def sample(self, m):
"""
Samples m samples from the current LpNestedSymmetric distribution.
:param m: Number of samples to draw.
:type m: int.
:returns: A Data object containing the samples
:rtype: natter.DataModule.Data
"""
ret = zeros((self.param['f'].n[()], m))
r = beta(float(self.param['f'].n[()]), 1.0, (1, m))
_recsample((), r, self.param['f'], m, ret)
ret = Data(ret, 'Samples from ' + self.name)
ret.scale(self.param['rp'].sample(m).X / self.param['f'].f(ret).X)
return ret
def sample(self,m):
"""
Samples m samples from the current LpSphericallySymmetric distribution.
:param m: Number of samples to draw.
:type m: int.
:returns: A Data object containing the samples
:rtype: natter.DataModule.Data
"""
# sample from a p-generlized normal with scale 1
z = gamma(1/self.param['p'],1.0,(self.param['n'],m))
z = abs(z)**(1/self.param['p'])
dat = Data(z * sign(randn(self.param['n'],m)),'Samples from ' + self.name, \
['sampled ' + str(m) + ' examples from Lp-generalized Normal'])
# normalize the samples to get a uniform distribution.
dat.normalize(self.param['p'])
r = self.param['rp'].sample(m)
dat.scale(r)
return dat
def sample(self, m):
"""
Samples m samples from the current LpSphericallySymmetric distribution.
:param m: Number of samples to draw.
:type m: int.
:returns: A Data object containing the samples
:rtype: natter.DataModule.Data
"""
# sample from a p-generlized normal with scale 1
z = gamma(1 / self.param['p'], 1.0, (self.param['n'], m))
z = abs(z)**(1 / self.param['p'])
dat = Data(z * sign(randn(self.param['n'],m)),'Samples from ' + self.name, \
['sampled ' + str(m) + ' examples from Lp-generalized Normal'])
# normalize the samples to get a uniform distribution.
dat.normalize(self.param['p'])
r = self.param['rp'].sample(m)
dat.scale(r)
return dat
