File List

Here is a list of all files with brief descriptions:

[detail level 123]

► xcsf
► utils
__init__.py
viz.py	Classes for visualising classifier knowledge representations
__init__.py
act_integer.c	Integer action functions
act_integer.h	Integer action functions
act_neural.c	Neural network action functions
act_neural.h	Neural network action functions
action.c	Interface for classifier actions
action.h	Interface for classifier actions
blas.c	Basic linear algebra functions
blas.h	Basic linear algebra functions
cl.c	Functions operating on classifiers
cl.h	Functions operating on classifiers
clset.c	Functions operating on sets of classifiers
clset.h	Functions operating on sets of classifiers
clset_neural.c	Functions operating on sets of neural classifiers
clset_neural.h	Functions operating on sets of neural classifiers
cond_dgp.c	Dynamical GP graph condition functions
cond_dgp.h	Dynamical GP graph condition functions
cond_dummy.c	Always-matching dummy condition functions
cond_dummy.h	Always-matching dummy condition functions
cond_ellipsoid.c	Hyperellipsoid condition functions
cond_ellipsoid.h	Hyperellipsoid condition functions
cond_gp.c	Tree GP condition functions
cond_gp.h	Tree GP condition functions
cond_neural.c	Multi-layer perceptron neural network condition functions
cond_neural.h	Multi-layer perceptron neural network condition functions
cond_rectangle.c	Hyperrectangle condition functions
cond_rectangle.h	Hyperrectangle condition functions
cond_ternary.c	Ternary condition functions
cond_ternary.h	Ternary condition functions
condition.c	Interface for classifier conditions
condition.h	Interface for classifier conditions
config.c	Configuration file (JSON) handling functions
config.h	Configuration file handling functions
dgp.c	An implementation of dynamical GP graphs with fuzzy activations
dgp.h	An implementation of dynamical GP graphs with fuzzy activations
ea.c	Evolutionary algorithm functions
ea.h	Evolutionary algorithm functions
env.c	Built-in problem environment interface
env.h	Built-in problem environment interface
env_csv.c	CSV input file handling functions
env_csv.h	CSV input file handling functions
env_maze.c	The discrete maze problem environment module
env_maze.h	The discrete maze problem environment module
env_mux.c	The real multiplexer problem environment
env_mux.h	The real multiplexer problem environment
gp.c	An implementation of GP trees based upon TinyGP
gp.h	An implementation of GP trees based upon TinyGP
image.c	Image handling functions
image.h	Image handling functions
loss.c	Loss functions for calculating prediction error
loss.h	Loss functions for calculating prediction error
main.c	Main function for stand-alone binary execution
neural.c	An implementation of a multi-layer perceptron neural network
neural.h	An implementation of a multi-layer perceptron neural network
neural_activations.c	Neural network activation functions
neural_activations.h	Neural network activation functions
neural_layer.c	Interface for neural network layers
neural_layer.h	Interface for neural network layers
neural_layer_args.c	Functions operating on neural network arguments/constants
neural_layer_args.h	Functions operating on neural network arguments/constants
neural_layer_avgpool.c	An implementation of an average pooling layer
neural_layer_avgpool.h	An implementation of an average pooling layer
neural_layer_connected.c	An implementation of a fully-connected layer of perceptrons
neural_layer_connected.h	An implementation of a fully-connected layer of perceptrons
neural_layer_convolutional.c	An implementation of a 2D convolutional layer
neural_layer_convolutional.h	An implementation of a 2D convolutional layer
neural_layer_dropout.c	An implementation of a dropout layer
neural_layer_dropout.h	An implementation of a dropout layer
neural_layer_lstm.c	An implementation of a long short-term memory layer
neural_layer_lstm.h	An implementation of a long short-term memory layer
neural_layer_maxpool.c	An implementation of a 2D maxpooling layer
neural_layer_maxpool.h	An implementation of a 2D maxpooling layer
neural_layer_noise.c	An implementation of a Gaussian noise adding layer
neural_layer_noise.h	An implementation of a Gaussian noise adding layer
neural_layer_recurrent.c	An implementation of a recurrent layer of perceptrons
neural_layer_recurrent.h	An implementation of a recurrent layer of perceptrons
neural_layer_softmax.c	An implementation of a softmax layer
neural_layer_softmax.h	An implementation of a softmax layer
neural_layer_upsample.c	An implementation of a 2D upsampling layer
neural_layer_upsample.h	An implementation of a 2D upsampling layer
pa.c	Prediction array functions
pa.h	Prediction array functions
param.c	Functions for setting and printing parameters
param.h	Functions for setting and printing parameters
perf.c	System performance printing
perf.h	System performance printing
pred_constant.c	Piece-wise constant prediction functions
pred_constant.h	Piece-wise constant prediction functions
pred_neural.c	Multi-layer perceptron neural network prediction functions
pred_neural.h	Multi-layer perceptron neural network prediction functions
pred_nlms.c	Normalised least mean squares prediction functions
pred_nlms.h	Normalised least mean squares prediction functions
pred_rls.c	Recursive least mean squares prediction functions
pred_rls.h	Recursive least mean squares prediction functions
prediction.c	Interface for classifier predictions
prediction.h	Interface for classifier predictions
pybind_callback.h	Interface for callbacks
pybind_callback_checkpoint.h	Checkpoint callback for Python library
pybind_callback_earlystop.h	Early stopping callback for Python library
pybind_utils.h	Utilities for Python library
pybind_wrapper.cpp	Python library wrapper functions
rule_dgp.c	Dynamical GP graph rule (condition + action) functions
rule_dgp.h	Dynamical GP graph rule (condition + action) functions
rule_neural.c	Neural network rule (condition + action) functions
rule_neural.h	Neural network rule (condition + action) functions
sam.c	Self-adaptive mutation functions
sam.h	Self-adaptive mutation functions
utils.c	Utility functions for random number handling, etc
utils.h	Utility functions for random number handling, etc
xcs_rl.c	Reinforcement learning functions
xcs_rl.h	Reinforcement learning functions
xcs_supervised.c	Supervised regression learning functions
xcs_supervised.h	Supervised regression learning functions
xcsf.c	System-level functions for initialising, saving, loading, etc
xcsf.h	XCSF data structures