def makeInputVector(name, d, randomSeed=None):
vec = []
if randomSeed == None:
randomSeed = long(time.clock() * 100000000000000000)
if randomSeed > -1:
PDFTools.setSeed(randomSeed)
length = 0
for i in range(d):
tmp = PDFTools.sampleFloat(GaussianPDF())
vec = vec + [tmp]
length = length + tmp**2
length = math.sqrt(length)
f = []
for i in range(d):
vec[i] = vec[i] / length
f = f + [ConstantFunction(1, vec[i])]
if randomSeed > -1:
PDFTools.setSeed(long(time.clock() * 1000000000000000))
print vec
return (FunctionInput(name, f, Units.UNK))
def genVectors(self, N, d):
if d != len(self.vocabulary[0]):
print "Error, vocabulary d != requested d in CleanupVectorGenerator"
if self.index >= len(self.vocabulary):
return ([None])
max = 0
for x in self.vocabulary[self.index]:
if abs(x) > max:
max = abs(x)
vecs = []
for i in range(N):
vec = [0 for x in range(d)]
length = 0
for j in range(d):
vec[j] = self.vocabulary[self.index][j] + PDFTools.sampleFloat(
GaussianPDF(0.0, self.variance)) * max
length = length + vec[j]**2
length = math.sqrt(length)
for j in range(d):
vec[j] = vec[j] / length
vecs = vecs + [vec]
return (vecs)
def genVectors(self, N, d):
vecs = []
for i in range(N):
if i < len(self.vectors):
vecs.append(self.vectors[i])
else:
tmp = [PDFTools.sampleFloat(GaussianPDF()) for x in range(d)]
length = math.sqrt(sum([x**2 for x in tmp]))
tmp = [x / length for x in tmp]
vecs.append(tmp)
return vecs
def __init__(self, frequency, dimension=1):
"""Initialize node variables.
:param frequency: frequency to update noise values
:param dimension: dimension of noise signal
"""
self.period = 1.0 / frequency
self.scale = 0.0
self.updatetime = 0.0
self.state = [0.0 for _ in range(dimension)]
self.pdf = GaussianPDF(0, 1)
nef.SimpleNode.__init__(self, "NoiseNode")
def genVector(d):
result = [PDFTools.sampleFloat(GaussianPDF()) for i in range(d)]
result = normalize(result)
return result
