f47485cc4a21fb18564ede7ab0cc9001dbd0f1d9,chaospy/descriptives/sensitivity/main.py,,Sens_m,#Any#Any#,9

Before Change


    dim = len(dist)
    poly = setdim(poly, dim)

    zero = [0]*dim
    out = numpy.zeros((dim,) + poly.shape)
    V = Var(poly, dist, **kws)
    for i in range(dim):
        zero[i] = 1
        out[i] = Var(E_cond(poly, zero, dist, **kws),
                     dist, **kws)/(V+(V == 0))*(V != 0)
        zero[i] = 0
    return out

After Change


    for idx, unit_vec in enumerate(numpy.eye(dim, dtype=int)):

        conditional = E_cond(poly[valids], unit_vec, dist, **kws)
        out[idx, valids] = Var(conditional, dist, **kws)
        out[idx, valids] /= variance[valids]

    return out
Italian Trulli
In pattern: SUPERPATTERN

Frequency: 3

Non-data size: 5

Instances


Project Name: jonathf/chaospy
Commit Name: f47485cc4a21fb18564ede7ab0cc9001dbd0f1d9
Time: 2020-06-10
Author: jonathf@gmail.com
File Name: chaospy/descriptives/sensitivity/main.py
Class Name:
Method Name: Sens_m


Project Name: pymc-devs/pymc3
Commit Name: 3f32776f0f58c961a1ad625f3e5e34ce31a9ebbb
Time: 2013-03-15
Author: jsalvatier@gmail.com
File Name: examples/stochastic_volatility.py
Class Name:
Method Name:


Project Name: jonathf/chaospy
Commit Name: f47485cc4a21fb18564ede7ab0cc9001dbd0f1d9
Time: 2020-06-10
Author: jonathf@gmail.com
File Name: chaospy/descriptives/sensitivity/total.py
Class Name:
Method Name: Sens_t