999d2ff75431496c25c7967b990e6b7fc09bb858,chaospy/spectral.py,,fit_quadrature,#Any#Any#Any#Any#Any#Any#,84

Before Change

    if norms is None:
        norms = numpy.sum(ovals**2*weights, -1)
    else:
        norms = numpy.array(norms).flatten()

    coefs = (numpy.sum(vals1, 1).T/norms).T
    coefs = coefs.reshape(len(coefs), *shape[1:])
    approx_model = numpoly.sum(orth*coefs.T, -1).T

After Change

    if norms is None:
        norms = numpy.sum(ovals**2*weights, -1)
    norms = numpy.asfarray(norms)
    assert norms.ndim == 1

    coeffs = (numpy.sum(vals1, 1).T/norms).T
    coeffs = coeffs.reshape(len(coeffs), *shape)
    approx_model = numpoly.sum(orth*coeffs.T, -1).T

Instances