hopsy.Model.compute_expected_fisher_information#
- Model.compute_expected_fisher_information()#
- deprecated:: 1.4
Use
log_curvature()instead.
For some proposals, the expected fisher information will help converging faster as long as the gradient computation is not too slow. If you can not compute a useful or fast enough expected fisher information for your custom model, you can just return a zero matrix with the correct dimensionality (number of rows and cols each equal to number of parameters).
:param : :type : param x: Input vector :param : :type : type x: numpy.ndarray[float64[n,1]]
- Returns:
return: The value of
model.compute_expected_fisher_information(x)rtype: numpy.ndarray[float64[n,n]]