参考書籍「詳細!Python3 入門ノート」の抜粋なのですが
>>> from sklearn import datasets >>> digits=datasets.load_digits() >>> dir(digits) ['DESCR', 'data', 'images', 'target', 'target_names'] >>> 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. >>> digits.data.shape (1797, 64) >>> digits.target.shape (1797,) >>> digits.data array([[ 0., 0., 5., ..., 0., 0., 0.], [ 0., 0., 0., ..., 10., 0., 0.], [ 0., 0., 0., ..., 16., 9., 0.], ..., [ 0., 0., 1., ..., 6., 0., 0.], [ 0., 0., 2., ..., 12., 0., 0.], [ 0., 0., 10., ..., 12., 1., 0.]]) >>> digits.data[0] 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.]) >>> digits.images[0] 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.]]) >>> import matplotlib.pyplot as plt >>> plt.matshow(digits.images[0],cmap="Greys") <matplotlib.image.AxesImage object at 0x0000026AA4F18438> >>>plt.show()
となって8×8の64マスのぼやっとした「0」が表示された後に書籍では本当に「0」を書いた手書き文字なのか確認するために
digits.target[0]
と打たなくてはいけないのですが
plt.show()
の後に<<<が現れません。。原因がわかる方、お答えいただけると嬉しいです。
自分では「plt.show()の後 書けない」や「anaconda prompt <<<が出ない」などで調べたのですがグラフが描画されないときの対処法など求めてる回答が得られませんでした。
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