a7d92f7020dfe8854430ef13ed2923b2606a6262,torchsample/utils.py,,th_random_choice,#Any#Any#Any#Any#,374

Before Change

    if p is None:
        if replace:
            idx = th.floor(th.rand(size)*a.size(0)).long()
        else:
            idx = th.randperm(a.size(0))[:size]
    else:
        if abs(1.0-sum(p)) > 1e-3:

After Change

        if replace:
            idx = th.floor(th.rand(n_samples)*a.size(0)).long()
        else:
            idx = th.randperm(len(a))[:n_samples]
    else:
        if abs(1.0-sum(p)) > 1e-3:
            raise ValueError("p must sum to 1.0")
        if not replace:
            raise ValueError("replace must equal true if probabilities given")
        idx_vec = th.cat([th.zeros(round(p[i]*1000))+i for i in range(len(p))])
        idx = (th.floor(th.rand(n_samples)*999)).long()
        idx = idx_vec[idx].long()
    selection = a[idx]
    if n_samples == 1:
        selection = selection[0]
    return selection


def save_transform(file, transform):

Instances