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ari-dasci · Jan 21, 2022 · 0ea8d97 · 0ea8d97
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diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree
diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0296ca83c7de98e5a331c0e059428bb3
+config: bdf77414e9e9ce716ef91ad58ab30f92
 tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html
@@ -5,7 +5,7 @@
 <html xmlns="http://www.w3.org/1999/xhtml">
   <head>
     <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-    <title>Overview: module code &#8212; pyCNN-LSTM  documentation</title>
+    <title>Overview: module code &#8212; TSFEDL  documentation</title>
     <link rel="stylesheet" href="../_static/alabaster.css" type="text/css" />
     <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
     <script type="text/javascript">
@@ -38,12 +38,10 @@
           <div class="body" role="main">
 
   <h1>All modules for which code is available</h1>
-<ul><li><a href="pyCNN_LSTM/blocks.html">pyCNN_LSTM.blocks</a></li>
-<li><a href="pyCNN_LSTM/blocks_pytorch.html">pyCNN_LSTM.blocks_pytorch</a></li>
-<li><a href="pyCNN_LSTM/data.html">pyCNN_LSTM.data</a></li>
-<li><a href="pyCNN_LSTM/models.html">pyCNN_LSTM.models</a></li>
-<li><a href="pyCNN_LSTM/models_pytorch.html">pyCNN_LSTM.models_pytorch</a></li>
-<li><a href="pyCNN_LSTM/utils.html">pyCNN_LSTM.utils</a></li>
+<ul><li><a href="TSFEDL/blocks_pytorch.html">TSFEDL.blocks_pytorch</a></li>
+<li><a href="TSFEDL/data.html">TSFEDL.data</a></li>
+<li><a href="TSFEDL/models_pytorch.html">TSFEDL.models_pytorch</a></li>
+<li><a href="TSFEDL/utils.html">TSFEDL.utils</a></li>
 </ul>
 
           </div>

diff --git a/docs/_build/html/_sources/blocks.rst.txt b/docs/_build/html/_sources/blocks.rst.txt
@@ -9,22 +9,22 @@ Here can be found all blocks implemented in Keras for the architecture's impleme
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.blocks.densenet_transition_block
-    pyCNN_LSTM.blocks.densenet_conv_block
-    pyCNN_LSTM.blocks.densenet_dense_block
-    pyCNN_LSTM.blocks.squeeze_excitation_module
-    pyCNN_LSTM.blocks.conv_block_YiboGao
-    pyCNN_LSTM.blocks.attention_branch_YiboGao
-    pyCNN_LSTM.blocks.RTA_block
-    pyCNN_LSTM.blocks.spatial_attention_block_ZhangJin
-    pyCNN_LSTM.blocks.temporal_attention_block_ZhangJin
+    TSFEDL.blocks.densenet_transition_block
+    TSFEDL.blocks.densenet_conv_block
+    TSFEDL.blocks.densenet_dense_block
+    TSFEDL.blocks.squeeze_excitation_module
+    TSFEDL.blocks.conv_block_YiboGao
+    TSFEDL.blocks.attention_branch_YiboGao
+    TSFEDL.blocks.RTA_block
+    TSFEDL.blocks.spatial_attention_block_ZhangJin
+    TSFEDL.blocks.temporal_attention_block_ZhangJin
 
-.. autofunction:: pyCNN_LSTM.blocks.densenet_transition_block
-.. autofunction:: pyCNN_LSTM.blocks.densenet_conv_block
-.. autofunction:: pyCNN_LSTM.blocks.densenet_dense_block
-.. autofunction:: pyCNN_LSTM.blocks.squeeze_excitation_module
-.. autofunction:: pyCNN_LSTM.blocks.conv_block_YiboGao
-.. autofunction:: pyCNN_LSTM.blocks.attention_branch_YiboGao
-.. autofunction:: pyCNN_LSTM.blocks.RTA_block
-.. autofunction:: pyCNN_LSTM.blocks.spatial_attention_block_ZhangJin
-.. autofunction:: pyCNN_LSTM.blocks.temporal_attention_block_ZhangJin
+.. autofunction:: TSFEDL.blocks.densenet_transition_block
+.. autofunction:: TSFEDL.blocks.densenet_conv_block
+.. autofunction:: TSFEDL.blocks.densenet_dense_block
+.. autofunction:: TSFEDL.blocks.squeeze_excitation_module
+.. autofunction:: TSFEDL.blocks.conv_block_YiboGao
+.. autofunction:: TSFEDL.blocks.attention_branch_YiboGao
+.. autofunction:: TSFEDL.blocks.RTA_block
+.. autofunction:: TSFEDL.blocks.spatial_attention_block_ZhangJin
+.. autofunction:: TSFEDL.blocks.temporal_attention_block_ZhangJin
diff --git a/docs/_build/html/_sources/blocks_pytorch.rst.txt b/docs/_build/html/_sources/blocks_pytorch.rst.txt
@@ -9,22 +9,22 @@ Here can be found all blocks implemented in PyTorch for the architecture's imple
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.blocks_pytorch.ConvBlockYiboGao
-    pyCNN_LSTM.blocks_pytorch.AttentionBranchYiboGao
-    pyCNN_LSTM.blocks_pytorch.RTABlock
-    pyCNN_LSTM.blocks_pytorch.SqueezeAndExcitationModule
-    pyCNN_LSTM.blocks_pytorch.DenseNetTransitionBlock
-    pyCNN_LSTM.blocks_pytorch.DenseNetConvBlock
-    pyCNN_LSTM.blocks_pytorch.DenseNetDenseBlock
-    pyCNN_LSTM.blocks_pytorch.SpatialAttentionBlockZhangJin
-    pyCNN_LSTM.blocks_pytorch.TemporalAttentionBlockZhangJin
+    TSFEDL.blocks_pytorch.ConvBlockYiboGao
+    TSFEDL.blocks_pytorch.AttentionBranchYiboGao
+    TSFEDL.blocks_pytorch.RTABlock
+    TSFEDL.blocks_pytorch.SqueezeAndExcitationModule
+    TSFEDL.blocks_pytorch.DenseNetTransitionBlock
+    TSFEDL.blocks_pytorch.DenseNetConvBlock
+    TSFEDL.blocks_pytorch.DenseNetDenseBlock
+    TSFEDL.blocks_pytorch.SpatialAttentionBlockZhangJin
+    TSFEDL.blocks_pytorch.TemporalAttentionBlockZhangJin
 
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.ConvBlockYiboGao
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.AttentionBranchYiboGao
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.RTABlock
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.SqueezeAndExcitationModule
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.DenseNetTransitionBlock
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.DenseNetConvBlock
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.DenseNetDenseBlock
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.SpatialAttentionBlockZhangJin
-.. autofunction:: pyCNN_LSTM.blocks_pytorch.TemporalAttentionBlockZhangJin
+.. autofunction:: TSFEDL.blocks_pytorch.ConvBlockYiboGao
+.. autofunction:: TSFEDL.blocks_pytorch.AttentionBranchYiboGao
+.. autofunction:: TSFEDL.blocks_pytorch.RTABlock
+.. autofunction:: TSFEDL.blocks_pytorch.SqueezeAndExcitationModule
+.. autofunction:: TSFEDL.blocks_pytorch.DenseNetTransitionBlock
+.. autofunction:: TSFEDL.blocks_pytorch.DenseNetConvBlock
+.. autofunction:: TSFEDL.blocks_pytorch.DenseNetDenseBlock
+.. autofunction:: TSFEDL.blocks_pytorch.SpatialAttentionBlockZhangJin
+.. autofunction:: TSFEDL.blocks_pytorch.TemporalAttentionBlockZhangJin
diff --git a/docs/_build/html/_sources/data.rst.txt b/docs/_build/html/_sources/data.rst.txt
@@ -9,10 +9,10 @@ Here can be found the implementation of the functions to read the data for the a
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.data.get_mit_bih_segments
-    pyCNN_LSTM.data.read_mit_bih
-    pyCNN_LSTM.data.MIT_BIH
+    TSFEDL.data.get_mit_bih_segments
+    TSFEDL.data.read_mit_bih
+    TSFEDL.data.MIT_BIH
 
-.. autofunction:: pyCNN_LSTM.data.get_mit_bih_segments
-.. autofunction:: pyCNN_LSTM.data.read_mit_bih
-.. autofunction:: pyCNN_LSTM.data.MIT_BIH
+.. autofunction:: TSFEDL.data.get_mit_bih_segments
+.. autofunction:: TSFEDL.data.read_mit_bih
+.. autofunction:: TSFEDL.data.MIT_BIH
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
@@ -1,8 +1,8 @@
 **********
-pyCNN-LSTM
+TSFEDL
 **********
 
-pyCNN-LSTM is a Python library that contains a wide range of models for working with time-series data
+TSFEDL is a Python library that contains a wide range of models for working with time-series data
 using hybrid deep learning models that uses both a Convolutional Neural Network (CNN) and a Recurrent
 Neural Network (RNN). The use of these models allow both to capture the spatial features and the time
 dependencies between timesteps.
@@ -12,11 +12,11 @@ This library contains the implementation of 22 architectures both in Tensorflow+
 Installation
 ============
 
-To install pyCNN-LSTM, we recommend using ``pip``.
+To install TSFEDL, we recommend using ``pip``.
 
 .. code-block:: bash
 
-    pip install pyCNN-LSTM
+    pip install TSFEDL
 
 .. toctree::
    :maxdepth: 2

diff --git a/docs/_build/html/_sources/models_keras.rst.txt b/docs/_build/html/_sources/models_keras.rst.txt
@@ -9,48 +9,48 @@ Here the different deep learning models included in the library are summarised.
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.models.OhShuLih
-    pyCNN_LSTM.models.KhanZulfiqar
-    pyCNN_LSTM.models.ZhengZhenyu
-    pyCNN_LSTM.models.HouBoroui
-    pyCNN_LSTM.models.WangKejun
-    pyCNN_LSTM.models.ChenChen
-    pyCNN_LSTM.models.KimTaeYoung
-    pyCNN_LSTM.models.GenMinxing
-    pyCNN_LSTM.models.FuJiangmeng
-    pyCNN_LSTM.models.ShiHaotian
-    pyCNN_LSTM.models.HuangMeiLing
-    pyCNN_LSTM.models.LihOhShu
-    pyCNN_LSTM.models.GaoJunLi
-    pyCNN_LSTM.models.WeiXiaoyan
-    pyCNN_LSTM.models.KongZhengmin
-    pyCNN_LSTM.models.YildirimOzal
-    pyCNN_LSTM.models.CaiWenjuan
-    pyCNN_LSTM.models.KimMinGu
-    pyCNN_LSTM.models.HtetMyetLynn
-    pyCNN_LSTM.models.ZhangJin
-    pyCNN_LSTM.models.YaoQihang
-    pyCNN_LSTM.models.YiboGao
+    TSFEDL.models.OhShuLih
+    TSFEDL.models.KhanZulfiqar
+    TSFEDL.models.ZhengZhenyu
+    TSFEDL.models.HouBoroui
+    TSFEDL.models.WangKejun
+    TSFEDL.models.ChenChen
+    TSFEDL.models.KimTaeYoung
+    TSFEDL.models.GenMinxing
+    TSFEDL.models.FuJiangmeng
+    TSFEDL.models.ShiHaotian
+    TSFEDL.models.HuangMeiLing
+    TSFEDL.models.LihOhShu
+    TSFEDL.models.GaoJunLi
+    TSFEDL.models.WeiXiaoyan
+    TSFEDL.models.KongZhengmin
+    TSFEDL.models.YildirimOzal
+    TSFEDL.models.CaiWenjuan
+    TSFEDL.models.KimMinGu
+    TSFEDL.models.HtetMyetLynn
+    TSFEDL.models.ZhangJin
+    TSFEDL.models.YaoQihang
+    TSFEDL.models.YiboGao
 
-.. autofunction:: pyCNN_LSTM.models.OhShuLih
-.. autofunction:: pyCNN_LSTM.models.KhanZulfiqar
-.. autofunction:: pyCNN_LSTM.models.ZhengZhenyu
-.. autofunction:: pyCNN_LSTM.models.HouBoroui
-.. autofunction:: pyCNN_LSTM.models.WangKejun
-.. autofunction:: pyCNN_LSTM.models.ChenChen
-.. autofunction:: pyCNN_LSTM.models.KimTaeYoung
-.. autofunction:: pyCNN_LSTM.models.GenMinxing
-.. autofunction:: pyCNN_LSTM.models.FuJiangmeng
-.. autofunction:: pyCNN_LSTM.models.ShiHaotian
-.. autofunction:: pyCNN_LSTM.models.HuangMeiLing
-.. autofunction:: pyCNN_LSTM.models.LihOhShu
-.. autofunction:: pyCNN_LSTM.models.GaoJunLi
-.. autofunction:: pyCNN_LSTM.models.WeiXiaoyan
-.. autofunction:: pyCNN_LSTM.models.KongZhengmin
-.. autofunction:: pyCNN_LSTM.models.YildirimOzal
-.. autofunction:: pyCNN_LSTM.models.CaiWenjuan
-.. autofunction:: pyCNN_LSTM.models.KimMinGu
-.. autofunction:: pyCNN_LSTM.models.HtetMyetLynn
-.. autofunction:: pyCNN_LSTM.models.ZhangJin
-.. autofunction:: pyCNN_LSTM.models.YaoQihang
-.. autofunction:: pyCNN_LSTM.models.YiboGao
+.. autofunction:: TSFEDL.models.OhShuLih
+.. autofunction:: TSFEDL.models.KhanZulfiqar
+.. autofunction:: TSFEDL.models.ZhengZhenyu
+.. autofunction:: TSFEDL.models.HouBoroui
+.. autofunction:: TSFEDL.models.WangKejun
+.. autofunction:: TSFEDL.models.ChenChen
+.. autofunction:: TSFEDL.models.KimTaeYoung
+.. autofunction:: TSFEDL.models.GenMinxing
+.. autofunction:: TSFEDL.models.FuJiangmeng
+.. autofunction:: TSFEDL.models.ShiHaotian
+.. autofunction:: TSFEDL.models.HuangMeiLing
+.. autofunction:: TSFEDL.models.LihOhShu
+.. autofunction:: TSFEDL.models.GaoJunLi
+.. autofunction:: TSFEDL.models.WeiXiaoyan
+.. autofunction:: TSFEDL.models.KongZhengmin
+.. autofunction:: TSFEDL.models.YildirimOzal
+.. autofunction:: TSFEDL.models.CaiWenjuan
+.. autofunction:: TSFEDL.models.KimMinGu
+.. autofunction:: TSFEDL.models.HtetMyetLynn
+.. autofunction:: TSFEDL.models.ZhangJin
+.. autofunction:: TSFEDL.models.YaoQihang
+.. autofunction:: TSFEDL.models.YiboGao
diff --git a/docs/_build/html/_sources/models_pytorch.rst.txt b/docs/_build/html/_sources/models_pytorch.rst.txt
@@ -9,46 +9,46 @@ Here the different deep learning models included in the library are summarised.
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.models_pytorch.OhShuLih
-    pyCNN_LSTM.models_pytorch.KhanZulfiqar
-    pyCNN_LSTM.models_pytorch.ZhengZhenyu
-    pyCNN_LSTM.models_pytorch.WangKejun
-    pyCNN_LSTM.models_pytorch.ChenChen
-    pyCNN_LSTM.models_pytorch.KimTaeYoung
-    pyCNN_LSTM.models_pytorch.GenMinxing
-    pyCNN_LSTM.models_pytorch.FuJiangmeng
-    pyCNN_LSTM.models_pytorch.ShiHaotian
-    pyCNN_LSTM.models_pytorch.HuangMeiLing
-    pyCNN_LSTM.models_pytorch.LihOhShu
-    pyCNN_LSTM.models_pytorch.GaoJunLi
-    pyCNN_LSTM.models_pytorch.WeiXiaoyan
-    pyCNN_LSTM.models_pytorch.KongZhengmin
-    pyCNN_LSTM.models_pytorch.YildirimOzal
-    pyCNN_LSTM.models_pytorch.CaiWenjuan
-    pyCNN_LSTM.models_pytorch.KimMinGu
-    pyCNN_LSTM.models_pytorch.HtetMyetLynn
-    pyCNN_LSTM.models_pytorch.ZhangJin
-    pyCNN_LSTM.models_pytorch.YaoQihang
-    pyCNN_LSTM.models_pytorch.YiboGao
+    TSFEDL.models_pytorch.OhShuLih
+    TSFEDL.models_pytorch.KhanZulfiqar
+    TSFEDL.models_pytorch.ZhengZhenyu
+    TSFEDL.models_pytorch.WangKejun
+    TSFEDL.models_pytorch.ChenChen
+    TSFEDL.models_pytorch.KimTaeYoung
+    TSFEDL.models_pytorch.GenMinxing
+    TSFEDL.models_pytorch.FuJiangmeng
+    TSFEDL.models_pytorch.ShiHaotian
+    TSFEDL.models_pytorch.HuangMeiLing
+    TSFEDL.models_pytorch.LihOhShu
+    TSFEDL.models_pytorch.GaoJunLi
+    TSFEDL.models_pytorch.WeiXiaoyan
+    TSFEDL.models_pytorch.KongZhengmin
+    TSFEDL.models_pytorch.YildirimOzal
+    TSFEDL.models_pytorch.CaiWenjuan
+    TSFEDL.models_pytorch.KimMinGu
+    TSFEDL.models_pytorch.HtetMyetLynn
+    TSFEDL.models_pytorch.ZhangJin
+    TSFEDL.models_pytorch.YaoQihang
+    TSFEDL.models_pytorch.YiboGao
 
-.. autofunction:: pyCNN_LSTM.models_pytorch.OhShuLih
-.. autofunction:: pyCNN_LSTM.models_pytorch.KhanZulfiqar
-.. autofunction:: pyCNN_LSTM.models_pytorch.ZhengZhenyu
-.. autofunction:: pyCNN_LSTM.models_pytorch.WangKejun
-.. autofunction:: pyCNN_LSTM.models_pytorch.ChenChen
-.. autofunction:: pyCNN_LSTM.models_pytorch.KimTaeYoung
-.. autofunction:: pyCNN_LSTM.models_pytorch.GenMinxing
-.. autofunction:: pyCNN_LSTM.models_pytorch.FuJiangmeng
-.. autofunction:: pyCNN_LSTM.models_pytorch.ShiHaotian
-.. autofunction:: pyCNN_LSTM.models_pytorch.HuangMeiLing
-.. autofunction:: pyCNN_LSTM.models_pytorch.LihOhShu
-.. autofunction:: pyCNN_LSTM.models_pytorch.GaoJunLi
-.. autofunction:: pyCNN_LSTM.models_pytorch.WeiXiaoyan
-.. autofunction:: pyCNN_LSTM.models_pytorch.KongZhengmin
-.. autofunction:: pyCNN_LSTM.models_pytorch.YildirimOzal
-.. autofunction:: pyCNN_LSTM.models_pytorch.CaiWenjuan
-.. autofunction:: pyCNN_LSTM.models_pytorch.KimMinGu
-.. autofunction:: pyCNN_LSTM.models_pytorch.HtetMyetLynn
-.. autofunction:: pyCNN_LSTM.models_pytorch.ZhangJin
-.. autofunction:: pyCNN_LSTM.models_pytorch.YaoQihang
-.. autofunction:: pyCNN_LSTM.models_pytorch.YiboGao
+.. autofunction:: TSFEDL.models_pytorch.OhShuLih
+.. autofunction:: TSFEDL.models_pytorch.KhanZulfiqar
+.. autofunction:: TSFEDL.models_pytorch.ZhengZhenyu
+.. autofunction:: TSFEDL.models_pytorch.WangKejun
+.. autofunction:: TSFEDL.models_pytorch.ChenChen
+.. autofunction:: TSFEDL.models_pytorch.KimTaeYoung
+.. autofunction:: TSFEDL.models_pytorch.GenMinxing
+.. autofunction:: TSFEDL.models_pytorch.FuJiangmeng
+.. autofunction:: TSFEDL.models_pytorch.ShiHaotian
+.. autofunction:: TSFEDL.models_pytorch.HuangMeiLing
+.. autofunction:: TSFEDL.models_pytorch.LihOhShu
+.. autofunction:: TSFEDL.models_pytorch.GaoJunLi
+.. autofunction:: TSFEDL.models_pytorch.WeiXiaoyan
+.. autofunction:: TSFEDL.models_pytorch.KongZhengmin
+.. autofunction:: TSFEDL.models_pytorch.YildirimOzal
+.. autofunction:: TSFEDL.models_pytorch.CaiWenjuan
+.. autofunction:: TSFEDL.models_pytorch.KimMinGu
+.. autofunction:: TSFEDL.models_pytorch.HtetMyetLynn
+.. autofunction:: TSFEDL.models_pytorch.ZhangJin
+.. autofunction:: TSFEDL.models_pytorch.YaoQihang
+.. autofunction:: TSFEDL.models_pytorch.YiboGao
diff --git a/docs/_build/html/_sources/utils.rst.txt b/docs/_build/html/_sources/utils.rst.txt
@@ -9,14 +9,14 @@ Here can be found the implementation of the auxiliary functions for the implemen
     :maxdepth: 1
 
 .. autosummary::
-    pyCNN_LSTM.utils.TimeDistributed
-    pyCNN_LSTM.utils.flip_indices_for_conv_to_lstm
-    pyCNN_LSTM.utils.flip_indices_for_conv_to_lstm_reshape
-    pyCNN_LSTM.utils.check_inputs
-    pyCNN_LSTM.utils.full_convolution
+    TSFEDL.utils.TimeDistributed
+    TSFEDL.utils.flip_indices_for_conv_to_lstm
+    TSFEDL.utils.flip_indices_for_conv_to_lstm_reshape
+    TSFEDL.utils.check_inputs
+    TSFEDL.utils.full_convolution
 
-.. autofunction:: pyCNN_LSTM.utils.TimeDistributed
-.. autofunction:: pyCNN_LSTM.utils.flip_indices_for_conv_to_lstm
-.. autofunction:: pyCNN_LSTM.utils.flip_indices_for_conv_to_lstm_reshape
-.. autofunction:: pyCNN_LSTM.utils.check_inputs
-.. autofunction:: pyCNN_LSTM.utils.full_convolution
+.. autofunction:: TSFEDL.utils.TimeDistributed
+.. autofunction:: TSFEDL.utils.flip_indices_for_conv_to_lstm
+.. autofunction:: TSFEDL.utils.flip_indices_for_conv_to_lstm_reshape
+.. autofunction:: TSFEDL.utils.check_inputs
+.. autofunction:: TSFEDL.utils.full_convolution