def buildDS(n, num, dur):
ds = SequentialDataSet(1,1)
dt = n['gfnn'].dt
t = np.arange(0, dur, dt)
length = len(t)
for i in range(num):
x = np.zeros(length)
# tempo between 1 and 2 bps
bps = 1 + np.random.random()
p = 1./bps
lastPulse = np.random.random() * p
for j in range(length):
if t[j] > lastPulse and t[j] >= lastPulse + p:
x[j] = 0.1
lastPulse = t[j]
else:
if j > 0:
if x[j-1] < 1e-5:
x[j] = 0
else:
x[j] = x[j-1] * 0.5
# try to predict the next sample
target = np.roll(x, -1)
ds.newSequence()
for j in range(length):
ds.addSample(x[j], target[j])
return ds
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 range(len(data)):
dataset.addSample([], data[i])
return dataset
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 makeMelodyDataSet(melodies, inspirationFunc=randomInspiration, inspirationLength=8):
seqDataSet = SequentialDataSet(sampleSize(), outputSize())
for m in melodies:
barCount = m.bars[-1]+1
assert barCount <= 8, "Bar counts greater than 8 unsupported"
inspiration = inspirationFunc(inspirationLength,m)
seqDataSet.newSequence()
for s in range(len(m.pitches)-1):
seqDataSet.addSample(
makeNoteSample(m.pitches[s], m.durations[s],
inspiration[s % inspirationLength], m.bars[s]),
makeNoteTarget(m.pitches[s+1], m.durations[s+1]))
return seqDataSet
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
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.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
if index == 'D':
outputVector[3] = 1
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
if index == 'D':
outputVector[3] = 1
# plt.show()
# i1 = np.sin(np.arange(0, 20))
# i2 = np.sin(np.arange(0, 20)) * 2
#
# t1 = np.ones([1, 20])
# t2 = np.ones([1, 20]) * 2
#
# input = np.array([i1, i2, i1, i2]).reshape(20 * 4, 1)
# target = np.array([t1, t2, t1, t2]).reshape(20 * 4, 1)
# Create datasets
print 'Preparing dataset ...'
# ts = sampler.load_csv('data/series.csv')
ds = SequentialDataSet(inLayerCount, outLayerCount)
ds.newSequence()
# ds = SupervisedDataSet(inLayerCount, outLayerCount)
for row in df.itertuples(index=False):
ds.addSample(row[0:columns-2], row[columns-2])
ds.endOfData()
# Create bp trainer
trainer = BackpropTrainer(net, ds)
# Trains the datasets
print 'Training ...'
epoch = 1000
error = 1.0
while error > delta_error and epoch >= 0:
b = BiasUnit('bias')
net.addModule(b)
net.addOutputModule(o)
net.addInputModule(i)
net.addModule(h)
net.addConnection(FullConnection(i, h, outSliceTo = 4*dim, name = 'f1'))
net.addConnection(FullConnection(b, h, outSliceTo = 4*dim, name = 'f2'))
net.addRecurrentConnection(FullConnection(h, h, inSliceTo = dim, outSliceTo = 4*dim, name = 'r1'))
net.addRecurrentConnection(IdentityConnection(h, h, inSliceFrom = dim, outSliceFrom = 4*dim, name = 'rstate'))
net.addConnection(FullConnection(h, o, inSliceTo = dim, name = 'f3'))
net.sortModules()
print net
ds = SequentialDataSet(15, 1)
ds.newSequence()
input = open(sys.argv[1], 'r')
for line in input.readlines():
row = np.array(line.split(','))
ds.addSample([float(x) for x in row[:15]], float(row[16]))
print ds
if len(sys.argv) > 2:
test = SequentialDataSet(15, 1)
test.newSequence()
input = open(sys.argv[2], 'r')
for line in input.readlines():
row = np.array(line.split(','))
test.addSample([float(x) for x in row[:15]], float(row[16]))
else:
def makeMelodyDataSet(melodies):
seqDataSet = SequentialDataSet(sampleSize(), outputSize())
for m in melodies:
seqDataSet.newSequence()
m.addSamples(seqDataSet)
return seqDataSet
