simpeg.utils.GaussianMixtureWithNonlinearRelationshipsWithPrior.score_samples_with_sensW#
- GaussianMixtureWithNonlinearRelationshipsWithPrior.score_samples_with_sensW(X, sensW)[source]#
Compute the weighted log probabilities for each sample.
[New function, modified from Scikit-Learn.mixture.gaussian_mixture.score_samples] Compute the weighted log probabilities for each sample.
- Parameters:
- X(
n_samples,n_features) array_like List of n_features-dimensional data points. Each row corresponds to a single data point.
- sensW(
n_samples) array_like Sensitivity weights
- X(
- Returns:
- (
n_samples)numpy.array Log probabilities of each data point in X.
- (