bump doxygen to latest version
This commit is contained in:
Laurent Perron
@@ -3,13 +3,13 @@
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.6.2" />
|
||||
<meta name="generator" content="pdoc 0.6.3" />
|
||||
<title>pywrapknapsack_solver API documentation</title>
|
||||
<meta name="description" content="" />
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<style type="text/css">
|
||||
@@ -28,17 +28,17 @@ a:link { color: #46641e; text-decoration: none}
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python"># This file was automatically generated by SWIG (http://www.swig.org).
|
||||
# Version 4.0.0
|
||||
# Version 4.0.1
|
||||
#
|
||||
# Do not make changes to this file unless you know what you are doing--modify
|
||||
# the SWIG interface file instead.
|
||||
|
||||
from sys import version_info as _swig_python_version_info
|
||||
if _swig_python_version_info < (2, 7, 0):
|
||||
raise RuntimeError('Python 2.7 or later required')
|
||||
raise RuntimeError("Python 2.7 or later required")
|
||||
|
||||
# Import the low-level C/C++ module
|
||||
if __package__ or '.' in __name__:
|
||||
if __package__ or "." in __name__:
|
||||
from . import _pywrapknapsack_solver
|
||||
else:
|
||||
import _pywrapknapsack_solver
|
||||
@@ -48,35 +48,6 @@ try:
|
||||
except ImportError:
|
||||
import __builtin__
|
||||
|
||||
def _swig_setattr_nondynamic(self, class_type, name, value, static=1):
|
||||
if name == "thisown":
|
||||
return self.this.own(value)
|
||||
if name == "this":
|
||||
if type(value).__name__ == 'SwigPyObject':
|
||||
self.__dict__[name] = value
|
||||
return
|
||||
method = class_type.__swig_setmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self, value)
|
||||
if not static:
|
||||
object.__setattr__(self, name, value)
|
||||
else:
|
||||
raise AttributeError("You cannot add attributes to %s" % self)
|
||||
|
||||
|
||||
def _swig_setattr(self, class_type, name, value):
|
||||
return _swig_setattr_nondynamic(self, class_type, name, value, 0)
|
||||
|
||||
|
||||
def _swig_getattr(self, class_type, name):
|
||||
if name == "thisown":
|
||||
return self.this.own()
|
||||
method = class_type.__swig_getmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self)
|
||||
raise AttributeError("'%s' object has no attribute '%s'" % (class_type.__name__, name))
|
||||
|
||||
|
||||
def _swig_repr(self):
|
||||
try:
|
||||
strthis = "proxy of " + self.this.__repr__()
|
||||
@@ -120,180 +91,31 @@ class _SwigNonDynamicMeta(type):
|
||||
|
||||
|
||||
class KnapsackSolver(object):
|
||||
r"""
|
||||
This library solves knapsack problems.
|
||||
|
||||
Problems the library solves include:
|
||||
- 0-1 knapsack problems,
|
||||
- Multi-dimensional knapsack problems,
|
||||
|
||||
Given n items, each with a profit and a weight, given a knapsack of
|
||||
capacity c, the goal is to find a subset of items which fits inside c
|
||||
and maximizes the total profit.
|
||||
The knapsack problem can easily be extended from 1 to d dimensions.
|
||||
As an example, this can be useful to constrain the maximum number of
|
||||
items inside the knapsack.
|
||||
Without loss of generality, profits and weights are assumed to be positive.
|
||||
|
||||
From a mathematical point of view, the multi-dimensional knapsack problem
|
||||
can be modeled by d linear constraints:
|
||||
|
||||
ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
||||
where item_i is a 0-1 integer variable.
|
||||
|
||||
Then the goal is to maximize:
|
||||
|
||||
Sum(i:1..n)(profit_i * item_i).
|
||||
|
||||
There are several ways to solve knapsack problems. One of the most
|
||||
efficient is based on dynamic programming (mainly when weights, profits
|
||||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
||||
NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
||||
That's the reason why the most of the following code is based on branch and
|
||||
bound search.
|
||||
|
||||
For instance to solve a 2-dimensional knapsack problem with 9 items,
|
||||
one just has to feed a profit vector with the 9 profits, a vector of 2
|
||||
vectors for weights, and a vector of capacities.
|
||||
E.g.:
|
||||
|
||||
**Python**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
|
||||
weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
|
||||
[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
|
||||
]
|
||||
capacities = [ 34, 4 ]
|
||||
|
||||
solver = pywrapknapsack_solver.KnapsackSolver(
|
||||
pywrapknapsack_solver.KnapsackSolver
|
||||
.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
'Multi-dimensional solver')
|
||||
solver.Init(profits, weights, capacities)
|
||||
profit = solver.Solve()
|
||||
|
||||
|
||||
**C++**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
const std::vectorstd::vector<int64 weights =
|
||||
{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
const std::vectorint64 capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver(
|
||||
KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.Init(profits, weights, capacities);
|
||||
const int64 profit = solver.Solve();
|
||||
|
||||
|
||||
**Java**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
final long[] capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver = new KnapsackSolver(
|
||||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.init(profits, weights, capacities);
|
||||
final long profit = solver.solve();
|
||||
|
||||
|
||||
"""
|
||||
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
KNAPSACK_BRUTE_FORCE_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_BRUTE_FORCE_SOLVER
|
||||
r"""
|
||||
Brute force method.
|
||||
|
||||
Limited to 30 items and one dimension, this
|
||||
solver uses a brute force algorithm, ie. explores all possible states.
|
||||
Experiments show competitive performance for instances with less than
|
||||
15 items.
|
||||
"""
|
||||
KNAPSACK_64ITEMS_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_64ITEMS_SOLVER
|
||||
r"""
|
||||
Optimized method for single dimension small problems
|
||||
|
||||
Limited to 64 items and one dimension, this
|
||||
solver uses a branch & bound algorithm. This solver is about 4 times
|
||||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.
|
||||
"""
|
||||
KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER
|
||||
r"""
|
||||
Dynamic Programming approach for single dimension problems
|
||||
|
||||
Limited to one dimension, this solver is based on a dynamic programming
|
||||
algorithm. The time and space complexity is O(capacity *
|
||||
number_of_items).
|
||||
"""
|
||||
KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER
|
||||
r"""
|
||||
CBC Based Solver
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on Integer Programming solver CBC.
|
||||
"""
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER
|
||||
r"""
|
||||
Generic Solver.
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on branch and bound.
|
||||
"""
|
||||
|
||||
def __init__(self, *args):
|
||||
_pywrapknapsack_solver.KnapsackSolver_swiginit(self, _pywrapknapsack_solver.new_KnapsackSolver(*args))
|
||||
__swig_destroy__ = _pywrapknapsack_solver.delete_KnapsackSolver
|
||||
|
||||
def Init(self, profits: 'std::vector< int64 > const &', weights: 'std::vector< std::vector< int64 > > const &', capacities: 'std::vector< int64 > const &') -> "void":
|
||||
r"""
|
||||
|
||||
Initializes the solver and enters the problem to be solved.
|
||||
"""
|
||||
def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)
|
||||
|
||||
def Solve(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Solves the problem and returns the profit of the optimal solution.
|
||||
"""
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Solve(self)
|
||||
|
||||
def BestSolutionContains(self, item_id: 'int') -> "bool":
|
||||
r"""
|
||||
|
||||
Returns true if the item 'item_id' is packed in the optimal knapsack.
|
||||
"""
|
||||
def BestSolutionContains(self, item_id: "int") -> "bool":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)
|
||||
|
||||
def set_use_reduction(self, use_reduction: 'bool') -> "void":
|
||||
def set_use_reduction(self, use_reduction: "bool") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)
|
||||
|
||||
def set_time_limit(self, time_limit_seconds: 'double') -> "void":
|
||||
r"""
|
||||
Time limit in seconds.
|
||||
|
||||
When a finite time limit is set the solution obtained might not be optimal
|
||||
if the limit is reached.
|
||||
"""
|
||||
def set_time_limit(self, time_limit_seconds: "double") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)
|
||||
|
||||
# Register KnapsackSolver in _pywrapknapsack_solver:
|
||||
@@ -314,292 +136,58 @@ _pywrapknapsack_solver.KnapsackSolver_swigregister(KnapsackSolver)</code></pre>
|
||||
<span>(</span><span>*args)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>This library solves knapsack problems.</p>
|
||||
<p>Problems the library solves include:
|
||||
- 0-1 knapsack problems,
|
||||
- Multi-dimensional knapsack problems,</p>
|
||||
<p>Given n items, each with a profit and a weight, given a knapsack of
|
||||
capacity c, the goal is to find a subset of items which fits inside c
|
||||
and maximizes the total profit.
|
||||
The knapsack problem can easily be extended from 1 to d dimensions.
|
||||
As an example, this can be useful to constrain the maximum number of
|
||||
items inside the knapsack.
|
||||
Without loss of generality, profits and weights are assumed to be positive.</p>
|
||||
<p>From a mathematical point of view, the multi-dimensional knapsack problem
|
||||
can be modeled by d linear constraints:</p>
|
||||
<pre><code>ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
||||
where item_i is a 0-1 integer variable.
|
||||
</code></pre>
|
||||
<p>Then the goal is to maximize:</p>
|
||||
<pre><code>Sum(i:1..n)(profit_i * item_i).
|
||||
</code></pre>
|
||||
<p>There are several ways to solve knapsack problems. One of the most
|
||||
efficient is based on dynamic programming (mainly when weights, profits
|
||||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
||||
NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
||||
That's the reason why the most of the following code is based on branch and
|
||||
bound search.</p>
|
||||
<p>For instance to solve a 2-dimensional knapsack problem with 9 items,
|
||||
one just has to feed a profit vector with the 9 profits, a vector of 2
|
||||
vectors for weights, and a vector of capacities.
|
||||
E.g.:</p>
|
||||
<p><strong>Python</strong>:</p>
|
||||
<p>.. code-block:: c++</p>
|
||||
<pre><code> profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
|
||||
weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
|
||||
[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
|
||||
]
|
||||
capacities = [ 34, 4 ]
|
||||
|
||||
solver = pywrapknapsack_solver.KnapsackSolver(
|
||||
pywrapknapsack_solver.KnapsackSolver
|
||||
.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
'Multi-dimensional solver')
|
||||
solver.Init(profits, weights, capacities)
|
||||
profit = solver.Solve()
|
||||
</code></pre>
|
||||
<p><strong>C++</strong>:</p>
|
||||
<p>.. code-block:: c++</p>
|
||||
<pre><code> const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
const std::vectorstd::vector<int64 weights =
|
||||
{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
const std::vectorint64 capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver(
|
||||
KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.Init(profits, weights, capacities);
|
||||
const int64 profit = solver.Solve();
|
||||
</code></pre>
|
||||
<p><strong>Java</strong>:</p>
|
||||
<p>.. code-block:: c++</p>
|
||||
<pre><code> final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
final long[] capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver = new KnapsackSolver(
|
||||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.init(profits, weights, capacities);
|
||||
final long profit = solver.solve();
|
||||
</code></pre></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class KnapsackSolver(object):
|
||||
r"""
|
||||
This library solves knapsack problems.
|
||||
|
||||
Problems the library solves include:
|
||||
- 0-1 knapsack problems,
|
||||
- Multi-dimensional knapsack problems,
|
||||
|
||||
Given n items, each with a profit and a weight, given a knapsack of
|
||||
capacity c, the goal is to find a subset of items which fits inside c
|
||||
and maximizes the total profit.
|
||||
The knapsack problem can easily be extended from 1 to d dimensions.
|
||||
As an example, this can be useful to constrain the maximum number of
|
||||
items inside the knapsack.
|
||||
Without loss of generality, profits and weights are assumed to be positive.
|
||||
|
||||
From a mathematical point of view, the multi-dimensional knapsack problem
|
||||
can be modeled by d linear constraints:
|
||||
|
||||
ForEach(j:1..d)(Sum(i:1..n)(weight_ij * item_i) <= c_j
|
||||
where item_i is a 0-1 integer variable.
|
||||
|
||||
Then the goal is to maximize:
|
||||
|
||||
Sum(i:1..n)(profit_i * item_i).
|
||||
|
||||
There are several ways to solve knapsack problems. One of the most
|
||||
efficient is based on dynamic programming (mainly when weights, profits
|
||||
and dimensions are small, and the algorithm runs in pseudo polynomial time).
|
||||
Unfortunately, when adding conflict constraints the problem becomes strongly
|
||||
NP-hard, i.e. there is no pseudo-polynomial algorithm to solve it.
|
||||
That's the reason why the most of the following code is based on branch and
|
||||
bound search.
|
||||
|
||||
For instance to solve a 2-dimensional knapsack problem with 9 items,
|
||||
one just has to feed a profit vector with the 9 profits, a vector of 2
|
||||
vectors for weights, and a vector of capacities.
|
||||
E.g.:
|
||||
|
||||
**Python**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
profits = [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
|
||||
weights = [ [ 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
|
||||
[ 1, 1, 1, 1, 1, 1, 1, 1, 1 ]
|
||||
]
|
||||
capacities = [ 34, 4 ]
|
||||
|
||||
solver = pywrapknapsack_solver.KnapsackSolver(
|
||||
pywrapknapsack_solver.KnapsackSolver
|
||||
.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
'Multi-dimensional solver')
|
||||
solver.Init(profits, weights, capacities)
|
||||
profit = solver.Solve()
|
||||
|
||||
|
||||
**C++**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
const std::vectorint64 profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
const std::vectorstd::vector<int64 weights =
|
||||
{ { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
const std::vectorint64 capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver(
|
||||
KnapsackSolver::KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.Init(profits, weights, capacities);
|
||||
const int64 profit = solver.Solve();
|
||||
|
||||
|
||||
**Java**:
|
||||
|
||||
|
||||
.. code-block:: c++
|
||||
|
||||
|
||||
final long[] profits = { 1, 2, 3, 4, 5, 6, 7, 8, 9 };
|
||||
final long[][] weights = { { 1, 2, 3, 4, 5, 6, 7, 8, 9 },
|
||||
{ 1, 1, 1, 1, 1, 1, 1, 1, 1 } };
|
||||
final long[] capacities = { 34, 4 };
|
||||
|
||||
KnapsackSolver solver = new KnapsackSolver(
|
||||
KnapsackSolver.SolverType.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER,
|
||||
"Multi-dimensional solver");
|
||||
solver.init(profits, weights, capacities);
|
||||
final long profit = solver.solve();
|
||||
|
||||
|
||||
"""
|
||||
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
KNAPSACK_BRUTE_FORCE_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_BRUTE_FORCE_SOLVER
|
||||
r"""
|
||||
Brute force method.
|
||||
|
||||
Limited to 30 items and one dimension, this
|
||||
solver uses a brute force algorithm, ie. explores all possible states.
|
||||
Experiments show competitive performance for instances with less than
|
||||
15 items.
|
||||
"""
|
||||
KNAPSACK_64ITEMS_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_64ITEMS_SOLVER
|
||||
r"""
|
||||
Optimized method for single dimension small problems
|
||||
|
||||
Limited to 64 items and one dimension, this
|
||||
solver uses a branch & bound algorithm. This solver is about 4 times
|
||||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.
|
||||
"""
|
||||
KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER
|
||||
r"""
|
||||
Dynamic Programming approach for single dimension problems
|
||||
|
||||
Limited to one dimension, this solver is based on a dynamic programming
|
||||
algorithm. The time and space complexity is O(capacity *
|
||||
number_of_items).
|
||||
"""
|
||||
KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER
|
||||
r"""
|
||||
CBC Based Solver
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on Integer Programming solver CBC.
|
||||
"""
|
||||
KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER = _pywrapknapsack_solver.KnapsackSolver_KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER
|
||||
r"""
|
||||
Generic Solver.
|
||||
|
||||
This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on branch and bound.
|
||||
"""
|
||||
|
||||
def __init__(self, *args):
|
||||
_pywrapknapsack_solver.KnapsackSolver_swiginit(self, _pywrapknapsack_solver.new_KnapsackSolver(*args))
|
||||
__swig_destroy__ = _pywrapknapsack_solver.delete_KnapsackSolver
|
||||
|
||||
def Init(self, profits: 'std::vector< int64 > const &', weights: 'std::vector< std::vector< int64 > > const &', capacities: 'std::vector< int64 > const &') -> "void":
|
||||
r"""
|
||||
|
||||
Initializes the solver and enters the problem to be solved.
|
||||
"""
|
||||
def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)
|
||||
|
||||
def Solve(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Solves the problem and returns the profit of the optimal solution.
|
||||
"""
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Solve(self)
|
||||
|
||||
def BestSolutionContains(self, item_id: 'int') -> "bool":
|
||||
r"""
|
||||
|
||||
Returns true if the item 'item_id' is packed in the optimal knapsack.
|
||||
"""
|
||||
def BestSolutionContains(self, item_id: "int") -> "bool":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)
|
||||
|
||||
def set_use_reduction(self, use_reduction: 'bool') -> "void":
|
||||
def set_use_reduction(self, use_reduction: "bool") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)
|
||||
|
||||
def set_time_limit(self, time_limit_seconds: 'double') -> "void":
|
||||
r"""
|
||||
Time limit in seconds.
|
||||
|
||||
When a finite time limit is set the solution obtained might not be optimal
|
||||
if the limit is reached.
|
||||
"""
|
||||
def set_time_limit(self, time_limit_seconds: "double") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)</code></pre>
|
||||
</details>
|
||||
<h3>Class variables</h3>
|
||||
<dl>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_64ITEMS_SOLVER"><code class="name">var <span class="ident">KNAPSACK_64ITEMS_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Optimized method for single dimension small problems</p>
|
||||
<p>Limited to 64 items and one dimension, this
|
||||
solver uses a branch & bound algorithm. This solver is about 4 times
|
||||
faster than KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER.</p></section>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_BRUTE_FORCE_SOLVER"><code class="name">var <span class="ident">KNAPSACK_BRUTE_FORCE_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Brute force method.</p>
|
||||
<p>Limited to 30 items and one dimension, this
|
||||
solver uses a brute force algorithm, ie. explores all possible states.
|
||||
Experiments show competitive performance for instances with less than
|
||||
15 items.</p></section>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER"><code class="name">var <span class="ident">KNAPSACK_DYNAMIC_PROGRAMMING_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Dynamic Programming approach for single dimension problems</p>
|
||||
<p>Limited to one dimension, this solver is based on a dynamic programming
|
||||
algorithm. The time and space complexity is O(capacity *
|
||||
number_of_items).</p></section>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER"><code class="name">var <span class="ident">KNAPSACK_MULTIDIMENSION_BRANCH_AND_BOUND_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Generic Solver.</p>
|
||||
<p>This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on branch and bound.</p></section>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt id="pywrapknapsack_solver.KnapsackSolver.KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER"><code class="name">var <span class="ident">KNAPSACK_MULTIDIMENSION_CBC_MIP_SOLVER</span></code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>CBC Based Solver</p>
|
||||
<p>This solver can deal with both large number of items and several
|
||||
dimensions. This solver is based on Integer Programming solver CBC.</p></section>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Instance variables</h3>
|
||||
@@ -609,7 +197,7 @@ dimensions. This solver is based on Integer Programming solver CBC.</p></section
|
||||
<section class="desc"><p>The membership flag</p></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')</code></pre>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
@@ -619,14 +207,10 @@ dimensions. This solver is based on Integer Programming solver CBC.</p></section
|
||||
<span>def <span class="ident">BestSolutionContains</span></span>(<span>self, item_id)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns true if the item 'item_id' is packed in the optimal knapsack.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def BestSolutionContains(self, item_id: 'int') -> "bool":
|
||||
r"""
|
||||
|
||||
Returns true if the item 'item_id' is packed in the optimal knapsack.
|
||||
"""
|
||||
<pre><code class="python">def BestSolutionContains(self, item_id: "int") -> "bool":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_BestSolutionContains(self, item_id)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -634,14 +218,10 @@ dimensions. This solver is based on Integer Programming solver CBC.</p></section
|
||||
<span>def <span class="ident">Init</span></span>(<span>self, profits, weights, capacities)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Initializes the solver and enters the problem to be solved.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Init(self, profits: 'std::vector< int64 > const &', weights: 'std::vector< std::vector< int64 > > const &', capacities: 'std::vector< int64 > const &') -> "void":
|
||||
r"""
|
||||
|
||||
Initializes the solver and enters the problem to be solved.
|
||||
"""
|
||||
<pre><code class="python">def Init(self, profits: "std::vector< int64 > const &", weights: "std::vector< std::vector< int64 > > const &", capacities: "std::vector< int64 > const &") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Init(self, profits, weights, capacities)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -649,14 +229,10 @@ dimensions. This solver is based on Integer Programming solver CBC.</p></section
|
||||
<span>def <span class="ident">Solve</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Solves the problem and returns the profit of the optimal solution.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Solve(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Solves the problem and returns the profit of the optimal solution.
|
||||
"""
|
||||
return _pywrapknapsack_solver.KnapsackSolver_Solve(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -664,18 +240,10 @@ dimensions. This solver is based on Integer Programming solver CBC.</p></section
|
||||
<span>def <span class="ident">set_time_limit</span></span>(<span>self, time_limit_seconds)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Time limit in seconds.</p>
|
||||
<p>When a finite time limit is set the solution obtained might not be optimal
|
||||
if the limit is reached.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def set_time_limit(self, time_limit_seconds: 'double') -> "void":
|
||||
r"""
|
||||
Time limit in seconds.
|
||||
|
||||
When a finite time limit is set the solution obtained might not be optimal
|
||||
if the limit is reached.
|
||||
"""
|
||||
<pre><code class="python">def set_time_limit(self, time_limit_seconds: "double") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_time_limit(self, time_limit_seconds)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -686,7 +254,7 @@ if the limit is reached.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def set_use_reduction(self, use_reduction: 'bool') -> "void":
|
||||
<pre><code class="python">def set_use_reduction(self, use_reduction: "bool") -> "void":
|
||||
return _pywrapknapsack_solver.KnapsackSolver_set_use_reduction(self, use_reduction)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -731,7 +299,7 @@ if the limit is reached.</p></section>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p><span style="color:#ddd">卐</span></p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.2</a>.</p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.3</a>.</p>
|
||||
</footer>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -3,13 +3,13 @@
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.6.2" />
|
||||
<meta name="generator" content="pdoc 0.6.3" />
|
||||
<title>pywrapgraph API documentation</title>
|
||||
<meta name="description" content="" />
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<style type="text/css">
|
||||
@@ -28,17 +28,17 @@ a:link { color: #46641e; text-decoration: none}
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python"># This file was automatically generated by SWIG (http://www.swig.org).
|
||||
# Version 4.0.0
|
||||
# Version 4.0.1
|
||||
#
|
||||
# Do not make changes to this file unless you know what you are doing--modify
|
||||
# the SWIG interface file instead.
|
||||
|
||||
from sys import version_info as _swig_python_version_info
|
||||
if _swig_python_version_info < (2, 7, 0):
|
||||
raise RuntimeError('Python 2.7 or later required')
|
||||
raise RuntimeError("Python 2.7 or later required")
|
||||
|
||||
# Import the low-level C/C++ module
|
||||
if __package__ or '.' in __name__:
|
||||
if __package__ or "." in __name__:
|
||||
from . import _pywrapgraph
|
||||
else:
|
||||
import _pywrapgraph
|
||||
@@ -48,35 +48,6 @@ try:
|
||||
except ImportError:
|
||||
import __builtin__
|
||||
|
||||
def _swig_setattr_nondynamic(self, class_type, name, value, static=1):
|
||||
if name == "thisown":
|
||||
return self.this.own(value)
|
||||
if name == "this":
|
||||
if type(value).__name__ == 'SwigPyObject':
|
||||
self.__dict__[name] = value
|
||||
return
|
||||
method = class_type.__swig_setmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self, value)
|
||||
if not static:
|
||||
object.__setattr__(self, name, value)
|
||||
else:
|
||||
raise AttributeError("You cannot add attributes to %s" % self)
|
||||
|
||||
|
||||
def _swig_setattr(self, class_type, name, value):
|
||||
return _swig_setattr_nondynamic(self, class_type, name, value, 0)
|
||||
|
||||
|
||||
def _swig_getattr(self, class_type, name):
|
||||
if name == "thisown":
|
||||
return self.this.own()
|
||||
method = class_type.__swig_getmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self)
|
||||
raise AttributeError("'%s' object has no attribute '%s'" % (class_type.__name__, name))
|
||||
|
||||
|
||||
def _swig_repr(self):
|
||||
try:
|
||||
strthis = "proxy of " + self.this.__repr__()
|
||||
@@ -120,13 +91,13 @@ class _SwigNonDynamicMeta(type):
|
||||
|
||||
|
||||
class SimpleMaxFlow(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.SimpleMaxFlow_swiginit(self, _pywrapgraph.new_SimpleMaxFlow())
|
||||
|
||||
def AddArcWithCapacity(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCapacity(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_AddArcWithCapacity(self, tail, head, capacity)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -135,26 +106,26 @@ class SimpleMaxFlow(object):
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_NumArcs(self)
|
||||
|
||||
def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Tail(self, arc)
|
||||
|
||||
def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Head(self, arc)
|
||||
|
||||
def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Capacity(self, arc)
|
||||
OPTIMAL = _pywrapgraph.SimpleMaxFlow_OPTIMAL
|
||||
POSSIBLE_OVERFLOW = _pywrapgraph.SimpleMaxFlow_POSSIBLE_OVERFLOW
|
||||
BAD_INPUT = _pywrapgraph.SimpleMaxFlow_BAD_INPUT
|
||||
BAD_RESULT = _pywrapgraph.SimpleMaxFlow_BAD_RESULT
|
||||
|
||||
def Solve(self, source: 'operations_research::NodeIndex', sink: 'operations_research::NodeIndex') -> "operations_research::SimpleMaxFlow::Status":
|
||||
def Solve(self, source: "operations_research::NodeIndex", sink: "operations_research::NodeIndex") -> "operations_research::SimpleMaxFlow::Status":
|
||||
return _pywrapgraph.SimpleMaxFlow_Solve(self, source, sink)
|
||||
|
||||
def OptimalFlow(self) -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_OptimalFlow(self)
|
||||
|
||||
def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Flow(self, arc)
|
||||
|
||||
def GetSourceSideMinCut(self) -> "void":
|
||||
@@ -163,7 +134,7 @@ class SimpleMaxFlow(object):
|
||||
def GetSinkSideMinCut(self) -> "void":
|
||||
return _pywrapgraph.SimpleMaxFlow_GetSinkSideMinCut(self)
|
||||
|
||||
def SetArcCapacity(self, arc: 'operations_research::ArcIndex', capacity: 'operations_research::FlowQuantity') -> "void":
|
||||
def SetArcCapacity(self, arc: "operations_research::ArcIndex", capacity: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMaxFlow_SetArcCapacity(self, arc, capacity)
|
||||
__swig_destroy__ = _pywrapgraph.delete_SimpleMaxFlow
|
||||
|
||||
@@ -171,7 +142,7 @@ class SimpleMaxFlow(object):
|
||||
_pywrapgraph.SimpleMaxFlow_swigregister(SimpleMaxFlow)
|
||||
|
||||
class MinCostFlowBase(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
NOT_SOLVED = _pywrapgraph.MinCostFlowBase_NOT_SOLVED
|
||||
OPTIMAL = _pywrapgraph.MinCostFlowBase_OPTIMAL
|
||||
@@ -189,16 +160,16 @@ class MinCostFlowBase(object):
|
||||
_pywrapgraph.MinCostFlowBase_swigregister(MinCostFlowBase)
|
||||
|
||||
class SimpleMinCostFlow(MinCostFlowBase):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.SimpleMinCostFlow_swiginit(self, _pywrapgraph.new_SimpleMinCostFlow())
|
||||
|
||||
def AddArcWithCapacityAndUnitCost(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity', unit_cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCapacityAndUnitCost(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity", unit_cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_AddArcWithCapacityAndUnitCost(self, tail, head, capacity, unit_cost)
|
||||
|
||||
def SetNodeSupply(self, node: 'operations_research::NodeIndex', supply: 'operations_research::FlowQuantity') -> "void":
|
||||
def SetNodeSupply(self, node: "operations_research::NodeIndex", supply: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMinCostFlow_SetNodeSupply(self, node, supply)
|
||||
|
||||
def Solve(self) -> "operations_research::MinCostFlowBase::Status":
|
||||
@@ -213,7 +184,7 @@ class SimpleMinCostFlow(MinCostFlowBase):
|
||||
def MaximumFlow(self) -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_MaximumFlow(self)
|
||||
|
||||
def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Flow(self, arc)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -222,19 +193,19 @@ class SimpleMinCostFlow(MinCostFlowBase):
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_NumArcs(self)
|
||||
|
||||
def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Tail(self, arc)
|
||||
|
||||
def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Head(self, arc)
|
||||
|
||||
def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Capacity(self, arc)
|
||||
|
||||
def Supply(self, node: 'operations_research::NodeIndex') -> "operations_research::FlowQuantity":
|
||||
def Supply(self, node: "operations_research::NodeIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Supply(self, node)
|
||||
|
||||
def UnitCost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
def UnitCost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.SimpleMinCostFlow_UnitCost(self, arc)
|
||||
__swig_destroy__ = _pywrapgraph.delete_SimpleMinCostFlow
|
||||
|
||||
@@ -242,13 +213,13 @@ class SimpleMinCostFlow(MinCostFlowBase):
|
||||
_pywrapgraph.SimpleMinCostFlow_swigregister(SimpleMinCostFlow)
|
||||
|
||||
class LinearSumAssignment(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.LinearSumAssignment_swiginit(self, _pywrapgraph.new_LinearSumAssignment())
|
||||
|
||||
def AddArcWithCost(self, left_node: 'operations_research::NodeIndex', right_node: 'operations_research::NodeIndex', cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCost(self, left_node: "operations_research::NodeIndex", right_node: "operations_research::NodeIndex", cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_AddArcWithCost(self, left_node, right_node, cost)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -257,13 +228,13 @@ class LinearSumAssignment(object):
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_NumArcs(self)
|
||||
|
||||
def LeftNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def LeftNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_LeftNode(self, arc)
|
||||
|
||||
def RightNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def RightNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightNode(self, arc)
|
||||
|
||||
def Cost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
def Cost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_Cost(self, arc)
|
||||
OPTIMAL = _pywrapgraph.LinearSumAssignment_OPTIMAL
|
||||
INFEASIBLE = _pywrapgraph.LinearSumAssignment_INFEASIBLE
|
||||
@@ -275,10 +246,10 @@ class LinearSumAssignment(object):
|
||||
def OptimalCost(self) -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_OptimalCost(self)
|
||||
|
||||
def RightMate(self, left_node: 'operations_research::NodeIndex') -> "operations_research::NodeIndex":
|
||||
def RightMate(self, left_node: "operations_research::NodeIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightMate(self, left_node)
|
||||
|
||||
def AssignmentCost(self, left_node: 'operations_research::NodeIndex') -> "operations_research::CostValue":
|
||||
def AssignmentCost(self, left_node: "operations_research::NodeIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_AssignmentCost(self, left_node)
|
||||
__swig_destroy__ = _pywrapgraph.delete_LinearSumAssignment
|
||||
|
||||
@@ -286,13 +257,13 @@ class LinearSumAssignment(object):
|
||||
_pywrapgraph.LinearSumAssignment_swigregister(LinearSumAssignment)
|
||||
|
||||
|
||||
def DijkstraShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
def DijkstraShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.DijkstraShortestPath(node_count, start_node, end_node, graph, disconnected_distance)
|
||||
|
||||
def BellmanFordShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
def BellmanFordShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.BellmanFordShortestPath(node_count, start_node, end_node, graph, disconnected_distance)
|
||||
|
||||
def AStarShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', heuristic: 'std::function< int64 (int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
def AStarShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", heuristic: "std::function< int64 (int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.AStarShortestPath(node_count, start_node, end_node, graph, heuristic, disconnected_distance)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
@@ -310,7 +281,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AStarShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', heuristic: 'std::function< int64 (int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
<pre><code class="python">def AStarShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", heuristic: "std::function< int64 (int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.AStarShortestPath(node_count, start_node, end_node, graph, heuristic, disconnected_distance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -321,7 +292,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def BellmanFordShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
<pre><code class="python">def BellmanFordShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.BellmanFordShortestPath(node_count, start_node, end_node, graph, disconnected_distance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -332,7 +303,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def DijkstraShortestPath(node_count: 'int', start_node: 'int', end_node: 'int', graph: 'std::function< int64 (int,int) >', disconnected_distance: 'int64') -> "std::vector< int > *":
|
||||
<pre><code class="python">def DijkstraShortestPath(node_count: "int", start_node: "int", end_node: "int", graph: "std::function< int64 (int,int) >", disconnected_distance: "int64") -> "std::vector< int > *":
|
||||
return _pywrapgraph.DijkstraShortestPath(node_count, start_node, end_node, graph, disconnected_distance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -349,13 +320,13 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class LinearSumAssignment(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.LinearSumAssignment_swiginit(self, _pywrapgraph.new_LinearSumAssignment())
|
||||
|
||||
def AddArcWithCost(self, left_node: 'operations_research::NodeIndex', right_node: 'operations_research::NodeIndex', cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCost(self, left_node: "operations_research::NodeIndex", right_node: "operations_research::NodeIndex", cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_AddArcWithCost(self, left_node, right_node, cost)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -364,13 +335,13 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_NumArcs(self)
|
||||
|
||||
def LeftNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def LeftNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_LeftNode(self, arc)
|
||||
|
||||
def RightNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def RightNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightNode(self, arc)
|
||||
|
||||
def Cost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
def Cost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_Cost(self, arc)
|
||||
OPTIMAL = _pywrapgraph.LinearSumAssignment_OPTIMAL
|
||||
INFEASIBLE = _pywrapgraph.LinearSumAssignment_INFEASIBLE
|
||||
@@ -382,10 +353,10 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def OptimalCost(self) -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_OptimalCost(self)
|
||||
|
||||
def RightMate(self, left_node: 'operations_research::NodeIndex') -> "operations_research::NodeIndex":
|
||||
def RightMate(self, left_node: "operations_research::NodeIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightMate(self, left_node)
|
||||
|
||||
def AssignmentCost(self, left_node: 'operations_research::NodeIndex') -> "operations_research::CostValue":
|
||||
def AssignmentCost(self, left_node: "operations_research::NodeIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_AssignmentCost(self, left_node)
|
||||
__swig_destroy__ = _pywrapgraph.delete_LinearSumAssignment</code></pre>
|
||||
</details>
|
||||
@@ -411,7 +382,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"><p>The membership flag</p></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')</code></pre>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
@@ -424,7 +395,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AddArcWithCost(self, left_node: 'operations_research::NodeIndex', right_node: 'operations_research::NodeIndex', cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
<pre><code class="python">def AddArcWithCost(self, left_node: "operations_research::NodeIndex", right_node: "operations_research::NodeIndex", cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_AddArcWithCost(self, left_node, right_node, cost)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -435,7 +406,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AssignmentCost(self, left_node: 'operations_research::NodeIndex') -> "operations_research::CostValue":
|
||||
<pre><code class="python">def AssignmentCost(self, left_node: "operations_research::NodeIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_AssignmentCost(self, left_node)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -446,7 +417,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Cost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
<pre><code class="python">def Cost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.LinearSumAssignment_Cost(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -457,7 +428,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def LeftNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def LeftNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_LeftNode(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -501,7 +472,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def RightMate(self, left_node: 'operations_research::NodeIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def RightMate(self, left_node: "operations_research::NodeIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightMate(self, left_node)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -512,7 +483,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def RightNode(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def RightNode(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.LinearSumAssignment_RightNode(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -537,7 +508,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class MinCostFlowBase(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
NOT_SOLVED = _pywrapgraph.MinCostFlowBase_NOT_SOLVED
|
||||
OPTIMAL = _pywrapgraph.MinCostFlowBase_OPTIMAL
|
||||
@@ -593,7 +564,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"><p>The membership flag</p></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')</code></pre>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
@@ -606,13 +577,13 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class SimpleMaxFlow(object):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.SimpleMaxFlow_swiginit(self, _pywrapgraph.new_SimpleMaxFlow())
|
||||
|
||||
def AddArcWithCapacity(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCapacity(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_AddArcWithCapacity(self, tail, head, capacity)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -621,26 +592,26 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_NumArcs(self)
|
||||
|
||||
def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Tail(self, arc)
|
||||
|
||||
def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Head(self, arc)
|
||||
|
||||
def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Capacity(self, arc)
|
||||
OPTIMAL = _pywrapgraph.SimpleMaxFlow_OPTIMAL
|
||||
POSSIBLE_OVERFLOW = _pywrapgraph.SimpleMaxFlow_POSSIBLE_OVERFLOW
|
||||
BAD_INPUT = _pywrapgraph.SimpleMaxFlow_BAD_INPUT
|
||||
BAD_RESULT = _pywrapgraph.SimpleMaxFlow_BAD_RESULT
|
||||
|
||||
def Solve(self, source: 'operations_research::NodeIndex', sink: 'operations_research::NodeIndex') -> "operations_research::SimpleMaxFlow::Status":
|
||||
def Solve(self, source: "operations_research::NodeIndex", sink: "operations_research::NodeIndex") -> "operations_research::SimpleMaxFlow::Status":
|
||||
return _pywrapgraph.SimpleMaxFlow_Solve(self, source, sink)
|
||||
|
||||
def OptimalFlow(self) -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_OptimalFlow(self)
|
||||
|
||||
def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Flow(self, arc)
|
||||
|
||||
def GetSourceSideMinCut(self) -> "void":
|
||||
@@ -649,7 +620,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def GetSinkSideMinCut(self) -> "void":
|
||||
return _pywrapgraph.SimpleMaxFlow_GetSinkSideMinCut(self)
|
||||
|
||||
def SetArcCapacity(self, arc: 'operations_research::ArcIndex', capacity: 'operations_research::FlowQuantity') -> "void":
|
||||
def SetArcCapacity(self, arc: "operations_research::ArcIndex", capacity: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMaxFlow_SetArcCapacity(self, arc, capacity)
|
||||
__swig_destroy__ = _pywrapgraph.delete_SimpleMaxFlow</code></pre>
|
||||
</details>
|
||||
@@ -679,7 +650,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"><p>The membership flag</p></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')</code></pre>
|
||||
<pre><code class="python">thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
@@ -692,7 +663,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AddArcWithCapacity(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity') -> "operations_research::ArcIndex":
|
||||
<pre><code class="python">def AddArcWithCapacity(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_AddArcWithCapacity(self, tail, head, capacity)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -703,7 +674,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
<pre><code class="python">def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Capacity(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -714,7 +685,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
<pre><code class="python">def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMaxFlow_Flow(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -747,7 +718,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Head(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -791,7 +762,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def SetArcCapacity(self, arc: 'operations_research::ArcIndex', capacity: 'operations_research::FlowQuantity') -> "void":
|
||||
<pre><code class="python">def SetArcCapacity(self, arc: "operations_research::ArcIndex", capacity: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMaxFlow_SetArcCapacity(self, arc, capacity)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -802,7 +773,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Solve(self, source: 'operations_research::NodeIndex', sink: 'operations_research::NodeIndex') -> "operations_research::SimpleMaxFlow::Status":
|
||||
<pre><code class="python">def Solve(self, source: "operations_research::NodeIndex", sink: "operations_research::NodeIndex") -> "operations_research::SimpleMaxFlow::Status":
|
||||
return _pywrapgraph.SimpleMaxFlow_Solve(self, source, sink)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -813,7 +784,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMaxFlow_Tail(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -827,16 +798,16 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class SimpleMinCostFlow(MinCostFlowBase):
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self):
|
||||
_pywrapgraph.SimpleMinCostFlow_swiginit(self, _pywrapgraph.new_SimpleMinCostFlow())
|
||||
|
||||
def AddArcWithCapacityAndUnitCost(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity', unit_cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
def AddArcWithCapacityAndUnitCost(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity", unit_cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_AddArcWithCapacityAndUnitCost(self, tail, head, capacity, unit_cost)
|
||||
|
||||
def SetNodeSupply(self, node: 'operations_research::NodeIndex', supply: 'operations_research::FlowQuantity') -> "void":
|
||||
def SetNodeSupply(self, node: "operations_research::NodeIndex", supply: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMinCostFlow_SetNodeSupply(self, node, supply)
|
||||
|
||||
def Solve(self) -> "operations_research::MinCostFlowBase::Status":
|
||||
@@ -851,7 +822,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def MaximumFlow(self) -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_MaximumFlow(self)
|
||||
|
||||
def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Flow(self, arc)
|
||||
|
||||
def NumNodes(self) -> "operations_research::NodeIndex":
|
||||
@@ -860,19 +831,19 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
def NumArcs(self) -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_NumArcs(self)
|
||||
|
||||
def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Tail(self, arc)
|
||||
|
||||
def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Head(self, arc)
|
||||
|
||||
def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Capacity(self, arc)
|
||||
|
||||
def Supply(self, node: 'operations_research::NodeIndex') -> "operations_research::FlowQuantity":
|
||||
def Supply(self, node: "operations_research::NodeIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Supply(self, node)
|
||||
|
||||
def UnitCost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
def UnitCost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.SimpleMinCostFlow_UnitCost(self, arc)
|
||||
__swig_destroy__ = _pywrapgraph.delete_SimpleMinCostFlow</code></pre>
|
||||
</details>
|
||||
@@ -889,7 +860,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AddArcWithCapacityAndUnitCost(self, tail: 'operations_research::NodeIndex', head: 'operations_research::NodeIndex', capacity: 'operations_research::FlowQuantity', unit_cost: 'operations_research::CostValue') -> "operations_research::ArcIndex":
|
||||
<pre><code class="python">def AddArcWithCapacityAndUnitCost(self, tail: "operations_research::NodeIndex", head: "operations_research::NodeIndex", capacity: "operations_research::FlowQuantity", unit_cost: "operations_research::CostValue") -> "operations_research::ArcIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_AddArcWithCapacityAndUnitCost(self, tail, head, capacity, unit_cost)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -900,7 +871,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Capacity(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
<pre><code class="python">def Capacity(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Capacity(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -911,7 +882,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Flow(self, arc: 'operations_research::ArcIndex') -> "operations_research::FlowQuantity":
|
||||
<pre><code class="python">def Flow(self, arc: "operations_research::ArcIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Flow(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -922,7 +893,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Head(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def Head(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Head(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -977,7 +948,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def SetNodeSupply(self, node: 'operations_research::NodeIndex', supply: 'operations_research::FlowQuantity') -> "void":
|
||||
<pre><code class="python">def SetNodeSupply(self, node: "operations_research::NodeIndex", supply: "operations_research::FlowQuantity") -> "void":
|
||||
return _pywrapgraph.SimpleMinCostFlow_SetNodeSupply(self, node, supply)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -1010,7 +981,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Supply(self, node: 'operations_research::NodeIndex') -> "operations_research::FlowQuantity":
|
||||
<pre><code class="python">def Supply(self, node: "operations_research::NodeIndex") -> "operations_research::FlowQuantity":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Supply(self, node)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -1021,7 +992,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Tail(self, arc: 'operations_research::ArcIndex') -> "operations_research::NodeIndex":
|
||||
<pre><code class="python">def Tail(self, arc: "operations_research::ArcIndex") -> "operations_research::NodeIndex":
|
||||
return _pywrapgraph.SimpleMinCostFlow_Tail(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -1032,7 +1003,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def UnitCost(self, arc: 'operations_research::ArcIndex') -> "operations_research::CostValue":
|
||||
<pre><code class="python">def UnitCost(self, arc: "operations_research::ArcIndex") -> "operations_research::CostValue":
|
||||
return _pywrapgraph.SimpleMinCostFlow_UnitCost(self, arc)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -1149,7 +1120,7 @@ def AStarShortestPath(node_count: 'int', start_node: 'int', end_
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p><span style="color:#ddd">卐</span></p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.2</a>.</p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.3</a>.</p>
|
||||
</footer>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -3,13 +3,13 @@
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.6.2" />
|
||||
<meta name="generator" content="pdoc 0.6.3" />
|
||||
<title>cp_model API documentation</title>
|
||||
<meta name="description" content="Methods for building and solving CP-SAT models …" />
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<style type="text/css">
|
||||
@@ -6077,7 +6077,7 @@ def Term(cls, expression, coefficient):
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p><span style="color:#ddd">卐</span></p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.2</a>.</p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.3</a>.</p>
|
||||
</footer>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
|
||||
@@ -3,13 +3,13 @@
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.6.2" />
|
||||
<meta name="generator" content="pdoc 0.6.3" />
|
||||
<title>sorted_interval_list API documentation</title>
|
||||
<meta name="description" content="" />
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
|
||||
<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
||||
<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase;cursor:pointer}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
<style type="text/css">
|
||||
@@ -28,7 +28,7 @@ a:link { color: #46641e; text-decoration: none}
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python"># This file was automatically generated by SWIG (http://www.swig.org).
|
||||
# Version 4.0.0
|
||||
# Version 4.0.1
|
||||
#
|
||||
# Do not make changes to this file unless you know what you are doing--modify
|
||||
# the SWIG interface file instead.
|
||||
@@ -47,10 +47,10 @@ __pdoc__['Domain.thisown'] = False
|
||||
|
||||
from sys import version_info as _swig_python_version_info
|
||||
if _swig_python_version_info < (2, 7, 0):
|
||||
raise RuntimeError('Python 2.7 or later required')
|
||||
raise RuntimeError("Python 2.7 or later required")
|
||||
|
||||
# Import the low-level C/C++ module
|
||||
if __package__ or '.' in __name__:
|
||||
if __package__ or "." in __name__:
|
||||
from . import _sorted_interval_list
|
||||
else:
|
||||
import _sorted_interval_list
|
||||
@@ -60,35 +60,6 @@ try:
|
||||
except ImportError:
|
||||
import __builtin__
|
||||
|
||||
def _swig_setattr_nondynamic(self, class_type, name, value, static=1):
|
||||
if name == "thisown":
|
||||
return self.this.own(value)
|
||||
if name == "this":
|
||||
if type(value).__name__ == 'SwigPyObject':
|
||||
self.__dict__[name] = value
|
||||
return
|
||||
method = class_type.__swig_setmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self, value)
|
||||
if not static:
|
||||
object.__setattr__(self, name, value)
|
||||
else:
|
||||
raise AttributeError("You cannot add attributes to %s" % self)
|
||||
|
||||
|
||||
def _swig_setattr(self, class_type, name, value):
|
||||
return _swig_setattr_nondynamic(self, class_type, name, value, 0)
|
||||
|
||||
|
||||
def _swig_getattr(self, class_type, name):
|
||||
if name == "thisown":
|
||||
return self.this.own()
|
||||
method = class_type.__swig_getmethods__.get(name, None)
|
||||
if method:
|
||||
return method(self)
|
||||
raise AttributeError("'%s' object has no attribute '%s'" % (class_type.__name__, name))
|
||||
|
||||
|
||||
def _swig_repr(self):
|
||||
try:
|
||||
strthis = "proxy of " + self.this.__repr__()
|
||||
@@ -132,153 +103,71 @@ class _SwigNonDynamicMeta(type):
|
||||
|
||||
|
||||
class Domain(object):
|
||||
r"""
|
||||
|
||||
We call "domain" any subset of Int64 = [kint64min, kint64max].
|
||||
|
||||
This class can be used to represent such set efficiently as a sorted and
|
||||
non-adjacent list of intervals. This is efficient as long as the size of such
|
||||
list stays reasonable.
|
||||
|
||||
In the comments below, the domain of *this will always be written 'D'.
|
||||
Note that all the functions are safe with respect to integer overflow.
|
||||
"""
|
||||
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self, *args):
|
||||
r"""
|
||||
*Overload 1:*
|
||||
By default, Domain will be empty.
|
||||
|
||||
|
|
||||
|
||||
*Overload 2:*
|
||||
Constructor for the common case of a singleton domain.
|
||||
|
||||
|
|
||||
|
||||
*Overload 3:*
|
||||
|
||||
Constructor for the common case of a single interval [left, right].
|
||||
If left > right, this will result in the empty domain.
|
||||
"""
|
||||
_sorted_interval_list.Domain_swiginit(self, _sorted_interval_list.new_Domain(*args))
|
||||
|
||||
@staticmethod
|
||||
def AllValues() -> "operations_research::Domain":
|
||||
r""" Returns the full domain Int64."""
|
||||
return _sorted_interval_list.Domain_AllValues()
|
||||
|
||||
@staticmethod
|
||||
def FromValues(values: 'std::vector< int64 >') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Creates a domain from the union of an unsorted list of integer values.
|
||||
Input values may be repeated, with no consequence on the output
|
||||
"""
|
||||
def FromValues(values: "std::vector< int64 >") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromValues(values)
|
||||
|
||||
@staticmethod
|
||||
def FromIntervals(intervals: 'std::vector< std::vector< int64 > > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a list of intervals (long[][] in Java and
|
||||
.NET, [[0, 2], [5, 5], [8, 10]] in python).
|
||||
"""
|
||||
def FromIntervals(intervals: "std::vector< std::vector< int64 > > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromIntervals(intervals)
|
||||
|
||||
@staticmethod
|
||||
def FromFlatIntervals(flat_intervals: 'std::vector< int64 > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a flattened list of intervals
|
||||
(long[] in Java and .NET, [0, 2, 5, 5, 8, 10] in python).
|
||||
"""
|
||||
def FromFlatIntervals(flat_intervals: "std::vector< int64 > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromFlatIntervals(flat_intervals)
|
||||
|
||||
def FlattenedIntervals(self) -> "std::vector< int64 >":
|
||||
r"""
|
||||
|
||||
This method returns the flattened list of interval bounds of the domain.
|
||||
|
||||
Thus the domain {0, 1, 2, 5, 8, 9, 10} will return [0, 2, 5, 5,
|
||||
8, 10] (as a C++ std::vector<int64>, as a java or C# long[], as
|
||||
a python list of integers).
|
||||
"""
|
||||
return _sorted_interval_list.Domain_FlattenedIntervals(self)
|
||||
|
||||
def IsEmpty(self) -> "bool":
|
||||
r""" Returns true if this is the empty set."""
|
||||
return _sorted_interval_list.Domain_IsEmpty(self)
|
||||
|
||||
def Size(self) -> "int64":
|
||||
r""" Returns the number of elements in the domain. It is capped at kint64max."""
|
||||
return _sorted_interval_list.Domain_Size(self)
|
||||
|
||||
def Min(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the min value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Min(self)
|
||||
|
||||
def Max(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the max value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Max(self)
|
||||
|
||||
def Contains(self, value: 'int64') -> "bool":
|
||||
r""" Returns true iff value is in Domain."""
|
||||
def Contains(self, value: "int64") -> "bool":
|
||||
return _sorted_interval_list.Domain_Contains(self, value)
|
||||
|
||||
def Complement(self) -> "operations_research::Domain":
|
||||
r""" Returns the set Int64 ∖ D."""
|
||||
return _sorted_interval_list.Domain_Complement(self)
|
||||
|
||||
def Negation(self) -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Returns {x ∈ Int64, ∃ e ∈ D, x = -e}.
|
||||
|
||||
Note in particular that if the negation of Int64 is not Int64 but
|
||||
Int64 \ {kint64min} !!
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Negation(self)
|
||||
|
||||
def IntersectionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∩ domain."""
|
||||
def IntersectionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_IntersectionWith(self, domain)
|
||||
|
||||
def UnionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∪ domain."""
|
||||
def UnionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_UnionWith(self, domain)
|
||||
|
||||
def AdditionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns {x ∈ Int64, ∃ a ∈ D, ∃ b ∈ domain, x = a + b}."""
|
||||
def AdditionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_AdditionWith(self, domain)
|
||||
|
||||
def __str__(self) -> "std::string":
|
||||
r""" Returns a compact std::string of a vector of intervals like "[1,4][6][10,20]"."""
|
||||
return _sorted_interval_list.Domain___str__(self)
|
||||
|
||||
def __lt__(self, other: 'Domain') -> "bool":
|
||||
r""" Lexicographic order on the intervals() representation."""
|
||||
def __lt__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___lt__(self, other)
|
||||
|
||||
def __eq__(self, other: 'Domain') -> "bool":
|
||||
def __eq__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___eq__(self, other)
|
||||
|
||||
def __ne__(self, other: 'Domain') -> "bool":
|
||||
def __ne__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___ne__(self, other)
|
||||
__swig_destroy__ = _sorted_interval_list.delete_Domain
|
||||
|
||||
@@ -286,33 +175,15 @@ class Domain(object):
|
||||
_sorted_interval_list.Domain_swigregister(Domain)
|
||||
|
||||
def Domain_AllValues() -> "operations_research::Domain":
|
||||
r""" Returns the full domain Int64."""
|
||||
return _sorted_interval_list.Domain_AllValues()
|
||||
|
||||
def Domain_FromValues(values: 'std::vector< int64 >') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Creates a domain from the union of an unsorted list of integer values.
|
||||
Input values may be repeated, with no consequence on the output
|
||||
"""
|
||||
def Domain_FromValues(values: "std::vector< int64 >") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromValues(values)
|
||||
|
||||
def Domain_FromIntervals(intervals: 'std::vector< std::vector< int64 > > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a list of intervals (long[][] in Java and
|
||||
.NET, [[0, 2], [5, 5], [8, 10]] in python).
|
||||
"""
|
||||
def Domain_FromIntervals(intervals: "std::vector< std::vector< int64 > > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromIntervals(intervals)
|
||||
|
||||
def Domain_FromFlatIntervals(flat_intervals: 'std::vector< int64 > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a flattened list of intervals
|
||||
(long[] in Java and .NET, [0, 2, 5, 5, 8, 10] in python).
|
||||
"""
|
||||
def Domain_FromFlatIntervals(flat_intervals: "std::vector< int64 > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromFlatIntervals(flat_intervals)
|
||||
|
||||
|
||||
@@ -334,171 +205,75 @@ def __lshift__(*args) -> "std::ostream &":
|
||||
<span>(</span><span>*args)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>We call "domain" any subset of Int64 = [kint64min, kint64max].</p>
|
||||
<p>This class can be used to represent such set efficiently as a sorted and
|
||||
non-adjacent list of intervals. This is efficient as long as the size of such
|
||||
list stays reasonable.</p>
|
||||
<p>In the comments below, the domain of *this will always be written 'D'.
|
||||
Note that all the functions are safe with respect to integer overflow.</p>
|
||||
<p><em>Overload 1:</em>
|
||||
By default, Domain will be empty.</p>
|
||||
<p>|</p>
|
||||
<p><em>Overload 2:</em>
|
||||
Constructor for the common case of a singleton domain.</p>
|
||||
<p>|</p>
|
||||
<p><em>Overload 3:</em></p>
|
||||
<p>Constructor for the common case of a single interval [left, right].
|
||||
If left > right, this will result in the empty domain.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">class Domain(object):
|
||||
r"""
|
||||
|
||||
We call "domain" any subset of Int64 = [kint64min, kint64max].
|
||||
|
||||
This class can be used to represent such set efficiently as a sorted and
|
||||
non-adjacent list of intervals. This is efficient as long as the size of such
|
||||
list stays reasonable.
|
||||
|
||||
In the comments below, the domain of *this will always be written 'D'.
|
||||
Note that all the functions are safe with respect to integer overflow.
|
||||
"""
|
||||
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc='The membership flag')
|
||||
thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag")
|
||||
__repr__ = _swig_repr
|
||||
|
||||
def __init__(self, *args):
|
||||
r"""
|
||||
*Overload 1:*
|
||||
By default, Domain will be empty.
|
||||
|
||||
|
|
||||
|
||||
*Overload 2:*
|
||||
Constructor for the common case of a singleton domain.
|
||||
|
||||
|
|
||||
|
||||
*Overload 3:*
|
||||
|
||||
Constructor for the common case of a single interval [left, right].
|
||||
If left > right, this will result in the empty domain.
|
||||
"""
|
||||
_sorted_interval_list.Domain_swiginit(self, _sorted_interval_list.new_Domain(*args))
|
||||
|
||||
@staticmethod
|
||||
def AllValues() -> "operations_research::Domain":
|
||||
r""" Returns the full domain Int64."""
|
||||
return _sorted_interval_list.Domain_AllValues()
|
||||
|
||||
@staticmethod
|
||||
def FromValues(values: 'std::vector< int64 >') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Creates a domain from the union of an unsorted list of integer values.
|
||||
Input values may be repeated, with no consequence on the output
|
||||
"""
|
||||
def FromValues(values: "std::vector< int64 >") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromValues(values)
|
||||
|
||||
@staticmethod
|
||||
def FromIntervals(intervals: 'std::vector< std::vector< int64 > > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a list of intervals (long[][] in Java and
|
||||
.NET, [[0, 2], [5, 5], [8, 10]] in python).
|
||||
"""
|
||||
def FromIntervals(intervals: "std::vector< std::vector< int64 > > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromIntervals(intervals)
|
||||
|
||||
@staticmethod
|
||||
def FromFlatIntervals(flat_intervals: 'std::vector< int64 > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a flattened list of intervals
|
||||
(long[] in Java and .NET, [0, 2, 5, 5, 8, 10] in python).
|
||||
"""
|
||||
def FromFlatIntervals(flat_intervals: "std::vector< int64 > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromFlatIntervals(flat_intervals)
|
||||
|
||||
def FlattenedIntervals(self) -> "std::vector< int64 >":
|
||||
r"""
|
||||
|
||||
This method returns the flattened list of interval bounds of the domain.
|
||||
|
||||
Thus the domain {0, 1, 2, 5, 8, 9, 10} will return [0, 2, 5, 5,
|
||||
8, 10] (as a C++ std::vector<int64>, as a java or C# long[], as
|
||||
a python list of integers).
|
||||
"""
|
||||
return _sorted_interval_list.Domain_FlattenedIntervals(self)
|
||||
|
||||
def IsEmpty(self) -> "bool":
|
||||
r""" Returns true if this is the empty set."""
|
||||
return _sorted_interval_list.Domain_IsEmpty(self)
|
||||
|
||||
def Size(self) -> "int64":
|
||||
r""" Returns the number of elements in the domain. It is capped at kint64max."""
|
||||
return _sorted_interval_list.Domain_Size(self)
|
||||
|
||||
def Min(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the min value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Min(self)
|
||||
|
||||
def Max(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the max value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Max(self)
|
||||
|
||||
def Contains(self, value: 'int64') -> "bool":
|
||||
r""" Returns true iff value is in Domain."""
|
||||
def Contains(self, value: "int64") -> "bool":
|
||||
return _sorted_interval_list.Domain_Contains(self, value)
|
||||
|
||||
def Complement(self) -> "operations_research::Domain":
|
||||
r""" Returns the set Int64 ∖ D."""
|
||||
return _sorted_interval_list.Domain_Complement(self)
|
||||
|
||||
def Negation(self) -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Returns {x ∈ Int64, ∃ e ∈ D, x = -e}.
|
||||
|
||||
Note in particular that if the negation of Int64 is not Int64 but
|
||||
Int64 \ {kint64min} !!
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Negation(self)
|
||||
|
||||
def IntersectionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∩ domain."""
|
||||
def IntersectionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_IntersectionWith(self, domain)
|
||||
|
||||
def UnionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∪ domain."""
|
||||
def UnionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_UnionWith(self, domain)
|
||||
|
||||
def AdditionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns {x ∈ Int64, ∃ a ∈ D, ∃ b ∈ domain, x = a + b}."""
|
||||
def AdditionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_AdditionWith(self, domain)
|
||||
|
||||
def __str__(self) -> "std::string":
|
||||
r""" Returns a compact std::string of a vector of intervals like "[1,4][6][10,20]"."""
|
||||
return _sorted_interval_list.Domain___str__(self)
|
||||
|
||||
def __lt__(self, other: 'Domain') -> "bool":
|
||||
r""" Lexicographic order on the intervals() representation."""
|
||||
def __lt__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___lt__(self, other)
|
||||
|
||||
def __eq__(self, other: 'Domain') -> "bool":
|
||||
def __eq__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___eq__(self, other)
|
||||
|
||||
def __ne__(self, other: 'Domain') -> "bool":
|
||||
def __ne__(self, other: "Domain") -> "bool":
|
||||
return _sorted_interval_list.Domain___ne__(self, other)
|
||||
__swig_destroy__ = _sorted_interval_list.delete_Domain</code></pre>
|
||||
</details>
|
||||
@@ -508,12 +283,11 @@ If left > right, this will result in the empty domain.</p></section>
|
||||
<span>def <span class="ident">AllValues</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the full domain Int64.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">@staticmethod
|
||||
def AllValues() -> "operations_research::Domain":
|
||||
r""" Returns the full domain Int64."""
|
||||
return _sorted_interval_list.Domain_AllValues()</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -521,19 +295,11 @@ def AllValues() -> "operations_research::Domain":
|
||||
<span>def <span class="ident">FromFlatIntervals</span></span>(<span>flat_intervals)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a flattened list of intervals
|
||||
(long[] in Java and .NET, [0, 2, 5, 5, 8, 10] in python).</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">@staticmethod
|
||||
def FromFlatIntervals(flat_intervals: 'std::vector< int64 > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a flattened list of intervals
|
||||
(long[] in Java and .NET, [0, 2, 5, 5, 8, 10] in python).
|
||||
"""
|
||||
def FromFlatIntervals(flat_intervals: "std::vector< int64 > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromFlatIntervals(flat_intervals)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -541,19 +307,11 @@ def FromFlatIntervals(flat_intervals: 'std::vector< int64 > const &
|
||||
<span>def <span class="ident">FromIntervals</span></span>(<span>intervals)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a list of intervals (long[][] in Java and
|
||||
.NET, [[0, 2], [5, 5], [8, 10]] in python).</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">@staticmethod
|
||||
def FromIntervals(intervals: 'std::vector< std::vector< int64 > > const &') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
This method is available in Python, Java and .NET. It allows
|
||||
building a Domain object from a list of intervals (long[][] in Java and
|
||||
.NET, [[0, 2], [5, 5], [8, 10]] in python).
|
||||
"""
|
||||
def FromIntervals(intervals: "std::vector< std::vector< int64 > > const &") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromIntervals(intervals)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -561,17 +319,11 @@ def FromIntervals(intervals: 'std::vector< std::vector< int64 > >
|
||||
<span>def <span class="ident">FromValues</span></span>(<span>values)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Creates a domain from the union of an unsorted list of integer values.
|
||||
Input values may be repeated, with no consequence on the output</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">@staticmethod
|
||||
def FromValues(values: 'std::vector< int64 >') -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Creates a domain from the union of an unsorted list of integer values.
|
||||
Input values may be repeated, with no consequence on the output
|
||||
"""
|
||||
def FromValues(values: "std::vector< int64 >") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_FromValues(values)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -582,11 +334,10 @@ def FromValues(values: 'std::vector< int64 >') -> "operatio
|
||||
<span>def <span class="ident">AdditionWith</span></span>(<span>self, domain)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns {x ∈ Int64, ∃ a ∈ D, ∃ b ∈ domain, x = a + b}.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def AdditionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns {x ∈ Int64, ∃ a ∈ D, ∃ b ∈ domain, x = a + b}."""
|
||||
<pre><code class="python">def AdditionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_AdditionWith(self, domain)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -594,11 +345,10 @@ def FromValues(values: 'std::vector< int64 >') -> "operatio
|
||||
<span>def <span class="ident">Complement</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the set Int64 ∖ D.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Complement(self) -> "operations_research::Domain":
|
||||
r""" Returns the set Int64 ∖ D."""
|
||||
return _sorted_interval_list.Domain_Complement(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -606,11 +356,10 @@ def FromValues(values: 'std::vector< int64 >') -> "operatio
|
||||
<span>def <span class="ident">Contains</span></span>(<span>self, value)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns true iff value is in Domain.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Contains(self, value: 'int64') -> "bool":
|
||||
r""" Returns true iff value is in Domain."""
|
||||
<pre><code class="python">def Contains(self, value: "int64") -> "bool":
|
||||
return _sorted_interval_list.Domain_Contains(self, value)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -618,21 +367,10 @@ def FromValues(values: 'std::vector< int64 >') -> "operatio
|
||||
<span>def <span class="ident">FlattenedIntervals</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>This method returns the flattened list of interval bounds of the domain.</p>
|
||||
<p>Thus the domain {0, 1, 2, 5, 8, 9, 10} will return [0, 2, 5, 5,
|
||||
8, 10] (as a C++ std::vector<int64>, as a java or C# long[], as
|
||||
a python list of integers).</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def FlattenedIntervals(self) -> "std::vector< int64 >":
|
||||
r"""
|
||||
|
||||
This method returns the flattened list of interval bounds of the domain.
|
||||
|
||||
Thus the domain {0, 1, 2, 5, 8, 9, 10} will return [0, 2, 5, 5,
|
||||
8, 10] (as a C++ std::vector<int64>, as a java or C# long[], as
|
||||
a python list of integers).
|
||||
"""
|
||||
return _sorted_interval_list.Domain_FlattenedIntervals(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -640,11 +378,10 @@ a python list of integers).</p></section>
|
||||
<span>def <span class="ident">IntersectionWith</span></span>(<span>self, domain)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the set D ∩ domain.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def IntersectionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∩ domain."""
|
||||
<pre><code class="python">def IntersectionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_IntersectionWith(self, domain)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -652,11 +389,10 @@ a python list of integers).</p></section>
|
||||
<span>def <span class="ident">IsEmpty</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns true if this is the empty set.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def IsEmpty(self) -> "bool":
|
||||
r""" Returns true if this is the empty set."""
|
||||
return _sorted_interval_list.Domain_IsEmpty(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -664,17 +400,10 @@ a python list of integers).</p></section>
|
||||
<span>def <span class="ident">Max</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the max value of the domain.</p>
|
||||
<p>This Checks that the domain is not empty.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Max(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the max value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Max(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -682,17 +411,10 @@ a python list of integers).</p></section>
|
||||
<span>def <span class="ident">Min</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the min value of the domain.</p>
|
||||
<p>This Checks that the domain is not empty.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Min(self) -> "int64":
|
||||
r"""
|
||||
|
||||
Returns the min value of the domain.
|
||||
|
||||
This Checks that the domain is not empty.
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Min(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -700,19 +422,10 @@ a python list of integers).</p></section>
|
||||
<span>def <span class="ident">Negation</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns {x ∈ Int64, ∃ e ∈ D, x = -e}.</p>
|
||||
<p>Note in particular that if the negation of Int64 is not Int64 but
|
||||
Int64 \ {kint64min} !!</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Negation(self) -> "operations_research::Domain":
|
||||
r"""
|
||||
|
||||
Returns {x ∈ Int64, ∃ e ∈ D, x = -e}.
|
||||
|
||||
Note in particular that if the negation of Int64 is not Int64 but
|
||||
Int64 \ {kint64min} !!
|
||||
"""
|
||||
return _sorted_interval_list.Domain_Negation(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -720,11 +433,10 @@ Int64 \ {kint64min} !!</p></section>
|
||||
<span>def <span class="ident">Size</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the number of elements in the domain. It is capped at kint64max.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def Size(self) -> "int64":
|
||||
r""" Returns the number of elements in the domain. It is capped at kint64max."""
|
||||
return _sorted_interval_list.Domain_Size(self)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -732,11 +444,10 @@ Int64 \ {kint64min} !!</p></section>
|
||||
<span>def <span class="ident">UnionWith</span></span>(<span>self, domain)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the set D ∪ domain.</p></section>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>Source code</summary>
|
||||
<pre><code class="python">def UnionWith(self, domain: 'Domain') -> "operations_research::Domain":
|
||||
r""" Returns the set D ∪ domain."""
|
||||
<pre><code class="python">def UnionWith(self, domain: "Domain") -> "operations_research::Domain":
|
||||
return _sorted_interval_list.Domain_UnionWith(self, domain)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
@@ -785,7 +496,7 @@ Int64 \ {kint64min} !!</p></section>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p><span style="color:#ddd">卐</span></p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.2</a>.</p>
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.6.3</a>.</p>
|
||||
</footer>
|
||||
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
|
||||
Reference in New Issue
Block a user