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.. _examples:

Gallery of examples
===================


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    <div class="sphx-glr-thumbcontainer" tooltip="Examining effect of adding more components">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_examining_different_number_of_components_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_plot_examining_different_number_of_components.py`

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      <div class="sphx-glr-thumbnail-title">Examining effect of adding more components</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Simple example with simulated non-negative components.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_simulated_nonnegative_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_simulated_nonnegative.py`

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      <div class="sphx-glr-thumbnail-title">Simple example with simulated non-negative components.</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Component models have been used to detect errors in semiconductor etch processes :citewise1999comparison, where the datasets have three natural modes: sample, measurement and time. However, the time required for measuring different samples may vary, which leads to a stack of matrices, one for each sample. This makes PARAFAC2 a natural choice :citewise2001application, as it naturally handles time profiles of different lengths.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_semiconductor_etch_analysis_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_semiconductor_etch_analysis.py`

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      <div class="sphx-glr-thumbnail-title">PARAFAC2 for semiconductor etch analysis</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Detecting behavioural patterns in bike sharing data">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_bikesharing_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_bikesharing.py`

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      <div class="sphx-glr-thumbnail-title">Detecting behavioural patterns in bike sharing data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to select the number of components for PARAFAC2 models by checking if patterns are replicable :citeerdos2025extracting. The process involves fitting the model to different subsets of your data to see if the results stay consistent (i.e. replicable across data subsets). To maximize explanatory power, typically, we select the highest number of components that remains replicable across the data subsets.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_replicability_thumb.png
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  :ref:`sphx_glr_auto_examples_plot_replicability.py`

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      <div class="sphx-glr-thumbnail-title">Determine the number of components through replicability analysis</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we first demonstrate how to specify exactly how the penalties are imposed in the AO-ADMM fitting procedure. Then, we create a custom penalty that imposes non-negativity on all component vectors and unimodality on all but one of the component vectors.">

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  .. image:: /auto_examples/images/thumb/sphx_glr_plot_custom_penalty_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_plot_custom_penalty.py`

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      <div class="sphx-glr-thumbnail-title">Example with custom penalty class for unimodality for all but one component</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/plot_examining_different_number_of_components
   /auto_examples/plot_simulated_nonnegative
   /auto_examples/plot_semiconductor_etch_analysis
   /auto_examples/plot_bikesharing
   /auto_examples/plot_replicability
   /auto_examples/plot_custom_penalty


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  .. container:: sphx-glr-footer sphx-glr-footer-gallery

    .. container:: sphx-glr-download sphx-glr-download-python

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

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


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 .. rst-class:: sphx-glr-signature

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