GroupLiNGAM

class lingam.GroupLiNGAM(alpha=0.01)

Implementation of GroupLiNGAM Algorithm [1]

References

[1]

Y. Kawahara, K. Bollen, S. Shimizu and T. Washio. GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. Arxiv preprint arXiv:1006.5041, 2010.

__init__(alpha=0.01)

Construct a GroupLiNGAM model.

Parameters:

alpha (float, optional (default=0.01)) – Alpha level for HSIC independence test.

property adjacency_matrix_

Estimated adjacency matrix.

Returns:

adjacency_matrix_ – The adjacency matrix B of fitted model, where n_features is the number of features.

Return type:

array-like, shape (n_features, n_features)

bootstrap(X, n_sampling)

Evaluate the statistical reliability of DAG based on the bootstrapping.

Parameters:

• X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

• n_sampling (int) – Number of bootstrapping samples.

Returns:

result – Returns the result of bootstrapping.

Return type:

BootstrapResult

property causal_order_

Estimated causal ordering.

Returns:

causal_order_ – The causal order of fitted model, where n_features is the number of features.

Return type:

array-like, shape (n_features)

fit(X)

Fit the model to X.

Parameters:

X (array-like, shape (n_samples, n_features)) – Training data, where n_samples is the number of samples and n_features is the number of features.

Returns:

self – Returns the instance itself.

Return type:

object