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| """Gen triple-set from a matrix.""" | |
| from typing import List, Tuple, Union # noqa | |
| import numpy as np | |
| import pandas as pd | |
| # fmt: off | |
| def cmat2tset( | |
| cmat1: Union[List[List[float]], np.ndarray, pd.DataFrame], | |
| # thirdcol: bool = True | |
| ) -> np.ndarray: | |
| # ) -> List[Union[Tuple[int, int], Tuple[int, int, float]]]: | |
| # fmt: on | |
| """Gen triple-set from a matrix. | |
| Args | |
| cmat: 2d-array or list, correlation or other metric matrix | |
| # thirdcol: bool, whether to output a third column (max value) | |
| Returns | |
| Obtain the max and argmax for each column, erase the row afterwards to eliminate one single row that would dominate | |
| every column. | |
| """ | |
| # if isinstance(cmat, list): | |
| cmat = np.array(cmat1) | |
| if not np.prod(cmat.shape): | |
| raise SystemError("data not 2d...") | |
| _ = """ | |
| # y00 = range(cmat.shape[1]) # cmat.shape[0] long time wasting bug | |
| yargmax = cmat.argmax(axis=0) | |
| if thirdcol: | |
| ymax = cmat.max(axis=0) | |
| res = [*zip(y00, yargmax, ymax)] # type: ignore | |
| # to unzip | |
| # a, b, c = zip(*res) | |
| return res | |
| _ = [*zip(y00, yargmax)] # type: ignore | |
| return _ | |
| """ | |
| low_ = cmat.min() - 1 | |
| argmax_max = [] | |
| src_len, tgt_len = cmat.shape # ylim, xlim | |
| for _ in range(min(src_len, tgt_len)): | |
| argmax = int(cmat.argmax()) | |
| row, col = divmod(argmax, tgt_len) | |
| argmax_max.append([col, row, cmat.max()]) # x-axis, y-axis | |
| # erase row-th row and col-th col of cmat | |
| cmat[row, :] = low_ | |
| cmat[:, col] = low_ | |
| return np.array(argmax_max) | |