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Gallery
=======

Examples and tutorials for coco-pipe workflows.


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Decoding Examples
=================


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the core functionality of the coco_pipe.decoding module. We generate a synthetic classification dataset and evaluate two classical machine learning models (Logistic Regression and Random Forest) using a rigorous cross-validation pipeline.">

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  :doc:`/auto_examples/decoding/plot_01_basic_decoding`

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      <div class="sphx-glr-thumbnail-title">Basic Decoding: Classification and Model Verification</div>
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Descriptors Examples
====================


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to use the DescriptorPipeline to extract EEG features (bands, parametric, complexity) from a standard NumPy array.">

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  :doc:`/auto_examples/descriptors/plot_01_basic_descriptors`

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      <div class="sphx-glr-thumbnail-title">Basic Feature Descriptors Extraction</div>
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Dimension Reduction Examples
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    <div class="sphx-glr-thumbcontainer" tooltip="This example compares several dimensionality reduction algorithms (PCA, t-SNE, UMAP, and Pacmap) across different parameter settings using a synthetic high-dimensional dataset. It demonstrates how hyperparameter choices can drastically affect the resulting embeddings.">

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      <div class="sphx-glr-thumbnail-title">Comprehensive Comparison of Dimensionality Reduction Methods</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the &quot;Advanced Exploration and Benchmarking&quot; pillar of the coco_pipe strategic vision. We move beyond &quot;looking good&quot; and use rigorous metrics (Trustworthiness, Continuity, LCMC) to quantify embedding distortion.">

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      <div class="sphx-glr-thumbnail-title">Benchmarking Dimensionality Reduction: The Epistemology of Embeddings</div>
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IO Examples
===========


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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the DataContainer and other core IO structures used in the coco-pipe package. The DataContainer is a powerful wrapper around N-dimensional numpy arrays that keeps track of dimensions, coordinates, and labels.">

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      <div class="sphx-glr-thumbnail-title">Data Structures Demo</div>
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   /auto_examples/decoding/index.rst
   /auto_examples/descriptors/index.rst
   /auto_examples/dim_reduction/index.rst
   /auto_examples/io/index.rst


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      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


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    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_