--- /dev/null
+/*
+Fast Artificial Neural Network Library (fann)
+Copyright (C) 2003 Steffen Nissen (lukesky@diku.dk)
+
+This library is free software; you can redistribute it and/or
+modify it under the terms of the GNU Lesser General Public
+License as published by the Free Software Foundation; either
+version 2.1 of the License, or (at your option) any later version.
+
+This library is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+Lesser General Public License for more details.
+
+You should have received a copy of the GNU Lesser General Public
+License along with this library; if not, write to the Free Software
+Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
+*/
+
+#ifndef __fann_data_h__
+#define __fann_data_h__
+
+#include <stdio.h>
+
+/* Section: FANN Datatypes
+
+ The two main datatypes used in the fann library is <struct fann>,
+ which represents an artificial neural network, and <struct fann_train_data>,
+ which represent training data.
+ */
+
+
+/* Type: fann_type
+ fann_type is the type used for the weights, inputs and outputs of the neural network.
+
+ fann_type is defined as a:
+ float - if you include fann.h or floatfann.h
+ double - if you include doublefann.h
+ int - if you include fixedfann.h (please be aware that fixed point usage is
+ only to be used during execution, and not during training).
+*/
+
+/* Enum: fann_train_enum
+ The Training algorithms used when training on <struct fann_train_data> with functions like
+ <fann_train_on_data> or <fann_train_on_file>. The incremental training looks alters the weights
+ after each time it is presented an input pattern, while batch only alters the weights once after
+ it has been presented to all the patterns.
+
+ FANN_TRAIN_INCREMENTAL - Standard backpropagation algorithm, where the weights are
+ updated after each training pattern. This means that the weights are updated many
+ times during a single epoch. For this reason some problems, will train very fast with
+ this algorithm, while other more advanced problems will not train very well.
+ FANN_TRAIN_BATCH - Standard backpropagation algorithm, where the weights are updated after
+ calculating the mean square error for the whole training set. This means that the weights
+ are only updated once during a epoch. For this reason some problems, will train slower with
+ this algorithm. But since the mean square error is calculated more correctly than in
+ incremental training, some problems will reach a better solutions with this algorithm.
+ FANN_TRAIN_RPROP - A more advanced batch training algorithm which achieves good results
+ for many problems. The RPROP training algorithm is adaptive, and does therefore not
+ use the learning_rate. Some other parameters can however be set to change the way the
+ RPROP algorithm works, but it is only recommended for users with insight in how the RPROP
+ training algorithm works. The RPROP training algorithm is described by
+ [Riedmiller and Braun, 1993], but the actual learning algorithm used here is the
+ iRPROP- training algorithm which is described by [Igel and Husken, 2000] which
+ is an variety of the standard RPROP training algorithm.
+ FANN_TRAIN_QUICKPROP - A more advanced batch training algorithm which achieves good results
+ for many problems. The quickprop training algorithm uses the learning_rate parameter
+ along with other more advanced parameters, but it is only recommended to change these
+ advanced parameters, for users with insight in how the quickprop training algorithm works.
+ The quickprop training algorithm is described by [Fahlman, 1988].
+
+ See also:
+ <fann_set_training_algorithm>, <fann_get_training_algorithm>
+*/
+enum fann_train_enum
+{
+ FANN_TRAIN_INCREMENTAL = 0,
+ FANN_TRAIN_BATCH,
+ FANN_TRAIN_RPROP,
+ FANN_TRAIN_QUICKPROP
+};
+
+/* Constant: FANN_TRAIN_NAMES
+
+ Constant array consisting of the names for the training algorithms, so that the name of an
+ training function can be received by:
+ (code)
+ char *name = FANN_TRAIN_NAMES[train_function];
+ (end)
+
+ See Also:
+ <fann_train_enum>
+*/
+static char const *const FANN_TRAIN_NAMES[] = {
+ "FANN_TRAIN_INCREMENTAL",
+ "FANN_TRAIN_BATCH",
+ "FANN_TRAIN_RPROP",
+ "FANN_TRAIN_QUICKPROP"
+};
+
+/* Enums: fann_activationfunc_enum
+
+ The activation functions used for the neurons during training. The activation functions
+ can either be defined for a group of neurons by <fann_set_activation_function_hidden> and
+ <fann_set_activation_function_output> or it can be defined for a single neuron by <fann_set_activation_function>.
+
+ The steepness of an activation function is defined in the same way by
+ <fann_set_activation_steepness_hidden>, <fann_set_activation_steepness_output> and <fann_set_activation_steepness>.
+
+ The functions are described with functions where:
+ * x is the input to the activation function,
+ * y is the output,
+ * s is the steepness and
+ * d is the derivation.
+
+ FANN_LINEAR - Linear activation function.
+ * span: -inf < y < inf
+ * y = x*s, d = 1*s
+ * Can NOT be used in fixed point.
+
+ FANN_THRESHOLD - Threshold activation function.
+ * x < 0 -> y = 0, x >= 0 -> y = 1
+ * Can NOT be used during training.
+
+ FANN_THRESHOLD_SYMMETRIC - Threshold activation function.
+ * x < 0 -> y = 0, x >= 0 -> y = 1
+ * Can NOT be used during training.
+
+ FANN_SIGMOID - Sigmoid activation function.
+ * One of the most used activation functions.
+ * span: 0 < y < 1
+ * y = 1/(1 + exp(-2*s*x))
+ * d = 2*s*y*(1 - y)
+
+ FANN_SIGMOID_STEPWISE - Stepwise linear approximation to sigmoid.
+ * Faster than sigmoid but a bit less precise.
+
+ FANN_SIGMOID_SYMMETRIC - Symmetric sigmoid activation function, aka. tanh.
+ * One of the most used activation functions.
+ * span: -1 < y < 1
+ * y = tanh(s*x) = 2/(1 + exp(-2*s*x)) - 1
+ * d = s*(1-(y*y))
+
+ FANN_SIGMOID_SYMMETRIC - Stepwise linear approximation to symmetric sigmoid.
+ * Faster than symmetric sigmoid but a bit less precise.
+
+ FANN_GAUSSIAN - Gaussian activation function.
+ * 0 when x = -inf, 1 when x = 0 and 0 when x = inf
+ * span: 0 < y < 1
+ * y = exp(-x*s*x*s)
+ * d = -2*x*s*y*s
+
+ FANN_GAUSSIAN_SYMMETRIC - Symmetric gaussian activation function.
+ * -1 when x = -inf, 1 when x = 0 and 0 when x = inf
+ * span: -1 < y < 1
+ * y = exp(-x*s*x*s)*2-1
+ * d = -2*x*s*(y+1)*s
+
+ FANN_ELLIOT - Fast (sigmoid like) activation function defined by David Elliott
+ * span: 0 < y < 1
+ * y = ((x*s) / 2) / (1 + |x*s|) + 0.5
+ * d = s*1/(2*(1+|x*s|)*(1+|x*s|))
+
+ FANN_ELLIOT_SYMMETRIC - Fast (symmetric sigmoid like) activation function defined by David Elliott
+ * span: -1 < y < 1
+ * y = (x*s) / (1 + |x*s|)
+ * d = s*1/((1+|x*s|)*(1+|x*s|))
+
+ FANN_LINEAR_PIECE - Bounded linear activation function.
+ * span: 0 < y < 1
+ * y = x*s, d = 1*s
+
+ FANN_LINEAR_PIECE_SYMMETRIC - Bounded Linear activation function.
+ * span: -1 < y < 1
+ * y = x*s, d = 1*s
+
+ See also:
+ <fann_set_activation_function_hidden>,
+ <fann_set_activation_function_output>
+*/
+enum fann_activationfunc_enum
+{
+ FANN_LINEAR = 0,
+ FANN_THRESHOLD,
+ FANN_THRESHOLD_SYMMETRIC,
+ FANN_SIGMOID,
+ FANN_SIGMOID_STEPWISE,
+ FANN_SIGMOID_SYMMETRIC,
+ FANN_SIGMOID_SYMMETRIC_STEPWISE,
+ FANN_GAUSSIAN,
+ FANN_GAUSSIAN_SYMMETRIC,
+ /* Stepwise linear approximation to gaussian.
+ * Faster than gaussian but a bit less precise.
+ * NOT implemented yet.
+ */
+ FANN_GAUSSIAN_STEPWISE,
+ FANN_ELLIOT,
+ FANN_ELLIOT_SYMMETRIC,
+ FANN_LINEAR_PIECE,
+ FANN_LINEAR_PIECE_SYMMETRIC
+};
+
+/* Constant: FANN_ACTIVATIONFUNC_NAMES
+
+ Constant array consisting of the names for the activation function, so that the name of an
+ activation function can be received by:
+ (code)
+ char *name = FANN_ACTIVATIONFUNC_NAMES[activation_function];
+ (end)
+
+ See Also:
+ <fann_activationfunc_enum>
+*/
+static char const *const FANN_ACTIVATIONFUNC_NAMES[] = {
+ "FANN_LINEAR",
+ "FANN_THRESHOLD",
+ "FANN_THRESHOLD_SYMMETRIC",
+ "FANN_SIGMOID",
+ "FANN_SIGMOID_STEPWISE",
+ "FANN_SIGMOID_SYMMETRIC",
+ "FANN_SIGMOID_SYMMETRIC_STEPWISE",
+ "FANN_GAUSSIAN",
+ "FANN_GAUSSIAN_SYMMETRIC",
+ "FANN_GAUSSIAN_STEPWISE",
+ "FANN_ELLIOT",
+ "FANN_ELLIOT_SYMMETRIC",
+ "FANN_LINEAR_PIECE",
+ "FANN_LINEAR_PIECE_SYMMETRIC"
+};
+
+/* Enum: fann_errorfunc_enum
+ Error function used during training.
+
+ FANN_ERRORFUNC_LINEAR - Standard linear error function.
+ FANN_ERRORFUNC_TANH - Tanh error function, usually better
+ but can require a lower learning rate. This error function agressively targets outputs that
+ differ much from the desired, while not targetting outputs that only differ a little that much.
+ This activation function is not recommended for cascade training and incremental training.
+
+ See also:
+ <fann_set_train_error_function>, <fann_get_train_error_function>
+*/
+enum fann_errorfunc_enum
+{
+ FANN_ERRORFUNC_LINEAR = 0,
+ FANN_ERRORFUNC_TANH
+};
+
+/* Constant: FANN_ERRORFUNC_NAMES
+
+ Constant array consisting of the names for the training error functions, so that the name of an
+ error function can be received by:
+ (code)
+ char *name = FANN_ERRORFUNC_NAMES[error_function];
+ (end)
+
+ See Also:
+ <fann_errorfunc_enum>
+*/
+static char const *const FANN_ERRORFUNC_NAMES[] = {
+ "FANN_ERRORFUNC_LINEAR",
+ "FANN_ERRORFUNC_TANH"
+};
+
+/* Enum: fann_stopfunc_enum
+ Stop criteria used during training.
+
+ FANN_STOPFUNC_MSE - Stop criteria is Mean Square Error (MSE) value.
+ FANN_STOPFUNC_BIT - Stop criteria is number of bits that fail. The number of bits; means the
+ number of output neurons which differ more than the bit fail limit
+ (see <fann_get_bit_fail_limit>, <fann_set_bit_fail_limit>).
+ The bits are counted in all of the training data, so this number can be higher than
+ the number of training data.
+
+ See also:
+ <fann_set_train_stop_function>, <fann_get_train_stop_function>
+*/
+enum fann_stopfunc_enum
+{
+ FANN_STOPFUNC_MSE = 0,
+ FANN_STOPFUNC_BIT
+};
+
+/* Constant: FANN_STOPFUNC_NAMES
+
+ Constant array consisting of the names for the training stop functions, so that the name of a
+ stop function can be received by:
+ (code)
+ char *name = FANN_STOPFUNC_NAMES[stop_function];
+ (end)
+
+ See Also:
+ <fann_stopfunc_enum>
+*/
+static char const *const FANN_STOPFUNC_NAMES[] = {
+ "FANN_STOPFUNC_MSE",
+ "FANN_STOPFUNC_BIT"
+};
+
+/* forward declarations for use with the callback */
+struct fann;
+struct fann_train_data;
+/* Type: fann_callback_type
+ This callback function can be called during training when using <fann_train_on_data>,
+ <fann_train_on_file> or <fann_cascade_train>.
+
+ >typedef int (FANN_API * fann_callback_type) (struct fann *ann, struct fann_train_data *train,
+ > unsigned int max_epochs,
+ > unsigned int epochs_between_reports,
+ > float desired_error, unsigned int epochs);
+
+ The callback can be set by using <fann_set_callback> and is very usefull for doing custom
+ things during training. It is recommended to use this function when implementing custom
+ training procedures, or when visualizing the training in a GUI etc. The parameters which the
+ callback function takes is the parameters given to the <fann_train_on_data>, plus an epochs
+ parameter which tells how many epochs the training have taken so far.
+
+ The callback function should return an integer, if the callback function returns -1, the training
+ will terminate.
+
+ Example of a callback function:
+ >int FANN_API test_callback(struct fann *ann, struct fann_train_data *train,
+ > unsigned int max_epochs, unsigned int epochs_between_reports,
+ > float desired_error, unsigned int epochs)
+ >{
+ > printf("Epochs %8d. MSE: %.5f. Desired-MSE: %.5f\n", epochs, fann_get_MSE(ann), desired_error);
+ > return 0;
+ >}
+
+ See also:
+ <fann_set_callback>, <fann_train_on_data>
+ */
+FANN_EXTERNAL typedef int (FANN_API * fann_callback_type) (struct fann *ann, struct fann_train_data *train,
+ unsigned int max_epochs,
+ unsigned int epochs_between_reports,
+ float desired_error, unsigned int epochs);
+
+
+/* ----- Data structures -----
+ * No data within these structures should be altered directly by the user.
+ */
+
+struct fann_neuron
+{
+ /* Index to the first and last connection
+ * (actually the last is a past end index)
+ */
+ unsigned int first_con;
+ unsigned int last_con;
+ /* The sum of the inputs multiplied with the weights */
+ fann_type sum;
+ /* The value of the activation function applied to the sum */
+ fann_type value;
+ /* The steepness of the activation function */
+ fann_type activation_steepness;
+ /* Used to choose which activation function to use */
+ enum fann_activationfunc_enum activation_function;
+#ifdef __GNUC__
+} __attribute__ ((packed));
+#else
+};
+#endif
+
+/* A single layer in the neural network.
+ */
+struct fann_layer
+{
+ /* A pointer to the first neuron in the layer
+ * When allocated, all the neurons in all the layers are actually
+ * in one long array, this is because we wan't to easily clear all
+ * the neurons at once.
+ */
+ struct fann_neuron *first_neuron;
+
+ /* A pointer to the neuron past the last neuron in the layer */
+ /* the number of neurons is last_neuron - first_neuron */
+ struct fann_neuron *last_neuron;
+};
+
+/* Struct: struct fann_error
+
+ Structure used to store error-related information, both
+ <struct fann> and <struct fann_train_data> can be casted to this type.
+
+ See also:
+ <fann_set_error_log>, <fann_get_errno>
+*/
+struct fann_error
+{
+ enum fann_errno_enum errno_f;
+ FILE *error_log;
+ char *errstr;
+};
+
+
+/* Struct: struct fann
+ The fast artificial neural network(fann) structure.
+
+ Data within this structure should never be accessed directly, but only by using the
+ *fann_get_...* and *fann_set_...* functions.
+
+ The fann structure is created using one of the *fann_create_...* functions and each of
+ the functions which operates on the structure takes *struct fann * ann* as the first parameter.
+
+ See also:
+ <fann_create_standard>, <fann_destroy>
+ */
+struct fann
+{
+ /* The type of error that last occured. */
+ enum fann_errno_enum errno_f;
+
+ /* Where to log error messages. */
+ FILE *error_log;
+
+ /* A string representation of the last error. */
+ char *errstr;
+
+ /* the learning rate of the network */
+ float learning_rate;
+
+ /* The learning momentum used for backpropagation algorithm. */
+ float learning_momentum;
+
+ /* the connection rate of the network
+ * between 0 and 1, 1 meaning fully connected
+ */
+ float connection_rate;
+
+ /* is 1 if shortcut connections are used in the ann otherwise 0
+ * Shortcut connections are connections that skip layers.
+ * A fully connected ann with shortcut connections are a ann where
+ * neurons have connections to all neurons in all later layers.
+ */
+ unsigned int shortcut_connections;
+
+ /* pointer to the first layer (input layer) in an array af all the layers,
+ * including the input and outputlayers
+ */
+ struct fann_layer *first_layer;
+
+ /* pointer to the layer past the last layer in an array af all the layers,
+ * including the input and outputlayers
+ */
+ struct fann_layer *last_layer;
+
+ /* Total number of neurons.
+ * very usefull, because the actual neurons are allocated in one long array
+ */
+ unsigned int total_neurons;
+
+ /* Number of input neurons (not calculating bias) */
+ unsigned int num_input;
+
+ /* Number of output neurons (not calculating bias) */
+ unsigned int num_output;
+
+ /* The weight array */
+ fann_type *weights;
+
+ /* The connection array */
+ struct fann_neuron **connections;
+
+ /* Used to contain the errors used during training
+ * Is allocated during first training session,
+ * which means that if we do not train, it is never allocated.
+ */
+ fann_type *train_errors;
+
+ /* Training algorithm used when calling fann_train_on_..
+ */
+ enum fann_train_enum training_algorithm;
+
+#ifdef FIXEDFANN
+ /* the decimal_point, used for shifting the fix point
+ * in fixed point integer operatons.
+ */
+ unsigned int decimal_point;
+
+ /* the multiplier, used for multiplying the fix point
+ * in fixed point integer operatons.
+ * Only used in special cases, since the decimal_point is much faster.
+ */
+ unsigned int multiplier;
+
+ /* When in choosen (or in fixed point), the sigmoid function is
+ * calculated as a stepwise linear function. In the
+ * activation_results array, the result is saved, and in the
+ * two values arrays, the values that gives the results are saved.
+ */
+ fann_type sigmoid_results[6];
+ fann_type sigmoid_values[6];
+ fann_type sigmoid_symmetric_results[6];
+ fann_type sigmoid_symmetric_values[6];
+#endif
+
+ /* Total number of connections.
+ * very usefull, because the actual connections
+ * are allocated in one long array
+ */
+ unsigned int total_connections;
+
+ /* used to store outputs in */
+ fann_type *output;
+
+ /* the number of data used to calculate the mean square error.
+ */
+ unsigned int num_MSE;
+
+ /* the total error value.
+ * the real mean square error is MSE_value/num_MSE
+ */
+ float MSE_value;
+
+ /* The number of outputs which would fail (only valid for classification problems)
+ */
+ unsigned int num_bit_fail;
+
+ /* The maximum difference between the actual output and the expected output
+ * which is accepted when counting the bit fails.
+ * This difference is multiplied by two when dealing with symmetric activation functions,
+ * so that symmetric and not symmetric activation functions can use the same limit.
+ */
+ fann_type bit_fail_limit;
+
+ /* The error function used during training. (default FANN_ERRORFUNC_TANH)
+ */
+ enum fann_errorfunc_enum train_error_function;
+
+ /* The stop function used during training. (default FANN_STOPFUNC_MSE)
+ */
+ enum fann_stopfunc_enum train_stop_function;
+
+ /* The callback function used during training. (default NULL)
+ */
+ fann_callback_type callback;
+
+ /* Variables for use with Cascade Correlation */
+
+ /* The error must change by at least this
+ * fraction of its old value to count as a
+ * significant change.
+ */
+ float cascade_output_change_fraction;
+
+ /* No change in this number of epochs will cause
+ * stagnation.
+ */
+ unsigned int cascade_output_stagnation_epochs;
+
+ /* The error must change by at least this
+ * fraction of its old value to count as a
+ * significant change.
+ */
+ float cascade_candidate_change_fraction;
+
+ /* No change in this number of epochs will cause
+ * stagnation.
+ */
+ unsigned int cascade_candidate_stagnation_epochs;
+
+ /* The current best candidate, which will be installed.
+ */
+ unsigned int cascade_best_candidate;
+
+ /* The upper limit for a candidate score
+ */
+ fann_type cascade_candidate_limit;
+
+ /* Scale of copied candidate output weights
+ */
+ fann_type cascade_weight_multiplier;
+
+ /* Maximum epochs to train the output neurons during cascade training
+ */
+ unsigned int cascade_max_out_epochs;
+
+ /* Maximum epochs to train the candidate neurons during cascade training
+ */
+ unsigned int cascade_max_cand_epochs;
+
+ /* An array consisting of the activation functions used when doing
+ * cascade training.
+ */
+ enum fann_activationfunc_enum *cascade_activation_functions;
+
+ /* The number of elements in the cascade_activation_functions array.
+ */
+ unsigned int cascade_activation_functions_count;
+
+ /* An array consisting of the steepnesses used during cascade training.
+ */
+ fann_type *cascade_activation_steepnesses;
+
+ /* The number of elements in the cascade_activation_steepnesses array.
+ */
+ unsigned int cascade_activation_steepnesses_count;
+
+ /* The number of candidates of each type that will be present.
+ * The actual number of candidates is then
+ * cascade_activation_functions_count *
+ * cascade_activation_steepnesses_count *
+ * cascade_num_candidate_groups
+ */
+ unsigned int cascade_num_candidate_groups;
+
+ /* An array consisting of the score of the individual candidates,
+ * which is used to decide which candidate is the best
+ */
+ fann_type *cascade_candidate_scores;
+
+ /* The number of allocated neurons during cascade correlation algorithms.
+ * This number might be higher than the actual number of neurons to avoid
+ * allocating new space too often.
+ */
+ unsigned int total_neurons_allocated;
+
+ /* The number of allocated connections during cascade correlation algorithms.
+ * This number might be higher than the actual number of neurons to avoid
+ * allocating new space too often.
+ */
+ unsigned int total_connections_allocated;
+
+ /* Variables for use with Quickprop training */
+
+ /* Decay is used to make the weights not go so high */
+ float quickprop_decay;
+
+ /* Mu is a factor used to increase and decrease the stepsize */
+ float quickprop_mu;
+
+ /* Variables for use with with RPROP training */
+
+ /* Tells how much the stepsize should increase during learning */
+ float rprop_increase_factor;
+
+ /* Tells how much the stepsize should decrease during learning */
+ float rprop_decrease_factor;
+
+ /* The minimum stepsize */
+ float rprop_delta_min;
+
+ /* The maximum stepsize */
+ float rprop_delta_max;
+
+ /* The initial stepsize */
+ float rprop_delta_zero;
+
+ /* Used to contain the slope errors used during batch training
+ * Is allocated during first training session,
+ * which means that if we do not train, it is never allocated.
+ */
+ fann_type *train_slopes;
+
+ /* The previous step taken by the quickprop/rprop procedures.
+ * Not allocated if not used.
+ */
+ fann_type *prev_steps;
+
+ /* The slope values used by the quickprop/rprop procedures.
+ * Not allocated if not used.
+ */
+ fann_type *prev_train_slopes;
+
+ /* The last delta applied to a connection weight.
+ * This is used for the momentum term in the backpropagation algorithm.
+ * Not allocated if not used.
+ */
+ fann_type *prev_weights_deltas;
+
+};
+
+#endif
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