ForceBru

This also doesn't let me silence the logger. I need to fit thousands of mixtures and don't want _any_ output while fitting individual mixtures - otherwise there's too much output,...

For me it happened when I had `using StatsBase, GaussianMixtures` in my code. This produced a warning and the `UndefVarError`: ``` WARNING: both GaussianMixtures and StatsBase export "weights"; uses of...

> It looks like you are following this Tutorial — is that correct? https://julianlsolvers.github.io/Optim.jl/stable/#examples/generated/ipnewton_basics/#generic-nonlinear-constraints Yes, that's right. > IPNewton allocates some memory without zero-ing it Looks like this is the...

> > It's unclear why Optim can't calculate the Hessian of constraints itself. > > It can Wow, that's a game changer! I've been calculating Hessians by hand for way...

Looks like I got it. Apparently, when I specify one initial point, the entire population will be just copies of this point: https://github.com/wildart/Evolutionary.jl/blob/81d8a7257624a8329424a9678845f4e72c1b3ed1/src/api/utilities.jl#L58-L64 And then there's probably not enough variability...

> default parameters are useless ... there is not way to set default parameters for any model because operators are population dependent Maybe there shouldn't be any default parameters then?...

I changed `mixture_loglikelihood` to use index access and remove `@views`, like in `f(x)` from the docs: ```julia function mixture_loglikelihood(params::AV{

I used brute force to test whether the matrix is positive definite with the Sylvester's criterion: ```julia using LinearAlgebra gp_covar = kernel_rbf_mat(X, X) determinants = @views [ det(gp_covar[begin:i, begin:i]) for...

> Constructors, how to interpret the inputs This is covered in the very next documentation entries: ``` help?> MvNormal search: MvNormal MvNormalCanon MvNormalKnownCov MvLogNormal AbstractMvNormal MvNormal Generally, users don't have...

I'm facing the same issue with this code: ```python >>> import statsmodels.formula.api as smf >>> import numpy as np >>> x = np.linspace(0.001, 5, 200) >>> y = (0.3 *...