Improved data structure management

supported features

• v0.0.1: in-library encoding using pre-trained models
• v0.0.3: search using tags that are not available in tag_list
• v0.0.4: int8 quantization
• v0.0.4: more intuitive validation technique
• v0.0.4: added "dot_product" and "PCA" options
• v0.0.5: added clustering feature
• v0.0.5: standardized indexing, encoding, search functions
• v0.0.5: corrected search function names

new features

• v0.0.6: replaced df_M with M and pointers
• v0.0.6: automatic clustering during encoding
• v0.0.6: added I/O module for easy storage/retrieval of files

deprecated features

• v0.0.1: computation of co-occurrence matrix
• v0.0.2: in-library compression/expansion of the one_hot vector

known issues

• length of the covariate vector can only be equivalent to the length of the ones in df_M
• does not support a sparse vector data structure, not necessary if it implementable
• search using custom tags is only available when vectors are encoded using the pre-trained encoder
• there is no code to handle the expansion of the existing tag_list
• there are no methods to optimize dot_product calculation
• code for encrypted covariate search exists but has not yet been implemented in the library
• code for covariate tagging exists but has not yet been implemented in the library

Added support for clustering tag_list

supported features

• v0.0.1: computation of co-occurrence matrix
• v0.0.1: in-library encoding using pre-trained models
• v0.0.3: search using tags that are not available in tag_list
• v0.0.4: int8 quantization
• v0.0.4: more intuitive validation technique
• v0.0.4: added "dot_product" and "PCA" options

new features

• v0.0.5: added clustering feature
• v0.0.5: standardized indexing, encoding, search functions
• v0.0.5: corrected search function names

deprecated features

• v0.0.2: in-library compression/expansion of the one_hot vector

known issues

• length of the covariate vector can only be equivalent to the length of the ones in df_M
• does not support a sparse vector data structure, not necessary if it implementable
• search using custom tags is only available when vectors are encoded using the pre-trained encoder
• there is no code to handle the expansion of the existing tag_list
• there are no methods to optimize dot_product calculation
• code for encrypted covariate search exists but has not yet been implemented in the library
• code for covariate tagging exists but has not yet been implemented in the library

Strong Improvements

supported features

• v0.0.1: computation of co-occurrence matrix
• v0.0.1: in-library encoding using pre-trained models
• v0.0.3: search using tags that are not available in tag_list

new features

• v0.0.4: int8 quantization
• v0.0.4: more intuitive validation technique
• v0.0.4: added "dot_product" and "PCA" options

deprecated features

• v0.0.2: in-library compression/expansion of the one_hot vector

known issues

• length of the covariate vector can only be equivalent to the length of the ones in df_M
• does not support a sparse vector data structure, necessary for highly scalable scenarios
• search using custom tags is only available when vectors are encoded using the pre-trained encoder
• no code to handle the expansion of the existing tag_list
• no methods to optimize dot_product calculation
• no clustering methods to maintain constant the size of tag_list
• code for encrypted covariate search exists but has not yet been implemented in the library
• code for covariate tagging exists but has not yet been implemented in the library
• search methods still maintain the old name "tag_filtering", and need to be renamed as "search"

Custom Tag Search

supported features

• computation of co-occurrence matrix
• in-library encoding using pre-trained models
• in-library compression/expansion of the one_hot vector

new features

• search using tags that are not available in tag_list

known issues

• length of the covariate vector can only be equivalent to the length of the ones in df_M
• does not support a sparse vector data structure, necessary for highly scalable scenarios
• search using custom tags is only available when vectors are encoded using the pre-trained encoder

Improved usabilty

supported features:

• computation of co-occurrence matrix
• in-library encoding using pre-trained models
• in-library compression/expansion of the oneshot vector

known issues:

• length of the covariate vector can only be equivalent to the length of the ones in df_M
• does not support a sparse vector data structure, necessary for highly scalable scenarios

Beta release

supported features:

• computation of co-occurrence matrix
• pre-trained encoders

known issues:

• n_tags < vector length