master > master: code py - hirschberg
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@ -16,6 +16,7 @@ from src.local.maths import *;
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from src.graphs.graph import *;
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from src.graphs.tarjan import *;
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from src.travel.naive import *;
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from src.string_alignment.hirschberg import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# GLOBAL CONSTANTS/VARIABLES
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@ -28,16 +29,22 @@ from src.travel.naive import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def enter():
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## Beispiel aus Seminarblatt 8
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tsp_naive_algorithm(
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dist = np.asarray([
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[0, 7, 2, 5],
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[7, 0, 5, 6],
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[2, 5, 0, 5],
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[2, 7, 4, 0],
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], dtype=float),
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optimise=max,
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verbose=True,
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# ## Beispiel für Seminarwoche 9 (Blatt 8):
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# tsp_naive_algorithm(
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# dist = np.asarray([
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# [0, 7, 4, 3],
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# [7, 0, 5, 6],
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# [2, 5, 0, 5],
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# [2, 7, 4, 0],
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# ], dtype=float),
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# optimise=min,
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# verbose=True,
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# );
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## Beispiel für Seminarwoche 10 (Blatt 9):
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hirschberg_algorithm_full(
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X = 'ACGAAG',
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Y = 'AGAT',
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verbose = True,
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);
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return;
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@ -0,0 +1,411 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# IMPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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from __future__ import annotations;
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from src.local.typing import *;
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from src.local.maths import *;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# EXPORTS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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__all__ = [
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'hirschberg_algorithm',
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'hirschberg_algorithm_full',
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];
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# CONSTANTS / SETUP
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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class Directions(Enum):
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UNSET = -1;
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DIAGONAL = 1;
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HORIZONTAL = 0;
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VERTICAL = 2;
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def gap_penalty(x: str):
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return 1;
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def missmatch_penalty(x: str, y: str):
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return 0 if x == y else 1;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# METHOD hirschberg_algorithm
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def hirschberg_algorithm(
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X: str,
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Y: str,
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verbose: bool = False,
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) -> Tuple[str, str]:
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Costs, Moves = hirschberg_match_matrix(X = '-' + X, Y = '-' + Y);
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path = reconstruct_optimal_path(Moves=Moves);
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word_x, word_y = reconstruct_words(X = '-' + X, Y = '-' + Y, Moves=Moves, path=path);
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if verbose:
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L = len(word_x);
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costs_repr, moves_repr = display_cost_matrix(Costs=Costs, path=path, X = '-' + X, Y = '-' + Y);
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print('');
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print('\x1b[1mAlignment:\x1b[0m');
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print(f' {word_y}');
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print(f' {L*"-"}');
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print(f' {word_x}');
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print('');
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print(costs_repr);
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print('');
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print(moves_repr);
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return word_x, word_y;
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def hirschberg_algorithm_full(
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X: str,
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Y: str,
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depth: int = 0,
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verbose: bool = False,
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) -> Tuple[str, str]:
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n = len(Y);
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if n > 1:
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n = int(np.ceil(n/2));
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# bilde linke Hälfte vom horizontalen Wort:
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Y1 = Y[:n];
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X1 = X;
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# bilde rechte Hälfte vom horizontalen Wort (und kehre h. + v. um):
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Y2 = Y[n:][::-1];
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X2 = X[::-1];
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# Löse Teilprobleme:
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Costs1, Moves1 = hirschberg_match_matrix(X = '-' + X1, Y = '-' + Y1);
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Costs2, Moves2 = hirschberg_match_matrix(X = '-' + X2, Y = '-' + Y2);
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path1, path2 = reconstruct_optimal_path_halves(
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Costs1=Costs1,
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Costs2=Costs2,
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Moves1=Moves1,
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Moves2=Moves2,
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);
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word_x_1, word_y_1 = reconstruct_words(X = '-' + X1, Y = '-' + Y1, Moves=Moves1, path=path1);
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word_x_2, word_y_2 = reconstruct_words(X = '-' + X2, Y = '-' + Y2, Moves=Moves2, path=path2);
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if verbose:
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L = len(word_x_1) + len(word_x_2);
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costs_repr, moves_repr = display_cost_matrix_halves(
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Costs1 = Costs1,
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Costs2 = Costs2,
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path1 = path1,
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path2 = path2,
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X1 = '-' + X1,
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X2 = '-' + X2,
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Y1 = '-' + Y1,
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Y2 = '-' + Y2,
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);
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print('');
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print(f'\x1b[1mRekursionstiefe: {depth}\x1b[0m')
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print('');
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print('\x1b[1mAlignment:\x1b[0m');
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print(f' {word_y_1} {word_y_2[::-1]}');
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print(f' {(L+1)*"-"}');
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print(f' {word_x_1} {word_x_2[::-1]}');
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print('');
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print(moves_repr);
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coord = path1[-1];
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m = coord[0];
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word_x_1, word_y_1 = hirschberg_algorithm_full(X=X[:m], Y=Y[:n], depth=depth+1, verbose=True);
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word_x_2, word_y_2 = hirschberg_algorithm_full(X=X[m:], Y=Y[n:], depth=depth+1, verbose=True);
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word_x = word_x_1 + word_x_2;
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word_y = word_y_1 + word_y_2;
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else:
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word_x, word_y = hirschberg_algorithm(X=X, Y=Y, verbose=False);
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if depth == 0:
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L = len(word_x);
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print('');
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print('\x1b[1mAlignment:\x1b[0m');
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print(f' {word_y}');
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print(f' {L*"-"}');
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print(f' {word_x}');
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print('');
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return word_x, word_y;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# METHODS cost matrix + optimal paths
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def hirschberg_match_matrix(
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X: str,
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Y: str,
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) -> Tuple[NDArray[(Any, Any), int], NDArray[(Any, Any), Directions]]:
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'''
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Berechnet Hirschberg-Costs-Matrix (ohne Rekursion).
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Annahmen:
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- X[0] = gap
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- Y[0] = gap
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'''
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m = len(X); # display vertically
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n = len(Y); # display horizontally
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Costs = np.full(shape=(m, n), dtype=int, fill_value=0);
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Moves = np.full(shape=(m, n), dtype=Directions, fill_value=Directions.UNSET);
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# zuerst 0. Spalte und 0. Zeile ausfüllen:
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for i, x in list(enumerate(X))[1:]:
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update_cost_matrix(Costs, Moves, x, '', i, 0);
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for j, y in list(enumerate(Y))[1:]:
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update_cost_matrix(Costs, Moves, '', y, 0, j);
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# jetzt alle »inneren« Werte bestimmen:
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for i, x in list(enumerate(X))[1:]:
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for j, y in list(enumerate(Y))[1:]:
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update_cost_matrix(Costs, Moves, x, y, i, j);
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return Costs, Moves;
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def update_cost_matrix(
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Costs: NDArray[(Any, Any), int],
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Moves: NDArray[(Any, Any), Directions],
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x: str,
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y: str,
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i: int,
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j: int,
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):
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'''
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Schrittweise Funktion zur Aktualisierung vom Eintrag `(i,j)` in der Kostenmatrix.
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Annahme:
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- alle »Vorgänger« von `(i,j)` in der Matrix sind bereits optimiert.
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@inputs
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- `Costs` - bisher berechnete Kostenmatrix
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- `Moves` - bisher berechnete optimale Schritte
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- `i`, `x` - Position und Wert in String `X` (»vertical« dargestellt)
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- `j`, `y` - Position und Wert in String `Y` (»horizontal« dargestellt)
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'''
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# nichts zu tun, wenn (i, j) == (0, 0):
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if i == 0 and j == 0:
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Costs[0, 0] = 0;
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return;
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################################
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# NOTE: Berechnung von möglichen Moves wie folgt.
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#
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# Fall 1: (i-1,j-1) ---> (i,j)
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# ==> Stringvergleich ändert sich wie folgt:
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# s1 s1 x
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# ---- ---> ------
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# s2 s2 y
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#
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# Fall 2: (i,j-1) ---> (i,j)
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# ==> Stringvergleich ändert sich wie folgt:
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# s1 s1 GAP
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# ---- ---> -------
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# s2 s2 y
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#
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# Fall 3: (i-1,j) ---> (i,j)
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# ==> Stringvergleich ändert sich wie folgt:
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# s1 s1 x
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# ---- ---> -------
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# s2 s2 GAP
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#
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# Diese Fälle berücksichtigen wir:
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################################
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edges = [];
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if i > 0 and j > 0:
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edges.append((
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Directions.DIAGONAL,
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Costs[i-1, j-1] + missmatch_penalty(x, y),
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));
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if j > 0:
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edges.append((
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Directions.HORIZONTAL,
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Costs[i, j-1] + gap_penalty(y),
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));
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if i > 0:
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edges.append((
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Directions.VERTICAL,
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Costs[i-1, j] + gap_penalty(x),
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));
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if len(edges) > 0:
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# Sortiere nach Priorität (festgelegt in Enum):
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edges = sorted(edges, key=lambda x: x[0].value);
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# Wähle erste Möglichkeit mit minimalen Kosten:
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index = np.argmin([ cost for _, cost in edges]);
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Moves[i, j], Costs[i, j] = edges[index];
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return;
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def reconstruct_words(
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X: str,
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Y: str,
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Moves: NDArray[(Any, Any), Directions],
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path: List[Tuple[int, int]],
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) -> Tuple[str, str]:
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word_x = '';
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word_y = '';
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for (i, j) in path:
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x = X[i];
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y = Y[j];
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match Moves[i, j]:
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case Directions.DIAGONAL:
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word_x += x;
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word_y += y;
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case Directions.HORIZONTAL:
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word_x += '-';
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word_y += y;
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case Directions.VERTICAL:
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word_x += x;
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word_y += '-';
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return word_x, word_y;
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def reconstruct_optimal_path(
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Moves: NDArray[(Any, Any), Directions],
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coord: Optional[Tuple[int, int]] = None,
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) -> List[Tuple[int, int]]:
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'''
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Liest Matrix mit optimalen Schritten den optimalen Pfad aus,
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angenfangen von Endkoordinaten.
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'''
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if coord is None:
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m, n = Moves.shape;
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(i, j) = (m-1, n-1);
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else:
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(i, j) = coord;
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path = [(i, j)];
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while (i, j) != (0, 0):
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match Moves[i, j]:
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case Directions.DIAGONAL:
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(i, j) = (i - 1, j - 1);
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case Directions.HORIZONTAL:
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(i, j) = (i, j - 1);
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case Directions.VERTICAL:
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(i, j) = (i - 1, j);
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case _:
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break;
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path.append((i, j));
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return path[::-1];
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def reconstruct_optimal_path_halves(
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Costs1: NDArray[(Any, Any), int],
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Costs2: NDArray[(Any, Any), int],
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Moves1: NDArray[(Any, Any), Directions],
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Moves2: NDArray[(Any, Any), Directions],
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) -> Tuple[List[Tuple[int, int]], List[Tuple[int, int]]]:
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(m, n1) = Costs1.shape;
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(m, n2) = Costs2.shape;
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info = [
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(
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Costs1[i, n1-1] + Costs2[m-1-i, n2-1],
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(i, n1-1),
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(m-1-i, n2-1),
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)
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for i in range(m)
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];
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index = np.argmin([ cost for cost, _, _ in info ]);
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path1 = reconstruct_optimal_path(Moves1, coord=info[index][1]);
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path2 = reconstruct_optimal_path(Moves2, coord=info[index][2]);
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return path1, path2;
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# AUXILIARY METHODS
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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def represent_cost_matrix(
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Costs: NDArray[(Any, Any), int],
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path: List[Tuple[int, int]],
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X: str,
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Y: str,
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pad: bool = False,
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) -> Tuple[NDArray[(Any, Any), Any], NDArray[(Any, Any), Any]]:
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m = len(X); # display vertically
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n = len(Y); # display horizontally
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# erstelle string-Array:
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if pad:
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table = np.full(shape=(3 + m + 3, 3 + n + 1), dtype=object, fill_value='');
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else:
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table = np.full(shape=(3 + m, 3 + n), dtype=object, fill_value='');
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# topmost rows:
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table[0, 3:(3+n)] = [str(j) for j in range(n)];
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table[1, 3:(3+n)] = [y for y in Y];
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table[2, 3:(3+n)] = '--';
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# leftmost columns:
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table[3:(3+m), 0] = [str(i) for i in range(m)];
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table[3:(3+m), 1] = [x for x in X];
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table[3:(3+m), 2] = '|';
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if pad:
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table[-3, 3:(3+n)] = '--';
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table[3:(3+m), -1] = '|';
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table_costs = table.copy();
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table_moves = table.copy();
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table_costs[3:(3+m), 3:(3+n)] = Costs;
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table_moves[3:(3+m), 3:(3+n)] = '.';
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for (i, j) in path:
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# table_costs[3 + i, 3 + j] = f'\x1b[92;1m{table_costs[3 + i, 3 + j]}\x1b[0m';
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table_moves[3 + i, 3 + j] = '@';
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return table_costs, table_moves;
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def display_cost_matrix(
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Costs: NDArray[(Any, Any), int],
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path: List[Tuple[int, int]],
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X: str,
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Y: str,
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) -> Tuple[str, str]:
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'''
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Zeigt Kostenmatrix + optimalen Pfad.
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@inputs
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- `Costs` - Kostenmatrix
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- `Moves` - Kodiert die optimalen Schritte
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- `X`, `Y` - Strings
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@returns
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- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
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'''
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table_costs, table_moves = represent_cost_matrix(Costs=Costs, path=path, X=X, Y=Y);
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# benutze pandas-Dataframe, um schöner darzustellen:
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costs_repr = pd.DataFrame(table_costs).to_string(index=False, header=False);
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moves_repr = pd.DataFrame(table_moves).to_string(index=False, header=False);
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return costs_repr, moves_repr;
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def display_cost_matrix_halves(
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Costs1: NDArray[(Any, Any), int],
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Costs2: NDArray[(Any, Any), int],
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path1: List[Tuple[int, int]],
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path2: List[Tuple[int, int]],
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X1: str,
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X2: str,
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Y1: str,
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Y2: str,
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) -> Tuple[str, str]:
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'''
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Zeigt Kostenmatrix + optimalen Pfad für Schritt im D & C Hirschberg-Algorithmus
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@inputs
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- `Costs1`, `Costs2` - Kostenmatrizen
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- `Moves1`, `Moves2` - Kodiert die optimalen Schritte
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- `X1`, `X2`, `Y1`, `Y2` - Strings
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@returns
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- eine 'printable' Darstellung der Matrix mit den Strings X, Y + Indexes.
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'''
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table_costs1, table_moves1 = represent_cost_matrix(Costs=Costs1, path=path1, X=X1, Y=Y1, pad=True);
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table_costs2, table_moves2 = represent_cost_matrix(Costs=Costs2, path=path2, X=X2, Y=Y2, pad=True);
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# merge Taellen:
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table_costs = np.concatenate([table_costs1, table_costs2[::-1, ::-1]], axis=1);
|
||||
table_moves = np.concatenate([table_moves1, table_moves2[::-1, ::-1]], axis=1);
|
||||
|
||||
# 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;
|
||||
Loading…
Reference in New Issue