dce3b66b54fac2040e826a5a465ff58cf1295e7f,hypertools/tools/reduce.py,,reduce,#Any#Any#Any#Any#Any#Any#,13

Before Change

    // 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

            return [x_r[0]]

    // dictionary of models
    models = {
        "PCA" : PCA,
        "FastICA" : FastICA,
        "TSNE" : TSNE,
        "Isomap" : Isomap,
        "SpectralEmbedding" : SpectralEmbedding,
        "LocallyLinearEmbedding" : LocallyLinearEmbedding
    }

    // main
    x = format_data(x)

    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
    if model_params=={}:
        model_params = {
            "n_components" : ndims
        }
    elif "n_components" in model_params:
        pass
    else:
        model_params["n_components"]=ndims

    // reduce data
    x_reduced = reduce_list(x, models[model], model_params)

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

Instances