#!/usr/bin/env python

import random
from nott_params import *

num_samples = int(gridDim[0] * gridDim[1] * 10)

def generate_data(numx, numy):
    stimulus = (random.randint(0, numx - 1), random.randint(0, numy - 1))
    return stimulus

def print_header():
    print("{0} {1} {2}".format(num_samples, num_inputs, num_outputs))

def print_input(left, right):
    data = left + right
    print(' '.join(data))

if __name__ == '__main__':
    random.seed()
    print_header()
    for i in range(num_samples):
        stimulus = generate_data(gridDim[0], gridDim[1])
        print('%d %d' % stimulus)
        scaled_x = 2 * float(stimulus[0]) / gridDim[0] - 1
        scaled_y = 2 * float(stimulus[1]) / gridDim[1] - 1
        print("{0} {1} {2} {3}".format(scaled_x, scaled_y,
                                       scaled_x, scaled_y))

#!/usr/bin/env python

import random
from nott_params import *

num_samples = int(gridDim[0] * gridDim[1] * 0.2)

def generate_data(numx, numy):
    stimulus = (random.randint(0, numx - 1), random.randint(0, numy - 1))
    return stimulus

def print_header():
    print("{0} {1} {2}".format(num_samples, num_inputs, num_outputs))

def print_input(left, right):
    data = left + right
    print(' '.join(data))

if __name__ == '__main__':
    random.seed()
    print_header()
    for i in range(num_samples):
        stimulus = generate_data(gridDim[0], gridDim[1])
        print('%d %d' % stimulus)
        scaled_x = 2 * float(stimulus[0]) / gridDim[0] - 1
        scaled_y = 2 * float(stimulus[1]) / gridDim[1] - 1
        print("{0} {1} {2} {3}".format(scaled_x, scaled_y,
                                       scaled_x, scaled_y))

Remove oversampling; use 20 percent instead
Remove oversampling; use 20 percent instead
