def generateSuperimposedSineData(sinefreqs, space, yScales=None):
sine = SuperimposedSine(sinefreqs)
if yScales is not None:
sine.yScales = array(yScales)
dataset = SequentialDataSet(0, 1)
data = sine.getFuncValues(space)
dataset.newSequence()
for i in xrange(len(data)):
dataset.addSample([], data[i])
return dataset
def list_to_dataset(inputs, outputs, dataset=None):
"""List to Dataset
Convert a standard list to a dataset. The list must be given in the
following format:
Inputs: 2 dimension list (N x M)
Outputs: 2 dimension list (N x K)
N: Number of time steps in data series
M: Number of inputs per time step
K: Number of outputs per time step
Arguments:
inputs: The input list given under the above conditions.
outputs: The output list given under the above conditions.
dataset: A SequentialDataSet object to add a new sequence. New dataset
generated if None. (Default: None)
Returns:
A SequentialDataSet object built from the retrieved input/output data.
"""
assert len(inputs) > 0
assert len(outputs) > 0
assert len(inputs) == len(outputs)
# The dataset object has not been initialized. We must determine the
# input and output size based on the unpacked data
num_samples = len(inputs)
in_dim = 1 if len(inputs.shape) == 1 else inputs.shape[1]
out_dim = 1 if len(outputs.shape) == 1 else outputs.shape[1]
# If the dataset does not exist, create it. Otherwise, use the dataset
# given
if not dataset:
dataset = SequentialDataSet(in_dim, out_dim)
# Make a new sequence for the given input/output pair
dataset.newSequence()
for i in range(num_samples):
dataset.addSample(inputs[i], outputs[i])
return dataset
rnn.addModule(TanhLayer(dim=hiddenSize, name='in_proc'))
rnn.addModule(LSTMLayer(dim=hiddenSize, peepholes=True, name='hidden'))
rnn.addModule(TanhLayer(dim=hiddenSize, name='out_proc'))
rnn.addOutputModule(SoftmaxLayer(dim=outputSize, name='out'))
rnn.addConnection(FullConnection(rnn['in'], rnn['in_proc'], name='c1'))
rnn.addConnection(FullConnection(rnn['in_proc'], rnn['hidden'], name='c2'))
rnn.addRecurrentConnection(
FullConnection(rnn['hidden'], rnn['hidden'], name='c3'))
rnn.addConnection(FullConnection(rnn['hidden'], rnn['out_proc'], name='c4'))
rnn.addConnection(FullConnection(rnn['out_proc'], rnn['out'], name='c5'))
rnn.sortModules()
# Construct dataset
trainingData = SequentialDataSet(inputSize, outputSize)
for index, row in df.iterrows():
trainingData.newSequence()
inputSequence = list((row.values)[0])
outputVector = [0, 0, 0, 0]
if index == 'A':
outputVector[0] = 1
if index == 'B':
outputVector[1] = 1
if index == 'C':
outputVector[2] = 1
