Problem with plot_Hinss2021_classification

[allwaysFindFood]

In the official example plot_Hinss2021_classification.py (https://neurotechx.github.io/moabb/auto_examples/plot_Hinss2021_classification.html), is there a logical error on line 76?

    """Select channels based on covariance information."""

    def __init__(self, n_chan, cov_est):
        self._chs_idx = None
        self.n_chan = n_chan
        self.cov_est = cov_est

    def fit(self, X, _y=None):
        # Get the covariances of the channels for each epoch.
        covs = Covariances(estimator=self.cov_est).fit_transform(X)
        # Get the average covariance between the channels
        m = np.mean(covs, axis=0)
        n_feats, _ = m.shape
        # Select the `n_chan` channels having the maximum covariances.
        all_max = []
        for i in range(n_feats):
            for j in range(n_feats):
                if len(all_max) <= self.n_chan:
                    all_max.append(m[i, j])
                else:
                    if m[i, j] > max(all_max):    # This is  line 76
                        all_max[np.argmin(all_max)] = m[i, j]
        indices = []
        for v in all_max:
            indices.extend(np.argwhere(m == v).flatten())
        # We will keep only these channels for the transform step.
        indices = np.unique(indices)
        self._chs_idx = indices
        return self

It should be: if m[i, j] is greater than the minimum value, then replace it.

                    if m[i, j] > min(all_max):
                        all_max[np.argmin(all_max)] = m[i, j]

Can the entire code segment for finding the largest n values be optimized to?

np.partition(m.flatten(), -n_feats)[-n_feats:]

[bruAristimunha]

Hey @gcattan,

Can you help @allwaysFindFood please?

[gcattan]

Hi @allwaysFindFood
Thanks for suggestions ! Indeed, looks like there is a place for improvement !

How do you feel about raising a PR?