master > master: code py - bei output Sortierung rückgängig machen
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@ -9,6 +9,7 @@ from src.thirdparty.types import *;
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from src.thirdparty.maths import *;
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from models.generated.config import *;
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from src.core.utils import *;
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from src.models.rucksack import *;
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from src.models.stacks import *;
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from src.algorithms.rucksack.display import *;
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@ -43,10 +44,11 @@ def rucksack_greedy_algorithm(
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'''
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# sortiere daten:
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order = resort_by_value_per_weight(weights=weights, values=values, items=items);
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uorder = iperm(order);
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# verbose output hier behandeln (irrelevant für Algorithmus):
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if verbose:
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repr = display_order(order=order, weights=weights, values=values, items=items, one_based=True);
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repr = display_order(order=order, weights=weights[uorder], values=values[uorder], items=items[uorder], one_based=True);
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print('');
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print('\x1b[1mRucksack Problem - Greedy\x1b[0m');
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print('');
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@ -57,21 +59,19 @@ def rucksack_greedy_algorithm(
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n = len(weights);
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weight_total = 0;
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vector = [ 0 for _ in range(n) ];
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rucksack = [];
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for i in range(n):
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# füge Item i hinzu, solange das Gesamtgewicht noch <= Schranke
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if weight_total + weights[i] <= capacity:
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weight_total += weights[i];
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rucksack.append(i);
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vector[i] = 1;
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# sonst abbrechen. Falls Bruchteile erlaubt, füge einen Bruchteil des i. Items hinzu
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else:
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if fractional:
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rucksack.append(i);
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vector[i] = (capacity - weight_total)/weights[i];
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break;
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# Aspekte der Lösung speichern:
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rucksack = [i for i, v in enumerate(vector) if v > 0]; # Indexes von Items im Rucksack
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soln = Solution(
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vector = vector,
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items = items[rucksack].tolist(),
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@ -81,13 +81,19 @@ def rucksack_greedy_algorithm(
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# verbose output hier behandeln (irrelevant für Algorithmus):
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if verbose:
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expr_value = display_sum(vector=soln.vector, values=values);
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expr_weight = display_sum(vector=soln.vector, values=weights);
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print('\x1b[1mEingeschätztes Maximum\x1b[0m');
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# Umsortierung rückgängig machen:
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vector = [ soln.vector[i] for i in order ];
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rucksack = [ uorder[r] for r in rucksack ];
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permute_data(weights=weights, values=values, items=items, perm=uorder);
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# Ausdrücke bestimmen:
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expr_value = display_sum(vector=vector, values=values);
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expr_weight = display_sum(vector=vector, values=weights);
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print('\x1b[1mEingeschätzte Lösung\x1b[0m');
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print('');
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print(f'Rucksack: {", ".join(soln.items)}.');
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if fractional:
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print(f'Vector: {soln.vector_support}');
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print(f'Mask: {soln.vector}');
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print(f' ---> {vector} (unter urspr. Sortierung)');
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print(f'Rucksack: {", ".join(items[rucksack])}.');
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print(f'max. Value ≈ {expr_value}');
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print(f'∑ Weights = {expr_weight}');
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print('');
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@ -112,10 +118,11 @@ def rucksack_branch_and_bound_algorithm(
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'''
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order = resort_by_value_per_weight(weights=weights, values=values, items=items);
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uorder = iperm(order);
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# verbose output hier behandeln (irrelevant für Algorithmus):
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if verbose:
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repr = display_order(order=order, weights=weights, values=values, items=items, one_based=True);
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repr = display_order(order=order, weights=weights[uorder], values=values[uorder], items=items[uorder], one_based=True);
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print('');
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print('\x1b[1mRucksack Problem - Branch & Bound\x1b[0m');
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print('');
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@ -158,14 +165,22 @@ def rucksack_branch_and_bound_algorithm(
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# verbose output hier behandeln (irrelevant für Algorithmus):
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if verbose:
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expr_value = display_sum(vector=soln.vector, values=values);
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expr_weight = display_sum(vector=soln.vector, values=weights);
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# NOTE: Information in Tabelle gemäß permutierten Daten:
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repr = display_branch_and_bound(values=values, steps=logged_steps);
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print('\x1b[1mMaximum\x1b[0m');
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# Umsortierung rückgängig machen:
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vector = [ soln.vector[i] for i in order ];
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rucksack = [ uorder[r] for r in rucksack ];
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permute_data(weights=weights, values=values, items=items, perm=uorder);
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# Ausdrücke bestimmen:
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expr_value = display_sum(vector=vector, values=values);
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expr_weight = display_sum(vector=vector, values=weights);
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print('\x1b[1mLösung\x1b[0m');
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print('');
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print(repr);
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print('');
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print(f'Rucksack: {", ".join(soln.items)}.');
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print(f'Mask: {soln.vector}');
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print(f' ---> {vector} (unter urspr. Sortierung)');
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print(f'Rucksack: {", ".join(items[rucksack])}.');
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print(f'max. Value ≈ {expr_value}');
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print(f'∑ Weights = {expr_weight}');
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print('');
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@ -188,11 +203,20 @@ def resort_by_value_per_weight(
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n = len(weights);
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indexes = list(range(n));
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order = sorted(indexes, key=lambda i: -values[i]/weights[i]);
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weights[:] = weights[order];
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values[:] = values[order];
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items[:] = items[order];
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permute_data(weights=weights, values=values, items=items, perm=order);
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return order;
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def permute_data(
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weights: np.ndarray,
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values: np.ndarray,
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items: np.ndarray,
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perm: List[int],
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):
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weights[:] = weights[perm];
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values[:] = values[perm];
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items[:] = items[perm];
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return;
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def estimate_lower_bound(
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mask: Mask,
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capacity: float,
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@ -9,7 +9,6 @@ from src.thirdparty.code import *;
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from src.thirdparty.maths import *;
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from src.thirdparty.types import *;
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from src.core.utils import *;
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from src.models.stacks import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@ -33,18 +32,16 @@ def display_order(
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items: np.ndarray,
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one_based: bool = False,
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) -> str:
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index = range(len(order));
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uorder = iperm(order);
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table = pd.DataFrame({
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'items': items,
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'index': index,
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'order': order,
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'values': values,
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'weights': weights,
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'u': (values/weights),
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}, index=index) \
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.reindex(uorder);
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}) \
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.reset_index(drop=True);
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if one_based:
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table['index'] += 1;
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table['order'] += 1;
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# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
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repr = tabulate(
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pd.DataFrame(table),
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@ -62,13 +59,17 @@ def display_order(
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def display_sum(
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vector: Union[List[int], List[float]],
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values: np.ndarray,
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as_maximum: bool = True,
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) -> str:
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value = sum([ u*x for u, x in zip(vector,values)]);
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expr = '+'.join([
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f'{x:g}' if u == 1 else f'{Fraction(str(u))}·{x:g}'
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for u, x in zip(vector,values) if u > 0
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]);
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return f'{value:g} (={expr})';
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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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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# METHOD display result of branch and bound
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@ -86,7 +87,7 @@ def display_branch_and_bound(
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rows.append((f'{lb_estimate:g}', f'{lb:g}', S));
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else:
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used_vectors.append(u)
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rows.append((f'{lb_estimate:g}', display_sum(vector=u, values=values), S));
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rows.append((f'{lb_estimate:g}', display_sum(vector=u, values=values, as_maximum=False), S));
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table = pd.DataFrame(rows) \
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.rename(columns={0: 'b', 1: 'g(TOP(S))', 2: 'S'}) \
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