layers) {
for (int x = 1; x < layers.size(); x++) {
Layer layer = layers.get(x);
for (int n = 0; n < layer.size(); n++) {
fann_set_activation_function(ann, layer.get(n)
.getActivationFunction().ordinal(), x, n);
fann_set_activation_steepness(ann, layer.get(n).getSteepness(),
x, n);
}
}
}
public int getNumInputNeurons() {
return fann_get_num_input(ann);
}
public int getNumOutputNeurons() {
return fann_get_num_output(ann);
}
public int getTotalNumNeurons() {
return fann_get_total_neurons(ann);
}
/**
* Save this FANN to a file.
*
* @param file
* @return true on success
*/
public boolean save(String file) {
return fann_save(ann, file) == 0;
}
/**
* Run the ANN on a set of inputs.
*
* @param input
* length == numInputNeurons
* @return the output of the ANN. (length = numOutputNeurons)
*/
public float[] run(float[] input) {
Pointer result = fann_run(ann, input);
float[] output = result.getFloatArray(0, getNumOutputNeurons());
return output;
}
/**
*
* Frees allocated memory.
*
* You must call this method when you are finished to prevent memory leaks.
*/
public void close() {
if (ann != null)
fann_destroy(ann);
}
/**
* Call {@link #close()} on garbage collection to catch memory leaks.
*/
@Override
public void finalize() throws Throwable {
close();
super.finalize();
}
/*
* A JNA Direct Mapping implementation of the FANN library. This instance
* should be more performant than #com.googlecode.fannj.jna.FannLibrary
*/
protected static native Pointer fann_create_standard_array(int numLayers,
int[] layers);
protected static native Pointer fann_create_sparse_array(
float connection_rate, int numLayers, int[] layers);
protected static native Pointer fann_create_shortcut_array(int numLayers,
int[] layers);
protected static native float fann_get_MSE(Pointer ann);
protected static native Pointer fann_run(Pointer ann, float[] input);
protected static native void fann_destroy(Pointer ann);
protected static native int fann_get_num_input(Pointer ann);
protected static native int fann_get_num_output(Pointer ann);
protected static native int fann_get_total_neurons(Pointer ann);
protected static native void fann_set_activation_function(Pointer ann,
int activation_function, int layer, int neuron);
protected static native void fann_set_activation_steepness(Pointer ann,
float steepness, int layer, int neuron);
protected static native Pointer fann_get_neuron(Pointer ann, int layer,
int neuron);
protected static native Pointer fann_create_from_file(
String configuration_file);
protected static native int fann_save(Pointer ann, String file);
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/FannShortcut.java 0000664 0000000 0000000 00000004130 12371213632 0025442 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import java.util.List;
/**
*
* A standard backpropagation neural network, which is not fully connected and
* which also has shortcut connections.
*
*
* @author krenfro, brandstaetter
*/
public class FannShortcut extends Fann {
public FannShortcut(List layers) {
super();
if (layers == null)
throw new IllegalArgumentException("layers == null");
if (layers.isEmpty())
throw new IllegalArgumentException("layers is empty");
int[] neurons = new int[layers.size()];
for (int x = 0; x < neurons.length; x++)
neurons[x] = layers.get(x).size();
ann = fann_create_shortcut_array(neurons.length, neurons);
addLayers(layers);
}
/**
* Create a new ANN with just the input and output layers for Cascade
* Training
*
* @param inputs
* The number of input neurons
* @param outputs
* The number of output neurons
*/
public FannShortcut(int inputs, int outputs) {
int[] layers = new int[2];
layers[0] = inputs;
layers[1] = outputs;
int numLayers = 2;
ann = fann_create_shortcut_array(numLayers, layers);
}
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/FannSparse.java 0000664 0000000 0000000 00000003502 12371213632 0025066 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import java.util.List;
/**
*
* A standard backpropagation neural network, which is not fully connected.
*
*
* @author krenfro
*/
public class FannSparse extends Fann {
public static final float DEFAULT_CONNECTION_RATE = 1f;
float connectionRate = 1f;
public FannSparse(List layers) {
this(DEFAULT_CONNECTION_RATE, layers);
}
public FannSparse(float connectionRate, List layers) {
super();
if (layers == null)
throw new IllegalArgumentException("layers == null");
if (layers.isEmpty())
throw new IllegalArgumentException("layers is empty");
this.connectionRate = connectionRate;
int[] neurons = new int[layers.size()];
for (int x = 0; x < neurons.length; x++)
neurons[x] = layers.get(x).size();
ann = fann_create_sparse_array(connectionRate, neurons.length, neurons);
addLayers(layers);
}
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/Layer.java 0000664 0000000 0000000 00000005043 12371213632 0024104 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import java.util.ArrayList;
/**
* A Layer of Neurons in an ANN.
*
* @author krenfro
*/
public class Layer extends ArrayList {
private static final long serialVersionUID = -6467294440860703773L;
/**
* Create a Layer with the specified number of neurons with the default
* Activation Function: {@link Neuron#DEFAULT_ACTIVATION_FUNCTION} with
* steepness: {@link Neuron#DEFAULT_ACTIVATION_STEEPNESS}
*
* @param numNeurons
* @return
*/
public static Layer create(int numNeurons) {
return create(numNeurons, Neuron.DEFAULT_ACTIVATION_FUNCTION,
Neuron.DEFAULT_ACTIVATION_STEEPNESS);
}
/**
* Create a Layer with the specified number of neurons and a particular
* ActivationFunction with the steepness:
* {@link Neuron#DEFAULT_ACTIVATION_STEEPNESS}
*
* @param numNeurons
* @param activationFunction
* @return
*/
public static Layer create(int numNeurons, ActivationFunction activationFunction) {
return create(numNeurons, activationFunction, Neuron.DEFAULT_ACTIVATION_STEEPNESS);
}
/**
* Create a Layer with the specified number of neurons and a particular
* ActivationFunction with specified steepness
*
* @param numNeurons
* @param activationFunction
* @param steepness
* @return
*/
public static Layer create(int numNeurons, ActivationFunction activationFunction,
float steepness) {
Layer layer = new Layer();
for (int i = 0; i < numNeurons; i++)
layer.add(new Neuron(activationFunction, steepness));
return layer;
}
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/Neuron.java 0000664 0000000 0000000 00000006257 12371213632 0024306 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
/**
* @author krenfro
*/
public class Neuron {
public static final ActivationFunction DEFAULT_ACTIVATION_FUNCTION = ActivationFunction.FANN_SIGMOID_STEPWISE;
public static final float DEFAULT_ACTIVATION_STEEPNESS = .5f;
ActivationFunction activationFunction;
float steepness;
/**
* Create a neuron with default activation function: FANN_SIGMOID_STEPWISE
* and activation steepness = .5
*/
public Neuron() {
this(DEFAULT_ACTIVATION_FUNCTION);
}
/**
* Create a neuron with the specified activation function and default
* activation steepness = .5
*
* @param activationFunction
*/
public Neuron(ActivationFunction activationFunction) {
this(activationFunction, DEFAULT_ACTIVATION_STEEPNESS);
}
/**
* Create a neuron with the specified activation function and steepness.
*
* @param activationFunction
* @param steepness
*/
public Neuron(ActivationFunction activationFunction, float steepness) {
if (activationFunction == null)
throw new IllegalArgumentException("activationFunction is null");
this.activationFunction = activationFunction;
this.steepness = steepness;
}
public float getSteepness() {
return steepness;
}
public ActivationFunction getActivationFunction() {
return activationFunction;
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result
+ ((activationFunction == null) ? 0 : activationFunction.hashCode());
result = prime * result + Float.floatToIntBits(steepness);
return result;
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (getClass() != obj.getClass())
return false;
Neuron other = (Neuron) obj;
if (activationFunction == null) {
if (other.activationFunction != null)
return false;
}
else if (!activationFunction.equals(other.activationFunction))
return false;
if (Float.floatToIntBits(steepness) != Float.floatToIntBits(other.steepness))
return false;
return true;
}
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/Trainer.java 0000664 0000000 0000000 00000012634 12371213632 0024440 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro, 2011 Daniel Thomas
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import com.sun.jna.Library;
import com.sun.jna.Native;
import com.sun.jna.NativeLibrary;
import com.sun.jna.Platform;
import com.sun.jna.Pointer;
import com.sun.jna.win32.StdCallLibrary;
import java.util.Map;
/**
* Trains an ANN. Currently only File based training is supported.
*
* @author krenfro, drt24, brandstaetter
*/
public class Trainer {
static {
NativeLibrary fann;
if (Platform.isWindows()){
fann = NativeLibrary.getInstance("fannfloat");
Map options = fann.getOptions();
options.put(Library.OPTION_CALLING_CONVENTION, StdCallLibrary.STDCALL_CONVENTION);
options.put(Library.OPTION_FUNCTION_MAPPER, new WindowsFunctionMapper());
}
else{
fann = NativeLibrary.getInstance("fann");
}
Native.register(fann);
}
Fann fann;
public Trainer(Fann fann) {
this.fann = fann;
}
/**
* @param trainingFile
* @param maxEpochs
* @param epochsBetweenReports
* @param desiredError
* @return MSE for the ann once trained
*/
public float train(String trainingFile, int maxEpochs,
int epochsBetweenReports, float desiredError) {
fann_train_on_file(fann.ann, trainingFile, maxEpochs,
epochsBetweenReports, desiredError);
return fann_get_MSE(fann.ann);
}
/**
*
* @param dataFile
* @param maxNeurons
* @param neuronsBetweenReports
* @param desiredError
* @return MSE for the ann once trained
*/
public float cascadeTrain(String dataFile, int maxNeurons,
int neuronsBetweenReports, float desiredError) {
setTrainingAlgorithm(TrainingAlgorithm.FANN_TRAIN_RPROP);
fann_cascadetrain_on_file(fann.ann, dataFile, maxNeurons,
neuronsBetweenReports, desiredError);
return fann_get_MSE(fann.ann);
}
public void setTrainingAlgorithm(TrainingAlgorithm algorithm) {
fann_set_training_algorithm(fann.ann, algorithm.ordinal());
}
/**
*
* @param testingFile
* @return MSE for the Fann which has been tested
*/
public float test(String testingFile) {
fann_reset_MSE(fann.ann);// so that only influence on the MSE is from
// testing data
Pointer testingData = fann_read_train_from_file(testingFile);
fann_test_data(fann.ann, testingData);
fann_destroy_train(testingData);// deallocate it.
return fann_get_MSE(fann.ann);
}
/* A JNA Direct Mapping implementation of the FANN library. */
protected static native void fann_train_on_file(Pointer ann,
String filename, int max_epochs, int epochs_between_reports,
float desired_error);
protected static native void fann_cascadetrain_on_file(Pointer ann,
String filename, int max_neurons, int neurons_between_reports,
float desired_error);
protected static native void fann_set_training_algorithm(Pointer ann,
int training_algorithm);
protected static native int fann_get_training_algorithm(Pointer ann);
/**
* Resets the mean square error from the network.
*
* @param ann
*/
protected static native void fann_reset_MSE(Pointer ann);
/**
* Reads the mean square error from the network.
*
* @param ann
* @return the mean square error of the network
*/
protected static native float fann_get_MSE(Pointer ann);
/**
* Test the network using data and return the MSE of the network.
*
* You might need to run {@link #fann_reset_MSE(Pointer)} first
*
* @param ann
* @param data
* the data to test with
* @return the mean square error of the network
*/
protected static native float fann_test_data(Pointer ann, Pointer data);
/**
* Read the training or testing data from a file
*
* You must call {@link #fann_destroy_train(Pointer)} on the {@link Pointer}
* you get from this after you have finished with it
*
* @param filename
* the file name of the file to read the data from
* @return pointer to the data which has been read for use with
* {@link #fann_test_data(Pointer,Pointer)}
*/
protected static native Pointer fann_read_train_from_file(String filename);
/**
* Deallocate the data
*
* @param data
* the training/testing data to deallocate
*/
protected static native void fann_destroy_train(Pointer data);
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/TrainingAlgorithm.java 0000664 0000000 0000000 00000002102 12371213632 0026443 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
public enum TrainingAlgorithm {
/* The order these appear must match the order in the FANN src! */
FANN_TRAIN_INCREMENTAL, FANN_TRAIN_BATCH, FANN_TRAIN_RPROP, FANN_TRAIN_QUICKPROP
}
fannj-fannj-0.7/src/main/java/com/googlecode/fannj/WindowsFunctionMapper.java 0000664 0000000 0000000 00000004065 12371213632 0027340 0 ustar 00root root 0000000 0000000 package com.googlecode.fannj;
import com.sun.jna.FunctionMapper;
import com.sun.jna.NativeLibrary;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.lang.reflect.Method;
import java.util.HashMap;
import java.util.Map;
import java.util.logging.Level;
import java.util.logging.Logger;
public class WindowsFunctionMapper implements FunctionMapper{
private static final Logger logger = Logger.getLogger(WindowsFunctionMapper.class.getName());
private final Map translations;
public WindowsFunctionMapper(){
translations = new HashMap();
loadTranslations();
}
private void loadTranslations(){
try{
BufferedReader in = new BufferedReader(
new InputStreamReader(
this.getClass().getResourceAsStream("WindowsFunctionNames.txt")));
String s = in.readLine();
while (s != null){
addTranslation(s);
s = in.readLine();
}
in.close();
}
catch(IOException ex){
throw new IllegalStateException("Unable to load windows function names", ex);
}
}
private void addTranslation(String windowsName){
if (windowsName != null && !windowsName.isEmpty() && !windowsName.startsWith("#")){
String cleanName = windowsName;
if (windowsName.startsWith("_"))
cleanName = windowsName.substring(1);
int pos = cleanName.indexOf("@");
if (pos > 0)
cleanName = cleanName.substring(0, pos);
logger.log(Level.FINE, "{0} = {1}", new Object[]{cleanName.trim(), windowsName.trim()});
translations.put(cleanName.trim(), windowsName.trim());
}
}
@Override
public String getFunctionName(NativeLibrary nl, Method method) {
String result = translations.get(method.getName());
return result == null ? method.getName() : result;
}
}
fannj-fannj-0.7/src/main/resources/ 0000775 0000000 0000000 00000000000 12371213632 0017273 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/main/resources/com/ 0000775 0000000 0000000 00000000000 12371213632 0020051 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/main/resources/com/googlecode/ 0000775 0000000 0000000 00000000000 12371213632 0022160 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/main/resources/com/googlecode/fannj/ 0000775 0000000 0000000 00000000000 12371213632 0023254 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/main/resources/com/googlecode/fannj/WindowsFunctionNames.txt 0000664 0000000 0000000 00000007755 12371213632 0030157 0 ustar 00root root 0000000 0000000 #copied from 'depends' windows utility
#these are processed by the WindowsFunctionMapper
_fann_cascadetrain_on_data@20
_fann_cascadetrain_on_file@20
_fann_clear_scaling_params@4
_fann_create_from_file@4
_fann_create_shortcut_array@8
_fann_create_sparse_array@12
_fann_create_standard_array@8
_fann_create_train_from_callback@16
_fann_descale_input@8
_fann_descale_output@8
_fann_descale_train@8
_fann_destroy@4
_fann_destroy_train@4
_fann_duplicate_train_data@4
_fann_get_activation_function@12
_fann_get_activation_steepness@12
_fann_get_bias_array@8
_fann_get_bit_fail@4
_fann_get_bit_fail_limit@4
_fann_get_callback@4
_fann_get_cascade_activation_functions@4
_fann_get_cascade_activation_functions_count@4
_fann_get_cascade_activation_steepnesses@4
_fann_get_cascade_activation_steepnesses_count@4
_fann_get_cascade_candidate_change_fraction@4
_fann_get_cascade_candidate_limit@4
_fann_get_cascade_candidate_stagnation_epochs@4
_fann_get_cascade_max_cand_epochs@4
_fann_get_cascade_max_out_epochs@4
_fann_get_cascade_num_candidate_groups@4
_fann_get_cascade_num_candidates@4
_fann_get_cascade_output_change_fraction@4
_fann_get_cascade_output_stagnation_epochs@4
_fann_get_cascade_weight_multiplier@4
_fann_get_connection_array@8
_fann_get_connection_rate@4
_fann_get_errno@4
_fann_get_errstr@4
_fann_get_layer@8
_fann_get_layer_array@8
_fann_get_learning_momentum@4
_fann_get_learning_rate@4
_fann_get_MSE@4
_fann_get_network_type@4
_fann_get_neuron@12
_fann_get_neuron_layer@12
_fann_get_num_input@4
_fann_get_num_layers@4
_fann_get_num_output@4
_fann_get_quickprop_decay@4
_fann_get_quickprop_mu@4
_fann_get_rprop_decrease_factor@4
_fann_get_rprop_delta_max@4
_fann_get_rprop_delta_min@4
_fann_get_rprop_delta_zero@4
_fann_get_rprop_increase_factor@4
_fann_get_total_connections@4
_fann_get_total_neurons@4
_fann_get_train_error_function@4
_fann_get_train_stop_function@4
_fann_get_training_algorithm@4
_fann_get_user_data@4
_fann_init_weights@8
_fann_length_train_data@4
_fann_merge_train_data@8
_fann_num_input_train_data@4
_fann_num_output_train_data@4
_fann_print_connections@4
_fann_print_error@4
_fann_print_parameters@4
_fann_randomize_weights@12
_fann_read_train_from_file@4
_fann_reset_errno@4
_fann_reset_errstr@4
_fann_reset_MSE@4
_fann_run@8
_fann_save@8
_fann_save_to_fixed@8
_fann_save_train@8
_fann_save_train_to_fixed@12
_fann_scale_input@8
_fann_scale_input_train_data@12
_fann_scale_output@8
_fann_scale_output_train_data@12
_fann_scale_train@8
_fann_scale_train_data@12
_fann_set_activation_function@16
_fann_set_activation_function_hidden@8
_fann_set_activation_function_layer@12
_fann_set_activation_function_output@8
_fann_set_activation_steepness@16
_fann_set_activation_steepness_hidden@8
_fann_set_activation_steepness_layer@12
_fann_set_activation_steepness_output@8
_fann_set_bit_fail_limit@8
_fann_set_callback@8
_fann_set_cascade_activation_functions@12
_fann_set_cascade_activation_steepnesses@12
_fann_set_cascade_candidate_change_fraction@8
_fann_set_cascade_candidate_limit@8
_fann_set_cascade_candidate_stagnation_epochs@8
_fann_set_cascade_max_cand_epochs@8
_fann_set_cascade_max_out_epochs@8
_fann_set_cascade_num_candidate_groups@8
_fann_set_cascade_output_change_fraction@8
_fann_set_cascade_output_stagnation_epochs@8
_fann_set_cascade_weight_multiplier@8
_fann_set_error_log@8
_fann_set_input_scaling_params@16
_fann_set_learning_momentum@8
_fann_set_learning_rate@8
_fann_set_output_scaling_params@16
_fann_set_quickprop_decay@8
_fann_set_quickprop_mu@8
_fann_set_rprop_decrease_factor@8
_fann_set_rprop_delta_max@8
_fann_set_rprop_delta_min@8
_fann_set_rprop_delta_zero@8
_fann_set_rprop_increase_factor@8
_fann_set_scaling_params@24
_fann_set_train_error_function@8
_fann_set_train_stop_function@8
_fann_set_training_algorithm@8
_fann_set_user_data@8
_fann_set_weight@16
_fann_set_weight_array@12
_fann_shuffle_train_data@4
_fann_subset_train_data@12
_fann_test@12
_fann_test_data@8
_fann_train@12
_fann_train_epoch@8
_fann_train_on_data@20
_fann_train_on_file@20
#fann_create_shortcut
#fann_create_sparse
#fann_create_standard
fannj-fannj-0.7/src/test/ 0000775 0000000 0000000 00000000000 12371213632 0015314 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/java/ 0000775 0000000 0000000 00000000000 12371213632 0016235 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/java/com/ 0000775 0000000 0000000 00000000000 12371213632 0017013 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/java/com/googlecode/ 0000775 0000000 0000000 00000000000 12371213632 0021122 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/java/com/googlecode/fannj/ 0000775 0000000 0000000 00000000000 12371213632 0022216 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/java/com/googlecode/fannj/FannTest.java 0000664 0000000 0000000 00000003435 12371213632 0024610 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import java.io.IOException;
import static org.junit.Assert.*;
import org.junit.Test;
import java.io.File;
import java.io.FileOutputStream;
import org.apache.commons.io.IOUtils;
public class FannTest {
@Test
public void testFromFile() throws IOException {
File temp = File.createTempFile("fannj_", ".tmp");
temp.deleteOnExit();
IOUtils.copy(this.getClass().getResourceAsStream("xor_float.net"),
new FileOutputStream(temp));
Fann fann = new Fann(temp.getPath());
assertEquals(2, fann.getNumInputNeurons());
assertEquals(1, fann.getNumOutputNeurons());
assertEquals(-1f, fann.run(new float[]{-1, -1})[0], .2f);
assertEquals(1f, fann.run(new float[]{-1, 1})[0], .2f);
assertEquals(1f, fann.run(new float[]{1, -1})[0], .2f);
assertEquals(-1f, fann.run(new float[]{1, 1})[0], .2f);
fann.close();
}
}
fannj-fannj-0.7/src/test/java/com/googlecode/fannj/FannTrainerTest.java 0000664 0000000 0000000 00000010515 12371213632 0026132 0 ustar 00root root 0000000 0000000 /* FannJ
* Copyright (C) 2009 Kyle Renfro
* 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 3 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
* Library General Public License for more details.
*
* You should have received a copy of the GNU Library 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. The text of license can be also found
* at http://www.gnu.org/copyleft/lgpl.html
*/
package com.googlecode.fannj;
import java.io.File;
import java.io.FileOutputStream;
import java.io.IOException;
import static org.junit.Assert.assertTrue;
import java.util.ArrayList;
import java.util.List;
import org.apache.commons.io.IOUtils;
import org.junit.Test;
public class FannTrainerTest {
@Test
public void testTrainingDefault() throws IOException {
File temp = File.createTempFile("fannj_", ".tmp");
temp.deleteOnExit();
IOUtils.copy(this.getClass().getResourceAsStream("xor.data"),
new FileOutputStream(temp));
List layers = new ArrayList();
layers.add(Layer.create(2));
layers.add(Layer.create(3, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
layers.add(Layer.create(1, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
Fann fann = new Fann(layers);
Trainer trainer = new Trainer(fann);
float desiredError = .001f;
float mse = trainer.train(temp.getPath(), 500000, 1000,
desiredError);
assertTrue("" + mse, mse <= desiredError);
}
@Test
public void testTrainingQuickprop() throws IOException {
File temp = File.createTempFile("fannj_", ".tmp");
temp.deleteOnExit();
IOUtils.copy(this.getClass().getResourceAsStream("xor.data"),
new FileOutputStream(temp));
List layers = new ArrayList();
layers.add(Layer.create(2));
layers.add(Layer.create(3, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
layers.add(Layer.create(1, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
Fann fann = new Fann(layers);
Trainer trainer = new Trainer(fann);
trainer.setTrainingAlgorithm(TrainingAlgorithm.FANN_TRAIN_QUICKPROP);
float desiredError = .001f;
float mse = trainer.train(temp.getPath(), 500000, 1000,
desiredError);
assertTrue("" + mse, mse <= desiredError);
}
@Test
public void testTrainingBackprop() throws IOException {
File temp = File.createTempFile("fannj_", ".tmp");
temp.deleteOnExit();
IOUtils.copy(this.getClass().getResourceAsStream("xor.data"),
new FileOutputStream(temp));
List layers = new ArrayList();
layers.add(Layer.create(2));
layers.add(Layer.create(3, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
layers.add(Layer.create(2, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
layers.add(Layer.create(1, ActivationFunction.FANN_SIGMOID_SYMMETRIC));
Fann fann = new Fann(layers);
Trainer trainer = new Trainer(fann);
trainer.setTrainingAlgorithm(TrainingAlgorithm.FANN_TRAIN_INCREMENTAL);
float desiredError = .001f;
float mse = trainer.train(temp.getPath(), 500000, 1000,
desiredError);
assertTrue("" + mse, mse <= desiredError);
}
@Test
public void testCascadeTraining() throws IOException {
File temp = File.createTempFile("fannj_", ".tmp");
temp.deleteOnExit();
IOUtils.copy(this.getClass().getResourceAsStream("parity8.train"),
new FileOutputStream(temp));
Fann fann = new FannShortcut(8, 1);
Trainer trainer = new Trainer(fann);
float desiredError = .00f;
float mse = trainer.cascadeTrain(temp.getPath(), 30, 1,
desiredError);
assertTrue("" + mse, mse <= desiredError);
}
}
fannj-fannj-0.7/src/test/resources/ 0000775 0000000 0000000 00000000000 12371213632 0017326 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/resources/com/ 0000775 0000000 0000000 00000000000 12371213632 0020104 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/resources/com/googlecode/ 0000775 0000000 0000000 00000000000 12371213632 0022213 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/ 0000775 0000000 0000000 00000000000 12371213632 0023307 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/jna/ 0000775 0000000 0000000 00000000000 12371213632 0024057 5 ustar 00root root 0000000 0000000 fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/jna/xor.data 0000664 0000000 0000000 00000000044 12371213632 0025520 0 ustar 00root root 0000000 0000000 4 2 1
-1 -1
-1
-1 1
1
1 -1
1
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-1
fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/parity8.train 0000664 0000000 0000000 00000012011 12371213632 0025741 0 ustar 00root root 0000000 0000000 256 8 1
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fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/xor.data 0000664 0000000 0000000 00000000044 12371213632 0024750 0 ustar 00root root 0000000 0000000 4 2 1
-1 -1
-1
-1 1
1
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1
1 1
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fannj-fannj-0.7/src/test/resources/com/googlecode/fannj/xor_float.net 0000664 0000000 0000000 00000003573 12371213632 0026024 0 ustar 00root root 0000000 0000000 FANN_FLO_2.1
num_layers=3
learning_rate=0.700000
connection_rate=1.000000
network_type=0
learning_momentum=0.000000
training_algorithm=2
train_error_function=1
train_stop_function=1
cascade_output_change_fraction=0.010000
quickprop_decay=-0.000100
quickprop_mu=1.750000
rprop_increase_factor=1.200000
rprop_decrease_factor=0.500000
rprop_delta_min=0.000000
rprop_delta_max=50.000000
rprop_delta_zero=0.100000
cascade_output_stagnation_epochs=12
cascade_candidate_change_fraction=0.010000
cascade_candidate_stagnation_epochs=12
cascade_max_out_epochs=150
cascade_max_cand_epochs=150
cascade_num_candidate_groups=2
bit_fail_limit=9.99999977648258209229e-03
cascade_candidate_limit=1.00000000000000000000e+03
cascade_weight_multiplier=4.00000005960464477539e-01
cascade_activation_functions_count=10
cascade_activation_functions=3 5 7 8 10 11 14 15 16 17
cascade_activation_steepnesses_count=4
cascade_activation_steepnesses=2.50000000000000000000e-01 5.00000000000000000000e-01 7.50000000000000000000e-01 1.00000000000000000000e+00
layer_sizes=3 4 2
scale_included=0
neurons (num_inputs, activation_function, activation_steepness)=(0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (0, 0, 0.00000000000000000000e+00) (3, 5, 1.00000000000000000000e+00) (3, 5, 1.00000000000000000000e+00) (3, 5, 1.00000000000000000000e+00) (0, 5, 1.00000000000000000000e+00) (4, 5, 1.00000000000000000000e+00) (0, 5, 1.00000000000000000000e+00)
connections (connected_to_neuron, weight)=(0, -2.22661828994750976562e+00) (1, 1.85336101055145263672e+00) (2, -1.52579700946807861328e+00) (0, -1.53036403656005859375e+00) (1, 2.32853889465332031250e+00) (2, 1.64283800125122070312e+00) (0, -9.78324294090270996094e-01) (1, -1.17475807666778564453e+00) (2, -1.95483958721160888672e+00) (3, 3.02897882461547851562e+00) (4, -2.84859561920166015625e+00) (5, -1.93521940708160400391e+00) (6, 1.01287376880645751953e+00)