spn.algorithms.Inference.log_likelihood

log_likelihood(node, data, dtype=<class 'numpy.float64'>, node_log_likelihood={<class 'spn.structure.Base.Sum'>: <function sum_log_likelihood>, <class 'spn.structure.Base.Product'>: <function prod_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.MultivariateGaussian'>: <function _get_log_likelihood.<locals>.f_log>, <class 'spn.structure.leaves.parametric.Parametric.Gaussian'>: <function continuous_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Hypergeometric'>: <function continuous_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Gamma'>: <function gamma_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.LogNormal'>: <function continuous_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Poisson'>: <function discrete_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Bernoulli'>: <function discrete_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Categorical'>: <function categorical_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Geometric'>: <function discrete_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Exponential'>: <function continuous_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.Uniform'>: <function uniform_log_likelihood>, <class 'spn.structure.leaves.parametric.Parametric.CategoricalDictionary'>: <function categorical_dictionary_log_likelihood>, <class 'spn.structure.leaves.piecewise.PiecewiseLinear.PiecewiseLinear'>: <function piecewise_log_likelihood>, <class 'spn.structure.leaves.histogram.Histograms.Histogram'>: <function histogram_log_likelihood>, <class 'spn.structure.leaves.cltree.CLTree.CLTree'>: <function cltree_log_likelihood>}, lls_matrix=None, debug=False, **kwargs)