The Complete SPRLIB & ANNLIB
Index
A
- ActEucDist
- activation function
- ActInprod
- activation function
- ActSqEucDist
- activation function
- adapt_mlnet
- perform one learning cycle on a maximum likelihood network
- adapt_wavnet
- adapt a wavelet network after presentation of one learning sample
- add_classerror
- add a pattern to the error estimation routines using Fisher's linear discriminant
- add_mean_var
- add a pattern to the summations needed for mean and variance
- add_unit_ff
- add a new unit to a layer
- addq_classerror
- add a pattern to the error estimation routines using a quadratic discriminant
- all_scatter_dataset
- determine the scatter matrices for a dataset
B
- best_matching_unit
- find the unit with the smallest output value in a Kohonen net
- BFGS-variables
- some variables used with the Broyden-Fletcher-Goldfarb-Shanno minimization algorithm (LOCAL)
- bfgs_free
- reset all the local variables and clean up memory (LOCAL)
- bfgs_init
- initialize the BFGS method (LOCAL)
- BLOCKPOINTERS_STRUCT
- local structure containing pointers to allocated blocks of wavelon- and wavelearn-structs (LOCAL)
- BP-flags
- back-propagation option flags
- bp_adapt_net
- perform one learning cycle using the backpropagation rule
- bp_adapt_unit
- adapt a unit according to the generalized delta rule
- bp_adapt_weights
- adapt weights marked ``changed'' according to the accumulated delta's
- bp_free
- free a network that was generated by bp_init
- bp_init
- initialise and set up a network for training with the backpropagation learning rule
- bp_learn
- perform a number of learning cycles with the backpropagation learning algorithm
- bp_learn_pocket
- train a network using the pocket algorithm
- BP_STRUCT
- a local structure for backpropagation learning (LOCAL)
- brent
- parabolic interpolation and brent's method in one dimension
C
- calc_classerror
- calculate the fraction of badly classified samples (linear classifier)
- Calc_Dist_Class
- calculate the minimal Mahalanobis distance of a pattern given the coefficients
- calc_marq_step
- calculate a Marquardt step (LOCAL)
- calc_mean_var
- calculate the mean and variance of patterns
- calc_multiclass_error
- calculate the error in classification using the Mahalanobis distance
- calcq_classerror
- calculate the fraction of badly classified samples (quadratic classifier)
- calloc_monitor
- an SPRANNLIB replacement for the calloc routine
- CDES-flags
- option flags for conjugate gradient descent learning
- CGDES-variables
- some variables used in the conjugate gradient descent training algorithm (LOCAL)
- cgdes_free
- reset all the local variables and clean up memory (LOCAL)
- cgdes_init
- initialize the conjugate gradient descent method (LOCAL)
- check_all_network
- check the consistency of a network
- check_conn_to_input
- check whether a unit is connected to an input unit (LOCAL)
- check_conn_to_output
- check whether a unit is connected to an output unit (LOCAL)
- check_decr_ids
- check whether unit and layer IDs decrease when running from output to input (LOCAL)
- check_ff_net
- check whether a net is a correctly configured feed forward network (LOCAL)
- check_incr_ids
- check whether unit and layer IDs increase when running from input to output (LOCAL)
- check_io_conn
- check whether all units are connected to an input and an output unit (LOCAL)
- check_layer_recipe
- checks whether a layer recipe is correct (LOCAL)
- check_links
- check if the links in a network are valid (LOCAL)
- check_map_recipes
- checks whether map recipes in a layer are correct (LOCAL)
- check_net
- check if the contents of a NET structure are valid (LOCAL)
- check_order_ids
- check whether the unit IDs and layer IDs are correctly ordered (LOCAL)
- check_sharednet_recipe
- checks whether a shared weights network recipe is correct (LOCAL)
- check_units
- check if the units in a network are valid (LOCAL)
- check_weights
- check if the weights in a network are valid (LOCAL)
- clear_auto_priority
- de-install a signal handler which can renice the process
- clear_user_signal
- reset the original SIGUSR1 signal handler
- cluster_pao
- perfom Pao clustering
- cluster_pao_init
- initialize the variables in order to perform Pao clustering
- compare_min_ed
- find the cluster having the minimal Euclidean distance (LOCAL)
- compute_error_dataset
- calculate the error between samples of two datasets (LOCAL)
- compute_euc_dist
- compute the Euclidean distance for the clusters (LOCAL)
- compute_means
- compute the means of the classes in the dataset
- conj_fr
- perform the Fletcher-Reeves variant of the conjugate gradient descent algorithm (LOCAL)
- conj_pr
- perform the Polak-Ribiere variant of the conjugate gradient descent algorithm (LOCAL)
- connect_layers
- connect maps in two successive layers (LOCAL)
- connect_maps
- connect units in two maps in two successive layers (LOCAL)
- create_ff_links
- create all links between units created by create_ff_units (LOCAL)
- create_ff_net
- create a feedforward network
- create_ff_units
- create all units for a feedforward network (LOCAL)
- create_ff_weights
- create all weights for the links created by create_ff_links (LOCAL)
- create_koh_links
- create all the links needed for a Kohonen network (LOCAL)
- create_koh_net
- create a Kohonen type network
- create_koh_units
- create all the units needed for a Kohonen network (LOCAL)
- create_koh_weights
- create all the weights needed for a Kohonen network (LOCAL)
- create_layer
- fill one LAYER structure (LOCAL)
- create_map
- fill one MAP structure (LOCAL)
- create_ml_links
- create all the links needed for a maximum likelihood network (LOCAL)
- create_ml_units
- create all the units needed for a maximum likelihood network (LOCAL)
- create_ml_weights
- create all the weights needed for a maximum likelihood network (LOCAL)
- create_mlnet
- create a maximum likelihood network
- create_sharednet
- create a shared weights neural network
- create_unit
- fill one UNIT structure (LOCAL)
- create_wavnet
- create a wavelet network
D
- d
- compute the squared distance between two patterns (LOCAL)
- d
- compute the squared distance between two patterns stored in the local variable points (LOCAL)
- DATASET-ACCESS-flags
- write enable/disable system/user-data in a dataset/sample
- DATASET-flags
- data set type specification flags
- DATASET
- structure holding general data set information
- delete_dataset
- free a complete dataset and all its samples
- delete_net
- delete and free memory of a complete network
- delete_theta_hist
- delete a network's bias (theta) history
- delete_unit_hist
- delete a network's unit history
- delete_weight_hist
- delete a network's weight history
- delta_ptron
- calculate the weight changes for the perceptron (LOCAL)
- DerDerTransDblSigmoid
- second derivative of transfer function
- DerDerTransParTanH
- second derivative of transfer function
- DerDerTransSigmoid
- second derivative of transfer function
- derf_bfgs
- compute the derivative of the objective function as needed by the BFGS method (LOCAL)
- derf_cgdes
- compute the derivative of the objective function as needed by the conjugate gradient descent method (LOCAL)
- DerTransDblSigmoid
- first derivative of transfer function
- DerTransGauss
- first derivative of transfer function
- DerTransLinear
- first derivative of transfer function
- DerTransParTanH
- first derivative of transfer function
- DerTransSigmoid
- first derivative of transfer function
- DerTransTanH
- first derivative of transfer function
- dfpmin
- perform Broyden-Fletcher-Goldfarb-Shanno variant of Davidon-Fletcher-Powell minimazation
- dfpminimize
- perform the Broyden-Fletcher-Goldfarb-Shanno variant of the Davidon-Fletcher-Powell minimization algorithm (LOCAL)
- distance
- calculate the Mahalanobis distance given the coefficients (LOCAL)
- DISTRIBUTION
- data structure holding a data set's distribution information
- DMEM
- a structure used to hold dynamically allocated memory information (LOCAL)
- draw_frame
- draw a 3D block (LOCAL)
- duin_dataset
- generate samples according to the Duin dataset
- duinset_error
- compute the classification error on the Duin set using a linear discriminant
- duinset_overlap
- compute the overlap of two classes for the Duin set
E
- eigsrt
- sort the given eigenvectors
- erf_bfgs
- compute the Mean Squared Error function used by the BFGS method (LOCAL)
- erf_cgdes
- compute the Mean Squared Error used by the conjugate gradient descent method (LOCAL)
- erfcc
- compute the complementary error function erfc(x) with fractional error everywhere less than 0.00000012
- ERROR_STRUCT
- network performance values datatype
- error_struct_array
- a local variable for the network performance routines (LOCAL)
- ESTERROR-variables
- local variables used in the error estimation routines.
- ESTIMATE-variables
- local variables used in the error estimation routines.
- ESTQERROR-variables
- local variables used in the error estimation routines.
- euclidian_distance
- calculate the euclidian distance between two arrays of doubles
- eval_error_ptron
- compute the error of a perceptron on the allocated dataset
- eval_ff_net
- evaluate a feed-forward network
- eval_koh_net
- evaluate a Kohonen typ network
- eval_ptron
- evaluate a perceptron
- eval_set_on_net
- determine the similarity between a (trained) SOM and a dataset
- eval_unit
- evaluate a unit
- EXIT-flags
- flags used in the SPRANNLIB exiting function
- extract_dataset_ff_net
- extract a dataset from a certain layer of anetwork
F
G
H
I
- INIT-flags
- flags used in the SPRANNLIB initialization function
- init_audio
- initialize the audio device (LOCAL)
- init_full_fname
- initializes the filename expansion mechanism
- init_mlnet
- initialize a maximum likelihood network
- init_wavnet
-initialize a wavelet network
- InvLinear
- the inverse of the linear function (LOCAL)
- InvSigmoid
- the inverse of the sigmoid function (LOCAL)
- InvTanH
- the inverse of the tanh function (LOCAL)
- IO-variables
- variable supporting network I/O functions
- IOFORMAT
- constant indicating the network I/O-format version number
- ioset_to_labelset
- convert an IOSET to a LABELSET
- IsClass
- check for legal class label (LOCAL)
- isempty
- a utility function (LOCAL)
- isodata_basic
- the isodata clustering algorithm as defined by Duda and Hart (k-means clustering)
J
- jacobi
- compute the eigenvalues and eigenvectors of a real symmetric matrix
- join_adapt_ids
- Update most IDs in a joined network (LOCAL)
- join_net
- construct a new network out of a number of networks
- join_relayer
- Rebuild maps and layers in a joined network (LOCAL)
K
- khighley_dataset
- generate samples according to the generalized Highleyman dataset
- khighley_overlap
- compute the overlap of two classes for the Higleyman set
- kl_coordinates
- compute the Karhunen-Loeve transform
- knn_classify
- classify a new pattern using the k-nearest neighbour classifier
- knn_free
- freeing routine for the k-nearest neighbour classifier
- knn_init
- initialization routine for the k-nearest neighbour classifier
- knn_loo_perf
- k-nearest neighbour classifier performance determination using the leave one out method
- knn_majority
- assign a classlabel according to the k-nearest neighbour rule
- KNNELM
- local structure for the k-nearest neighbour classifier
- koh_adapt_net
- adapt a net's weights according to the Kohonen learning rule
- koh_adapt_unit
- adapt a unit's weights according to the Kohonen learning rule
- koh_draw_free
- clear all data used in drawing Kohonen maps
- koh_draw_init
- initialize the variables for drawing a Kohonen map
- koh_learn
- perform a number of learning cycles with the Kohonen learning rule
- koh_test_net
- constructs a two-dimensional Kohonen network with two inputs
- koh_weight_draw
- draw a Kohonen map
- KOHONEN-constants
- some constants used in creating Kohonen networks (LOCAL)
- KOHONEN-DRAW-flags
- option flags for Kohonen weight drawing
- KOHONEN-DRAW-variables
- variables used for graphical display of Kohonen type networks
- KOHONEN-flags
- option flags for Kohonen learning
- kvar_dataset
- generate samples according to the generalized var dataset
- kvar_overlap
- compute the overlap of two classes for the varset
L
- labelset_to_ioset
- convert a LABELSET to an IOSET
- LAYER-flags
- flags to indicate the type of a layer
- LAYER
- layer datatype
- LAYER_RECIPE
- simplified description of a layer of a shared weights network
- learn_bfgs_ffnet
- apply the BFGS optimization method to train a feedforward neural network
- learn_cgdes_ffnet
- apply the conjugate gradient descent method to a feedforward neural network
- learn_marquardt_ffnet
- train a feedforward or radial-basis function network with the dataset pointed by dset for steps number of times, using the Marquardt optimization method
- learn_mlnet
- perform a number of learning cycles on a maximum likelihood network
- learn_ptron
- train a perceptron with the loaded training samples
- learn_ptron_free
- free the space allocated for perceptron learning
- learn_ptron_init
- initialization routine for perceptron training
- learn_wavnet
- train a wavelet network on a dataset
- LIBRARY-flags
- various constants influencing library operation
- LINK-flags
- flags to indicate the type of a link
- LINK
- link (unit connection) datatype
- linmin
- line minimization
- load_dataset
- read a (possibly) compressed dataset from a file
- load_network
- load a (compressed) network
- log_lhood_mlnet
- determine the log-likelihood function of all samples in a dataset
- loo_min_dist_perf
- compute the performance of the minimum distance using the leave-one-out method
- LPAT
- local structure for pattern storage (LOCAL)
- lubksb
- solve a set of n linear equations
- ludcmp
- lu decomposition of a matrix
M
- mahal_dataset
- compute the Mahalanobis coefficients for a class (LOCAL)
- mahal_multiclass_dataset
- determine the Mahalanobis coefficients for all classes in the set
- make_matrix3D
- construct a transformation matrix to display three-dimensional data
- make_source_ffnet
- write a stand-alone C program containing a network evaluation routine
- make_spcost_matrix
- compute the shortest-path distance matrix between all nodes in a SOM
- MALINFO
- a structure used by the SPRANNLIB memory allocation routines (LOCAL)
- malloc_dataset
- allocate a DATASET structure
- malloc_estimate
- allocate memory for covariance estimation (LOCAL)
- malloc_layer
- allocate a LAYER structure from a pool
- malloc_link
- allocate a LINK structure from a pool
- malloc_local
- a local allocation routine (LOCAL)
- malloc_lspace
- allocate space for learning samples (LOCAL)
- malloc_map
- allocate a MAP structure from a pool
- malloc_monitor
- an SPRANNLIB replacement for the malloc routine
- malloc_net
- allocate a NET structure from a pool
- malloc_netlib
- a malloc stub (LOCAL)
- malloc_ptron
- allocate space for a perceptron and initialize
- malloc_sample
- allocate a SAMPLE structure
- malloc_sortspace
- memory allocation routine for the k-nearest neighbour classifier (LOCAL)
- malloc_space
- allocate memory for samples (LOCAL)
- malloc_unit
- allocate a UNIT structure from a pool
- malloc_unit_value
- allocate a UNIT_VALUE structure from a pool
- malloc_weight
- allocate a WEIGHT structure from a pool
- malloc_weight_value
- allocate a WEIGHT_VALUE structure from a pool
- MAP
- unit group datatype
- MAP_RECIPE
- simplified description of a map of a shared weights network
- MARQ-variables
- some local variables used with the Levenberg-Marquardt algorithm (LOCAL)
- marq_free
- free al data used by the Marquardt method (LOCAL)
- marq_grad_mse
- calculate the gradient of the MSE with resp. to the parameters (LOCAL)
- marq_grad_net
- calculate the gradient of all the parameters belonging to the complete network (LOCAL)
- marq_grad_output
- calculate the gradient of the network weights (LOCAL)
- marq_grad_unit
- calculate the gradient of all the parameters belonging to one unit (LOCAL)
- marq_init
- initialize the Marquardt optimization method (LOCAL)
- matcopy
- copy matrix A to B
- matinv
- invert a matrix A into matrix B
- matinvd
- invert a matrix A into matrix B and return the determinant
- matmult
- multiply two matrices and store result in a new matrix
- matrix
- a Numerical Recipes in C datatype
- mattransp
- transpose matrix A to At
- matvecmult
- multiply a matrix with a vector and store the result in a new vector
- mean_var_dataset
- estimate the mean and variance of a class in a data set
- meanset_error
- compute the classification error on the meanset using a linear discriminant
- memory_monitor
- return the total amount of dynamically allocated memory
- MESSAGE-functions
- front-ends to the sprmessage function
- ML_STRUCT
- a local structure for maximum likelihood learning
- MLNET-constants
- some constants used in the creation of maximum likelihood networks (LOCAL)
- MNBRAK
- search for a bracketed minimum of a function in downhill direction
- MONITOR-ALLOCATION-functions
- sprlib memory allocation routines replacing the original C-functions
- MONITOR-ALLOCATION-variables
- some variables used by the SPRANNLIB memory allocation routines (LOCAL)
N
P
- PAO-variables
- local variables used for Pao clustering
- PARZELM
- local structure for Parzen classification
- parzen_best_s
- determine the best smoothing value for a class
- parzen_classify
- two class Parzen classifier
- parzen_dataset_class
- classify a sample, given a dataset, using the parzen estimator
- parzen_dset_best_s
- compute the optimal smoothing parameter for a class
- parzen_init
- parzen_init
- initialization for the Parzen classifier
- parzen_mclass
- pattern classification using the Parzen classifier
- parzen_probability
- determine the probability of a new pattern using the Parzen classifier
- PERF-flags
- network performance calculation option flags
- perf_free
- free the performance testing facilities of a learning set
- perf_init
- initialize the performance testing facilities of a learning set
- perf_mlnet
- auxiliary function to determine the performance of a maximum likelihood network
- pn_adapt_net
- perform one learning cycle with the pseudo-Newton variation of backpropagation
- pn_adapt_unit
- adapt a unit according to the pseudo-Newton variation on the generalized delta rule
- pn_adapt_weights
- adapt weights marked ``changed'' according to the accumulated (pseudo-Newton) delta's
- pn_free
- free a network that was generated by pn_init
- pn_init
- initialise and set up a network for training with the pseudo-Newton backpropagation learning rule
- pn_learn
- perform a number of learning cycles with the pseudo-Newton learning algorithm
- PN_STRUCT
- a local structure for pseudo-Newton backpropagation learning (LOCAL)
- pocket_weights
- store network weights in a pocket (LOCAL)
- pr_cum_dbl_exp
- compute the cumulative (integrated) probability function
- pr_cum_normal
- compute the cumulative probability distribution function of a standardized normal variable
- pr_dbl_exp
- the probability density function for the double exponential distribution
- pr_error
- compute the classification error for a one-dimensional classifier
- pr_error_multi
- compute the classification error for a classifier
- pr_k_normal
- compute the probability density for a general k-dimensional normal distribution
- pr_normal
- compute the probability density of a standardized normal random variable
- print_bot_up_wts
- create a dataset of the cluster values (LOCAL)
- print_c_code
- write the code necessary to evaluate a network to a C file (LOCAL)
- print_c_header
- write all global settings (constants, include files) to a C file (LOCAL)
- print_inweights
- write the weights between the input and hidden layer to a C file (LOCAL)
- print_outweights
- write the weights between the hidden and output layer to a C file (LOCAL)
- printfile
- write a buffer to a file and check for errors (LOCAL)
- PTRON-flags
- flags for perceptron learning
- PTRON
- a structure for perceptron learning
R
S
- SAMANN-flags
- SAMANN option flags
- samann_adapt_net
- perform one learning cycle using the SAMANN rule
- samann_adapt_unit
- adapt a unit according to SAMANN learning rule
- samann_der_linear
- a local version of the derivative of the linear transfer function (LOCAL)
- samann_der_sigmoid
- a local version of the derivative of the sigmoid transfer function (LOCAL)
- samann_free
- free a network that was generated by samann_init
- samann_init
- initialise and set up a network for training with the SAMANN backpropagation learning rule
- samann_lambda
- calculate the Sammon mapping constant (LOCAL)
- samann_learn
- perform a number of learning cycles with the backpropagation learning algorithm
- samann_learn_sample
- perform one learning cycles with the SAMANN learning algorithm
- samann_linear
- a local version of the linear transfer function (LOCAL)
- samann_scale_dataset
- scale a dataset to make it suitable for SAMANN backpropagation training
- samann_sigmoid
- a local version of the sigmoid transfer function (LOCAL)
- SAMANN_STRUCT
- a local structure for SAMANN backpropagation learning (LOCAL)
- SAMMON-flags
- flags for Sammon map routines
- SAMMON-MAP-flags
- flags for Sammon map injection/extraction routines
- SAMMON-VARIABLES
- some global variables for the Sammon mapping routines (LOCAL)
- sammon_backup_map
- make a backup of the current map (LOCAL)
- sammon_constant
- query the Sammon constant
- sammon_dstress
- calculate the derivative of Sammon's stress measure
- sammon_exit
- free memory allocated by sammon_init
- sammon_extract_map
- extract the current Sammon mapping in the form ofa DATASET
- sammon_init
- initialize the Sammon mapping routines
- sammon_inject_map
- replace the current Sammon mapping by a DATASET
- sammon_rand
- initialize a Sammon map with random values
- sammon_restore_map
- restore a backup of a map (LOCAL)
- sammon_solve
- minimize Sammon's stress measure
- sammon_stress
- calculate Sammon's stress measure
- sammon_triangulate
- map a new point using an existing Sammon mapping (LOCAL)
- sammon_triangulate_set
- map a dataset using an existing Sammon mapping
- SAMPLE-flags
- enabling or disabling a data sample
- SAMPLE
- structure holding information of specific data point
- sample_distance
- compute the Euclidean between two SPRANNLIB samples
- sample_mse
- calculate the MSE of one sample and the corresponding network output
- sample_perf
- compute the performance of a network on a sample
- scale_output_dataset
- scale one output element of an IOSET dataset
- scawi_check
- check if the network can be initialized using the SCAWI method (LOCAL)
- scawi_ffnet
- initialize a feedforward neural net using the SCAWI method
- send_audio
- send data to the audio device (LOCAL)
- set_auto_priority
- install a signal handler which can renice the process
- set_sigsegv_handler
- install the SPRANNLIB segmentation violation handler (LOCAL)
- set_user_signal
- set the user definable signal SIGUSR1 to a userdefined function
- SHAREDNET-constants
- some constants for the shared weights networks creation routines (LOCAL)
- SHAREDNET-variables
- some variables used during the shared weights networks creation process (LOCAL)
- SHAREDNET_RECIPE
- simplified description of a shared weights network
- sigsegv_handler
- an SPRANNLIB replacement segmentation violation handler (LOCAL)
- simple_df
- derivative of the standard perceptron (LOCAL)
- simple_f
- simple perceptron transfer function (LOCAL)
- som_goodness
- compute the goodness-of-fit between a trained SOM and a dataset
- SOUND-flags
- some flags for playing sound samples
- SOUND-variables
- some variables used in the sound sample routines
- sound_sample
- play a sound sample to the audio device
- sprexit
- terminate the use of SPRANNLIB
- sprinit
- initialize SPRANNLIB and print version and copyright information
- sprmessage
- a general stdout error message / warning function
- sqrdistance
- calculate the squared distance (LOCAL)
- sqrdistance
- determine the squared distance (LOCAL)
- STATISTICS
- data structure with all statistics about data set
- status_layer
- print a LAYER structure's contents to a stream
- status_link
- print a LINK structure's contents to a stream
- status_map
- print a MAP structure's contents to a stream
- status_monitor
- print a memory allocation status report
- status_net
- print a NET structure's contents to a stream
- status_total
- print a network's contents to a stream
- status_unit
- print a UNIT structure's contents to a stream
- status_weight
- print a WEIGHT structure's contents to a stream
- std_mean_dataset
- generate samples according to the mean dataset
- std_var_dataset
- generate samples according to the var dataset
- STOCK-values
- constants used in the allocation routines (LOCAL)
- STOCK-variables
- variables used in the allocation routines (LOCAL)
- store_hist_all_thetas
- store the current values of the thetas in the linked lists of their history
- store_hist_all_units
- store the current unit values in the linked list of their history
- store_hist_all_weights
- store the current weight values in the linked lists of their history
- store_hist_theta
- store the current value of theta in a newly allocated structure in the linked list of values
- store_hist_unit
- store the current state of a unit in a newly allocated structure in the linked list of unit states
- store_hist_weight
- store the current weight value in a newly allocated structure in the linked list of weight values
- subspace_dataset
- create a new dataset from another dataset with a higher dimension
- svdcmp
- perform a single value decomposition of matrix
- system_data
- a union used to store external data
- system_time
- get information about used CPU time
T
U
V
W
- warning_system_load
- get system load averages and warn the user if necessary
- WAVELEARN_STRUCT
- a local structure for wavelet network gradient descent minimization (LOCAL)
- WAVELON_STRUCT
- a local structure for wavelet network gradient descent minimization (LOCAL)
- WEIGHT-flags
- flags to indicate the type of a weight
- WEIGHT
- connection weight datatype
- weight0_vector
- compute the constant value of the quadratic decision function (LOCAL)
- WEIGHT_VALUE
- connection weight value datatype
- weight_vector
- compute the Fisher discriminant vector
- widrow_hoff_ptron
- update the perceptron weight using the Widrow-Hoff delta rule (LOCAL)
- wien_out_free
- free the variables allocated by wien_out_init (LOCAL)
- wien_out_init
- initialization routine for determining the Wiener weight for an output unit (LOCAL)
- wien_output_ff
- calculate the Wiener solution for an output unit
- wiener_output_calc
- calculate the Wiener weights for an unit (LOCAL)
This index was automatically generated by api2html
This document was generated using api2html on Thu Mar 5 09:00:00 MET DST 1998