Heuristic¶

class samplesizelib.linear.heuristic.BootstrapEstimator(statmodel, **kwards)[source]¶

Description of Bootstrap Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
averaging (float) – to do
epsilon (float) – to do
begin (int) – to do
end (int) – to do
num (int) – to do
multiprocess (bool) – to do
progressbar (bool) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict

class samplesizelib.linear.heuristic.CrossValidationEstimator(statmodel, **kwards)[source]¶

Description of Cross Validation Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
averaging (float) – to do
epsilon (float) – to do
begin (int) – to do
end (int) – to do
num (int) – to do
test_size (float) – to do
multiprocess (bool) – to do
progressbar (bool) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict

class samplesizelib.linear.heuristic.LogisticRegressionEstimator(statmodel, **kwards)[source]¶

Description of Logistic Regression Method

Parameters

statmodel (RegressionModel or LogisticModel) – the machine learning algorithm
ind (int) – to do
alpha (float) – to do
beta (float) – to do

forward(features, target)[source]¶

Returns sample size prediction for the given dataset.

Parameters

features (array.) – The tensor of shape num_elements \(\times\) num_feature.
target (array.) – The tensor of shape num_elements.

Returns

sample size estimation for the given dataset.

Return type

dict