def __init__(self, executionTime, startPos, startVel, goalPos, cs,
numWeights, overlap, use_scaling):
self.T = executionTime
self.cs = cs
self.alpha = 25.0
self.beta = 6.25
self.g = goalPos
self.y = startPos
self.startPos = startPos
self.z = self.T * startVel
self.startZ = self.z
self.rbf = Rbf(cs, executionTime, numWeights, overlap)
self.amplitude = 0
self.use_scaling = use_scaling
def main():
#read dataset and preprocess it
dataset = PreProcessing("seeds_dataset.txt", separator='\s+')
dataset.normalize()
dataset.normalize_class()
#divide dataset into training and test sets
train, test = training.holdout(0.7, dataset.normalized_dataframe)
nn = Rbf(7, 3)
nn.train(train, eta=0.5, max_iterations=500)
print("RBF:", training.accuracy(nn, test, 3))
mm = Mlp(7, 3, 3)
mm.backpropagation(train.values.tolist(), max_iterations=500)
print("MLP:", training.accuracy(mm, test, 3))
def __init__(self, executionTime, startPos, startVel, startAcc, goalPos,
goalVel, cs, numWeights, overlap, use_vel_scaling):
self.T = executionTime
self.cs = cs
self.alpha = 25.0
self.beta = 6.25
self.g = goalPos
self.gd = goalVel
self.gdd = 0.0 #has to be 0
self.y = startPos
self.y0 = startPos
self.yd0 = startVel
self.ydd0 = startAcc
self.ydd = startAcc
self.v = self.T * self.yd0
self.startV = self.v
self.fop = Fop(0.0, startPos, startVel, startAcc, executionTime,
goalPos, goalVel, self.gdd)
self.rbf = Rbf(cs, executionTime, numWeights, overlap)
self.amplitude = goalPos - startPos
self.amplitude2 = goalVel - startVel
self.use_vel_scaling = use_vel_scaling
def seed_test():
# Carregando e Normalizando os dados da base de vinhos
dataset = PreProcessing("seeds_dataset.txt", separator='\s+')
dataset.normalize()
dataset.normalize_class()
# Atributos a serem variados nos testes
n_layers = [1, 2]
hidden_layer = [3, [6, 6]]
momentums = [0.3, 0.5]
max_iterations = [100, 250, 500]
etas = [0.3, 0.5]
ps = [0.7, 0.9]
rbf_accuracy = 0
mlp_accuracy = 0
tests = 0
# Teste
for layer in n_layers:
for momentum in momentums:
for eta in etas:
for max_iteration in max_iterations:
for p in ps:
tests += 1
print("Test number", tests)
train, test = training.holdout(
p, dataset.normalized_dataframe)
print("INPUT NEURONS = 7 HIDDEN NEURONS = " +
str(int(6 / layer)) +
" OUTPUT NEURONS = 3 HIDDEN LAYER = " +
str(layer) + " ETA = " + str(eta) +
" MAX ITERATIONS = " + str(max_iteration) +
" MOMENTUM = " + str(momentum) + " P = " +
str(p))
print()
print("RBF")
nn = Rbf(7, 3)
nn.train(train, eta=0.5, max_iterations=max_iteration)
ac = training.accuracy(nn, test, 3)
rbf_accuracy += ac
print("ACCURACY =", ac)
print()
print("MLP")
example = test.values.tolist()
mm = Mlp(7,
hidden_layer[layer - 1],
3,
n_hidden_layers=layer)
mm.backpropagation(train.values.tolist(),
eta=eta,
max_iterations=max_iteration)
ac = training.accuracy(mm, test, n_classes=3)
mlp_accuracy += ac
print("ACCURACY =", ac)
print()
print("Rbf:")
nn.feed_forward(example[15][:(-1 * 3)])
print(example[15])
print("Result 1")
nn.show_class()
print()
print("Mlp")
print(example[15])
nn.feed_forward(example[15][:(-1 * 3)])
print("Result 2")
mm.show_class()
print()
print(
"******************************************************//******************************************************"
)
print()
print(tests, " tests executed. Rbf accuracy:", rbf_accuracy / tests,
" Mlp accuracy:", mlp_accuracy / tests)
