pydbm.clustering.interface package

Submodules

pydbm.clustering.interface.auto_encodable module

class pydbm.clustering.interface.auto_encodable.AutoEncodable

Bases: object

The interface of the Deep Embedded Clustering(DEC).

References

  • Xie, J., Girshick, R., & Farhadi, A. (2016, June). Unsupervised deep embedding for clustering analysis. In International conference on machine learning (pp. 478-487).
auto_encoder_model

Model object of Auto-Encoder.

backward_auto_encoder

Pass down to the Auto-Encoder as backward.

Parameters:
  • delta_arr – np.ndarray of delta.
  • encoder_only_flag – Pass down to encoder only or decoder/encoder.
Returns:

np.ndarray of delta.
embed_feature_points

Embed and extract feature points.

Parameters:observed_arr – np.ndarray of observed data points.
Returns:np.ndarray of feature points.
inference

Inferencing.

Parameters:observed_arr – np.ndarray of observed data points.
Returns:np.ndarray of inferenced data.
inferencing_mode

inferencing_mode for auto_encoder_model.

optimize_auto_encoder

Optimize Auto-Encoder.

Parameters:
  • learning_rate – Learning rate.
  • epoch – Now epoch.
  • encoder_only_flag – Optimize encoder only or decoder/encoder.
pre_learn

Pre-learning.

Parameters:
  • observed_arr – np.ndarray of observed data points.
  • target_arr – np.ndarray of noised observed data points.

pydbm.clustering.interface.computable_clustering_loss module

class pydbm.clustering.interface.computable_clustering_loss.ComputableClusteringLoss

Bases: object

The interface of Loss functions in framework of the Deep Embedded Clustering(DEC).

References

  • Aljalbout, E., Golkov, V., Siddiqui, Y., Strobel, M., & Cremers, D. (2018). Clustering with deep learning: Taxonomy and new methods. arXiv preprint arXiv:1801.07648.
  • Guo, X., Gao, L., Liu, X., & Yin, J. (2017, June). Improved Deep Embedded Clustering with Local Structure Preservation. In IJCAI (pp. 1753-1759).
  • Xie, J., Girshick, R., & Farhadi, A. (2016, June). Unsupervised deep embedding for clustering analysis. In International conference on machine learning (pp. 478-487).
  • Zhao, J., Mathieu, M., & LeCun, Y. (2016). Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126.
compute_clustering_loss

Compute clustering loss.

Parameters:
  • observed_arr – np.ndarray of observed data points.
  • reconstructed_arr – np.ndarray of reconstructed data.
  • feature_arr – np.ndarray of feature points.
  • delta_arr – np.ndarray of differences between feature points and centroids.
  • p_arr – np.ndarray of result of soft assignment.
  • q_arr – np.ndarray of target distribution.
Returns:

Tuple data. - np.ndarray of delta for the encoder. - np.ndarray of delta for the decoder. - np.ndarray of delta for the centroids.

pydbm.clustering.interface.extractable_centroids module

class pydbm.clustering.interface.extractable_centroids.ExtractableCentroids

Bases: object

The interface of clustering only to get information on centroids to be mentioned as initial parameters in framework of the Deep Embedded Clustering(DEC).

References

  • Xie, J., Girshick, R., & Farhadi, A. (2016, June). Unsupervised deep embedding for clustering analysis. In International conference on machine learning (pp. 478-487).
extract_centroids

Clustering and extract centroids.

Parameters:
  • observed_arr – np.ndarray of observed data points.
  • k – The number of clusters.
Returns:

np.ndarray of centroids.

Module contents