):
value_map = value_map.fa
if a.max() >= len(value_map):
raise ValueError("value_map is not large enough!" +
" max val: %s, map size: %s" % (a.max(),
len(value_map)))
libinference.vector_map(a, value_map)
if isinstance(prop, PropertyMap):
prop.fa = a
def reverse_map(prop, value_map):
"""Modify `value_map` such that the positions indexed by the values in `prop`
correspond to their index in `prop`."""
if isinstance(prop, PropertyMap):
prop = prop.fa
if isinstance(value_map, PropertyMap):
a = value_map.fa
else:
a = value_map
if prop.max() >= len(a):
raise ValueError("value map is not large enough!" +
" max val: %s, map size: %s" % (prop.max(), len(a)))
if prop.dtype != a.dtype:
prop = array(prop, dtype=a.dtype)
libinference.vector_rmap(prop, a)
if isinstance(value_map, PropertyMap):
value_map.fa = a
[docs]
def contiguous_map(prop):
"""Remap the values of ``prop`` in the contiguous range :math:`[0, N-1]`."""
prop = prop.copy()
if isinstance(prop, PropertyMap):
a = prop.fa
else:
a = prop
libinference.vector_contiguous_map(a)
if isinstance(prop, PropertyMap):
prop.fa = a
return prop
[docs]
def nested_contiguous_map(bs):
"""Remap the values of the nested partition ``bs`` in the contiguous range :math:`[0, N_l-1]` for each level :math:`l`."""
cs = [b for b in bs]
for i, b in enumerate(cs):
c = contiguous_map(b)
cs[i] = c
if i == len(cs) - 1:
break
nb = zeros(c.max() + 1, dtype=c.dtype)
for j, r in enumerate(c):
nb[r] = cs[i+1][b[j]]
cs[i+1] = nb
return cs
