def generate_example(char_list, char=-1, noise=0):
if char == -1:
char = randint(0, 25)
res = np.zeros((26, 1))
res[char][0] = 1 - res[char][0]
# generates random noise from 0 to 3 if noise == -1
if noise == -1:
noise = randint(0, 3)
img = generate_char_img(char_list, char, noise)
img = img.reshape(126, 1)
return Example(img, res)
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
AND_AI = NeuralNetwork([2, 10, 1], 0.1)
MAX_LEARN = 10000
example_list = []
for i in range(MAX_LEARN):
a = randint(0, 1)
b = randint(0, 1)
example_list.append(Example(np.array([[a],[b]]), np.array([[ int(a == 1 and b == 1) ]])))
AND_AI.learn(example_list)
total = 500
correct = 0
for i in range(total):
a = randint(0, 1)
b = randint(0, 1)
c = AND_AI.output((np.array([[a], [b]])))
if int(a == 1 and b == 1) == int(c[0][0] + 0.5):
correct = correct + 1
print('Set [{},{}] -> {} | AI: {} -> {}'.format(a, b, int(a == 1 and b == 1), c, round(c[0,0])))
print('Correct: {}%'.format(correct / total * 100))
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
xorAI = NeuralNetwork([2, 2, 1], 0.1)
MAX_LEARN = 10000
example_list = []
for i in range(MAX_LEARN):
a = randint(0, 1)
b = randint(0, 1)
example_list.append(
Example(np.array([[a], [b]]), np.array([[int(a != b)]])))
xorAI.learn(example_list)
total = 500
correct = 0
for i in range(total):
a = randint(0, 1)
b = randint(0, 1)
c = xorAI.output((np.array([[a], [b]])))
if int(a != b) == int(c[0][0] + 0.5):
correct = correct + 1
print('Set [{},{}] -> {} | AI: {}'.format(a, b, int(a != b), c))
print('Correct: {}%'.format(correct / total * 100))
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
AND2 = NeuralNetwork([2, 1], 0.003)
e1 = Example(np.array([[0], [0]]), np.array([[0]]))
e2 = Example(np.array([[0], [255]]), np.array([[0]]))
e3 = Example(np.array([[255], [0]]), np.array([[0]]))
e4 = Example(np.array([[255], [255]]), np.array([[1]]))
e = [e1, e2, e3, e4]
MAX_LEARN = 100000
example_list = []
for i in range(MAX_LEARN):
r = randint(0, 3)
example_list.append(e[r])
AND2.learn(example_list)
total = 500
correct = 0
for i in range(total):
r = randint(0, 3)
c = AND2.output(e[r].input)
if int(round(c[0][0]) == e[r].result[0][0]):
correct = correct + 1
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
xorAI = NeuralNetwork([1, 1], 0.1)
e1 = Example(np.array([[-1]]), np.array([[1]]))
e2 = Example(np.array([[1]]), np.array([[-1]]))
e = [e1, e2]
MAX_LEARN = 10000
example_list = []
for i in range(MAX_LEARN):
r = randint(0, 1)
example_list.append(e[r])
# example_list.append(Example(np.array([[a],[b]]), np.array([[ int(a == 1 or b == 1) ]])))
xorAI.learn(example_list)
total = 500
correct = 0
for i in range(total):
a = randint(0, 1)
c = xorAI.output(e[a].input)
# if int(a == 1 or b == 1) == int(c[0][0] + 0.5):
# correct = correct + 1
# r = randint(0,1)
#
# if r == 0:
# e = Example(np.array([[0],[1]]),np.array([[0.3],[1]]))
# elif r == 1:
# e = Example(np.array([[1],[0]]),np.array([[1],[0.3]]))
#
# example.append( e )
mj_example = []
majorityFunction = NeuralNetwork([1, 1])
for i in range(40000):
r = randint(0, 1)
mj_example.append(Example(np.array([[r]]), np.array([[r]])))
majorityFunction.learn(mj_example)
for i in range(30):
print('------')
print(i)
r = randint(0, 1)
solution = majorityFunction.output(np.array([[r]]))
print(r)
print(solution)
print(mj_example[0].result)
val = majorityFunction.output(mj_example[0].result)
print(val)
print(val)
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
xorAI = NeuralNetwork([2, 4, 4, 1], 0.1)
e1 = Example(np.array([[-1], [-1]]), np.array([[1]]))
e2 = Example(np.array([[-1], [1]]), np.array([[1]]))
e3 = Example(np.array([[1], [-1]]), np.array([[-1]]))
e4 = Example(np.array([[1], [1]]), np.array([[1]]))
e = [e1, e2, e3, e4]
MAX_LEARN = 10000
example_list = []
for i in range(MAX_LEARN):
r = randint(0, 3)
example_list.append(e[r])
# example_list.append(Example(np.array([[a],[b]]), np.array([[ int(a == 1 or b == 1) ]])))
xorAI.learn(example_list)
total = 500
correct = 0
for i in range(total):
a = randint(0, 1)
b = randint(0, 1)
c = xorAI.output((np.array([[a], [b]])))
from csce420_final_project.NeuralNetwork import NeuralNetwork
from csce420_final_project.NeuralNetwork import Example
import numpy as np
from random import randint
xorAI = NeuralNetwork([2, 10, 1], 0.1)
MAX_LEARN = 10000
example_list = []
for i in range(MAX_LEARN):
a = randint(0, 1)
b = randint(0, 1)
example_list.append(
Example(np.array([[a], [b]]), np.array([[int(a == 1 or b == 1)]])))
xorAI.learn(example_list)
total = 500
correct = 0
for i in range(total):
a = randint(0, 1)
b = randint(0, 1)
c = xorAI.output((np.array([[a], [b]])))
if int(a == 1 or b == 1) == int(c[0][0] + 0.5):
correct = correct + 1
print('Set [{},{}] -> {} | AI: {}'.format(a, b, int(a == 1 or b == 1), c))
print('Correct: {}%'.format(correct / total * 100))