def _pdf(self, x, norm_range=False):
x = x.unstack_x()
mu = self.params['mu']
sigma = self.params['sigma']
return z.exp((-(x - mu) ** 2) / (
2 * sigma ** 2)) # non-normalized gaussian
def _unnormalized_pdf(self, x):
x = x.unstack_x()
mu = self.params['mu']
sigma = self.params['sigma']
from zfit import z
return z.exp(
(-(x - mu)**2) / (2 * sigma**2)) # non-normalized gaussian
def _unnormalized_pdf(self, x):
lambda_ = self.params['lambda']
x = x.unstack_x()
probs = z.exp(lambda_ * (self._shift_x(x)))
tf.debugging.assert_all_finite(probs, f"Exponential PDF {self} has non valid values. This is likely caused"
f" by numerical problems: if the exponential is too steep, this will"
f" yield NaNs or infs. Make sure that your lambda is small enough and/or"
f" the initial space is in the same"
f" region as your data (and norm_range, if explicitly set differently)."
f" If this issue still persists, please oben an issue on Github:"
f" https://github.com/zfit/zfit")
return probs # Don't use exp! will overflow.
def _numerics_shifted_exp(self, x, lambda_): # needed due to overflow in exp otherwise, prevents by shift
return z.exp(lambda_ * (x - self._numerics_data_shift))
def _unnormalized_pdf(self, x): # implement function
data = z.unstack_x(x)
alpha = self.params['alpha']
return z.exp(alpha * data)
def raw_integral(x):
return (z.exp(lambd * (model._shift_x(x))) / lambd
) # needed due to overflow in exp otherwise
def _unnormalized_pdf(self, x):
x = x.unstack_x()
mu = self.params["mu"]
sigma = self.params["sigma"]
return z.exp((-((x - mu) ** 2)) / (2 * sigma**2)) # non-normalized gaussian
def _func(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def _unnormalized_pdf(self, x):
mu = self.params['mu']
sigma = self.params['sigma']
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
def _func(self, x):
mu = self.params["mu"]
sigma = self.params["sigma"]
x = z.unstack_x(x)
return z.exp(-z.square((x - mu) / sigma))
