Learning to recognize handwritten digits with a K-nearest neighbors classifier

In this recipe, we will see how to recognize handwritten digits with a K-nearest neighbors (K-NN) classifier. This classifier is a simple but powerful model, well-adapted to complex, highly nonlinear datasets such as images. We will explain how it works later in this recipe.

How to do it...

1. We import the modules:

   In [1]: import numpy as np
           import sklearn
           import sklearn.datasets as ds
           import sklearn.cross_validation as cv
           import sklearn.neighbors as nb
           import matplotlib.pyplot as plt
           %matplotlib inline

2. Let's load the digits dataset, part of the datasets module of scikit-learn. This dataset contains handwritten digits that have been manually labeled:

   In [2]: digits = ds.load_digits()
           X = digits.data
           y = digits.target
           print((X.min(), X.max()))
           print(X.shape)
   0.0 16.0
   (1797L, 64L)

   In the matrix X, each row contains 8 * 8=64 pixels (in grayscale...