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master > master: code py - requirements kompakteres Display

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This commit is contained in:
RD 2022-06-09 15:00:06 +02:00
parent 61841a5368
commit a536d16c1d
2 changed files with 29 additions and 20 deletions
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1
code/python/src/main.py
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@ -52,6 +52,7 @@ def enter():
# Y = 'apple',
# X = 'happily',
verbose = True,
just_moves = False,
);
return;

48
code/python/src/string_alignment/hirschberg.py
View File

@ -45,14 +45,16 @@ def hirschberg_algorithm_once(
X: str,
Y: str,
verbose: bool = False,
just_moves: bool = False,
) -> Tuple[str, str]:
Costs, Moves = compute_cost_matrix(X = '-' + X, Y = '-' + Y);
path = reconstruct_optimal_path(Moves=Moves);
word_x, word_y = reconstruct_words(X = '-' + X, Y = '-' + Y, moves=[Moves[coord] for coord in path], path=path);
if verbose:
repr = display_cost_matrix(Costs=Costs, path=path, X = '-' + X, Y = '-' + Y);
repr = display_cost_matrix(Costs=Costs, path=path, X = '-' + X, Y = '-' + Y, just_moves=just_moves);
print(f'\n{repr}');
print(f'\n\x1b[1mOptimales Alignment:\x1b[0m');
print('');
print(word_y);
print(len(word_x) * '-');
print(word_x);
@ -63,14 +65,16 @@ def hirschberg_algorithm(
X: str,
Y: str,
verbose: bool = False,
just_moves: bool = False,
) -> Tuple[str, str]:
alignments_x, alignments_y = hirschberg_algorithm_step(X=X, Y=Y, depth=1, verbose=verbose);
alignments_x, alignments_y = hirschberg_algorithm_step(X=X, Y=Y, depth=1, verbose=verbose, just_moves=just_moves);
word_x = ''.join(alignments_x);
word_y = ''.join(alignments_y);
if verbose:
display_x = '|'.join(alignments_x);
display_y = '|'.join(alignments_y);
display_x = f'[{"][".join(alignments_x)}]';
display_y = f'[{"][".join(alignments_y)}]';
print(f'\n\x1b[1mOptimales Alignment:\x1b[0m');
print('');
print(display_y);
print(len(display_x) * '-');
print(display_x);
@ -82,6 +86,7 @@ def hirschberg_algorithm_step(
Y: str,
depth: int = 0,
verbose: bool = False,
just_moves: bool = False,
) -> Tuple[List[str], List[str]]:
n = len(Y);
if n == 1:
@ -118,6 +123,7 @@ def hirschberg_algorithm_step(
X2 = '-' + X2,
Y1 = '-' + Y1,
Y2 = '-' + Y2,
just_moves = just_moves,
);
print(f'\n\x1b[1mRekursionstiefe: {depth}\x1b[0m\n\n{repr}')
@ -125,8 +131,8 @@ def hirschberg_algorithm_step(
coord1, coord2 = get_optimal_transition(Costs1=Costs1, Costs2=Costs2);
p = coord1[0];
# Divide and Conquer ausführen:
alignments_x_1, alignments_y_1 = hirschberg_algorithm_step(X=X[:p], Y=Y[:n], depth=depth+1, verbose=verbose);
alignments_x_2, alignments_y_2 = hirschberg_algorithm_step(X=X[p:], Y=Y[n:], depth=depth+1, verbose=verbose);
alignments_x_1, alignments_y_1 = hirschberg_algorithm_step(X=X[:p], Y=Y[:n], depth=depth+1, verbose=verbose, just_moves=just_moves);
alignments_x_2, alignments_y_2 = hirschberg_algorithm_step(X=X[p:], Y=Y[n:], depth=depth+1, verbose=verbose, just_moves=just_moves);
# Resultate zusammensetzen:
alignments_x = alignments_x_1 + alignments_x_2;
@ -375,10 +381,10 @@ def represent_cost_matrix(
table_costs = table.copy();
table_moves = table.copy();
table_costs[3:(3+m), 3:(3+n)] = Costs;
table_costs[3:(3+m), 3:(3+n)] = Costs.copy();
table_moves[3:(3+m), 3:(3+n)] = '·';
for (i, j) in path:
# table_costs[3 + i, 3 + j] = f'\x1b[92;1m{table_costs[3 + i, 3 + j]}\x1b[0m';
table_costs[3 + i, 3 + j] = f'{{{table_costs[3 + i, 3 + j]}}}';
table_moves[3 + i, 3 + j] = '*';
return table_costs, table_moves;
@ -388,6 +394,7 @@ def display_cost_matrix(
path: List[Tuple[int, int]],
X: str,
Y: str,
just_moves: bool = False,
) -> str:
'''
Zeigt Kostenmatrix + optimalen Pfad.
@ -402,12 +409,13 @@ def display_cost_matrix(
'''
table_costs, table_moves = represent_cost_matrix(Costs=Costs, path=path, X=X, Y=Y);
# benutze pandas-Dataframe, um schöner darzustellen:
h = table_costs.shape[0];
costs_repr = pd.DataFrame(table_costs).to_string(index=False, header=False);
moves_repr = pd.DataFrame(table_moves).to_string(index=False, header=False);
table = np.concatenate([table_costs, np.full(shape=(h, 1), dtype=object, fill_value=' '), table_moves], axis=1);
if just_moves:
table = table_moves;
else:
table = table_costs;
repr = pd.DataFrame(table).to_string(index=False, header=False);
# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');
return repr;
def display_cost_matrix_halves(
@ -419,6 +427,7 @@ def display_cost_matrix_halves(
X2: str,
Y1: str,
Y2: str,
just_moves: bool = False,
) -> str:
'''
Zeigt Kostenmatrix + optimalen Pfad für Schritt im D & C Hirschberg-Algorithmus
@ -435,14 +444,13 @@ def display_cost_matrix_halves(
table_costs2, table_moves2 = represent_cost_matrix(Costs=Costs2, path=path2, X=X2, Y=Y2, pad=True);
# merge Taellen:
h = table_costs1.shape[0];
table_costs = np.concatenate([table_costs1, table_costs2[::-1, ::-1]], axis=1);
table_moves = np.concatenate([table_moves1, table_moves2[::-1, ::-1]], axis=1);
table = np.concatenate([table_costs, np.full(shape=(h, 1), dtype=object, fill_value=' '), table_moves], axis=1);
if just_moves:
table = table_moves;
else:
table = table_costs;
# benutze pandas-Dataframe, um schöner darzustellen:
# costs_repr = pd.DataFrame(table_costs).to_string(index=False, header=False);
# moves_repr = pd.DataFrame(table_moves).to_string(index=False, header=False);
# return costs_repr, moves_repr;
repr = pd.DataFrame(table).to_string(index=False, header=False);
# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');
return repr;