def test_nand(self):
random.seed(1)
my_model = ANNModel(2,
2,
2, ["0", "1"],
verbose=False,
add_bias=True,
weight_bail=0.00001)
input_list = read_csv("NAND.csv")
my_model.train(input_list)
for x in [[0, 0], [0, 1], [1, 0], [1, 1]]:
print("for input [%i,%i]" % (x[0], x[1]))
print(my_model.classify(x))
def test_iris(self):
random.seed(1)
my_model = ANNModel(
4,
4,
3, ["Iris-setosa", "Iris-versicolor", "Iris-virginica"],
verbose=False,
add_bias=False)
input_list = read_csv("iris.data")
# input_list=input_list[1:2]
my_model.train(input_list)
print("expecting Iris-setosa")
print(my_model.classify([5.1, 3.5, 1.4, 0.2]))
print("expecting Iris-versicolor")
print(my_model.classify([7.0, 3.2, 4.7, 1.4]))
print("expecting Iris-virginica")
print(my_model.classify([6.3, 3.3, 6.0, 2.5]))
import random
from ANN import ANNModel, read_csv
random.seed(0)
my_model = ANNModel(2, 2, 2, ["0", "1"], verbose=True)
input_list = read_csv("XOR.csv")
# input_list=input_list[1:2]
my_model.train(input_list)