Belle II Software  release-08-01-10
GroupedDEDXEstimationTrainer Class Reference
Inheritance diagram for GroupedDEDXEstimationTrainer:
Collaboration diagram for GroupedDEDXEstimationTrainer:

Public Member Functions

def create_dedx_bins (self, data)
 
def create_p_bins (self, data)
 
def use_only_the_highest_values (self, data, number_of_values=None)
 
def create_fit_data (self, dedx_bin)
 
def fit_p_to_dedx_bin (self, dedx_bin)
 
def train (self, data)
 
def test (self, data)
 

Public Attributes

 dedx_estimator_function
 by default, the dE/dx-particle-identification trainer has not run yet
 
 dedx_column
 the default data column is 'dedx'
 

Static Public Attributes

int number_of_bins_in_dedx = 20
 number of dE/dx bins
 
int number_of_bins_in_p = 29
 number of track-momentum bins
 
int number_of_head_values_used_to_fit = 20
 number of head values in fit
 

Detailed Description

Train a neural network for dE/dx-based particle identification

Definition at line 42 of file train.py.

Member Function Documentation

◆ create_dedx_bins()

def create_dedx_bins (   self,
  data 
)
Construct the dE/dx bins and then populate them with the data

Definition at line 52 of file train.py.

◆ create_fit_data()

def create_fit_data (   self,
  dedx_bin 
)
Fit track-momentum values

Definition at line 74 of file train.py.

◆ create_p_bins()

def create_p_bins (   self,
  data 
)
Construct the momentum bins and then populate them with the data

Definition at line 61 of file train.py.

◆ fit_p_to_dedx_bin()

def fit_p_to_dedx_bin (   self,
  dedx_bin 
)
Fit the track-momentum values in the selected dE/dx bin, then train on the fitted values

Definition at line 86 of file train.py.

◆ test()

def test (   self,
  data 
)
inherited
Get the trained neural-network output value for test data

Reimplemented in MVADEDXEstimationTrainer.

Definition at line 34 of file train.py.

◆ train()

def train (   self,
  data 
)
inherited
Train on the input data

Reimplemented in MVADEDXEstimationTrainer, and FittedGroupedDEDXEstimatorTrainer.

Definition at line 29 of file train.py.

◆ use_only_the_highest_values()

def use_only_the_highest_values (   self,
  data,
  number_of_values = None 
)
Sort the data then select only the highest N values

Definition at line 67 of file train.py.


The documentation for this class was generated from the following file: