master > master: code py - darstellung hervorhebung + moves
- Hervorhebung von Summanden, die vom Greedyalgorithmus betroffen sind - Moves Spalte hinzugefügt - Summen + padding + moves jetzt abgedunkelt
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@ -122,24 +122,31 @@ def rucksack_branch_and_bound_algorithm(
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print('');
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logged_steps = [];
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step: Step;
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mask = empty_mask(n=len(costs));
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lb_estimate = np.inf;
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bound = np.inf;
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S = Stack();
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S.push(mask);
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while not S.empty():
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lb, choice, order_, pad = estimate_lower_bound(mask=S.top(), max_cost=max_cost, costs=costs, values=values, items=items);
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# top-Element auslesen und Bound berechnen:
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A: Mask = S.top();
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bound_subtree, choice, order_, pad = estimate_lower_bound(mask=A, max_cost=max_cost, costs=costs, values=values, items=items);
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# für logging:
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if verbose:
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logged_steps.append((lb_estimate, lb, str(S), choice, order_, pad));
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step = Step(bound=bound, bound_subtree=bound_subtree, stack_str=str(S), choice=choice, order=order_, indexes=A.indexes_unset, pad=pad);
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S.pop();
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# Update nur nötig, wenn die (eingeschätzte) untere Schranke von A das bisherige Minimum verbessert:
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A: Mask = S.pop();
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if lb < lb_estimate:
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# Bound, wenn sich A nicht weiter aufteilen lässt od. man A wie eine einelementige Option behandeln kann:
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if bound_subtree < bound:
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# Bound aktualisieren, wenn sich A nicht weiter aufteilen od. wenn sich A wie eine einelementige Option behandeln läst:
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if not A.splittable() or pad != MaskValue.UNSET:
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lb_estimate = lb;
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bound = bound_subtree;
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# falls A als einelementige Menge betrachtet werden kann, ersetze unbekannte Werte:
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if pad != MaskValue.UNSET:
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A = A.pad(pad);
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mask = A;
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# für logging:
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if verbose:
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step.move = EnumBranchAndBoundMove.BOUND;
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# Branch sonst
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else:
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B, C = A.split();
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@ -147,6 +154,11 @@ def rucksack_branch_and_bound_algorithm(
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# Nur dann C auf Stack legen, wenn mind. eine Möglichkeit in C die Kapazitätsschranke erfüllt:
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if sum(costs[C.indexes_one]) <= max_cost:
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S.push(C);
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# für logging:
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if verbose:
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step.move = EnumBranchAndBoundMove.BRANCH;
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if verbose:
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logged_steps.append(step);
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# Aspekte der Lösung speichern
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rucksack = mask.indexes_one; # Indexes von Items im Rucksack
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@ -87,30 +87,36 @@ def display_rucksack(
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# METHOD display result of branch and bound
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def display_branch_and_bound(
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values: np.ndarray,
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steps: List[Tuple[float, float, Stack, List[Fraction], List[int], MaskValue]],
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) -> str:
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def display_branch_and_bound(values: np.ndarray, steps: List[Step]) -> str:
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# füge Summen-Ausdrücke für Greedy-Alg hinzu:
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rows = [];
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used_choices = [];
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for lb_estimate, lb, S, choice, order, pad in steps:
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if choice in used_choices:
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expr = f'{lb:g}';
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index_soln = max([-1] + [ i for i, step in enumerate(steps) if step.move == EnumBranchAndBoundMove.BOUND ]);
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for i, step in enumerate(steps):
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if step.choice in used_choices:
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expr = f'{step.bound_subtree:g}';
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else:
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used_choices.append(choice);
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expr = display_sum(choice=choice, values=values, as_maximum=False, order=order);
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rows.append((f'{lb_estimate:+g}', expr, S, ('' if pad == MaskValue.UNSET else pad.value)));
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used_choices.append(step.choice);
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expr = display_sum(choice=step.choice, values=values, as_maximum=False, order=step.order, indexes=step.indexes);
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pad_str = ('' if step.pad == MaskValue.UNSET else step.pad.value);
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move_str = ('' if step.move == EnumBranchAndBoundMove.NONE else step.move.value);
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if i == index_soln:
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move_str = f'{move_str} *';
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rows.append({
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'bound': f'{step.bound:+g}',
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'bound_subtree': expr,
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'stack': step.stack_str,
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'pad': f'\x1b[2m{pad_str}\x1b[0m',
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'move': f'\x1b[2m{move_str}\x1b[0m',
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});
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table = pd.DataFrame(rows) \
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.rename(columns={0: 'bound', 1: 'g(TOP(S))', 2: 'S', 3: 'pad?'}) \
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.reset_index(drop=True);
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table = pd.DataFrame(rows).reset_index(drop=True);
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# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
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repr = tabulate(
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table,
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headers=['bound', 'g(TOP(S))', 'S — stack', 'pad?'],
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headers=['bound', 'g(TOP(S))', 'S — stack', '\x1b[2mpad?\x1b[0m', '\x1b[2mmove\x1b[0m'],
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showindex=False,
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colalign=('left', 'left', 'right', 'center'),
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colalign=('left', 'left', 'right', 'center', 'left'),
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tablefmt='rst'
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);
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return repr;
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@ -123,17 +129,21 @@ def display_sum(
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choice: List[Fraction],
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values: np.ndarray,
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order: Optional[List[int]] = None,
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indexes: List[int] = [],
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as_maximum: bool = True,
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) -> str:
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parts = [ (u, x) for u, x in zip(choice, values)];
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def render(x: Tuple[bool, Fraction, float]):
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b, u, value = x;
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expr = f'\x1b[91m{value:g}\x1b[0m' if b else f'\x1b[2m{value:g}\x1b[0m';
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return expr if u == 1 else f'\x1b[4;2m{u}\x1b[0m\x1b[2m·\x1b[0m{expr}';
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parts = [ (i in indexes, u, x) for i, (u, x) in enumerate(zip(choice, values)) ];
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if not (order is None):
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parts = [ parts[j] for j in order ];
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value = sum([ u*x for u, x in parts]);
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expr = '+'.join([
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f'{x:g}' if u == 1 else f'{u}·{x:g}'
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for u, x in parts if u > 0
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]);
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parts = list(filter(lambda x: x[1] > 0, parts));
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value = sum([ u*x for _, u, x in parts ]);
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expr = '\x1b[2m+\x1b[0m'.join(map(render, parts));
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if as_maximum:
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return f'{value:g} = {expr}';
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else:
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return f'-{value:g} = -({expr})';
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return f'{value:g} \x1b[2m=\x1b[0m {expr}';
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return f'-{value:g} \x1b[2m= -(\x1b[0m{expr}\x1b[2m)\x1b[0m';
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@ -7,6 +7,7 @@
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from src.models.rucksack.mask import *;
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from src.models.rucksack.solution import *;
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from src.models.rucksack.logging import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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@ -17,4 +18,6 @@ __all__ = [
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'MaskValue',
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'Mask',
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'Solution',
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'EnumBranchAndBoundMove',
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'Step',
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];
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@ -0,0 +1,47 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# IMPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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from __future__ import annotations;
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from src.thirdparty.maths import *;
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from src.thirdparty.types import *;
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from src.models.rucksack.mask import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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__all__ = [
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'EnumBranchAndBoundMove',
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'Step',
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];
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# CLASS Move
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class EnumBranchAndBoundMove(Enum):
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NONE = -1;
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BOUND = 'bound';
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BRANCH = 'branch';
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# CLASS Step
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@dataclass
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class Step():
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bound: float = field();
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bound_subtree: float = field();
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stack_str: str = field();
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choice: List[Fraction] = field();
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order: List[int] = field();
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# the indexes upon which the greedy algorithm is carried out:
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indexes: List[int] = field();
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pad: MaskValue = field();
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move: EnumBranchAndBoundMove = field(default=EnumBranchAndBoundMove.NONE);
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