pyxpcm.pcm.predict_proba

pcm.predict_proba(self, ds, features=None, dim=None, inplace=False, name='PCM_POST', classdimname='pcm_class')[source]

Predict posterior probability of each components given the data

This method adds these properties to the PCM instance:

  • llh: The log likelihood of the model with regard to new data
Parameters:
ds: :class:`xarray.Dataset`

The dataset to work with

features: dict()

Definitions of PCM features in the input xarray.Dataset. If not specified or set to None, features are identified using xarray.DataArray attributes ‘feature_name’.

dim: str

Name of the vertical dimension in the input xarray.Dataset

inplace: boolean, False by default

If False, return a xarray.DataArray with predicted probabilities If True, return the input xarray.Dataset with probabilities added as a new xarray.DataArray

name: str, default is ‘PCM_POST’

Name of the DataArray with prediction probability (posteriors)

classdimname: str, default is ‘pcm_class’

Name of the dimension holding classes
Returns:
xarray.DataArray

Probability of each Gaussian (state) in the model given each sample (if option ‘inplace’ = False)

or
xarray.Dataset

Input dataset with Component Probability as a ‘PCM_POST’ new xarray.DataArray (if option ‘inplace’ = True)