def _unnormalized_pdf(self, x, norm_range=False):
x = x.unstack_x()
mu = self.params['mu']
sigma = self.params['sigma']
return ztf.exp(
(-(x - mu)**2) / (2 * sigma**2)) # non-normalized gaussian
def _unnormalized_pdf(self, x, norm_range=False):
x = x.unstack_x()
return ztf.exp(
(-(x - mu)**2) / (2 * sigma**2)) # non-normalized gaussian
def _func(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = ztf.unstack_x(x)
return ztf.exp(-ztf.square((x - mu) / sigma))
def _unnormalized_pdf(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = ztf.unstack_x(x)
return ztf.exp(-ztf.square((x - mu) / sigma))
def _numerics_shifted_exp(self, x, lambda_): # needed due to overflow in exp otherwise, prevents by shift
return ztf.exp(lambda_ * (x - self._numerics_data_shift))
def _unnormalized_pdf(self, x): # implement function
data = x.unstack_x()
alpha = self.params['alpha']
return ztf.exp(alpha * data)