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main.py
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main.py
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import numpy as np
from ELM import ELM
from MLP import MLP
import DataUtils as ut
def main():
#run_iris()
#run_vertebral()
#run_dermatology()
#run_cancer()
#run_xor()
#run_elm_iris()
#run_elm_vertebral()
#run_elm_dermatology()
#run_elm_cancer()
run_elm_xor()
def run_iris():
acs = []
att = [0, 1, 2, 3]
d = ut.get_iris_data().to_numpy()
print('======== MLP IRIS ========')
for i in range(20):
mlp = MLP(4, 0.1, 500, 6, 3)
acs.append(realization(mlp, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_vertebral():
acs = []
att = [0, 1, 2, 3, 4, 5]
d = ut.get_column_data().to_numpy()
print('======== MLP VERTEBRAL ========')
for i in range(20):
mlp = MLP(6, 0.1, 500, 12, 3)
acs.append(realization(mlp, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_dermatology():
acs = []
att = [x for x in range(34)]
d = ut.get_dermatology().to_numpy()
print('======== MLP DERMATOLOGY ========')
for i in range(20):
mlp = MLP(34, 0.1, 500, 12, 6)
acs.append(realization(mlp, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_cancer():
acs = []
att = [x for x in range(10)]
d = ut.get_cancer().to_numpy()
print('======== MLP BREAST CANCER ========')
for i in range(20):
mlp = MLP(10, 0.1, 500, 4, 2)
acs.append(realization(mlp, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_xor():
acs = []
att = [0, 1]
d = ut.get_xor().to_numpy()
print('======== MLP XOR ========')
for i in range(20):
mlp = MLP(2, 0.1, 500, 4, 2)
acs.append(realization(mlp, d, att, xor=True))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_elm_iris():
acs = []
att = [0, 1, 2, 3]
d = ut.get_iris_data().to_numpy()
print('======== ELM IRIS ========')
for i in range(20):
elm = ELM(18, 3)
acs.append(realization_elm(elm, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_elm_vertebral():
acs = []
att = [0, 1, 2, 3, 4, 5]
d = ut.get_column_data().to_numpy()
print('======== ELM VERTEBRAL ========')
for i in range(20):
elm = ELM(16, 3)
acs.append(realization_elm(elm, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_elm_dermatology():
acs = []
att = [x for x in range(34)]
d = ut.get_dermatology().to_numpy()
print('======== ELM DERMATOLOGY ========')
for i in range(20):
elm = ELM(30, 6)
acs.append(realization_elm(elm, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_elm_cancer():
acs = []
att = [x for x in range(10)]
d = ut.get_cancer().to_numpy()
print('======== ELM BREAST CANCER ========')
for i in range(20):
elm = ELM(8, 2)
acs.append(realization_elm(elm, d, att))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_elm_xor():
acs = []
att = [0, 1]
d = ut.get_xor().to_numpy()
print('======== ELM XOR ========')
for i in range(20):
elm = ELM(8, 2)
acs.append(realization_elm(elm, d, att, xor=True))
print('Realização: {0}, Acurácia: {1}%'.format(i, acs[i]))
print('Acurácia após 20 realizações: {0}%, Desvio padrão: {1}'.format(calc_accuracy(acs), np.std(acs)))
def run_artificial():
att = [0]
mlp = MLP(1, 0.1, 500, 1, 1)
d = ut.get_artificial()
realization_regression(mlp, d, att)
def realization(mlp, d, att, xor=False):
np.random.shuffle(d)
qt_training = int(0.8 * len(d))
train_data, test_data = d[:qt_training], d[qt_training:]
mlp.train(train_data, att)
accuracy = mlp.test(test_data, att)
if (xor): ut.plot_decision_surface_mlp(mlp, test_data, att)
return accuracy
def realization_elm(elm, d, att, xor=False):
np.random.shuffle(d)
qt_training = int(0.8 * len(d))
train_data, test_data = d[:qt_training], d[qt_training:]
elm.train(train_data, att)
accuracy = elm.test(test_data, att)
if (xor): ut.plot_decision_surface_elm(elm, test_data, att)
return accuracy
def realization_regression(mlp, d, att):
np.random.shuffle(d)
qt_training = int(0.8 * len(d))
train_data, test_data = d[:qt_training], d[qt_training:]
mlp.train(train_data, att)
ut.plot(mlp, test_data)
def calc_accuracy(array):
return sum(array) / len(array)
if __name__ == '__main__':
main()