| """Reference repair for validation only; excluded from training images.""" |
| from pathlib import Path |
|
|
| Path('libfmp/c3/c3s2_dtw.py').write_text('''import numpy as np |
| from scipy.spatial.distance import cdist |
| |
| |
| def compute_cost_matrix(X, Y, metric="euclidean"): |
| return cdist(X.T, Y.T, metric=metric) |
| |
| |
| def _accumulate(C, steps): |
| D = np.full(C.shape, np.inf) |
| D[0, 0] = C[0, 0] |
| for i in range(C.shape[0]): |
| for j in range(C.shape[1]): |
| if i == j == 0: |
| continue |
| previous = [D[i-di, j-dj] for di, dj in steps if i >= di and j >= dj] |
| if previous: |
| D[i, j] = C[i, j] + min(previous) |
| return D |
| |
| |
| def _path(D, steps): |
| i, j = D.shape[0]-1, D.shape[1]-1 |
| points = [(i, j)] |
| while i or j: |
| candidates = [(i-di, j-dj) for di, dj in steps if i >= di and j >= dj] |
| if not candidates: |
| raise ValueError("No feasible warping path") |
| i, j = min(candidates, key=lambda p: D[p]) |
| points.append((i, j)) |
| return np.array(points[::-1]) |
| |
| |
| def compute_accumulated_cost_matrix(C): |
| return _accumulate(C, [(1, 1), (1, 0), (0, 1)]) |
| |
| |
| def compute_optimal_warping_path(D): |
| return _path(D, [(1, 1), (1, 0), (0, 1)]) |
| |
| |
| def compute_accumulated_cost_matrix_21(C): |
| return _accumulate(C, [(1, 1), (2, 1), (1, 2)]) |
| |
| |
| def compute_optimal_warping_path_21(D): |
| return _path(D, [(1, 1), (2, 1), (1, 2)]) |
| ''') |
| with Path('libfmp/c3/__init__.py').open('a') as output: |
| output.write('\nfrom .c3s2_dtw import compute_cost_matrix, compute_accumulated_cost_matrix, compute_optimal_warping_path, compute_accumulated_cost_matrix_21, compute_optimal_warping_path_21\n') |
|
|
