def test():
data = []
data.append([0, 0, 1, 1])
data.append([0, 0, 1, 0.9])
data.append([0, 0, 0.9, 0.9])
data.append([0.8, 1, 0, 0])
data.append([1, 1, 0.1, 0])
data.append([1, 0.9, 0, 0.2])
targets = []
targets.append(0)
targets.append(0)
targets.append(0)
targets.append(1)
targets.append(1)
targets.append(1)
layers = [4, 2, 1]
dnn = AutoEncoder(data,
data,
targets,
layers,
hidden_layer="TANH",
final_layer="TANH",
compression_epochs=50,
bias=True,
autoencoding_only=True)
#dnn = DNNRegressor(data, targets, layers, hidden_layer="TanhLayer", final_layer="TanhLayer", compression_epochs=50, bias=True, autoencoding_only=False)
dnn = dnn.fit()
data.append([0.9, 0.8, 0, 0.1])
print "\n-----"
for d in data:
print dnn.activate(d)
def test():
data = []
data.append([0,0,1,1])
data.append([0,0,1,0.9])
data.append([0,0,0.9,0.9])
data.append([0.8,1,0,0])
data.append([1,1,0.1,0])
data.append([1,0.9,0,0.2])
targets = []
targets.append(0)
targets.append(0)
targets.append(0)
targets.append(1)
targets.append(1)
targets.append(1)
layers = [4,2,1]
dnn = AutoEncoder(data, data, targets, layers, hidden_layer="TANH", final_layer="TANH", compression_epochs=50, bias=True, autoencoding_only=True)
#dnn = DNNRegressor(data, targets, layers, hidden_layer="TanhLayer", final_layer="TanhLayer", compression_epochs=50, bias=True, autoencoding_only=False)
dnn = dnn.fit()
data.append([0.9, 0.8, 0, 0.1])
print "\n-----"
for d in data:
print dnn.activate(d)
def autoencode(self, data, targets, cv, epochs=1, new=True):
if new:
end = 1000
autoencoder = AutoEncoder(
data[:end],
targets[:end],
[1875, 900, 9],
hidden_layer="SigmoidLayer",
final_layer="SigmoidLayer",
compression_epochs=epochs,
bias=True,
autoencoding_only=True,
)
autoencoder = autoencoder.fit()
save(filename, autoencoder)
else:
autoencoder = load(filename)
data = [autoencoder.activate(d) for d in data]
cv = [autoencoder.activate(c) for c in cv]
print data[0][:10]
return data, cv