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master > master: code py - fractional Werte + Sortierung in Greedy-Summen

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RD 2022-06-14 20:02:22 +02:00
parent c6149c230a
commit 3791220cee
4 changed files with 61 additions and 65 deletions
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61
code/python/src/algorithms/rucksack/algorithms.py
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@ -57,15 +57,15 @@ def rucksack_greedy_algorithm(
# führe greedy aus:
n = len(costs);
cost_total = 0;
vector = [ 0 for _ in range(n) ];
vector = [ Fraction(0) for _ in range(n) ];
for i in order:
# füge Item i hinzu, solange das Gesamtgewicht noch <= Schranke
if cost_total + costs[i] <= max_cost:
cost_total += costs[i];
vector[i] = 1;
vector[i] = Fraction(1);
# falls Bruchteile erlaubt sind, füge einen Bruchteil des i. Items hinzu und abbrechen
elif fractional:
vector[i] = (max_cost - cost_total)/costs[i];
vector[i] = Fraction(Fraction(max_cost - cost_total)/Fraction(costs[i]), _normalize=False);
break;
# ansonsten weiter machen:
else:
@ -74,7 +74,8 @@ def rucksack_greedy_algorithm(
# Aspekte der Lösung speichern:
rucksack = [i for i, v in enumerate(vector) if v > 0]; # Indexes von Items im Rucksack
soln = Solution(
vector = vector,
order = order,
choice = vector,
items = items[rucksack].tolist(),
costs = costs[rucksack].tolist(),
values = values[rucksack].tolist(),
@ -85,7 +86,7 @@ def rucksack_greedy_algorithm(
repr_rucksack = display_rucksack(items=items[rucksack], costs=costs[rucksack], values=values[rucksack]);
print('\x1b[1mEingeschätzte Lösung\x1b[0m');
print('');
print(f'Mask: {soln.vector}');
print(f'Mask: [{", ".join(map(str, soln.choice))}]');
print('Rucksack:')
print(repr_rucksack);
print('');
@ -126,21 +127,19 @@ def rucksack_branch_and_bound_algorithm(
S = Stack();
S.push(vector);
while not S.empty():
lb, u, can_add_all, can_add_none = estimate_lower_bound(mask=S.top(), max_cost=max_cost, costs=costs, values=values, items=items);
lb, choice, order_, pad = estimate_lower_bound(mask=S.top(), max_cost=max_cost, costs=costs, values=values, items=items);
if verbose:
logged_steps.append((lb_estimate, lb, str(S), u, can_add_all, can_add_none));
logged_steps.append((lb_estimate, lb, str(S), choice, order_, pad));
# Update nur nötig, wenn die (eingeschätzte) untere Schranke von A das bisherige Minimum verbessert:
A: Mask = S.pop();
if lb < lb_estimate:
# Bound, wenn sich A nicht weiter aufteilen lässt od. man A wie eine einelementige Option behandeln kann:
if not A.splittable() or can_add_all or can_add_none:
if not A.splittable() or pad != MaskValue.UNSET:
lb_estimate = lb;
if can_add_all:
vector = A.pad_ones();
elif can_add_none:
vector = A.pad_zeros();
else:
vector = A;
# falls A als einelementige Menge betrachtet werden kann, ersetze unbekannte Werte:
if pad != MaskValue.UNSET:
A = A.pad(pad);
vector = A;
# Branch sonst
else:
B, C = A.split();
@ -152,7 +151,8 @@ def rucksack_branch_and_bound_algorithm(
# Aspekte der Lösung speichern
rucksack = vector.indexes_one; # Indexes von Items im Rucksack
soln = Solution(
vector = vector.decision,
order = order,
choice = vector.choice,
items = items[rucksack].tolist(),
values = values[rucksack].tolist(),
costs = costs[rucksack].tolist(),
@ -160,13 +160,13 @@ def rucksack_branch_and_bound_algorithm(
# verbose output hier behandeln (irrelevant für Algorithmus):
if verbose:
repr = display_branch_and_bound(values=values, steps=logged_steps, order=order);
repr = display_branch_and_bound(values=values, steps=logged_steps);
repr_rucksack = display_rucksack(items=items[rucksack], costs=costs[rucksack], values=values[rucksack]);
print('\x1b[1mLösung\x1b[0m');
print('');
print(repr);
print('');
print(f'Mask: {soln.vector}');
print(f'Mask: [{", ".join(map(str, soln.choice))}]');
print('Rucksack:');
print(repr_rucksack);
print('');
@ -184,7 +184,8 @@ def get_sort_order(costs: np.ndarray, values: np.ndarray) -> List[int]:
'''
n = len(costs);
indexes = list(range(n));
order = sorted(indexes, key=lambda i: -values[i]/costs[i]);
margin = [ value/cost for cost, value in zip(costs, values) ];
order = sorted(indexes, key=lambda i: -margin[i]);
return order;
def estimate_lower_bound(
@ -193,7 +194,7 @@ def estimate_lower_bound(
costs: np.ndarray,
values: np.ndarray,
items: np.ndarray,
) -> Tuple[float, List[float], bool]:
) -> Tuple[float, List[Fraction], List[int], MaskValue]:
'''
Wenn partielle Information über den Rucksack festgelegt ist,
kann man bei dem unbekannten Teil das Rucksack-Problem
@ -205,25 +206,25 @@ def estimate_lower_bound(
indexes_one = mask.indexes_one;
indexes_unset = mask.indexes_unset;
n = len(mask);
vector = np.zeros(shape=(n,), dtype=float);
choice = np.zeros(shape=(n,), dtype=Fraction);
order = np.asarray(range(n));
# Berechnungen bei Items mit bekanntem Status in Rucksack:
value_rucksack = sum(values[indexes_one]);
cost_rucksack = sum(costs[indexes_one]);
vector[indexes_one] = 1;
choice[indexes_one] = Fraction(1);
# Für Rest des Rucksacks (Items mit unbekanntem Status):
cost_rest = max_cost - cost_rucksack;
can_add_all = False;
can_add_none = False;
pad = MaskValue.UNSET;
# Prüfe, ob man als Lösung alles/nichts hinzufügen kann:
if len(indexes_unset) > 0 and sum(costs[indexes_unset]) <= cost_rest:
can_add_all = True;
vector[indexes_unset] = 1;
pad = MaskValue.ONE;
choice[indexes_unset] = Fraction(1);
value_rest = sum(values[indexes_unset]);
elif len(indexes_unset) > 0 and min(costs[indexes_unset]) > cost_rest:
can_add_none = True;
vector[indexes_unset] = 0;
pad = MaskValue.ZERO;
choice[indexes_unset] = Fraction(0);
value_rest = 0;
# Sonst mit Greedy-Algorithmus lösen:
# NOTE: Lösung ist eine Überschätzung des max-Wertes.
@ -236,11 +237,13 @@ def estimate_lower_bound(
fractional = True,
verbose = False,
);
choice[indexes_unset] = soln_rest.choice;
value_rest = soln_rest.total_value;
vector[indexes_unset] = soln_rest.vector;
# Berechne Permutation für Teilrucksack
permute_part(order, indexes=indexes_unset, order=soln_rest.order, in_place=True);
# Einschätzung des max-Wertes:
value_max_est = value_rucksack + value_rest;
# Ausgabe mit -1 multiplizieren (weil maximiert wird):
return -value_max_est, vector.tolist(), can_add_all, can_add_none;
return -value_max_est, choice.tolist(), order.tolist(), pad;

30
code/python/src/algorithms/rucksack/display.py
View File

@ -10,6 +10,7 @@ from src.thirdparty.maths import *;
from src.thirdparty.types import *;
from src.models.stacks import *;
from src.models.rucksack import *;
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# EXPORTS
@ -38,7 +39,7 @@ def display_order(
'order': order,
'values': values,
'costs': costs,
'u': (values/costs),
'margin': [str(Fraction(Fraction(value), Fraction(cost))) for cost, value in zip(costs, values)],
}) \
.reset_index(drop=True);
if one_based:
@ -62,10 +63,11 @@ def display_rucksack(
costs: np.ndarray,
values: np.ndarray,
) -> str:
render = lambda r: f'{r:g}';
table = pd.DataFrame({
'items': items.tolist() + ['----', '∑'],
'costs': costs.tolist() + ['', f'\x1b[92;1m{sum(costs)}\x1b[0m'],
'values': values.tolist() + ['', f'\x1b[92;1m{sum(values)}\x1b[0m'],
'costs': list(map(render, costs)) + ['', f'\x1b[92;1m{sum(costs):g}\x1b[0m'],
'values': list(map(render, values)) + ['', f'\x1b[92;1m{sum(values):g}\x1b[0m'],
});
repr = tabulate(
table,
@ -82,20 +84,18 @@ def display_rucksack(
def display_branch_and_bound(
values: np.ndarray,
steps: List[Tuple[float, float, Stack, List[float], bool, bool]],
order: Optional[List[int]] = None,
steps: List[Tuple[float, float, Stack, List[Fraction], List[int], MaskValue]],
) -> str:
# füge Summen-Ausdrücke für Greedy-Alg hinzu:
rows = [];
used_vectors = [];
for lb_estimate, lb, S, u, can_add_all, can_add_none in steps:
pad = '1' if can_add_all else ('0' if can_add_none else '');
if u in used_vectors:
used_choices = [];
for lb_estimate, lb, S, choice, order, pad in steps:
if choice in used_choices:
expr = f'{lb:g}';
else:
used_vectors.append(u)
expr = display_sum(vector=u, values=values, as_maximum=False, order=order);
rows.append((f'{lb_estimate:g}', expr, pad, S));
used_choices.append(choice);
expr = display_sum(choice=choice, values=values, as_maximum=False, order=order);
rows.append((f'{lb_estimate:g}', expr, ('' if pad == MaskValue.UNSET else pad.value), S));
table = pd.DataFrame(rows) \
.rename(columns={0: 'b', 1: 'g(TOP(S))', 2: 'pad?', 3: 'S'}) \
@ -115,17 +115,17 @@ def display_branch_and_bound(
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
def display_sum(
vector: List[float],
choice: List[Fraction],
values: np.ndarray,
order: Optional[List[int]] = None,
as_maximum: bool = True,
) -> str:
parts = [ (u, x) for u, x in zip(vector, values)];
parts = [ (u, x) for u, x in zip(choice, values)];
if not (order is None):
parts = [ parts[j] for j in order ];
value = sum([ u*x for u, x in parts]);
expr = '+'.join([
f'{x:g}' if u == 1 else f'{Fraction(str(u))}·{x:g}'
f'{x:g}' if u == 1 else f'{u}·{x:g}'
for u, x in parts if u > 0
]);
if as_maximum:

18
code/python/src/models/rucksack/mask.py
View File

@ -7,6 +7,7 @@
from __future__ import annotations;
from src.thirdparty.maths import *;
from src.thirdparty.types import *;
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -47,8 +48,9 @@ class Mask():
return ''.join([ str(m.value) for m in self.values ]);
@property
def decision(self) -> List[int]:
return [ x.value for x in self.values ];
def choice(self) -> List[Fraction]:
assert all(x != MaskValue.UNSET for x in self.values);
return [ Fraction(x.value) for x in self.values ];
@property
def indexes_set(self) -> List[int]:
@ -76,17 +78,11 @@ class Mask():
vector2[self.index] = MaskValue.ONE;
return Mask(vector1), Mask(vector2);
def pad_zeros(self) -> Mask:
def pad(self, x: MaskValue) -> Mask:
'''
Completes mask by filling in unset values with zeros
Pads unset values with a give by given value.
'''
return Mask([ MaskValue.ZERO if u == MaskValue.UNSET else u for u in self.values ]);
def pad_ones(self) -> Mask:
'''
Completes mask by filling in unset values with zeros
'''
return Mask([ MaskValue.ONE if u == MaskValue.UNSET else u for u in self.values ]);
return Mask([ x if u == MaskValue.UNSET else u for u in self.values ]);
@property
def support(self) -> List[int]:

17
code/python/src/models/rucksack/solution.py
View File

@ -5,9 +5,9 @@
# IMPORTS
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
from __future__ import annotations
from dataclasses import asdict;
from __future__ import annotations;
from src.thirdparty.maths import *;
from src.thirdparty.types import *;
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@ -24,7 +24,8 @@ __all__ = [
@dataclass
class SolutionRaw():
vector: List[float] = field();
order: List[int] = field();
choice: List[Fraction] = field();
items: List[str] = field();
values: List[float] = field(repr=False);
costs: List[float] = field(repr=False);
@ -32,16 +33,12 @@ class SolutionRaw():
class Solution(SolutionRaw):
@property
def support(self) -> List[float]:
return [ i for i, v in enumerate(self.vector) if v > 0 ];
@property
def vector_support(self) -> List[float]:
return [ v for v in self.vector if v > 0 ];
return [ i for i, v in enumerate(self.choice) if v > 0 ];
@property
def total_weight(self) -> float:
return sum([ self.vector[i]*x for (i, x) in zip(self.support, self.costs) ]);
return sum([ self.choice[i]*x for (i, x) in zip(self.support, self.costs) ]);
@property
def total_value(self) -> float:
return sum([ self.vector[i]*x for (i, x) in zip(self.support, self.values) ]);
return sum([ self.choice[i]*x for (i, x) in zip(self.support, self.values) ]);