class NeuronDecider(BaseDecider):
"""
The class is responsible for learning to recognize certain group of objects.
Attributes
-----------
hiden_neurons : int
Number of hiden neurons.
OUTPUT_NEURONS : int
Number of output neurons.
input_neurons : int
Number of input neurons.
X_train : numpy array of array of floats
Each item of the array contains specific "coordinates" of the train
object in array.
X_test : numpy array of array of floats
Each item of the array contains specific "coordinates" of the test
object in array.
y_train : numpy array of ints
Each item of the array contains a number of the group which the train
object belongs. Position in the array
corresponds to item in X_train.
y_test : numpy array of ints
Each item of the array contains a number of the group which the test
object belongs. Position in the array
corresponds to item in X_test.
maxErr : float
Maximal error which would satisfy trainer
maxEpochs : int
Maximum number of epochs for training
"""
OUTPUT_NEURONS = 1
def __init__(self,
treshold=0.5,
hidden_neurons=2,
maxErr=None,
maxEpochs=20000):
"""
Parameters
-----------
hidden_neurons: int
Number of hidden neurons
maxErr : NoneType, float
Maximal error which would satisfy the trainer. In case of None trainUntilConvergence methods
is used
maxEpochs : int
Maximum number of epochs for training
Note
-----
Attributes with None values will be updated by setTrainer
and train methods
"""
self.hiden_neurons = hidden_neurons
self.input_neurons = None
self.X = None
self.y = None
self.treshold = treshold
self.maxEpochs = maxEpochs
self.maxErr = maxErr
self.net = None
def learn(self, searched, others):
"""
This method loads lists of specific values of searched objects and
others. Then the sample will be divided into train and
test samples according to user.
Parameters
-----------
searched : iterable
List of searched objects values (their "coordinates")
others : iterable
List of other objects values (their "coordinates")
Returns
-------
NoneType
None
"""
if not len(searched) or not len(others):
raise QueryInputError(
"Decider can't be learned on an empty sample")
# Resolve number of input neurons
self.input_neurons = len(searched[0])
# Input is accepted as a numpy array or as a list
if type(searched) != list:
try:
X = searched.tolist() + others.tolist()
except AttributeError as err:
raise AttributeError("Wrong coordinates input: %s" % err)
elif type(searched) == list:
X = np.array(searched + others)
# Note searched objects as 1 and others as 0
self.y = np.array([1 for i in range(len(searched))] +
[0 for i in range(len(others))])
self.X = X
self.train()
def train(self):
"""Train neuron grid by training sample"""
self.net = FeedForwardNetwork()
inLayer = LinearLayer(self.input_neurons)
hiddenLayer = SigmoidLayer(self.hiden_neurons)
outLayer = LinearLayer(self.OUTPUT_NEURONS)
self.net.addInputModule(inLayer)
self.net.addModule(hiddenLayer)
self.net.addOutputModule(outLayer)
in_to_hidden = FullConnection(inLayer, hiddenLayer)
hidden_to_out = FullConnection(hiddenLayer, outLayer)
self.net.addConnection(in_to_hidden)
self.net.addConnection(hidden_to_out)
self.net.sortModules()
ds = ClassificationDataSet(self.input_neurons,
self.OUTPUT_NEURONS,
nb_classes=3)
for i, coord in enumerate(self.X):
ds.addSample(coord, (self.y[i], ))
trainer = BackpropTrainer(self.net,
dataset=ds,
momentum=0.1,
verbose=True,
weightdecay=0.01)
if self.maxErr:
for i in range(self.maxEpochs):
if trainer.train() < self.maxErr:
print "Desired error reached"
break
else:
trainer.trainUntilConvergence(maxEpochs=self.maxEpochs)
print "Successfully finished"
def evaluate(self, coords):
"""
Find if inspected parameter-space coordinates belongs to searched
object
Parameter
---------
coords : list of lists
Parameter-space coordinates of inspected objects
Returns
------
numpy.array
Probabilities of membership to searched group objects
"""
pred = []
for coord in coords:
p = self.net.activate(coord)[0]
if p < 0:
p = 0
elif p > 1:
p = 1
pred.append(p)
return np.array(pred)
ds = SupervisedDataSet(len(inputs_training), len(target_training))
ds.setField('input', inputs_training)
ds.setField('target', target_training)
trainer = BackpropTrainer(n, ds)
trainer.trainUntilConvergence(verbose=True, validationProportion=0.15, maxEpochs=100, continueEpochs=10)
for i in range(700):
mse = trainer.train()
rmse = np.sqrt(mse)
print "training RSME, epoch {}: {}".format(i+1, rmse)
#==============================================================================
# trainer = BackpropTrainer(n, ds, learningrate=0.01, momentum=0.1)
#
# for epoch in range(1, 100000000):
# if epoch % 10000000 == 0:
# error = trainer.train()
# print 'Epoch: ', epoch
# print 'Error: ', error
#==============================================================================
predict = np.array([n.activate(x) for x in inputs_validation])
plt.plot(predict, color='blue', label='predict')
plt.plot(target_validation, color='red', label='target')
plt.legend(loc='best')
print 'True'
ds.setField('target', target_training)
trainer = BackpropTrainer(n, ds)
trainer.trainUntilConvergence(verbose=True,
validationProportion=0.15,
maxEpochs=100,
continueEpochs=10)
for i in range(700):
mse = trainer.train()
rmse = np.sqrt(mse)
print "training RSME, epoch {}: {}".format(i + 1, rmse)
#==============================================================================
# trainer = BackpropTrainer(n, ds, learningrate=0.01, momentum=0.1)
#
# for epoch in range(1, 100000000):
# if epoch % 10000000 == 0:
# error = trainer.train()
# print 'Epoch: ', epoch
# print 'Error: ', error
#==============================================================================
predict = np.array([n.activate(x) for x in inputs_validation])
plt.plot(predict, color='blue', label='predict')
plt.plot(target_validation, color='red', label='target')
plt.legend(loc='best')
print 'True'
