

In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd

from ipywidgets import interact, interactive, fixed



In [2]:

    
#blow up an image by a factor of x
def blowUp(image,x):
    w,h = image.shape
    newimage = np.zeros((w*x,h*x))
    for row in range(h):
        for col in range(w):
            px = image[row,col]
            for i in range(col*x,col*x+x):
                for j in range(row*x,row*x+x):
                    newimage[j,i] = px
    return newimage

Set the fn variable to the filename of either the training or test dataset



In [3]:

    
#training data
#fn = 'data/ocr/optdigits.tra'

#testing data
fn = 'data/ocr/optdigits.tes'

header="x11,x12,x13,x14,x15,x16,x17,x18,x21,x22,x23,x24,x25,x26,x27,x28,x31,x32,x33,x34,x35,x36,x37,x38,x41,x42,x43,x44,x45,x46,x47,x48,x51,x52,x53,x54,x55,x56,x57,x58,x61,x62,x63,x64,x65,x66,x67,x68,x71,x72,x73,x74,x75,x76,x77,x78,x81,x82,x83,x84,x85,x86,x87,x88,digit".split(",")

df = pd.read_csv(fn, header=None)
df.columns = header
df.head()









    Out[3]:






  
    
      
      x11
      x12
      x13
      x14
      x15
      x16
      x17
      x18
      x21
      x22
      ...
      x78
      x81
      x82
      x83
      x84
      x85
      x86
      x87
      x88
      digit
    
  
  
    
      0
      0
      0
      5
      13
      9
      1
      0
      0
      0
      0
      ...
      0
      0
      0
      6
      13
      10
      0
      0
      0
      0
    
    
      1
      0
      0
      0
      12
      13
      5
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      11
      16
      10
      0
      0
      1
    
    
      2
      0
      0
      0
      4
      15
      12
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      3
      11
      16
      9
      0
      2
    
    
      3
      0
      0
      7
      15
      13
      1
      0
      0
      0
      8
      ...
      0
      0
      0
      7
      13
      13
      9
      0
      0
      3
    
    
      4
      0
      0
      0
      1
      11
      0
      0
      0
      0
      0
      ...
      0
      0
      0
      0
      2
      16
      4
      0
      0
      4
    
  

5 rows × 65 columns



In [4]:

    
y = df.digit.copy().values
X = df.drop("digit", axis=1).values
X.shape, y.shape









    Out[4]:





((1797, 64), (1797,))



In [5]:

    
X = X.reshape((-1, 8,8))
X.shape









    Out[5]:





(1797, 8, 8)

After running the cell below, you can move the slider to visualize the various instances of the dataset, change factor slider to increase the sharpness of the image



In [6]:

    
@interact(X=fixed(X), y=fixed(y), idx=(0,X.shape[0]), factor=(1,50))
def show_item(X, y, idx=0, factor=5):
    x = X[idx]
    print("Instance %s:\t[%s]" % (
            idx+1, ", ".join("'%s'" % str(k) for k in 
                list(x.flatten()) + [y[idx]])))
    x = (((x-16)/16.0)*255).astype("int")
    x = blowUp(x, factor)
    fig, ax = plt.subplots(figsize=(5,5))
    ax.imshow(x, cmap="Greys")
    ax.set_title("Instance=%s, Digit=%s" % (idx+1, y[idx]))
    plt.axis('off')









    



Instance 1:	['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', '0']



In [ ]:

	x13	x14	x15	x16	x22	...	x83	x84	x85	x86	x87	digit
0	5	13	9	1	0	...	6	13	10	0	0	0
1	0	12	13	5	0	...	0	11	16	10	0	1
2	0	4	15	12	0	...	0	3	11	16	9	2
3	7	15	13	1	8	...	7	13	13	9	0	3
4	0	1	11	0	0	...	0	2	16	4	0	4

	x13	x14	x15	x16	x22	...	x83	x84	x85	x86	x87	digit
0	5	13	9	1	0	...	6	13	10	0	0	0
1	0	12	13	5	0	...	0	11	16	10	0	1
2	0	4	15	12	0	...	0	3	11	16	9	2
3	7	15	13	1	8	...	7	13	13	9	0	3
4	0	1	11	0	0	...	0	2	16	4	0	4

	x13	x14	x15	x16	x22	...	x83	x84	x85	x86	x87	digit
0	5	13	9	1	0	...	6	13	10	0	0	0
1	0	12	13	5	0	...	0	11	16	10	0	1
2	0	4	15	12	0	...	0	3	11	16	9	2
3	7	15	13	1	8	...	7	13	13	9	0	3
4	0	1	11	0	0	...	0	2	16	4	0	4