pydbm.clustering.autoencodable package

Submodules

pydbm.clustering.autoencodable.convolutional_dec module

class pydbm.clustering.autoencodable.convolutional_dec.ConvolutionalDEC

Bases: pydbm.clustering.interface.auto_encodable.AutoEncodable

The Deep Embedded Clustering(DEC) with Convolutional Neural Networks.

References

  • Guo, X., Liu, X., Zhu, E., & Yin, J. (2017, November). Deep clustering with convolutional autoencoders. In International Conference on Neural Information Processing (pp. 373-382). Springer, Cham.
  • 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).
auto_encoder_model

getter

backward_auto_encoder

Pass down to the Auto-Encoder as backward.

Parameters:
  • delta_arrnp.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_arrnp.ndarray of observed data points.
Returns:np.ndarray of feature points.
get_auto_encoder_model

getter

get_inferencing_mode

getter

inference

Inferencing.

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

getter

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_arrnp.ndarray of observed data points.
  • target_arrnp.ndarray of noised observed data points.
set_auto_encoder_model

setter

set_inferencing_mode

setter

pydbm.clustering.autoencodable.simple_dec module

class pydbm.clustering.autoencodable.simple_dec.SimpleDEC

Bases: pydbm.clustering.interface.auto_encodable.AutoEncodable

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

getter

backward_auto_encoder

Pass down to the Auto-Encoder as backward.

Parameters:
  • delta_arrnp.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_arrnp.ndarray of observed data points.
Returns:np.ndarray of feature points.
get_auto_encoder_model

getter

get_inferencing_mode

getter

inference

Inferencing.

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

getter

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_arrnp.ndarray of observed data points.
  • target_arrnp.ndarray of noised observed data points.
set_auto_encoder_model

setter

set_inferencing_mode

setter

Module contents