sklearn中digits手写字体数据集_digits数据集

1. 导入

from sklearn import datasets
digits = datasets.load_digits()

2. 属性查看

• digits: bunch类型

print(digits.keys())
 
dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])

3. 具体数据

• 1797个样本，每个样本包括8*8像素的图像和一个[0, 9]整数的标签

3.1 images

• ndarray类型，保存8*8的图像，里面的元素是float64类型，共有1797张图片
• 用于显示图片

• import matplotlib.pyplot as plt
  plt.imshow(digits.images[0])
                        <matplotlib.image.AxesImage at 0x1676ca13ba8>
  plt.show()
  

•  

 

# 获取第一张图片
print(digits.images[0])
[[  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.]]

• 

• 或者

• from skimage import io
  im=plt.imshow(digits.images[0])
  print(type(im))
                <class 'matplotlib.image.AxesImage'>
  io.show()
  

  

 

3.2 data

• ndarray类型，将images按行展开成一行，共有1797行
• 输入数据

• print(digits.data[0])
  [  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.]
  

3.3 target

• ndarray类型，指明每张图片的标签，也就是每张图片代表的数字
• 输出数据，标签

• print(digits.target[0])
   
  0
  

  3.4 target_names

• ndarray类型，数据集中所有标签值

• print(digits.target_names)
  [0 1 2 3 4 5 6 7 8 9]
  

  3.5 DESCR

• 数据集的描述，作者，数据来源等

• print(digits.DESCR)
  .. _digits_dataset:
   
  Optical recognition of handwritten digits dataset
  --------------------------------------------------
   
  **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.
   
  .. topic:: 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.