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)