449ccc820a0558d742bc7055bc37c1690dff4496,hypertools/tools/reduce.py,,reduce,#Any#Any#Any#Any#Any#Any#,13

Before Change

        x_reduced = reducePCA(x,ndims)

    // pad cols with zeros if ndims returned is less than ndims
    if x_reduced[0].shape[1] < ndims:
        for idx, x_r in enumerate(x_reduced):
            x_reduced[idx] = np.hstack([x_r, np.zeros((x_r.shape[0], ndims-x_reduced[0].shape[1]))])

    if align == True:
        // Import is here to avoid circular imports with reduce.py
        from .align import align as aligner
        x_reduced = aligner(x_reduced)

After Change

    assert all([i.shape[1]>ndims for i in x]), "In order to reduce the data, ndims must be less than the number of dimensions"

    // if there are any nans in any of the lists, use ppca
    if np.isnan(np.vstack(x)).any():
        warnings.warn("Missing data: Inexact solution computed with PPCA (see https://github.com/allentran/pca-magic for details)")
        x = fill_missing(x)

    // normalize
    if normalize:
        x = normalizer(x, normalize=normalize)

    // build model params dict

Instances