K-Means Initialization with Missing Values

[jaanisfehling]

Hi, I think it should not be possible to initialize StepMix with a NaN compatible measurement model (e.g. GaussianNan) and init_params="kmeans".
I get the following error:

ValueError: Input X contains NaN.
KMeans does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

It makes sense tho, since the data has NaN values and the default sklearn implementation of k-means does not handle that.