neural_network 0.0.4 
neural_network: ^0.0.4 copied to clipboard
Library for creating artificial neural networks.
// Copyright (c) 2014, <Alvaro Arcas Garcia>. All rights reserved. Use of this source code
// is governed by a BSD-style license that can be found in the LICENSE file.
library neural_network.example;
import 'package:neural_network/neural_network.dart';
main() {
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////////////////////////////// DATASET /////////////////////////////////////////
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//Create a new dataset
// The first parameter is the title of the dataSet.
// The second parameter sets the number of attributes of the dataset.
// The third parameter is optional and indicates the number of class values of the dataset.
// In this case the dataset is suspervised because it has class values (2).
DataSet supervisedDataSetExample = new DataSet("SupervisedDataSetTest", 2, numClassValues: 2);
// For an unsupervised dataset dont use the optional parameter.
DataSet unsupervisedDataSetExample = new DataSet("UnSupervisedDataSetTest", 2);
//
// You can set the labels of the attributes and class values. Be sure that the number of labels is the same as the
// number of attributes and class values. It is not equal an exception will be throw.
//
supervisedDataSetExample.labels = ["First", "Second", "Third", "Fourth"];
unsupervisedDataSetExample.labels = ["First", "Second"];
//
// Adding instances to the dataset. The number of values in the list must be the same as the number of values and class values.
// If not an exception will be throw.If you add an instance that is already in the dataset, this instance will not be added.
//
supervisedDataSetExample.addInstance([1.0,1.1,1.2,1.3]);
unsupervisedDataSetExample.addInstance([1.0,1.1]);
//
// You can set if the new instance for add will be for train or for test.
//
supervisedDataSetExample.addInstance([0.0,0.1,0.2,0.3], true); // For train
unsupervisedDataSetExample.addInstance([0.0,0.1], false); // For test
//
// Adding multiple instance. All instance must have the same number of values as the number of attributes and class values
// of the dataset.
//
supervisedDataSetExample.addInstances([[2.0,2.1,2.2,2.3],[3.0,3.1,3.2,3.3]]);
unsupervisedDataSetExample.addInstances([[2.0,2.1],[3.0,3.1]]);
//
// Get all the values for all instances.
//
List<List<double>> instancesSupervisedValues = supervisedDataSetExample.instancesValues;
List<List<double>> instancesUnsupervisedValues = unsupervisedDataSetExample.instancesValues;
//
// Get attributes values for an instance. Just provide the index of the instance.
//
List<double> instanceAttributesSupervised = supervisedDataSetExample.instanceValues(0);
List<double> instanceAttributesUnsupervised = unsupervisedDataSetExample.instanceValues(0);
//
// Get class values for an instance. Just provide the index of the instance. The dataSet must be supervised.
//
List<double> instanceClassValuesSupervised = supervisedDataSetExample.instanceClassValues(0);
//
// Set the number of instance for train and the instance for test.
//
supervisedDataSetExample.trainTestSet = 2; /// The dataset has 3 instances. 2 will be for train and 1 for test.
unsupervisedDataSetExample.trainTestSet = 2; /// The dataset has 3 instances. 2 will be for train and 1 for test.
//
// Getting all instance used for train.
//
List <Instance> trainSupervisedDataSet = supervisedDataSetExample.trainSet;
List <Instance> trainUnsupervisedDataSet = unsupervisedDataSetExample.trainSet;
//
// Getting all instance used for test.
//
List <Instance> testSupervisedDataSet = supervisedDataSetExample.testSet;
List <Instance> testUnsupervisedDataSet = unsupervisedDataSetExample.testSet;
//
// Removing instances. You can remove one instance or all instance from the dataSet.
// For removing one instance just provide the index of that instance.
//
supervisedDataSetExample.removeInstance(3);
unsupervisedDataSetExample.removeInstance(2);
supervisedDataSetExample.removeAllInstances();
unsupervisedDataSetExample.removeAllInstances();
//
// DataSet info. You can get a lot of information about the dataset.
//
supervisedDataSetExample.addInstances([[2.0,2.1,2.2,2.3],[3.0,3.1,3.2,3.3]]);
unsupervisedDataSetExample.addInstances([[2.0,2.1],[3.0,3.1]]);
// Get the number of attributes.
int numAttributesSupervised = supervisedDataSetExample.numValues;
int numAttributesUnsupervised = unsupervisedDataSetExample.numValues;
// Get the number of class values. If the number of class values is 0, the dataset will be unsupervised.
int numClassValuesSupervised = supervisedDataSetExample.numClassValues;
int numClassValuesUnsupervised = unsupervisedDataSetExample.numClassValues; //Its 0
// Get if the dataset is supervised. If true, the dataset is supervised, if not the dataset is unsupervised.
supervisedDataSetExample.isSupervised; //True
unsupervisedDataSetExample.isSupervised; //False
// Get the minimum value present in each attribute or class value. If there are no instances it will return an empty array.
List <double> minValuesSupervised = supervisedDataSetExample.minValues;
List <double> minValuesUnsupervised = unsupervisedDataSetExample.minValues;
// Get the maximum value present in each attribute or class value. If there are no instances it will return an empty array.
List <double> maxValuesSupervised = supervisedDataSetExample.maxValues;
List <double> maxValuesUnsupervised = unsupervisedDataSetExample.maxValues;
// Get the mean value in each attribute or class value. If there are no instances it will return an empty array.
List <double> meanValuesSupervised = supervisedDataSetExample.meanValues;
List <double> meanValuesUnsupervised = unsupervisedDataSetExample.meanValues;
// Filters
//
// This library constains 2 basic filters that you can apply to a dataset.
//
//
// Normalization filter. If there are no instances it will throw an exception.
//
NormalizationFilter normalization = new NormalizationFilter();
supervisedDataSetExample.instances = normalization.applyFilter(supervisedDataSetExample);
unsupervisedDataSetExample.instances = normalization.applyFilter(unsupervisedDataSetExample);
//
// Randomization filter. If there are no instances it will throw an exception.
//
RandomizeFilter random = new RandomizeFilter();
supervisedDataSetExample.instances = random.applyFilter(supervisedDataSetExample);
unsupervisedDataSetExample.instances = random.applyFilter(unsupervisedDataSetExample);
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
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////////////////////////////// NETWORKS /////////////////////////////////////////
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// Adaline network.
// The first parameter determines the number of cells input from the network.
// The second parameter sets the maximum number of iterations of the network during learning.
Adaline adalineNetwork = new Adaline(3,100);
// Learn Process
// Set the learning rate.
adalineNetwork.learningRule.learningRate = 0.01;
// Learn
adalineNetwork.learningRule.learn(supervisedDataSetExample); //The dataset must be supervised
// Test your network with the test instance of your dataSet.
TestingRule testRuleAdaline = new TestingRule(new MeanSquareError(),adalineNetwork);
testRuleAdaline.test(supervisedDataSetExample);
List<List<double>>outputNetworkTestAdaline = testRuleAdaline.outputNetworkTest;
List<double> errorTestAdaline = testRuleAdaline.errorTests;
// Get the error of all iterations.
List<double> errorIterationsAdaline = (adalineNetwork.learningRule as BasicLearningRule).errorIterations;
// Get the outputs of the network in the learn process.
List<List<double>> outputNetworkTrainAdaline = (adalineNetwork.learningRule as BasicLearningRule).outputNetworkTrain;
// You can set multiples stop conditions
adalineNetwork.learningRule.addStopCondition(new MinError(0.01, adalineNetwork.learningRule));
adalineNetwork.learningRule.addStopCondition(new WeightVariation(0.05, adalineNetwork.learningRule));
// Simple Perceptron network.
// The first parameter determines the number of cells input from the network.
// The second parameter sets the maximum number of iterations of the network during learning.
Perceptron simplePerceptron = new Perceptron(3,100);
// Learn Process
// Set the learning rate.
simplePerceptron.learningRule.learningRate = 0.01;
// Learn
simplePerceptron.learningRule.learn(supervisedDataSetExample); //The dataset must be supervised. The class value has to ve 1.o for one class and -1 for the other.
// Get the error of all iterations.
List<double> errorIterationsPerceptron = (simplePerceptron.learningRule as BasicLearningRule).errorIterations;
// Get the outputs of the network in the learn process.
List<List<double>> outputNetworkTrainPerceptron = (simplePerceptron.learningRule as BasicLearningRule).outputNetworkTrain;
// Test your network with the test instance of your dataSet.
TestingRule testRuleSimplePerceptron = new TestingRule(new MeanSquareError(),simplePerceptron);
testRuleSimplePerceptron.test(supervisedDataSetExample);
List<List<double>>outputNetworkTestSimplePerceptron = testRuleSimplePerceptron.outputNetworkTest;
List<double> errorTestSimplePerceptron = testRuleSimplePerceptron.errorTests;
// You can set multiples stop conditions to learn process
simplePerceptron.learningRule.addStopCondition(new MinError(0.01, simplePerceptron.learningRule));
simplePerceptron.learningRule.addStopCondition(new WeightVariation(0.05, simplePerceptron.learningRule));
// Multilayer Perceptron
// The first parameter is a list of integer values. Each value indicates the number of neurons in one layer. The lenght
// of the list indicates the number of network layers.
// The second parameter sets the maximum number of iterations of the network during learning.
MultilayerPerceptron multilayerPerceptron = new MultilayerPerceptron([3,3,3,3,3,1], 100);
multilayerPerceptron.learningRule.learn(supervisedDataSetExample); //Supervised dataset needed.
// Get the error of all iterations.
List<double> errorIterationsMultilayer = (multilayerPerceptron.learningRule as BackPropagationLearningRule).errorIterations;
// Test your network with the test instance of your dataSet.
TestingRule testRuleMultilayerPerceptron = new TestingRule(new MeanSquareError(),multilayerPerceptron);
testRuleMultilayerPerceptron.test(supervisedDataSetExample);
List<List<double>>outputNetworkTestMultilayerPerceptron = testRuleMultilayerPerceptron.outputNetworkTest;
List<double> errorTestMultilayerPerceptron = testRuleMultilayerPerceptron.errorTests;
// Radial base network
DataSet dataSetTest = new DataSet("TEST", 8,numClassValues: 1);
List<List<double>> instances = [[0.908675799, 0.0, 0.0, 0.62460063, 0.0, 0.941860465, 0.047666837, 0.0, 0.128441513], [0.291552505, 0.0, 0.590704615, 0.586261947, 0.189440988, 0.660174349, 0.410436492, 0.035714286, 0.249034515], [0.0, 0.425709523, 0.0, 0.560702865, 0.0, 0.25, 0.873055747, 0.074175824, 0.186246435], [0.420776228, 0.558987194, 0.0, 0.182907305, 0.347826076, 0.591860395, 0.526091381, 0.016483516, 0.444375245], [0.202511409, 0.0, 0.626686648, 0.321885016, 0.242236017, 0.840116279, 0.526843986, 0.035714286, 0.233337479], [0.633561644, 0.420701167, 0.0, 0.256389711, 0.493788805, 0.968895491, 0.02759659, 0.005494505, 0.32727047], [0.634703196, 0.264329442, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 1.0, 0.515385599], [0.342009139, 0.0, 0.49925037, 0.194089485, 0.385093156, 0.595930233, 0.767185164, 0.005494505, 0.232839174], [0.502283105, 0.0, 0.0, 0.648562292, 0.0, 0.502906977, 0.516808862, 0.491758242, 0.339603846], [0.337899543, 0.0, 0.0, 0.480830658, 0.0, 0.869186047, 0.56698448, 0.074175824, 0.230970493], [0.341095877, 0.0, 0.591204395, 0.532747593, 0.180124218, 0.661046581, 0.410687461, 0.151098901, 0.431543564], [0.089726034, 0.589872019, 0.0, 0.652555902, 0.0, 0.496511593, 0.387606647, 0.244505495, 0.465055449], [0.48173516, 0.403450202, 0.0, 0.041533523, 0.248447197, 0.578488372, 0.572002041, 0.074175824, 0.525601109], [0.52283105, 0.0, 0.0, 0.560702865, 0.0, 0.651162791, 0.569493261, 0.016483516, 0.188239708], [0.46803653, 0.0, 0.0, 0.568690086, 0.0, 0.485465116, 0.546914233, 0.244505495, 0.38108881], [0.650684932, 0.055648304, 0.469765103, 0.281150143, 0.360248436, 0.398255814, 0.629704002, 0.005494505, 0.404136051], [0.430136973, 0.0, 0.480759591, 0.36980834, 0.291925457, 0.465697709, 0.679879619, 0.005494505, 0.251276947], [0.085844763, 0.582637723, 0.0, 0.560702865, 0.0, 0.715116279, 0.53411951, 0.074175824, 0.322785607], [0.474885845, 0.0, 0.0, 0.560702865, 0.0, 0.613372093, 0.592072289, 0.016483516, 0.185748105], [0.105251155, 0.0, 0.68265868, 0.289936133, 0.499999984, 0.583720895, 0.592323106, 0.074175824, 0.19471782], [0.109589041, 0.658875924, 0.0, 0.415335473, 0.369565206, 0.779942003, 0.202709465, 0.074175824, 0.43727421], [0.639269406, 0.0, 0.0, 0.50479232, 0.0, 0.715116279, 0.363773228, 0.016483516, 0.269963879], [0.228310502, 0.030606567, 0.704647655, 0.672523954, 0.052795029, 0.409883721, 0.519317643, 0.074175824, 0.244674218], [0.101598174, 0.318864774, 0.446276863, 0.639776301, 0.273291917, 0.171511628, 0.590817898, 0.074175824, 0.22000748], [0.908675799, 0.0, 0.0, 0.62460063, 0.0, 0.941860465, 0.047666837, 0.005494505, 0.295627253], [0.526255708, 0.396494164, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 0.244505495, 0.44088703], [0.395205493, 0.0, 0.606696643, 0.301118194, 0.307453407, 0.734302256, 0.460361292, 0.035714286, 0.45396787], [0.399771703, 0.0, 0.486756617, 0.309904185, 0.366459616, 0.502616349, 0.706472637, 0.271978022, 0.664133553], [0.735159817, 0.061213134, 0.659670145, 0.448881776, 0.263975147, 0.235465116, 0.391369818, 0.005494505, 0.369752071], [0.447488584, 0.0, 0.534732618, 0.704472836, 0.341614896, 0.229651163, 0.376317133, 0.074175824, 0.368007995], [0.341095877, 0.0, 0.591204395, 0.532747593, 0.180124218, 0.661046581, 0.410687461, 0.074175824, 0.340351323], [0.152739712, 0.117417923, 0.621189402, 0.291533553, 0.335403716, 0.813372235, 0.507275525, 0.074175824, 0.358664529], [0.095205473, 0.473567056, 0.662668676, 0.557508025, 0.263975147, 0.038081326, 0.530105371, 0.074175824, 0.343092085], [0.132420091, 0.523094054, 0.729635161, 0.648562292, 0.341614896, 0.081395349, 0.291018582, 0.074175824, 0.380092198], [0.182420078, 0.759042828, 0.0, 0.510383351, 0.0, 0.614534953, 0.30180631, 0.016483516, 0.125077865], [0.187214612, 0.239287706, 0.949525209, 0.728434498, 0.186335398, 0.354651163, 0.072754646, 0.074175824, 0.256633867], [0.474885845, 0.0, 0.0, 0.560702865, 0.0, 0.49127907, 0.642247906, 0.986263736, 0.445745632], [0.464155224, 0.56622149, 0.0, 0.652555902, 0.0, 0.477907047, 0.092824893, 0.005494505, 0.147128445], [0.123287671, 0.495269903, 0.934532706, 0.792332263, 0.217391298, 0.154069767, 0.050175618, 0.074175824, 0.337361414], [0.20159818, 0.0, 0.625687123, 0.357827509, 0.307453407, 0.808139535, 0.514049263, 0.005494505, 0.127320297], [0.132420091, 0.695603796, 0.0, 0.36900957, 0.372670796, 0.720930233, 0.235825403, 0.074175824, 0.462439291], [0.420776228, 0.558987194, 0.0, 0.182907305, 0.347826076, 0.591860395, 0.526091381, 0.074175824, 0.814376462], [0.341095877, 0.0, 0.591204395, 0.567891316, 0.180124218, 0.705232488, 0.402157546, 0.035714286, 0.229226367], [0.680365297, 0.0, 0.0, 0.520766762, 0.0, 0.651162791, 0.378825913, 0.016483516, 0.346455708], [0.424657534, 0.534223715, 0.0, 0.560702865, 0.0, 0.380813953, 0.310587043, 0.016483516, 0.26398406], [0.248173509, 0.879521456, 0.0, 0.510383351, 0.0, 0.511627907, 0.239086788, 0.074175824, 0.442008246], [0.594977183, 0.52587647, 0.0, 0.344249138, 0.360248436, 0.417732593, 0.405920717, 0.074175824, 0.859225174], [0.150684932, 0.117139674, 0.818590695, 0.0, 0.177018628, 0.749127765, 0.466884062, 0.271978022, 0.459698529], [0.735159817, 0.061213134, 0.659670145, 0.448881776, 0.263975147, 0.061046512, 0.391369818, 0.151098901, 0.789460553], [0.362785374, 0.279632726, 0.391304351, 0.62939301, 0.267080737, 0.184593023, 0.420220798, 0.074175824, 0.374610723], [0.173515982, 0.361157499, 0.59270362, 0.464057447, 0.111801239, 0.599709267, 0.383341689, 0.035714286, 0.39753336], [0.394977169, 0.500834733, 0.599700132, 0.321086246, 0.322981356, 0.084302326, 0.429001531, 0.074175824, 0.920767408], [0.737442922, 0.295770742, 0.0, 0.25319487, 0.512422344, 0.148546442, 0.735323617, 0.016483516, 0.58390435], [0.248173509, 0.879521456, 0.0, 0.510383351, 0.0, 0.511627907, 0.239086788, 0.016483516, 0.242805537], [0.54109589, 0.0, 0.0, 0.600638968, 0.0, 0.485465116, 0.469142025, 0.074175824, 0.370125847], [0.863013699, 0.0, 0.0, 0.560702865, 0.0, 0.393023291, 0.296537931, 0.244505495, 0.647689066], [0.181278525, 0.0, 0.834582684, 0.38178917, 0.236024837, 0.740116209, 0.461113896, 0.151098901, 0.414102415], [0.146347046, 0.0, 0.816091945, 0.436900945, 0.139751548, 0.74883714, 0.466884062, 0.035714286, 0.288401652], [0.785388128, 0.066777964, 0.394802587, 0.321086246, 0.360248436, 0.48255814, 0.296036144, 0.016483516, 0.460570573], [0.163926947, 0.259877584, 0.799100396, 0.403354642, 0.301242227, 0.59941864, 0.382839903, 0.035714286, 0.339105515], [0.785388128, 0.066777964, 0.394802587, 0.321086246, 0.319875766, 0.48255814, 0.296036144, 0.074175824, 0.636103155], [0.341095877, 0.0, 0.591204395, 0.532747593, 0.198757758, 0.661046581, 0.410687461, 0.074175824, 0.377849766], [0.634703196, 0.264329442, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 0.244505495, 0.476267621], [0.220547959, 0.368391754, 0.0, 0.560702865, 0.0, 0.515697744, 0.580782775, 0.244505495, 0.445247301], [0.625570776, 0.0, 0.0, 0.741214099, 0.0, 0.588662791, 0.422478761, 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[0.452054795, 0.0, 0.0, 0.4968051, 0.0, 0.796511628, 0.504264958, 0.016483516, 0.1650679], [0.623287671, 0.260990552, 0.0, 0.038338619, 0.726708052, 0.148546442, 1.0, 0.074175824, 0.67733901], [0.657305922, 0.52587647, 0.0, 0.192491946, 0.683229792, 0.417732593, 0.405920717, 0.005494505, 0.476765902], [0.538584461, 0.52587647, 0.0, 0.424121344, 0.295031047, 0.417732593, 0.405920717, 0.005494505, 0.271209674], [0.529680365, 0.048970507, 0.789605174, 0.536741203, 0.475155265, 0.48255814, 0.097842454, 0.074175824, 0.523234123], [0.337899543, 0.0, 0.47826084, 0.523961602, 0.170807448, 0.453197744, 0.670346282, 0.035714286, 0.281425197], [0.111872146, 0.0, 0.924537703, 0.361022349, 0.496894395, 0.793604651, 0.210737594, 0.074175824, 0.138657036], [0.673515982, 0.0, 0.0, 0.510383351, 0.0, 0.696511558, 0.351981928, 0.074175824, 0.383082132], [0.547945205, 0.105731777, 0.0, 0.848242808, 0.0, 0.380813953, 0.190667347, 0.244505495, 0.599601348], [0.735159817, 0.061213134, 0.659670145, 0.36900957, 0.276397507, 0.061046512, 0.391369818, 0.016483516, 0.472903973], [0.965753425, 0.0, 0.0, 0.536741203, 0.0, 0.941860465, 0.047666837, 0.005494505, 0.392051822], [0.503424658, 0.413466913, 0.0, 0.511182121, 0.263975147, 0.436046512, 0.289764192, 0.074175824, 0.624143531], [0.310502283, 0.0, 0.0, 0.512779541, 0.0, 0.924418605, 0.489212272, 0.016483516, 0.12109132], [0.228310502, 0.030606567, 0.704647655, 0.672523954, 0.052795029, 0.409883721, 0.519317643, 0.151098901, 0.260620413], [0.337899543, 0.0, 0.47826084, 0.523961602, 0.170807448, 0.453197744, 0.670346282, 0.005494505, 0.1431419], [0.335844763, 0.0, 0.493753124, 0.289936133, 0.397515516, 0.543023221, 0.740090361, 0.271978022, 0.607076146], [0.076940632, 0.56622149, 0.0, 0.510383351, 0.0, 0.799999858, 0.41470145, 0.016483516, 0.064532205], [0.095890411, 0.041736228, 0.974512714, 0.432907335, 0.186335398, 0.639534884, 0.288509802, 0.074175824, 0.162077991], [0.300913249, 0.0, 0.472763594, 0.607827419, 0.142857138, 0.424418605, 0.647767254, 0.035714286, 0.24679207], [0.365296804, 0.308848085, 0.429785095, 0.584664527, 0.155279498, 0.273255814, 0.348720543, 0.074175824, 0.391055211], [0.511415525, 0.0, 0.689655152, 0.616613409, 0.341614896, 0.0, 0.496738615, 0.074175824, 0.477762563], [0.447488584, 0.0, 0.534732618, 0.704472836, 0.341614896, 0.229651163, 0.376317133, 0.074175824, 0.368007995], [0.52283105, 0.0, 0.0, 0.560702865, 0.0, 0.226744186, 0.579528384, 0.005494505, 0.139404513], [0.202511409, 0.0, 0.626686648, 0.321885016, 0.242236017, 0.840116279, 0.526843986, 0.035714286, 0.233337479], [0.424657534, 0.534223715, 0.0, 0.560702865, 0.0, 0.380813953, 0.310587043, 0.016483516, 0.26398406], [0.390410959, 0.0, 0.449775099, 0.616613409, 0.341614896, 0.377906977, 0.421475188, 0.074175824, 0.37261745], [0.623287671, 0.260990552, 0.0, 0.038338619, 0.726708052, 0.148546442, 1.0, 0.247252747, 0.749595135], [0.148401826, 0.520311639, 0.974512714, 0.50479232, 0.217391298, 0.281976744, 0.105368797, 0.074175824, 0.268593491], [0.255251148, 0.272954924, 0.122438777, 0.478434468, 0.208074528, 0.770348837, 0.480431539, 0.271978022, 0.593496975], [0.252511429, 0.0, 0.50174912, 0.300319424, 0.322981356, 0.589534849, 0.777220288, 0.151098901, 0.52410618], [0.566210046, 0.0, 0.0, 0.648562292, 0.0, 0.502906977, 0.45408934, 0.074175824, 0.311573447], [0.116438356, 0.283806349, 0.0, 0.560702865, 0.0, 0.252906977, 0.875815346, 0.244505495, 0.298866334], [0.640410959, 0.0, 0.0, 0.510383351, 0.0, 0.717442003, 0.364525832, 0.016483516, 0.270835935], [0.594977183, 0.52587647, 0.0, 0.344249138, 0.360248436, 0.417732593, 0.405920717, 0.005494505, 0.410738754], [0.070776256, 0.556483037, 0.0, 0.560702865, 0.0, 0.36744193, 0.615153103, 0.016483516, 0.141148627], [0.341095877, 0.0, 0.591204395, 0.532747593, 0.180124218, 0.661046581, 0.410687461, 0.151098901, 0.431543564], [0.769406393, 0.492487487, 0.0, 0.512779541, 0.344720486, 0.243895419, 0.285750203, 0.247252747, 0.884141083], [0.223744292, 0.370061219, 0.0, 0.560702865, 0.0, 0.477907047, 0.532363334, 0.016483516, 0.184876048], [0.633561644, 0.420701167, 0.0, 0.256389711, 0.493788805, 0.968895491, 0.02759659, 0.005494505, 0.32727047], [0.737442922, 0.295770742, 0.0, 0.25319487, 0.512422344, 0.148546442, 0.735323617, 0.005494505, 0.387068678], [0.52283105, 0.0, 0.0, 0.560702865, 0.0, 0.514534884, 0.579528384, 0.005494505, 0.139404513], [0.577625571, 0.052865888, 0.484757607, 0.185303495, 0.406832286, 0.48255814, 0.694932305, 0.074175824, 0.519496699], [0.762557078, 0.0, 0.0, 0.768370601, 0.0, 0.108721, 0.315353787, 0.074175824, 0.268095185], [0.280821918, 0.0, 0.0, 0.472843438, 0.0, 0.906976744, 0.599598631, 0.016483516, 0.11012832], [0.482420064, 0.729549294, 0.0, 0.428913724, 0.267080737, 0.714825651, 0.04465627, 0.016483516, 0.504173415], [0.863013699, 0.0, 0.0, 0.560702865, 0.0, 0.393023291, 0.296537931, 0.005494505, 0.274947061], [0.785388128, 0.066777964, 0.394802587, 0.321086246, 0.319875766, 0.48255814, 0.296036144, 0.151098901, 0.653295145], [0.634703196, 0.264329442, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 0.491758242, 0.478759175], [0.296347039, 0.0, 0.607696124, 0.416932893, 0.208074528, 0.74244193, 0.462870073, 0.271978022, 0.54204561], [0.474885845, 0.0, 0.0, 0.560702865, 0.0, 0.494186047, 0.643753115, 0.005494505, 0.093932979], [0.500913228, 0.0, 0.639180401, 0.484824269, 0.357142846, 0.200872023, 0.46588064, 0.074175824, 0.435280938], [0.54109589, 0.0, 0.0, 0.600638968, 0.0, 0.485465116, 0.469142025, 0.074175824, 0.370125847], [0.068493151, 0.575959943, 0.804597677, 0.456868997, 0.155279498, 0.191860465, 0.356246885, 0.074175824, 0.385822845], [0.143835616, 0.357540351, 0.660169925, 0.425718884, 0.251552787, 0.595348802, 0.382839903, 0.005494505, 0.212906446], [0.118721461, 0.484140242, 0.924537703, 0.848242808, 0.217391298, 0.127906977, 0.045158056, 0.074175824, 0.274199583], [0.737442922, 0.295770742, 0.0, 0.25319487, 0.512422344, 0.148546442, 0.735323617, 0.074175824, 0.722063068], [0.337899543, 0.0, 0.47826084, 0.559105445, 0.164596268, 0.42994193, 0.660311159, 0.151098901, 0.464806278], [0.296347039, 0.0, 0.607696124, 0.416932893, 0.208074528, 0.74244193, 0.462870073, 0.005494505, 0.163323786], [0.625570776, 0.0, 0.0, 0.741214099, 0.0, 0.588662791, 0.422478761, 0.035714286, 0.290145778], [0.851598174, 0.330550932, 0.0, 0.473642208, 0.276397507, 0.148546442, 0.470396416, 0.016483516, 0.663635223], [0.310730607, 0.0, 0.0, 0.510383351, 0.0, 0.923837352, 0.489964876, 0.074175824, 0.189983809], [0.563926941, 0.0, 0.0, 0.560702865, 0.0, 0.715116279, 0.531861547, 0.074175824, 0.378597256], [0.673515982, 0.0, 0.0, 0.50479232, 0.0, 0.694767442, 0.351229324, 0.074175824, 0.457954415], [0.502283105, 0.0, 0.0, 0.648562292, 0.0, 0.502906977, 0.516808862, 0.074175824, 0.284664265], [0.464155224, 0.56622149, 0.0, 0.652555902, 0.0, 0.477907047, 0.092824893, 0.005494505, 0.147128445], [0.547945205, 0.105731777, 0.0, 0.848242808, 0.0, 0.380813953, 0.190667347, 0.739010989, 0.656907965], [0.093607306, 0.471341115, 0.713143392, 0.550319454, 0.260869557, 0.483721, 0.124184654, 0.074175824, 0.341347959], [0.63789953, 0.0, 0.0, 0.510383351, 0.0, 0.88255807, 0.477420972, 0.016483516, 0.152111627], [0.908675799, 0.0, 0.0, 0.62460063, 0.0, 0.941860465, 0.047666837, 0.244505495, 0.5592376], [0.494977142, 0.591263226, 0.0, 0.270766732, 0.444099365, 0.148546442, 0.718514905, 0.016483516, 0.387068678], [1.0, 0.0, 0.0, 0.408945673, 0.0, 0.941860465, 0.047666837, 0.244505495, 0.838794141], [0.211643829, 0.0, 0.502248861, 0.386581431, 0.232919247, 0.572674419, 0.772202726, 0.271978022, 0.522611188], [0.594977183, 0.52587647, 0.0, 0.344249138, 0.360248436, 0.417732593, 0.405920717, 0.247252747, 0.958888812], [0.405936046, 0.3586533, 0.50174912, 0.404153292, 0.295031047, 0.07005807, 0.529101949, 0.074175824, 0.629126738], [0.563926941, 0.0, 0.0, 0.560702865, 0.0, 0.74127907, 0.53938789, 0.244505495, 0.477513404], [0.105251155, 0.0, 0.68265868, 0.289936133, 0.499999984, 0.583720895, 0.592323106, 0.074175824, 0.19471782], [0.664383562, 0.0, 0.0, 0.560702865, 0.0, 0.404069767, 0.411440065, 0.074175824, 0.464058838], [0.362785374, 0.279632726, 0.391304351, 0.62939301, 0.267080737, 0.184593023, 0.420220798, 0.074175824, 0.374610723], [0.121004566, 0.511964394, 0.714642657, 0.576677306, 0.279503097, 0.229651163, 0.263421993, 0.074175824, 0.332129073], [0.426940639, 0.0, 0.0, 0.560702865, 0.0, 0.326162826, 0.75589565, 0.244505495, 0.370499573], [0.076940632, 0.56622149, 0.0, 0.510383351, 0.0, 0.799999858, 0.41470145, 0.074175824, 0.197707754], [0.54109589, 0.0, 0.0, 0.50479232, 0.0, 0.779069767, 0.401404941, 0.016483516, 0.234583287], [0.395205493, 0.0, 0.606696643, 0.301118194, 0.307453407, 0.734302256, 0.460361292, 0.074175824, 0.610439806], [0.594977183, 0.52587647, 0.0, 0.344249138, 0.360248436, 0.417732593, 0.405920717, 0.016483516, 0.667372659], [0.445662128, 0.0, 0.587206379, 0.423322694, 0.295031047, 0.644767407, 0.400150551, 0.151098901, 0.61828828], [0.107305936, 0.656649983, 0.0, 0.432907335, 0.403726696, 0.13372093, 0.750125484, 0.074175824, 0.38158714], [0.337899543, 0.0, 0.47826084, 0.523961602, 0.170807448, 0.453197744, 0.670346282, 0.074175824, 0.33499439], [0.107305936, 0.328324992, 0.459770101, 0.488817879, 0.217391298, 0.441860465, 0.466633244, 0.074175824, 0.26398406], [0.474885845, 0.0, 0.0, 0.560702865, 0.0, 0.49127907, 0.642247906, 0.491758242, 0.43602844], [0.534246575, 0.0, 0.0, 0.480830658, 0.093167699, 0.537790698, 0.559458137, 0.074175824, 0.529836826], [0.474885845, 0.0, 0.0, 0.560702865, 0.0, 0.49127907, 0.642247906, 0.016483516, 0.157717707], [0.310730607, 0.0, 0.470264843, 0.518370572, 0.217391298, 0.432848907, 0.634721564, 0.151098901, 0.464183393], [0.257762543, 0.149693934, 0.609195394, 0.269968082, 0.298136637, 0.620058105, 0.468138453, 0.074175824, 0.621278214], [0.945205479, 0.0, 0.0, 0.321086246, 0.254658377, 0.0, 0.521826424, 0.074175824, 0.48635854], [0.785388128, 0.066777964, 0.394802587, 0.321086246, 0.360248436, 0.48255814, 0.296036144, 0.005494505, 0.282670992], [0.265296797, 0.151919863, 0.618690651, 0.151757192, 0.369565206, 0.798546369, 0.498494792, 0.005494505, 0.312570084], [0.192237436, 0.345297719, 0.0, 0.510383351, 0.0, 0.820930302, 0.427245354, 0.016483516, 0.070636603], [0.223744292, 0.0, 0.0, 0.464856217, 0.0, 0.941860465, 0.629704002, 0.074175824, 0.123582911], [0.182420078, 0.759042828, 0.0, 0.510383351, 0.0, 0.614534953, 0.30180631, 0.016483516, 0.125077865], [0.251141553, 0.0, 0.623688152, 0.297124584, 0.242236017, 0.826744256, 0.515554472, 0.151098901, 0.532577589], [0.888127854, 0.072342795, 0.614692635, 0.704472836, 0.121118009, 0.235465116, 0.263421993, 0.151098901, 0.713342487], [0.851598174, 0.0, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 0.016483516, 0.451849993], [0.735159817, 0.061213134, 0.659670145, 0.448881776, 0.263975147, 0.061046512, 0.391369818, 0.151098901, 0.789460553], [0.136986301, 0.528658885, 0.739630163, 0.456868997, 0.590062093, 0.10755814, 0.36879079, 0.074175824, 0.391553492], [0.945205479, 0.0, 0.0, 0.321086246, 0.257763967, 0.0, 0.521826424, 0.074175824, 0.48635854], [0.136986301, 0.575959943, 0.859570189, 0.75239616, 0.310558997, 0.061046512, 0.110386359, 0.074175824, 0.467297881], [0.743150685, 0.132164721, 0.0, 0.848242808, 0.0, 0.380813953, 0.0, 0.491758242, 0.492213791], [0.888127854, 0.072342795, 0.614692635, 0.704472836, 0.121118009, 0.235465116, 0.263421993, 0.005494505, 0.290021198], [0.186986288, 0.341124096, 0.0, 0.652555902, 0.0, 0.459883756, 0.516808862, 0.005494505, 0.032016944], [0.141552511, 0.0, 0.99950022, 0.472843438, 0.403726696, 0.139534884, 0.632212783, 0.074175824, 0.158963502], [0.202511409, 0.0, 0.626686648, 0.321885016, 0.242236017, 0.840116279, 0.526843986, 0.074175824, 0.29986297], [0.300913249, 0.0, 0.472763594, 0.607827419, 0.142857138, 0.424418605, 0.647767254, 0.005494505, 0.100286535], [0.108447489, 0.656649983, 0.0, 0.431309914, 0.391304336, 0.1331395, 0.74937288, 0.074175824, 0.38158714], [0.116438356, 0.664997229, 0.0, 0.62460063, 0.186335398, 0.584302326, 0.22579028, 0.074175824, 0.305593642], [0.586757991, 0.052865888, 0.704647655, 0.257188481, 0.338509306, 0.409883721, 0.519317643, 0.074175824, 0.712096716], [0.255251148, 0.272954924, 0.122438777, 0.478434468, 0.208074528, 0.770348837, 0.480431539, 0.151098901, 0.558116384], [0.389954311, 0.506121305, 0.0, 0.510383351, 0.0, 0.614534953, 0.30180631, 0.074175824, 0.361903572]];
dataSetTest.addInstances(instances);
dataSetTest.trainTestSet = 500;
// The first parameter defines the number of neurons in the input layer .
// The second parameter defines the number of neurons in the hidden layer.
// The third parameter refers to the number of neurons in the output layer.
// The last parameter is the maximum number of iterations for learning.
RadialBase radialNetwork = new RadialBase(8,3,1,500);
//An initialization of the network its needed.
List<List<double>> trainInstances = [];
for(int i = 0; i < dataSetTest.instances.length; i++){
if(dataSetTest.instances[i].isForTrain!=null && dataSetTest.instances[i].isForTrain)
trainInstances.add(dataSetTest.instanceValues(i));
}
(radialNetwork.learningRule as RadialLearning).initialization(trainInstances);
//Learn
radialNetwork.learningRule.learningRate = 0.0001;
radialNetwork.learningRule.learn(dataSetTest);
// Test
TestingRule testRule = new TestingRule(new MeanSquareError(),radialNetwork);
testRule.test(dataSetTest);
}Metadata
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Library for creating artificial neural networks.
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BSD-3-Clause (license)
