pyanno4rt.learning_model.frequentist.addons

Additional model files module.

The module aims to provide functions as a supplement for the frequentist learning models.

Overview

Function

build_iocnn(input_shape, output_shape, labels, hyperparameters, squash_output)

Build the input-output convex neural network architecture with the functional API.

build_standard_nn(input_shape, output_shape, labels, hyperparameters, squash_output)

Build the standard neural network architecture with the functional API.

linear_decision_function(svm, features)

Compute the linear decision function for the SVM.

rbf_decision_function(svm, features)

Compute the rbf decision function for the SVM.

poly_decision_function(svm, features)

Compute the poly decision function for the SVM.

sigmoid_decision_function(svm, features)

Compute the sigmoid decision function for the SVM.

linear_decision_gradient(svm, _)

Compute the linear decision function gradient for the SVM.

rbf_decision_gradient(svm, features)

Compute the rbf decision function gradient for the SVM.

poly_decision_gradient(svm, features)

Compute the poly decision function gradient for the SVM.

sigmoid_decision_gradient(svm, features)

Compute the sigmoid decision function gradient for the SVM.
Attributes

loss_map

-

optimizer_map

-

Functions

pyanno4rt.learning_model.frequentist.addons.build_iocnn(input_shape, output_shape, labels, hyperparameters, squash_output)[source]

Build the input-output convex neural network architecture with the functional API.

Parameters:

  • input_shape (int) – Shape of the input features.

  • output_shape (int) – Shape of the output labels.

  • hyperparameters (dict) – Dictionary with the hyperparameter names and values for the neural network outcome prediction model.

  • squash_output (bool) – Indicator for the use of a sigmoid activation function in the output layer.

Returns:

Instance of the class Functional, which provides a functional input-output convex neural network architecture.

Return type:

object of class ‘Functional’

pyanno4rt.learning_model.frequentist.addons.build_standard_nn(input_shape, output_shape, labels, hyperparameters, squash_output)[source]

Build the standard neural network architecture with the functional API.

Parameters:

  • input_shape (int) – Shape of the input features.

  • output_shape (int) – Shape of the output labels.

  • hyperparameters (dict) – Dictionary with the hyperparameter names and values for the neural network outcome prediction model.

  • squash_output (bool) – Indicator for the use of a sigmoid activation function in the output layer.

Returns:

Instance of the class Functional, which provides a functional standard neural network architecture.

Return type:

object of class ‘Functional’

pyanno4rt.learning_model.frequentist.addons.linear_decision_function(svm, features)[source]

Compute the linear decision function for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Value of the decision function with linear kernel.

Return type:

float

pyanno4rt.learning_model.frequentist.addons.rbf_decision_function(svm, features)[source]

Compute the rbf decision function for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Value of the decision function with rbf kernel.

Return type:

float

pyanno4rt.learning_model.frequentist.addons.poly_decision_function(svm, features)[source]

Compute the poly decision function for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Value of the decision function with poly kernel.

Return type:

float

pyanno4rt.learning_model.frequentist.addons.sigmoid_decision_function(svm, features)[source]

Compute the sigmoid decision function for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Value of the decision function with sigmoid kernel.

Return type:

float

pyanno4rt.learning_model.frequentist.addons.linear_decision_gradient(svm, _)[source]

Compute the linear decision function gradient for the SVM.

Parameters:

svm (object of class SVC) – Instance of scikit-learn’s SVC class.

Returns:

Gradient of the decision function with linear kernel.

Return type:

ndarray

pyanno4rt.learning_model.frequentist.addons.rbf_decision_gradient(svm, features)[source]

Compute the rbf decision function gradient for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Gradient of the decision function with rbf kernel.

Return type:

ndarray

pyanno4rt.learning_model.frequentist.addons.poly_decision_gradient(svm, features)[source]

Compute the poly decision function gradient for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Gradient of the decision function with poly kernel.

Return type:

ndarray

pyanno4rt.learning_model.frequentist.addons.sigmoid_decision_gradient(svm, features)[source]

Compute the sigmoid decision function gradient for the SVM.

Parameters:

  • svm (object of class SVC) – Instance of scikit-learn’s SVC class.

  • features (ndarray) – Vector of feature values.

Returns:

Gradient of the decision function with sigmoid kernel.

Return type:

ndarray

Attributes

pyanno4rt.learning_model.frequentist.addons.loss_map
pyanno4rt.learning_model.frequentist.addons.optimizer_map