pydbm.clustering.autoencodable package¶
Submodules¶
pydbm.clustering.autoencodable.convolutional_dec module¶
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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).
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auto_encoder_model
¶ getter
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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.
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embed_feature_points
¶ Embed and extract feature points.
Parameters: observed_arr – np.ndarray of observed data points. Returns: np.ndarray of feature points.
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get_auto_encoder_model
¶ getter
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get_inferencing_mode
¶ getter
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inference
¶ Inferencing.
Parameters: observed_arr – np.ndarray of observed data points. Returns: np.ndarray of inferenced data.
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inferencing_mode
¶ getter
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optimize_auto_encoder
¶ Optimize Auto-Encoder.
Parameters: - learning_rate – Learning rate.
- epoch – Now epoch.
- encoder_only_flag – Optimize encoder only or decoder/encoder.
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pre_learn
¶ Pre-learning.
Parameters: - observed_arr – np.ndarray of observed data points.
- target_arr – np.ndarray of noised observed data points.
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set_auto_encoder_model
¶ setter
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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_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.
-
get_auto_encoder_model
¶ getter
-
get_inferencing_mode
¶ getter
-
inference
¶ Inferencing.
Parameters: observed_arr – np.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_arr – np.ndarray of observed data points.
- target_arr – np.ndarray of noised observed data points.
-
set_auto_encoder_model
¶ setter
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set_inferencing_mode
¶ setter