19efebad71288796c490f7dfcc4092afee646611,stellargraph/data/explorer.py,,naive_weighted_choices,#Any#Any#,295

Before Change

    // divide the interval [0, sum(weights)) into len(weights)
    // subintervals [x_i, x_{i+1}), where the width x_{i+1} - x_i ==
    // weights[i]
    subinterval_ends = []
    running_total = 0
    for w in weights:
        if w < 0:
            raise ValueError("Detected negative weight: {}".format(w))
        running_total += w
        subinterval_ends.append(running_total)

    // pick a place in the overall interval
    x = rs.random() * running_total

    // find the subinterval that contains the place, by looking for the
    // first subinterval where the end is (strictly) after it

After Change

    requires a lot of preprocessing (normalized probabilties), and
    does a lot of conversions/checks/preprocessing internally.
    
    probs = np.cumsum(weights)
    idx = np.searchsorted(probs, rs.random() * probs[-1], side="left")

    return idx

Instances