scikits.learn.datasets.load_digits

scikits.learn.datasets.load_digits(n_class=10)

load the digits dataset and returns it.

Parameters :

n_class : integer, between 0 and 10

Number of classes to return, defaults to 10

Returns :

data : Bunch

Dictionnary-like object, the interesting attributes are: ‘data’, the data to learn, images, the images corresponding to each sample, ‘target’, the classification labels for each sample, ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.

Notes

Data Set Characteristics:

Number of Instances:  

5620

Number of Attributes:  

64

Attribute Information:  

8x8 image of integer pixels in the range 0..16.

Missing Attribute Values:  

None

Creator:

5. Alpaydin (alpaydin ‘@’ boun.edu.tr)

Date:

July; 1998

This is a copy of the test set of the UCI ML hand-written digits datasets http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

The data set contains images of hand-written digits: 10 classes where each class refers to a digit.

Preprocessing programs made available by NIST were used to extract normalized bitmaps of handwritten digits from a preprinted form. From a total of 43 people, 30 contributed to the training set and different 13 to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of 4x4 and the number of on pixels are counted in each block. This generates an input matrix of 8x8 where each element is an integer in the range 0..16. This reduces dimensionality and gives invariance to small distortions.

For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G. T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C. L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469, 1994.