create_train_file.py

import numpy as np
import matplotlib.pyplot as mpl
trainning_input_file        = 'train_input.txt'
testing_file = 'test_set.txt'

m=12345
n=2
K=3
#generate a circle region at center and size is R
center1 = np.random.rand(2).astype(np.float32)/4 + 0.4
center2 = -1 * np.random.rand(2).astype(np.float32)/4 - 0.4
r1 = np.random.rand(1).astype(np.float32) * 0.1 + 0.5
r2 = np.random.rand(1).astype(np.float32) * 0.1 + 0.5

x_data = np.random.rand(m,n).astype(np.float32) * 2 - 1
#calc the y for each training sets
y_data = np.zeros([m,K]).astype(np.float32)
for idx in range(m):
    if (x_data[idx,0] - center1[0])**2 + (x_data[idx,1] - center1[1])**2 <= r1**2:
        y_data[idx,1] = 1
    elif (x_data[idx,0] - center2[0])**2 + (x_data[idx,1] - center2[1])**2 <= r2**2:
        y_data[idx,2] = 1
    else:
        y_data[idx,0] = 1

mpl.plot(x_data[y_data[:,0]==1,0],x_data[y_data[:,0]==1,1],'k.')
mpl.plot(x_data[y_data[:,1]==1,0],x_data[y_data[:,1]==1,1],'b.')
mpl.plot(x_data[y_data[:,2]==1,0],x_data[y_data[:,2]==1,1],'r.')
mpl.show()


with open(trainning_input_file, 'wt') as f:
    for idx in range(m-2000):
        print('%10f,%10f,%1d,%1d,%1d'%(x_data[idx][0],x_data[idx][1],y_data[idx][0],y_data[idx][1],y_data[idx][2]),file = f)


with open(testing_file, 'wt') as f:
    for idx in range(m-2000,m):
        print('%10f,%10f,%1d,%1d,%1d'%(x_data[idx][0],x_data[idx][1],y_data[idx][0],y_data[idx][1],y_data[idx][2]),file = f)