d65cddeddda46bb20f82b1e233cd5305ace7b1c7,chaospy/descriptives/conditional.py,,E_cond,#Any#Any#Any#,10
 
Before Change
    poly = polynomials.decompose(poly)
    cache = {}
    if len(freeze.shape) == 1:
        out = _E_cond(poly, freeze, dist, cache, **kws)
    else:
        out = polynomials.concatenate([
            _E_cond(poly, freeze_, dist, cache, **kws)[numpy.newaxis]
            for freeze_ in freeze
        ])
    if out.isconstant():
        out = out.tonumpy()
    return out
After Change
        return polynomials.sum(frozen, 0)
    // Remove frozen coefficients, such that poly == sum(frozen*unfrozen) holds
    for key in unfrozen.keys:
        unfrozen[key] = unfrozen[key] != 0
    return polynomials.sum(frozen*E(unfrozen, dist), 0)

In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 6
Instances
 Project Name: jonathf/chaospy
 Commit Name: d65cddeddda46bb20f82b1e233cd5305ace7b1c7
 Time: 2019-12-29
 Author: jonathf@gmail.com
 File Name: chaospy/descriptives/conditional.py
 Class Name: 
 Method Name: E_cond
 Project Name: SheffieldML/GPy
 Commit Name: 929cf0a4890e418ecec0b000ed7fefa2372bc082
 Time: 2015-09-07
 Author: ibinbei@gmail.com
 File Name: GPy/core/gp.py
 Class Name: GP
 Method Name: predict_jacobian
 Project Name: NifTK/NiftyNet
 Commit Name: 5af1994def9a52fe1ffd2847b2519f1e27cfbc64
 Time: 2017-08-12
 Author: wenqi.li@ucl.ac.uk
 File Name: niftynet/io/misc_io.py
 Class Name: 
 Method Name: do_resampling