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Implementation for Fast Siamese Recurrent Neural Network Approximation for the Triangular Global Alignment Kernel by Shota Nagayama, Zoltan Milacski, Andras Lorincz.

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Fast Siamese Recurrent Neural Network Approximation for the Triangular Global Alignment Kernel

Global Alignment Kernel

  1. Copy compute_gram_matrix.json_sample to any name what you want. $cd experiments
    $cp compute_gram_matrix.json_sample (new_json_file_name)
  2. Specify
  • dataset_type: UCIauslan, UCIcharacter or upperChar
  • dataset_location: the directory which holds the dataset
  • output_dir: directory to save the results
  • output_filename_format: format of the output file
  • sigma and triangular: gak variables
  • data_augmentation_size: number of times to augment
  1. Run the experiment $python3 compute_gram_matrix.py with (new_json_file_name)

GRAM Matrix-based approach

Matrix completion

  1. Copy complete_matrix_(algorithm).json_sample to any name what you want. $cd experiments $cp compute_gram_matrix.json_sample (new_json_file_name)
  2. Modify the parameters "fast_rnn" requires a pretrained model.
  3. Run the experiment $python3 complete_matrix.py with (new_json_file_name)

Classification error

  1. Run the experiment $python3 compute_classification_errors.py with (parameter)=(new_value)

Feature-based approach

Feature mapping approximation

  1. Copy linear_svm.json_sample to any name what you want. $cd experiments $cp linear_svm.json_sample (new_json_file_name)
  2. Modify the parameters This method requires a pretrained model.
  3. Run the experiment $python3 linear_svm.py with (new_json_file_name)

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Implementation for Fast Siamese Recurrent Neural Network Approximation for the Triangular Global Alignment Kernel by Shota Nagayama, Zoltan Milacski, Andras Lorincz.

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