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# EXAMPLE SEE http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html#sphx-glr-auto-examples-plot-digits-pipe-py


    

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print(__doc__)


# Code source: Gaël Varoquaux
# Modified for documentation by Jaques Grobler
# License: BSD 3 clause


import numpy as np
import matplotlib.pyplot as plt

from sklearn import linear_model, decomposition, datasets
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

logistic = linear_model.LogisticRegression()

pca = decomposition.PCA()
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target


    

    




Startup script for IPython kernel.

Installs an import hook to configure the matplotlib backend on the fly.

Originally from @minrk at 
https://github.com/minrk/profile_default/blob/master/startup/mplimporthook.py
Repurposed for docker-stacks to address repeat bugs like
https://github.com/jupyter/docker-stacks/issues/235.

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pca.fit(X_digits)

plt.figure(1, figsize=(4, 3))
plt.clf()
plt.axes([.2, .2, .7, .7])
plt.plot(pca.explained_variance_, linewidth=2)
plt.axis('tight')
plt.xlabel('n_components')
plt.ylabel('explained_variance_')


    

    
Out[3]:





<matplotlib.text.Text at 0x7f02ef9323c8>






    

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