custom

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

Bases: Model

eval_nll_end(nll, norm=None)

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 BaseCustomModel2(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel

function_order = [{'function': 'eval_normal_factors', 'input': ['mcdata', 'mc_weight'], 'output': 'norm'}, {'function': 'eval_nll_part', 'input': ['data', 'weight', 'norm', 'idx'], 'output': 'nll'}, {'function': 'eval_nll_end', 'input': ['nll', 'norm'], 'output': 'ret'}]

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:

class SimpleBinnningChi2Model(amp, w_bkg=1.0, resolution_size=1, extended=False, **kwargs)

Bases: BaseCustomModel2

eval_nll_end(nll, norm)

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

eval_normal_factors(mcdata, weight)

required_params = ['n_bins']

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)

get_weight_data(*args, **kwargs)

Blend data and background data together multiplied by their weights.

Parameters:

• data – Data array

• weight – Weight for data

• bg – Data array for background

• alpha – Boolean. If it’s true, weight will be multiplied by a factor \(\alpha=\)???

Returns:

Data, weight. Their length both equals len(data)+len(bg).

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)

create_histogram(binning, weight, n_bins)