master > master: code py - algorithmus angepasst:

- korrekte behandlung von Permutationen
- hervorhebung von Summanden
- Spalte mit Infos über Moves
- optionen, um alle Gewichte zu zeigen / alle Summen zu zeigen

code/python/src/algorithms/rucksack/algorithms.py

@@ -57,25 +57,25 @@ def rucksack_greedy_algorithm(
     # führe greedy aus:
     n = len(costs);
     cost_total = 0;
-    vector = [ Fraction(0) for _ in range(n) ];
+    choice = [ 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] = Fraction(1);
+            choice[i] = Fraction(1);
         # falls Bruchteile erlaubt sind, füge einen Bruchteil des i. Items hinzu und abbrechen
         elif fractional:
-            vector[i] = Fraction(Fraction(max_cost - cost_total)/Fraction(costs[i]), _normalize=False);
+            choice[i] = Fraction(Fraction(max_cost - cost_total)/Fraction(costs[i]), _normalize=False);
             break;
         # ansonsten weiter machen:
         else:
             continue;
 
     # Aspekte der Lösung speichern:
-    rucksack = [i for i, v in enumerate(vector) if v > 0]; # Indexes von Items im Rucksack
+    rucksack = [i for i, v in enumerate(choice) if v > 0]; # Indexes von Items im Rucksack
     soln = Solution(
         order = order,
-        choice = vector,
+        choice = choice,
         items = items[rucksack].tolist(),
         costs = costs[rucksack].tolist(),
         values = values[rucksack].tolist(),
@@ -83,7 +83,7 @@ def rucksack_greedy_algorithm(
 
     # verbose output hier behandeln (irrelevant für Algorithmus):
     if verbose:
-        repr_rucksack = display_rucksack(items=items[rucksack], costs=costs[rucksack], values=values[rucksack]);
+        repr_rucksack = display_rucksack(items=items, costs=costs, values=values, choice=choice);
         print('\x1b[1mEingeschätzte Lösung\x1b[0m');
         print('');
         print(f'Mask: [{", ".join(map(str, soln.choice))}]');
@@ -122,24 +122,31 @@ def rucksack_branch_and_bound_algorithm(
         print('');
 
     logged_steps = [];
-    vector = empty_mask(n=len(costs));
-    lb_estimate = np.inf;
+    step: Step;
+    mask = empty_mask(n=len(costs));
+    bound = np.inf;
     S = Stack();
-    S.push(vector);
+    S.push(mask);
     while not S.empty():
-        lb, choice, order_, pad = estimate_lower_bound(mask=S.top(), max_cost=max_cost, costs=costs, values=values, items=items);
+        # top-Element auslesen und Bound berechnen:
+        A: Mask = S.top();
+        bound_subtree, choice, order_, pad = estimate_lower_bound(mask=A, max_cost=max_cost, costs=costs, values=values, items=items);
+        # für logging:
         if verbose:
-            logged_steps.append((lb_estimate, lb, str(S), choice, order_, pad));
+            step = Step(bound=bound, bound_subtree=bound_subtree, stack_str=str(S), choice=choice, order=order_, indexes=A.indexes_unset, pad=pad);
+        S.pop();
         # 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 bound_subtree < bound:
+            # Bound aktualisieren, wenn sich A nicht weiter aufteilen od. wenn sich A wie eine einelementige Option behandeln läst:
             if not A.splittable() or pad != MaskValue.UNSET:
-                lb_estimate = lb;
+                bound =  bound_subtree;
                 # falls A als einelementige Menge betrachtet werden kann, ersetze unbekannte Werte:
                 if pad != MaskValue.UNSET:
                     A = A.pad(pad);
-                vector = A;
+                mask = A;
+                # für logging:
+                if verbose:
+                    step.move = EnumBranchAndBoundMove.BOUND;
             # Branch sonst
             else:
                 B, C = A.split();
@@ -147,12 +154,17 @@ def rucksack_branch_and_bound_algorithm(
                 # Nur dann C auf Stack legen, wenn mind. eine Möglichkeit in C die Kapazitätsschranke erfüllt:
                 if sum(costs[C.indexes_one]) <= max_cost:
                     S.push(C);
+                # für logging:
+                if verbose:
+                    step.move = EnumBranchAndBoundMove.BRANCH;
+        if verbose:
+            logged_steps.append(step);
 
     # Aspekte der Lösung speichern
-    rucksack = vector.indexes_one; # Indexes von Items im Rucksack
+    rucksack = mask.indexes_one; # Indexes von Items im Rucksack
     soln = Solution(
         order = order,
-        choice = vector.choice,
+        choice = mask.choice,
         items = items[rucksack].tolist(),
         values = values[rucksack].tolist(),
         costs = costs[rucksack].tolist(),
@@ -161,11 +173,11 @@ 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);
-        repr_rucksack = display_rucksack(items=items[rucksack], costs=costs[rucksack], values=values[rucksack]);
-        print('\x1b[1mLösung\x1b[0m');
-        print('');
+        repr_rucksack = display_rucksack(items=items, costs=costs, values=values, choice=mask.choice);
         print(repr);
         print('');
+        print('\x1b[1mLösung\x1b[0m');
+        print('');
         print(f'Mask: [{", ".join(map(str, soln.choice))}]');
         print('Rucksack:');
         print(repr_rucksack);
@@ -201,7 +213,7 @@ def estimate_lower_bound(
     mit Greedy-Algorithmus »lösen«,
     um schnell eine gute Einschätzung zu bestimmen.
 
-    NOTE: Diese Funktion wird `g(vector)` im Skript bezeichnet.
+    NOTE: Diese Funktion wird `g(mask)` im Skript bezeichnet.
     '''
     indexes_one = mask.indexes_one;
     indexes_unset = mask.indexes_unset;

code/python/src/algorithms/rucksack/display.py

@@ -9,6 +9,8 @@ from src.thirdparty.code import *;
 from src.thirdparty.maths import *;
 from src.thirdparty.types import *;
 
+from src.setup import config;
+from models.generated.config import *;
 from src.models.stacks import *;
 from src.models.rucksack import *;
 
@@ -62,18 +64,31 @@ def display_rucksack(
     items: np.ndarray,
     costs: np.ndarray,
     values: np.ndarray,
+    choice: List[Fraction],
 ) -> str:
+    show_options = config.OPTIONS.rucksack.show;
     render = lambda r: f'{r:g}';
+    choice = np.asarray(choice);
+    rucksack = np.where(choice > 0);
+    if not(EnumRucksackShow.all_weights in show_options):
+        items = items[rucksack];
+        costs = costs[rucksack];
+        values = values[rucksack];
+        choice = choice[rucksack];
     table = pd.DataFrame({
         'items':  items.tolist() + ['----', '∑'],
-        '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'],
+        'nr':  list(map(str, choice))
+            + ['----', f'\x1b[92;1m{float(sum(choice)):g}\x1b[0m'],
+        'costs':  list(map(render, costs))
+            + ['----', f'\x1b[92;1m{sum(choice*costs):g}\x1b[0m'],
+        'values': list(map(render, values))
+            + ['----', f'\x1b[92;1m{sum(choice*values):g}\x1b[0m'],
     });
     repr = tabulate(
         table,
-        headers=['item', 'cost', 'value'],
+        headers=['item', 'nr', 'cost', 'value'],
         showindex=False,
-        colalign=('left', 'center', 'center'),
+        colalign=('left', 'center', 'center', 'center'),
         tablefmt='rst'
     );
     return repr;
@@ -82,30 +97,39 @@ def display_rucksack(
 # METHOD display result of branch and bound
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-def display_branch_and_bound(
-    values: np.ndarray,
-    steps: List[Tuple[float, float, Stack, List[Fraction], List[int], MaskValue]],
-) -> str:
-    # füge Summen-Ausdrücke für Greedy-Alg hinzu:
+def display_branch_and_bound(values: np.ndarray, steps: List[Step]) -> str:
+    show_options = config.OPTIONS.rucksack.show;
+    show_all_sums = (EnumRucksackShow.all_sums in show_options);
+
     rows = [];
     used_choices = [];
-    for lb_estimate, lb, S, choice, order, pad in steps:
-        if choice in used_choices:
-            expr = f'{lb:g}';
+    index_soln = max([-1] + [ i for i, step in enumerate(steps) if step.move == EnumBranchAndBoundMove.BOUND ]);
+    for i, step in enumerate(steps):
+        if show_all_sums or step.choice not in used_choices:
+            # Füge Summen-Ausdrücke für Greedy-Alg hinzu:
+            used_choices.append(step.choice);
+            expr = display_sum(choice=step.choice, values=values, as_maximum=False, order=step.order, indexes=step.indexes);
         else:
-            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));
+            expr = f'{step.bound_subtree:g}';
+        pad_str = ('' if step.pad == MaskValue.UNSET else step.pad.value);
+        move_str = ('' if step.move == EnumBranchAndBoundMove.NONE else step.move.value);
+        if i == index_soln:
+            move_str = f'{move_str} *';
+        rows.append({
+            'bound': f'{step.bound:+g}',
+            'bound_subtree': expr,
+            'stack': step.stack_str,
+            'pad': f'\x1b[2m{pad_str}\x1b[0m',
+            'move': f'\x1b[2m{move_str}\x1b[0m',
+        });
 
-    table = pd.DataFrame(rows) \
-        .rename(columns={0: 'b', 1: 'g(TOP(S))', 2: 'pad?', 3: 'S'}) \
-        .reset_index(drop=True);
+    table = pd.DataFrame(rows).reset_index(drop=True);
     # benutze pandas-Dataframe + tabulate, um schöner darzustellen:
     repr = tabulate(
         table,
-        headers=['b', 'g(TOP(S))', 'pad?', 'S'],
+        headers=['bound', 'g(TOP(S))', 'S — stack', '\x1b[2mpad?\x1b[0m', '\x1b[2mmove\x1b[0m'],
         showindex=False,
-        colalign=('left', 'left', 'center', 'right'),
+        colalign=('left', 'left', 'right', 'center', 'left'),
         tablefmt='rst'
     );
     return repr;
@@ -118,17 +142,31 @@ def display_sum(
     choice: List[Fraction],
     values: np.ndarray,
     order: Optional[List[int]] = None,
+    indexes: List[int] = [],
     as_maximum: bool = True,
 ) -> str:
-    parts = [ (u, x) for u, x in zip(choice, values)];
+    show_options = config.OPTIONS.rucksack.show;
+    show_all_weights = (EnumRucksackShow.all_weights in show_options);
+
+    def render(x: Tuple[bool, Fraction, float]):
+        b, u, value = x;
+        if u == 0:
+            expr = f'\x1b[94;2m{value:g}\x1b[0m' if b else f'\x1b[2m{value:g}\x1b[0m';
+        else:
+            expr = f'\x1b[94m{value:g}\x1b[0m' if b else f'\x1b[0m{value:g}\x1b[0m';
+        if not show_all_weights and u == 1:
+            return expr;
+        return f'\x1b[2;4m{u}\x1b[0m\x1b[2m·\x1b[0m{expr}';
+
+    parts = [ (i in indexes, u, x) for i, (u, x) in enumerate(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'{u}·{x:g}'
-        for u, x in parts if u > 0
-    ]);
+    if not show_all_weights:
+        parts = list(filter(lambda x: x[1] > 0, parts));
+
+    value = sum([ u*x for _, u, x in parts ]);
+    expr = '\x1b[2m+\x1b[0m'.join(map(render, parts));
+
     if as_maximum:
-        return f'{value:g} = {expr}';
-    else:
-        return f'-{value:g} = -({expr})';
+        return f'{value:g} \x1b[2m=\x1b[0m {expr}';
+    return f'-{value:g} \x1b[2m= -(\x1b[0m{expr}\x1b[2m)\x1b[0m';

code/python/src/models/rucksack/__init__.py

@@ -7,6 +7,7 @@
 
 from src.models.rucksack.mask import *;
 from src.models.rucksack.solution import *;
+from src.models.rucksack.logging import *;
 
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 # EXPORTS
@@ -17,4 +18,6 @@ __all__ = [
     'MaskValue',
     'Mask',
     'Solution',
+    'EnumBranchAndBoundMove',
+    'Step',
 ];

code/python/src/models/rucksack/logging.py

@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# IMPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+from __future__ import annotations;
+
+from src.thirdparty.maths import *;
+from src.thirdparty.types import *;
+
+from src.models.rucksack.mask import *;
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# EXPORTS
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+__all__ = [
+    'EnumBranchAndBoundMove',
+    'Step',
+];
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# CLASS Move
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+class EnumBranchAndBoundMove(Enum):
+    NONE = -1;
+    BOUND = 'bound';
+    BRANCH = 'branch';
+
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# CLASS Step
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+@dataclass
+class Step():
+    bound: float = field();
+    bound_subtree: float = field();
+    stack_str: str = field();
+    choice: List[Fraction] = field();
+    order: List[int] = field();
+    # the indexes upon which the greedy algorithm is carried out:
+    indexes: List[int] = field();
+    pad: MaskValue = field();
+    move: EnumBranchAndBoundMove = field(default=EnumBranchAndBoundMove.NONE);