custom

class BaseCustomModel(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: Model

eval_nll_part(data, weight=None, norm=None, idx=0)

eval_normal_factors(mcdata, weight=None)

nll(data, mcdata, weight: Tensor = 1.0, batch=None, bg=None, mc_weight=1.0)

Calculate NLL.

\[-\ln L = -\sum_{x_i \in data } w_i \ln f(x_i;\theta_k) + (\sum w_j ) \ln \sum_{x_i \in mc } f(x_i;\theta_k)\]

Parameters:

• data – Data array

• mcdata – MCdata array

• weight – Weight of data???

• batch – The length of array to calculate as a vector at a time. How to fold the data array may depend on the GPU computability.

• bg – Background data array. It can be set to None if there is no such thing.

Returns:

Real number. The value of NLL.

nll_grad_batch(data, mcdata, weight, mc_weight)

batch version of self.nll_grad()

\[- \frac{\partial \ln L}{\partial \theta_k } = -\sum_{x_i \in data } w_i \frac{\partial}{\partial \theta_k} \ln f(x_i;\theta_k) + (\sum w_j ) \left( \frac{ \partial }{\partial \theta_k} \sum_{x_i \in mc} f(x_i;\theta_k) \right) \frac{1}{ \sum_{x_i \in mc} f(x_i;\theta_k) }\]

Parameters:

• data

• mcdata

• weight

• mc_weight

Returns:

nll_grad_hessian(data, mcdata, weight=1.0, batch=24000, bg=None, mc_weight=1.0)

The parameters are the same with self.nll(), but it will return Hessian as well.

Return NLL:

Real number. The value of NLL.

Return gradients:

List of real numbers. The gradients for each variable.

Return Hessian:

2-D Array of real numbers. The Hessian matrix of the variables.

value_and_grad(fun)

class SimpleCFitModel(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel

eval_nll_part(data, weight, norm, idx=0)

eval_normal_factors(mcdata, weight)

required_params = ['bg_frac']

class SimpleChi2Model(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel

fit amp = weight directly. Required set extended = True.

eval_nll_part(data, weight, norm, idx=0)

class SimpleClipNllModel(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: SimpleNllModel

eval_nll_part(data, weight, norm, idx=0)

class SimpleNllFracModel(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel

eval_nll_part(data, weight, norm, idx=0)

eval_normal_factors(mcdata, weight)

required_params = ['constr_frac', 'bg_frac']

class SimpleNllModel(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel

eval_nll_part(data, weight, norm, idx=0)

eval_normal_factors(mcdata, weight)