def neuro_start(inputs, targets, training):
#initializes the neural network
network=neuro.setup_network(inputs)
#The number of repetitions that you will
#be training your network with
training_reps= training
#trains your neural network
neuro.train(network, inputs, targets, training_reps)
total = 0
correct = 0
for i in range(1,10):
for x in range(1,10):
pred = neuro.predict(network, [i+x])
#rounds the predicted value to either 0 or 1
#print '{} == pred {}'.format(i+x,pred)
pred = np.round(pred)
if (i+x)%2 == pred:
correct+= 1
total += 1
percent = (correct/total) * 100
#print 'with training: {}'.format(training_reps)
#print 'correct: {}\n total: {} \n {:.2f}%'.format(correct, total, (correct/total) * 100)
return training_reps, percent, correct, total
network = []
data = open('test_cases.csv')
data = data.read()
data = data.split('\n')
data.pop()
for i in data:
tokens = i.split(',')
inputs.append([float(tokens[0]), float(tokens[1]), float(tokens[2])])
targets.append([float(tokens[3])])
network = neuro.setup_network(inputs)
neuro.train(network, inputs, targets, reps)
user_answer = 'y'
while user_answer == 'y':
r = float(raw_input("Enter a value for red between 0 and 255: ")) / 255
g = float(raw_input("Enter a value for green between 0 and 255: ")) / 255
b = float(raw_input("Enter a value for blue between 0 and 255: ")) / 255
pred = round(neuro.predict(network, [r, g, b]), 2)
if (pred >= 0.0 and pred < 0.33):
print("\nThe color is RED!\n")
elif (pred >= 0.33 and pred < 0.66):
print("\nThe color is GREEN!\n")
elif (pred >= 0.66 and pred < 1.0):
print("\nThe color is BLUE!\n")
user_answer = raw_input("Again? (y/n): ")
targets = [[1], [0], [0], [1], [0], [0], [1], [1], [0], [0], [0], [1], [1],
[1], [1]]
reps = 1000
inputs = np.float32(inputs) / 255.0
network = []
network = neuro.setup_network(inputs)
neuro.train(network, inputs, targets, reps)
#TAKE INPUT
red_value = input("Enter Red Value: ")
green_value = input("Enter Green Value: ")
blue_value = input("Enter Blue Value: ")
red_value = float(red_value) / 255.0
green_value = float(green_value) / 255.0
blue_value = float(blue_value) / 255.0
color = [red_value, green_value, blue_value]
pred = neuro.predict(network, color)
print("Is it blue?")
if (pred >= float(0.70)):
print("YES")
else:
print("NO")
neural_counter = 0
naive_counter = 0
tree_counter = 0
static_inputs = list(all_inputs)
for index in range(len(result)):
test_input = all_inputs.pop(index)
inputs = all_inputs
all_inputs = list(static_inputs)
reps = 50
network = []
network = neuro.setup_network(inputs)
neuro.train(network, inputs, targets, reps)
pred = neuro.predict(network, test_input)
if (pred >= 0 and pred < 0.5):
pred = 0
elif (pred >= 0.5 and pred < 1.0):
pred = 1
else:
pred = -1
if (pred == 0):
party_guess = "Republican"
elif (pred == 1):
party_guess = "Democrat"
else:
party_guess = "error - out of range"
#training targets placed in the same order as their #corresponding inputs (listed above) targets = [[0], [0], [0], [1]] #initializes the neural network network = neuro.setup_network(inputs) #The number of repetitions that you will #be training your network with training_reps = 100 #trains your neural network neuro.train(network, inputs, targets, training_reps) #gets the predicted value for input [0,0] pred = neuro.predict(network, [0, 0]) #rounds the predicted value to either 0 or 1 pred = int(np.round(pred)) print "0 & 0 =", pred #the following lines are the same as the lines #documented above except they are for inputs [0,1], [1,0], and [1,1] pred = neuro.predict(network, [0, 1]) pred = int(np.round(pred)) print "0 & 1 =", pred pred = neuro.predict(network, [1, 0]) pred = int(np.round(pred))
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
testing5 = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0,
1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0,
0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
]
testing6 = [
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0,
1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0
]
pred1 = neuro.predict(network, testing1)
pred2 = neuro.predict(network, testing2)
pred3 = neuro.predict(network, testing3)
pred4 = neuro.predict(network, testing4)
pred5 = neuro.predict(network, testing5)
pred6 = neuro.predict(network, testing6)
print(pred1, pred2, pred3, pred4, pred5, pred6)