o = output.T.tolist()[0]

o_max = 0

max_i = -1

for i in range(0,len(o)):

if (o[i] > o_max):

max_i = i

o_max = o[i]

if (max_i == 0):

return "shakespeare"

elif (max_i == 1):

return "twain"

elif (max_i == 2):

shake = "docs2/shakespeare/"

twain = "docs2/twain/"

dickens = "docs2/dickens/"

txt = ".txt"

nn = nn_matrix.neural_network(6,3,[8],1, .3)

prsr = ip.parser()

shake_desired = np.array([[1], [0], [0]])

twain_desired = np.array([[0], [1], [0]])

dickens_desired = np.array([[0], [0], [1]])

input_shake = []

input_twain = []

input_dickens = []

for j in range(1, 15):

input_shake.append(np.array([prsr.parse(shake+str(j)+txt)]).T)

input_twain.append(np.array([prsr.parse(twain+str(j)+txt)]).T)

input_dickens.append(np.array([prsr.parse(dickens+str(j)+txt)]).T)

# print (input_shake)

# print(input_twain)

# print(input_dickens)

for x in range(0, 100):

for i in range(0, 14):

# input_shake = np.array([prsr.parse(shake+str(i)+txt)]).T

# print("input_shake: ", input_shake)

# input_twain = np.array([prsr.parse(twain+str(i)+txt)]).T

# print("input_twain: ", input_twain)

nn.output(input_shake[i])

nn.back_prop(shake_desired)

nn.output(input_twain[i])

nn.back_prop(twain_desired)

nn.output(input_dickens[i])

nn.back_prop(dickens_desired)

for a in range(14,19):

stra = str(a)

test_shake = prsr.parse(shake+stra+txt)

test_twain = prsr.parse(twain+stra+txt)

test_dickens = prsr.parse(dickens+stra+txt)

shake_out = nn.output(test_shake)

twain_out = nn.output(test_twain)

dickens_out = nn.output(test_dickens)

print("shake_out"+stra + ": ", test(shake_out))

print("twain_out"+stra+": ", test(twain_out))

print("dickens_out"+stra+": ", test(dickens_out))

print("\n")

print(shake_out)

print(twain_out)

print(dickens_out)