识别手写体数字

导入数据集



In [3]:

    
from sklearn import datasets
digits = datasets.load_digits()
print digits.DESCR









    



Optical Recognition of Handwritten Digits Data Set
===================================================

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: E. 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.

References
----------
  - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
    Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
    Graduate Studies in Science and Engineering, Bogazici University.
  - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
  - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
    Linear dimensionalityreduction using relevance weighted LDA. School of
    Electrical and Electronic Engineering Nanyang Technological University.
    2005.
  - Claudio Gentile. A New Approximate Maximal Margin Classification
    Algorithm. NIPS. 2000.

手写体数字图像的数据，则存储在digit.images，数组中每个元素表示一张图像，每个元素为 $8 \times 8$形状的矩阵，矩阵各项为数值类型，每个数值对应着一种灰度等级，0代表白色，15代表黑色



In [4]:

    
digits.images[0]









    Out[4]:





array([[  0.,   0.,   5.,  13.,   9.,   1.,   0.,   0.],
       [  0.,   0.,  13.,  15.,  10.,  15.,   5.,   0.],
       [  0.,   3.,  15.,   2.,   0.,  11.,   8.,   0.],
       [  0.,   4.,  12.,   0.,   0.,   8.,   8.,   0.],
       [  0.,   5.,   8.,   0.,   0.,   9.,   8.,   0.],
       [  0.,   4.,  11.,   0.,   1.,  12.,   7.,   0.],
       [  0.,   2.,  14.,   5.,  10.,  12.,   0.,   0.],
       [  0.,   0.,   6.,  13.,  10.,   0.,   0.,   0.]])

借助matplotlib库，生成图像



In [6]:

    
import matplotlib.pyplot as plt
%matplotlib inline
plt.imshow(digits.images[0],cmap=plt.cm.gray_r,interpolation='nearest')









    Out[6]:





<matplotlib.image.AxesImage at 0x10fbaa990>



In [7]:

    
digits.target









    Out[7]:





array([0, 1, 2, ..., 8, 9, 8])



In [8]:

    
digits.target.size









    Out[8]:





1797

学习预测

digits数据集有1797个元素，考虑使用前1791个作为训练集，剩余的6个作为验证集，查看细节



In [10]:

    
import matplotlib.pyplot as plt
%matplotlib inline
plt.subplot(321)
plt.imshow(digits.images[1791], cmap=plt.cm.gray_r, interpolation='nearest')
plt.subplot(322)
plt.imshow(digits.images[1792], cmap=plt.cm.gray_r, interpolation='nearest')
plt.subplot(323)
plt.imshow(digits.images[1793], cmap=plt.cm.gray_r, interpolation='nearest')
plt.subplot(324)
plt.imshow(digits.images[1794], cmap=plt.cm.gray_r, interpolation='nearest')
plt.subplot(325)
plt.imshow(digits.images[1795], cmap=plt.cm.gray_r, interpolation='nearest')
plt.subplot(326)
plt.imshow(digits.images[1796], cmap=plt.cm.gray_r, interpolation='nearest')









    Out[10]:





<matplotlib.image.AxesImage at 0x110014cd0>

定义svc估计器进行学习



In [13]:

    
from sklearn import svm
svc = svm.SVC(gamma=0.0001,C=100.)
svc.fit(digits.data[1:1790],digits.target[1:1790])









    Out[13]:





SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape=None, degree=3, gamma=0.0001, kernel='rbf',
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False)



In [14]:

    
svc.predict(digits.data[1791:1976])









    Out[14]:





array([4, 9, 0, 8, 9, 8])



In [19]:

    
digits.target[1791:1976]









    Out[19]:





array([4, 9, 0, 8, 9, 8])



In [ ]: