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master > master: code py - verbose/display mode als enum

This commit is contained in:
RD 2022-06-09 15:14:03 +02:00
parent b79cc24bc4
commit 67aa70edfa
2 changed files with 42 additions and 44 deletions
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9
code/python/src/main.py
View File

@ -41,8 +41,8 @@ def enter():
# verbose=True,
# );
## Beispiel für Seminarwoche 10 (Blatt 9):
# hirschberg_algorithm_once(
hirschberg_algorithm(
hirschberg_algorithm_once(
# hirschberg_algorithm(
# Y = 'ANSPANNEN',
# X = 'ANSTRENGEN',
# Y = 'AGAT',
@ -51,8 +51,9 @@ def enter():
X = 'happily ever, lol',
# Y = 'apple',
# X = 'happily',
verbose = True,
just_moves = False,
mode = DisplayMode.COSTS,
# mode = DisplayMode.MOVES,
# mode = DisplayMode.COSTS_AND_MOVES,
);
return;

77
code/python/src/string_alignment/hirschberg.py
View File

@ -18,12 +18,19 @@ from src.local.maths import *;
__all__ = [
'hirschberg_algorithm',
'hirschberg_algorithm_once',
'DisplayMode'
];
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# CONSTANTS / SETUP
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
class DisplayMode(Enum):
NONE = -1;
COSTS = 0;
MOVES = 1;
COSTS_AND_MOVES = 2;
class Directions(Enum):
UNSET = -1;
# Prioritäten hier setzen
@ -44,14 +51,13 @@ def missmatch_penalty(x: str, y: str):
def hirschberg_algorithm_once(
X: str,
Y: str,
verbose: bool = False,
just_moves: bool = False,
mode: DisplayMode = DisplayMode.NONE,
) -> 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, just_moves=just_moves);
if mode != DisplayMode.NONE:
repr = display_cost_matrix(Costs=Costs, path=path, X = '-' + X, Y = '-' + Y, mode=mode);
print(f'\n{repr}');
print(f'\n\x1b[1mOptimales Alignment:\x1b[0m');
print('');
@ -64,13 +70,12 @@ def hirschberg_algorithm_once(
def hirschberg_algorithm(
X: str,
Y: str,
verbose: bool = False,
just_moves: bool = False,
mode: DisplayMode = DisplayMode.NONE,
) -> Tuple[str, str]:
alignments_x, alignments_y = hirschberg_algorithm_step(X=X, Y=Y, depth=1, verbose=verbose, just_moves=just_moves);
alignments_x, alignments_y = hirschberg_algorithm_step(X=X, Y=Y, depth=1, mode=mode);
word_x = ''.join(alignments_x);
word_y = ''.join(alignments_y);
if verbose:
if mode != DisplayMode.NONE:
display_x = f'[{"][".join(alignments_x)}]';
display_y = f'[{"][".join(alignments_y)}]';
print(f'\n\x1b[1mOptimales Alignment:\x1b[0m');
@ -85,8 +90,7 @@ def hirschberg_algorithm_step(
X: str,
Y: str,
depth: int = 0,
verbose: bool = False,
just_moves: bool = False,
mode: DisplayMode = DisplayMode.NONE,
) -> Tuple[List[str], List[str]]:
n = len(Y);
if n == 1:
@ -112,7 +116,7 @@ def hirschberg_algorithm_step(
Costs1, Moves1 = compute_cost_matrix(X = '-' + X1, Y = '-' + Y1);
Costs2, Moves2 = compute_cost_matrix(X = '-' + X2, Y = '-' + Y2);
if verbose:
if mode != DisplayMode.NONE:
path1, path2 = reconstruct_optimal_path_halves(Costs1=Costs1, Costs2=Costs2, Moves1=Moves1, Moves2=Moves2);
repr = display_cost_matrix_halves(
Costs1 = Costs1,
@ -123,7 +127,7 @@ def hirschberg_algorithm_step(
X2 = '-' + X2,
Y1 = '-' + Y1,
Y2 = '-' + Y2,
just_moves = just_moves,
mode = mode,
);
print(f'\n\x1b[1mRekursionstiefe: {depth}\x1b[0m\n\n{repr}')
@ -131,8 +135,8 @@ 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, 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);
alignments_x_1, alignments_y_1 = hirschberg_algorithm_step(X=X[:p], Y=Y[:n], depth=depth+1, verbose=verbose, mode=mode);
alignments_x_2, alignments_y_2 = hirschberg_algorithm_step(X=X[p:], Y=Y[n:], depth=depth+1, verbose=verbose, mode=mode);
# Resultate zusammensetzen:
alignments_x = alignments_x_1 + alignments_x_2;
@ -355,8 +359,9 @@ def represent_cost_matrix(
path: List[Tuple[int, int]],
X: str,
Y: str,
mode: DisplayMode,
pad: bool = False,
) -> Tuple[NDArray[(Any, Any), Any], NDArray[(Any, Any), Any]]:
) -> NDArray[(Any, Any), Any]:
m = len(X); # display vertically
n = len(Y); # display horizontally
@ -379,22 +384,25 @@ def represent_cost_matrix(
table[-3, 3:(3+n)] = '--';
table[3:(3+m), -1] = '|';
table_costs = table.copy();
table_moves = table.copy();
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'{{{table_costs[3 + i, 3 + j]}}}';
table_moves[3 + i, 3 + j] = '*';
match mode:
case DisplayMode.MOVES:
table[3:(3+m), 3:(3+n)] = '.';
for (i, j) in path:
table[3 + i, 3 + j] = '*';
case DisplayMode.COSTS | DisplayMode.COSTS_AND_MOVES:
table[3:(3+m), 3:(3+n)] = Costs.copy();
if mode == DisplayMode.COSTS_AND_MOVES:
for (i, j) in path:
table[3 + i, 3 + j] = f'{{{table[3 + i, 3 + j]}}}';
return table_costs, table_moves;
return table;
def display_cost_matrix(
Costs: NDArray[(Any, Any), int],
path: List[Tuple[int, int]],
X: str,
Y: str,
just_moves: bool = False,
mode: DisplayMode,
) -> str:
'''
Zeigt Kostenmatrix + optimalen Pfad.
@ -407,13 +415,7 @@ def display_cost_matrix(
@returns
- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
'''
table_costs, table_moves = represent_cost_matrix(Costs=Costs, path=path, X=X, Y=Y);
# benutze pandas-Dataframe, um schöner darzustellen:
if just_moves:
table = table_moves;
else:
table = table_costs;
table = represent_cost_matrix(Costs=Costs, path=path, X=X, Y=Y, mode=mode);
# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');
return repr;
@ -427,7 +429,7 @@ def display_cost_matrix_halves(
X2: str,
Y1: str,
Y2: str,
just_moves: bool = False,
mode: DisplayMode,
) -> str:
'''
Zeigt Kostenmatrix + optimalen Pfad für Schritt im D & C Hirschberg-Algorithmus
@ -440,16 +442,11 @@ def display_cost_matrix_halves(
@returns
- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
'''
table_costs1, table_moves1 = represent_cost_matrix(Costs=Costs1, path=path1, X=X1, Y=Y1, pad=True);
table_costs2, table_moves2 = represent_cost_matrix(Costs=Costs2, path=path2, X=X2, Y=Y2, pad=True);
table1 = represent_cost_matrix(Costs=Costs1, path=path1, X=X1, Y=Y1, mode=mode, pad=True);
table2 = represent_cost_matrix(Costs=Costs2, path=path2, X=X2, Y=Y2, mode=mode, pad=True);
# merge Taellen:
table_costs = np.concatenate([table_costs1[:, :-1], table_costs2[::-1, ::-1]], axis=1);
table_moves = np.concatenate([table_moves1[:, :-1], table_moves2[::-1, ::-1]], axis=1);
if just_moves:
table = table_moves;
else:
table = table_costs;
table = np.concatenate([table1[:, :-1], table2[::-1, ::-1]], axis=1);
# benutze pandas-Dataframe + tabulate, um schöner darzustellen:
repr = tabulate(pd.DataFrame(table), showindex=False, stralign='center', tablefmt='plain');