.. _sphx_glr_auto_examples_plot_PPCA.py:


=============================
Interpolating missing data with probabalistic PCA
=============================

When you pass a matrix with with missing data, hypertools will attempt to
fill in the values using probabalistic principal components analysis (PPCA).
Here is an example where we generate some synthetic data, remove some of the
values, and then use PPCA to interpolate those missing values. Then, we plot
both the original and data with missing values together to see how it performed.




.. image:: /auto_examples/images/sphx_glr_plot_PPCA_001.png
    :align: center





.. code-block:: python


    # Code source: Andrew Heusser
    # License: MIT

    # import
    from scipy.linalg import toeplitz
    import numpy as np
    from copy import copy
    import hypertools as hyp

    # simulate data
    K = 10 - toeplitz(np.arange(10))
    data1 = np.cumsum(np.random.multivariate_normal(np.zeros(10), K, 250), axis=0)
    data2 = copy(data1)

    # simulate missing data
    missing = .1
    inds = [(i,j) for i in range(data2.shape[0]) for j in range(data2.shape[1])]
    missing_data = [inds[i] for i in np.random.choice(int(len(inds)), int(len(inds)*missing))]
    for i,j in missing_data:
        data2[i,j]=np.nan

    # plot
    hyp.plot([data1, data2], linestyle=['-',':'], legend=['Original', 'PPCA'])

**Total running time of the script:** ( 0 minutes  0.153 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_PPCA.py <plot_PPCA.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_PPCA.ipynb <plot_PPCA.ipynb>`


.. only:: html

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

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