def generalization_error(net, length, inp_len, med, numb, punct):
sentence_tuples = get_nice_sentences_as_tuples(MIN=length,
MAX=length,
include_numbers=numb,
include_punctuation=punct)
exper_data = SequenceClassificationDataSet(inp=inp_len, target=2)
sentence_matrices = construct_sentence_matrices(sentence_tuples,
medium=med)
for s in sentence_matrices:
insert_grammatical_sequence(exper_data, s)
insert_randomized_sequence(exper_data, s)
return 1 - testOnSequenceData(net, exper_data)
def generateNoisySines(npoints, nseq, noise=0.3):
""" construct a 2-class dataset out of noisy sines """
x = np.arange(npoints) / float(npoints) * 20.
y1 = np.sin(x + rand(1) * 3.)
y2 = np.sin(x / 2. + rand(1) * 3.)
DS = SequenceClassificationDataSet(1, 1, nb_classes=2)
for _ in xrange(nseq):
DS.newSequence()
buf = rand(npoints) * noise + y1 + (rand(1) - 0.5) * noise
for i in xrange(npoints):
DS.addSample([buf[i]], [0])
DS.newSequence()
buf = rand(npoints) * noise + y2 + (rand(1) - 0.5) * noise
for i in xrange(npoints):
DS.addSample([buf[i]], [1])
return DS
def __createDataset(data):
ds = SequenceClassificationDataSet(inputs,
1,
nb_classes=nClasses,
class_labels=labels.values())
for target in classes:
tupt = np.asarray([target])
# print("Target " + str(tupt))
for x in data[target]:
ds.newSequence()
for y in x:
tup = tuple(y)
ds.appendLinked(tup, tupt)
print(ds.calculateStatistics())
# ds._convertToOneOfMany(bounds=[0, 1])
# print ds.getField('target')
print("DS entries " + str(ds.getNumSequences()))
return ds
for x in train_index:
X_train.append(X[x])
y_train.append(y[x])
for x in test_index:
X_test.append(X[x])
y_test.append(y[x])
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)
# SequenceClassificationDataset(inp,target, nb_classes)
# inp = input dimension
# target = number of targets
# nb_classes = number of classes
trndata = SequenceClassificationDataSet(100,1, nb_classes=2)
tstdata = SequenceClassificationDataSet(100,1, nb_classes=2)
for index in range(len(y_train)):
trndata.addSample(X_train[index], y_train[index])
for index in range(len(y_test)):
tstdata.addSample(X_test[index], y_test[index])
trndata._convertToOneOfMany( bounds=[0.,1.] )
tstdata._convertToOneOfMany( bounds=[0.,1.] )
if exists("params.xml"):
rnn = NetworkReader.readFrom('params.xml')
else:
# construct LSTM network - note the missing output bias
correct += 1.0
train_accuracy2 = correct / float(len(Y_train))
print "training accuracy is ", train_accuracy2
test_out = net.activateOnDataset(test_ds)
Y_pred = convert_output(test_out)
correct = 0.0
for i in range(len(Y_test)):
if compare_list(Y_test[i], Y_pred[i]):
correct += 1.0
test_accuracy2 = correct / float(len(Y_test))
print "test accuracy is ", test_accuracy2
"""
x_dimension = len(X_train[0])
y_dimension = len(Y_train[0])
DS = SequenceClassificationDataSet(x_dimension, y_dimension, nb_classes=3)
ds = SupervisedDataSet(x_dimension, y_dimension)
for i in range(len(X_train)):
ds.addSample(X_train[i], Y_train[i])
# construct LSTM network - note the missing output bias
rnn = buildNetwork(x_dimension,
x_dimension,
y_dimension,
hiddenclass=LSTMLayer,
outclass=SoftmaxLayer,
outputbias=False,
recurrent=True)
# define a training method
trainer = RPropMinusTrainer(rnn, dataset=ds, verbose=True)
for _ in range(nseq):
DS.newSequence()
buf = rand(npoints) * noise + y1 + (rand(1) - 0.5) * noise
for i in range(npoints):
DS.addSample([buf[i]], [0])
DS.newSequence()
buf = rand(npoints) * noise + y2 + (rand(1) - 0.5) * noise
for i in range(npoints):
DS.addSample([buf[i]], [1])
return DS
DS = SequenceClassificationDataSet
# create training and test data
trndata = SequenceClassificationDataSet(X_train, Y_train)
tstdata = SequenceClassificationDataSet(X_test, Y_test)
# construct LSTM network - note the missing output bias
rnn = buildNetwork(trndata.indim, (),
trndata.outdim,
hiddenclass=LSTMLayer,
outclass=SoftmaxLayer,
outclass=SoftmaxLayer)
#buildNetwork( MultiDimensionalLSTM
#rnn.addInputModule(LinearLayer(3, name='in'))
#rnn.addModule(MDLSTMLayer(5,2, name='hidden'))
#rnn.addOutputModule(SoftmaxLayer(1, name='out'))
#
