├── .gitignore ├── LICENSE ├── README.md ├── main.go ├── media ├── first_example.png └── second_example.png ├── model └── neural │ ├── multiLayerNetwork.go │ ├── neuralLayer.go │ ├── neuronUnit.go │ ├── pattern.go │ └── transferFunction.go ├── res ├── iris.all_data.csv └── sonar.all_data.csv ├── util └── util.go └── validation └── validation.go /.gitignore: -------------------------------------------------------------------------------- 1 | # Binaries for programs and plugins 2 | *.exe 3 | *.dll 4 | *.so 5 | *.dylib 6 | 7 | # Test binary, build with `go test -c` 8 | *.test 9 | 10 | # Output of the go coverage tool, specifically when used with LiteIDE 11 | *.out 12 | 13 | # Project-local glide cache, RE: https://github.com/Masterminds/glide/issues/736 14 | .glide/ 15 | .idea/ 16 | run.sh 17 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Go Perceptron 2 | 3 | A single / multi level perceptron classifier with weights estimated from sonar training data set using stochastic gradient descent. Recently I added back propagation algorithm over multilayer perceptron network. 4 | The implementation is in dev. Planned features: 5 | 6 | - complete future features XD (see above) 7 | - find co-workers 8 | - create a ml library in openqasm (just kidding) 9 | - brainstorming / devtesting other an models 10 | 11 | ### Updates 12 | 13 | 2017-10-04: Introduced Recurrent Neural Network (Elman Network) with "learn to sum integer" task. Big refactoring in code (working on) 14 | 15 | 2017-08-08: Introduced multi layer perceptron network definition with parametric number of hidden layer and neurons. Back propagation algorithm with different transfer function actived - I wanna thank you [dakk](https://github.com/dakk) because I was truly inspired by your code. 16 | 17 | 2017-08-01: Introduced validation package and k-fold cross validation. 18 | 19 | 2017-07-31: I started working on ```mlp``` branch for MLP + back prop. It doens't work yet but...I push first commit after some exp in dev. I delete ```dev``` because of some structs optimization. 20 | 21 | 2017-07-31: we started working on k-fold validation. 22 | 23 | ### Dependencies 24 | 25 | - [logrus](https://github.com/sirupsen/logrus) 26 | 27 | ### Run test 28 | 29 | To run a simple test just open a shell and run the following: 30 | 31 | ``` 32 | git clone https://github.com/made2591/go-perceptron-go 33 | cd go-perceptron-go 34 | go get github.com/sirupsen/logrus 35 | go run main.go 36 | ``` 37 | 38 | You can setup a MultiLayerPerceptron using ```PrepareMLPNet```. The first parameter, a simple ```[]int```, define the entire network struct. Example: 39 | 40 | - [4, 3, 3] will define a network struct with 3 layer: input, hidden, output, with respectively 4, 3 and 3 neurons. For classification problems the input layers has to be define with a number of neurons that match features of pattern shown to network. Of course, the output layer should have a number of unit equals to the number of class in training set. 41 | The network will have this topology: 42 | 43 | ![](./media/first_example.png) 44 | 45 | - [4, 6, 4, 3] will have this topology: 46 | 47 | ![](./media/second_example.png) 48 | 49 | ### To complete yet 50 | 51 | - test methods 52 | 53 | ### Future features 54 | 55 | - [mathgl](https://github.com/go-gl/mathgl.git) for better vector space handling 56 | - some other cool neural model XD 57 | -------------------------------------------------------------------------------- /main.go: -------------------------------------------------------------------------------- 1 | // Main package provide main to test library 2 | package main 3 | 4 | import ( 5 | // sys import 6 | "os" 7 | 8 | // third part import 9 | log "github.com/sirupsen/logrus" 10 | 11 | // this repo internal import 12 | mn "github.com/made2591/go-perceptron-go/model/neural" 13 | mu "github.com/made2591/go-perceptron-go/util" 14 | v "github.com/made2591/go-perceptron-go/validation" 15 | ) 16 | 17 | func init() { 18 | // Output to stdout instead of the default stderr 19 | log.SetOutput(os.Stdout) 20 | // Only log the warning severity or above. 21 | log.SetLevel(log.InfoLevel) 22 | } 23 | 24 | //############################ MAIN ############################ 25 | 26 | func main() { 27 | 28 | // ############################################################################################################# 29 | // ###################################### Single layer perceptron model ###################################### 30 | // ############################################################################################################# 31 | 32 | if true { 33 | 34 | log.WithFields(log.Fields{ 35 | "level": "info", 36 | "place": "main", 37 | "msg": "single layer perceptron train and test over sonar dataset", 38 | }).Info("Compute single layer perceptron on sonar data set (binary classification problem)") 39 | 40 | // percentage and shuffling in dataset 41 | var filePath string = "./res/sonar.all_data.csv" 42 | var percentage float64 = 0.67 43 | var shuffle = 1 44 | 45 | // single layer neuron parameters 46 | var bias float64 = 0.0 47 | var learningRate float64 = 0.01 48 | 49 | // training parameters 50 | var epochs int = 500 51 | var folds int = 5 52 | 53 | // Patterns initialization 54 | var patterns, _, _ = mn.LoadPatternsFromCSVFile(filePath) 55 | 56 | // NeuronUnit initialization 57 | var neuron mn.NeuronUnit = mn.NeuronUnit{Weights: make([]float64, len(patterns[0].Features)), Bias: bias, Lrate: learningRate} 58 | 59 | // compute scores for each folds execution 60 | var scores []float64 = v.KFoldValidation(&neuron, patterns, epochs, folds, shuffle) 61 | 62 | // use simpler validation 63 | var neuron2 mn.NeuronUnit = mn.NeuronUnit{Weights: make([]float64, len(patterns[0].Features)), Bias: bias, Lrate: learningRate} 64 | var scores2 []float64 = v.RandomSubsamplingValidation(&neuron2, patterns, percentage, epochs, folds, shuffle) 65 | 66 | log.WithFields(log.Fields{ 67 | "level": "info", 68 | "place": "main", 69 | "scores": scores, 70 | }).Info("Scores reached: ", scores) 71 | 72 | log.WithFields(log.Fields{ 73 | "level": "info", 74 | "place": "main", 75 | "scores": scores2, 76 | }).Info("Scores reached: ", scores2) 77 | 78 | } 79 | 80 | // ############################################################################################################# 81 | // ###################################### Multilayer perceptron model ######################################## 82 | // ############################################################################################################# 83 | 84 | if false { 85 | 86 | log.WithFields(log.Fields{ 87 | "level": "info", 88 | "place": "main", 89 | "msg": "multi layer perceptron train and test over iris dataset", 90 | }).Info("Compute backpropagation multi layer perceptron on sonar data set (binary classification problem)") 91 | 92 | // percentage and shuffling in dataset 93 | var filePath = "./res/iris.all_data.csv" 94 | //filePath = "./res/sonar.all_data.csv" 95 | 96 | // single layer neuron parameters 97 | var learningRate = 0.01 98 | var percentage = 0.67 99 | var shuffle = 1 100 | 101 | // training parameters 102 | var epochs = 500 103 | var folds = 3 104 | 105 | // Patterns initialization 106 | var patterns, _ , mapped = mn.LoadPatternsFromCSVFile(filePath) 107 | 108 | //input layer : 4 neuron, represents the feature of Iris, more in general dimensions of pattern 109 | //hidden layer : 3 neuron, activation using sigmoid, number of neuron in hidden level 110 | // 2° hidden l : * neuron, insert number of level you want 111 | //output layer : 3 neuron, represents the class of Iris, more in general dimensions of mapped values 112 | var layers []int = []int{len(patterns[0].Features), 20, len(mapped)} 113 | 114 | //Multilayer perceptron model, with one hidden layer. 115 | var mlp mn.MultiLayerNetwork = mn.PrepareMLPNet(layers, learningRate, mn.SigmoidalTransfer, mn.SigmoidalTransferDerivate) 116 | 117 | // compute scores for each folds execution 118 | var scores = v.MLPKFoldValidation(&mlp, patterns, epochs, folds, shuffle, mapped) 119 | 120 | // use simpler validation 121 | var mlp2 mn.MultiLayerNetwork = mn.PrepareMLPNet(layers, learningRate, mn.SigmoidalTransfer, mn.SigmoidalTransferDerivate) 122 | var scores2 = v.MLPRandomSubsamplingValidation(&mlp2, patterns, percentage, epochs, folds, shuffle, mapped) 123 | 124 | log.WithFields(log.Fields{ 125 | "level": "info", 126 | "place": "main", 127 | "scores": scores, 128 | }).Info("Scores reached: ", scores) 129 | 130 | log.WithFields(log.Fields{ 131 | "level": "info", 132 | "place": "main", 133 | "scores": scores2, 134 | }).Info("Scores reached: ", scores2) 135 | 136 | } 137 | 138 | // ############################################################################################################# 139 | // ######################################### Recurrent Neural Network ######################################## 140 | // ############################################################################################################# 141 | 142 | if true { 143 | 144 | log.WithFields(log.Fields{ 145 | "level": "info", 146 | "place": "main", 147 | "msg": "multi layer perceptron train and test over iris dataset", 148 | }).Info("Compute training algorithm on elman network using iris data set (binary classification problem)") 149 | 150 | // percentage and shuffling in dataset 151 | //var filePath = ".\\res\\iris.all_data.csv" 152 | //var filePath = "./res/sonar.all_data.csv" 153 | 154 | // single layer neuron parameters 155 | var learningRate = 0.01 156 | var shuffle = 1 157 | 158 | // training parameters 159 | var epochs = 500 160 | 161 | // Patterns initialization 162 | var patterns = mn.CreateRandomPatternArray(8, 30) 163 | 164 | //log.Info(patterns[0].Features[:int(len(patterns[0].Features)/2)]) 165 | //n := mu.ConvertBinToInt(patterns[0].Features[:int(len(patterns[0].Features)/2)]) 166 | //log.Info(n) 167 | //os.Exit(1) 168 | 169 | //input layer : 4 neuron, represents the feature of Iris, more in general dimensions of pattern 170 | //hidden layer : 3 neuron, activation using sigmoid, number of neuron in hidden level 171 | // 2° hidden l : * neuron, insert number of level you want 172 | //output layer : 3 neuron, represents the class of Iris, more in general dimensions of mapped values 173 | 174 | //Multilayer perceptron model, with one hidden layer. 175 | var mlp mn.MultiLayerNetwork = 176 | mn.PrepareElmanNet(len(patterns[0].Features)+10, 177 | 10, len(patterns[0].MultipleExpectation), learningRate, 178 | mn.SigmoidalTransfer, mn.SigmoidalTransferDerivate) 179 | 180 | // compute scores for each folds execution 181 | var mean, _ = v.RNNValidation(&mlp, patterns, epochs, shuffle) 182 | 183 | log.WithFields(log.Fields{ 184 | "level": "info", 185 | "place": "main", 186 | "precision": mu.Round(mean, .5, 2), 187 | }).Info("Scores reached: ", mu.Round(mean, .5, 2)) 188 | 189 | } 190 | 191 | } 192 | -------------------------------------------------------------------------------- /media/first_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/made2591/go-perceptron-go/f9cb1d41709be57f3f5d407c39e08defd021f9ce/media/first_example.png -------------------------------------------------------------------------------- /media/second_example.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/made2591/go-perceptron-go/f9cb1d41709be57f3f5d407c39e08defd021f9ce/media/second_example.png -------------------------------------------------------------------------------- /model/neural/multiLayerNetwork.go: -------------------------------------------------------------------------------- 1 | // Neural provides struct to represents most common neural networks model and algorithms to train / test them. 2 | package neural 3 | 4 | import ( 5 | 6 | // sys import 7 | "os" 8 | //"fmt" 9 | "math" 10 | 11 | // third part import 12 | log "github.com/sirupsen/logrus" 13 | mu "github.com/made2591/go-perceptron-go/util" 14 | 15 | "time" 16 | "math/rand" 17 | ) 18 | 19 | func init() { 20 | // Output to stdout instead of the default stderr 21 | log.SetOutput(os.Stdout) 22 | // Only log the warning severity or above. 23 | log.SetLevel(log.InfoLevel) 24 | } 25 | 26 | type MultiLayerNetwork struct { 27 | 28 | // Lrate represents learning rate of neuron 29 | L_rate float64 30 | 31 | // NeuralLayers represents layer of neurons 32 | NeuralLayers []NeuralLayer 33 | 34 | // Transfer function 35 | T_func transferFunction 36 | 37 | // Transfer function derivative 38 | T_func_d transferFunction 39 | 40 | } 41 | 42 | // PrepareMLPNet create a multi layer Perceptron neural network. 43 | // [l:[]int] is an int array with layers neurons number [input, ..., output] 44 | // [lr:int] is the learning rate of neural network 45 | // [tr:transferFunction] is a transfer function 46 | // [tr:transferFunction] the respective transfer function derivative 47 | func PrepareMLPNet(l []int, lr float64, tf transferFunction, trd transferFunction) (mlp MultiLayerNetwork) { 48 | 49 | // setup learning rate and transfer function 50 | mlp.L_rate = lr 51 | mlp.T_func = tf 52 | mlp.T_func_d = trd 53 | 54 | // setup layers 55 | mlp.NeuralLayers = make([]NeuralLayer, len(l)) 56 | 57 | // for each layers specified 58 | for il, ql := range l { 59 | 60 | // if it is not the first 61 | if il != 0 { 62 | 63 | // prepare the GENERIC layer with specific dimension and correct number of links for each NeuronUnits 64 | mlp.NeuralLayers[il] = PrepareLayer(ql, l[il-1]) 65 | 66 | } else { 67 | 68 | // prepare the INPUT layer with specific dimension and No links to previous. 69 | mlp.NeuralLayers[il] = PrepareLayer(ql, 0) 70 | 71 | } 72 | 73 | } 74 | 75 | log.WithFields(log.Fields{ 76 | "level": "info", 77 | "msg": "multilayer perceptron init completed", 78 | "layers": len(mlp.NeuralLayers), 79 | "learningRate: ": mlp.L_rate, 80 | }).Info("Complete Multilayer Perceptron init.") 81 | 82 | return 83 | 84 | } 85 | 86 | 87 | // PrepareElmanNet create a recurrent neUral network neural network. 88 | // [l:[]int] is an int array with layers neurons number [input, ..., output] 89 | // [lr:int] is the learning rate of neural network 90 | // [tr:transferFunction] is a transfer function 91 | // [tr:transferFunction] the respective transfer function derivative 92 | func PrepareElmanNet(i int, h int, o int, lr float64, tf transferFunction, trd transferFunction) (rnn MultiLayerNetwork) { 93 | 94 | // setup a three layer network with Input Context dimension 95 | rnn = PrepareMLPNet([]int{i, h, o}, lr, tf, trd); 96 | 97 | log.WithFields(log.Fields{ 98 | "level": "info", 99 | "msg": "recurrent neural network init completed", 100 | "inputLayer": i, 101 | "hiddenLayer": h, 102 | "outputLayer": o, 103 | "learningRate: ": rnn.L_rate, 104 | }).Info("Complete RNN init.") 105 | 106 | return 107 | 108 | } 109 | 110 | // Execute a multi layer Perceptron neural network. 111 | // [mlp:MultiLayerNetwork] multilayer perceptron network pointer, [s:Pattern] input value 112 | // It returns output values by network 113 | func Execute(mlp *MultiLayerNetwork, s *Pattern, options ...int) (r []float64) { 114 | 115 | // new value 116 | nv := 0.0 117 | 118 | // result of execution for each OUTPUT NeuronUnit in OUTPUT NeuralLayer 119 | r = make([]float64, mlp.NeuralLayers[len(mlp.NeuralLayers)-1].Length) 120 | 121 | // show pattern to network => 122 | for i := 0; i < len(s.Features); i++ { 123 | 124 | // setup value of each neurons in first layers to respective features of pattern 125 | mlp.NeuralLayers[0].NeuronUnits[i].Value = s.Features[i] 126 | 127 | } 128 | 129 | // init context 130 | for i := len(s.Features); i < mlp.NeuralLayers[0].Length; i++ { 131 | 132 | // setup value of each neurons in context layers to 0.5 133 | mlp.NeuralLayers[0].NeuronUnits[i].Value = 0.5 134 | 135 | } 136 | 137 | //OLD: TODO REMOVE 138 | // show pattern to network => 139 | //for i := 0; i < mlp.NeuralLayers[0].Length; i++ { 140 | // 141 | // // setup value of each neurons in first layers to respective features of pattern 142 | // mlp.NeuralLayers[0].NeuronUnits[i].Value = s.Features[i] 143 | // 144 | //} 145 | //OLD: END REMOVE 146 | 147 | // execute - hiddens + output 148 | // for each layers from first hidden to output 149 | for k := 1; k < len(mlp.NeuralLayers); k++ { 150 | 151 | // for each neurons in focused level 152 | for i := 0; i < mlp.NeuralLayers[k].Length; i++ { 153 | 154 | // init new value 155 | nv = 0.0 156 | 157 | // for each neurons in previous level (for k = 1, INPUT) 158 | for j := 0; j < mlp.NeuralLayers[k - 1].Length; j++ { 159 | 160 | // sum output value of previous neurons multiplied by weight between previous and focused neuron 161 | nv += mlp.NeuralLayers[k].NeuronUnits[i].Weights[j] * mlp.NeuralLayers[k - 1].NeuronUnits[j].Value 162 | 163 | log.WithFields(log.Fields{ 164 | "level": "debug", 165 | "msg": "multilayer perceptron execution", 166 | "len(mlp.NeuralLayers)": len(mlp.NeuralLayers), 167 | "layer: ": k, 168 | "neuron: ": i, 169 | "previous neuron: ": j, 170 | }).Debug("Compute output propagation.") 171 | 172 | } 173 | 174 | // add neuron bias 175 | nv += mlp.NeuralLayers[k].NeuronUnits[i].Bias 176 | 177 | // compute activation function to new output value 178 | mlp.NeuralLayers[k].NeuronUnits[i].Value = mlp.T_func(nv) 179 | 180 | // save output of hidden layer to context if nextwork is RECURRENT 181 | if k == 1 && len(options) > 0 && options[0] == 1 { 182 | 183 | for z := len(s.Features); z < mlp.NeuralLayers[0].Length; z++ { 184 | 185 | log.WithFields(log.Fields{ 186 | "level" : "debug", 187 | "len z" : z, 188 | "s.Features" : s.Features, 189 | "len(s.Features)" : len(s.Features), 190 | "len mlp.NeuralLayers[0].NeuronUnits" : len(mlp.NeuralLayers[0].NeuronUnits), 191 | "len mlp.NeuralLayers[k].NeuronUnits" : len(mlp.NeuralLayers[k].NeuronUnits), 192 | }).Debug("Save output of hidden layer to context.") 193 | 194 | mlp.NeuralLayers[0].NeuronUnits[z].Value = mlp.NeuralLayers[k].NeuronUnits[z-len(s.Features)].Value 195 | 196 | } 197 | 198 | } 199 | 200 | log.WithFields(log.Fields{ 201 | "level": "debug", 202 | "msg": "setup new neuron output value after transfer function application", 203 | "len(mlp.NeuralLayers)": len(mlp.NeuralLayers), 204 | "layer: ": k, 205 | "neuron: ": i, 206 | "outputvalue" : mlp.NeuralLayers[k].NeuronUnits[i].Value, 207 | }).Debug("Setup new neuron output value after transfer function application.") 208 | 209 | } 210 | 211 | } 212 | 213 | 214 | // get ouput values 215 | for i := 0; i < mlp.NeuralLayers[len(mlp.NeuralLayers)-1].Length; i++ { 216 | 217 | // simply accumulate values of all neurons in last level 218 | r[i] = mlp.NeuralLayers[len(mlp.NeuralLayers)-1].NeuronUnits[i].Value 219 | 220 | } 221 | 222 | return r 223 | 224 | } 225 | 226 | 227 | 228 | // BackPropagation algorithm for assisted learning. Convergence is not guaranteed and very slow. 229 | // Use as a stop criterion the average between previous and current errors and a maximum number of iterations. 230 | // [mlp:MultiLayerNetwork] input value [s:Pattern] input value (scaled between 0 and 1) 231 | // [o:[]float64] expected output value (scaled between 0 and 1) 232 | // return [r:float64] delta error between generated output and expected output 233 | func BackPropagate(mlp *MultiLayerNetwork, s *Pattern, o []float64, options ...int) (r float64) { 234 | 235 | var no []float64; 236 | // execute network with pattern passed over each level to output 237 | if len(options) == 1 { 238 | no = Execute(mlp, s, options[0]) 239 | } else { 240 | no = Execute(mlp, s) 241 | } 242 | 243 | // init error 244 | e := 0.0 245 | 246 | // compute output error and delta in output layer 247 | for i := 0; i < mlp.NeuralLayers[len(mlp.NeuralLayers)-1].Length; i++ { 248 | 249 | // compute error in output: output for given pattern - output computed by network 250 | e = o[i] - no[i] 251 | 252 | // compute delta for each neuron in output layer as: 253 | // error in output * derivative of transfer function of network output 254 | mlp.NeuralLayers[len(mlp.NeuralLayers)-1].NeuronUnits[i].Delta = e * mlp.T_func_d(no[i]) 255 | 256 | } 257 | 258 | // backpropagate error to previous layers 259 | // for each layers starting from the last hidden (len(mlp.NeuralLayers)-2) 260 | for k := len(mlp.NeuralLayers)-2; k >= 0; k-- { 261 | 262 | // compute actual layer errors and re-compute delta 263 | for i := 0; i < mlp.NeuralLayers[k].Length; i++ { 264 | 265 | // reset error accumulator 266 | e = 0.0 267 | 268 | // for each link to next layer 269 | for j := 0; j < mlp.NeuralLayers[k + 1].Length; j++ { 270 | 271 | // sum delta value of next neurons multiplied by weight between focused neuron and all neurons in next level 272 | e += mlp.NeuralLayers[k + 1].NeuronUnits[j].Delta * mlp.NeuralLayers[k + 1].NeuronUnits[j].Weights[i] 273 | 274 | } 275 | 276 | // compute delta for each neuron in focused layer as error * derivative of transfer function 277 | mlp.NeuralLayers[k].NeuronUnits[i].Delta = e * mlp.T_func_d(mlp.NeuralLayers[k].NeuronUnits[i].Value) 278 | 279 | } 280 | 281 | // compute weights in the next layer 282 | // for each link to next layer 283 | for i := 0; i < mlp.NeuralLayers[k + 1].Length; i++ { 284 | 285 | // for each neurons in actual level (for k = 0, INPUT) 286 | for j := 0; j < mlp.NeuralLayers[k].Length; j++ { 287 | 288 | // sum learning rate * next level next neuron Delta * actual level actual neuron output value 289 | mlp.NeuralLayers[k + 1].NeuronUnits[i].Weights[j] += 290 | mlp.L_rate * mlp.NeuralLayers[k + 1].NeuronUnits[i].Delta * mlp.NeuralLayers[k].NeuronUnits[j].Value 291 | 292 | } 293 | 294 | // learning rate * next level next neuron Delta * actual level actual neuron output value 295 | mlp.NeuralLayers[k + 1].NeuronUnits[i].Bias += mlp.L_rate * mlp.NeuralLayers[k + 1].NeuronUnits[i].Delta 296 | 297 | } 298 | 299 | // copy hidden output to context 300 | if k == 1 && len(options) > 0 && options[0] == 1 { 301 | 302 | for z := len(s.Features); z < mlp.NeuralLayers[0].Length; z++ { 303 | 304 | // save output of hidden layer to context 305 | mlp.NeuralLayers[0].NeuronUnits[z].Value = mlp.NeuralLayers[k].NeuronUnits[z-len(s.Features)].Value 306 | 307 | } 308 | 309 | } 310 | 311 | } 312 | 313 | // compute global errors as sum of abs difference between output execution for each neuron in output layer 314 | // and desired value in each neuron in output layer 315 | for i := 0; i < len(o); i++ { 316 | 317 | r += math.Abs(no[i] - o[i]) 318 | 319 | } 320 | 321 | // average error 322 | r = r / float64(len(o)) 323 | 324 | return 325 | 326 | } 327 | 328 | // MLPTrain train a mlp MultiLayerNetwork with BackPropagation algorithm for assisted learning. 329 | func MLPTrain(mlp *MultiLayerNetwork, patterns []Pattern, mapped []string, epochs int) { 330 | 331 | epoch := 0 332 | output := make([]float64, len(mapped)) 333 | 334 | // for fixed number of epochs 335 | for { 336 | 337 | // for each pattern in training set 338 | for _, pattern := range patterns { 339 | 340 | // setup desired output for each unit 341 | for io, _ := range output { 342 | output[io] = 0.0 343 | } 344 | // setup desired output for specific class of pattern focused 345 | output[int(pattern.SingleExpectation)] = 1.0 346 | // back propagation 347 | BackPropagate(mlp, &pattern, output) 348 | 349 | } 350 | 351 | log.WithFields(log.Fields{ 352 | "level": "info", 353 | "place": "validation", 354 | "method": "MLPTrain", 355 | "epoch": epoch, 356 | }).Debug("Training epoch completed.") 357 | 358 | // if max number of epochs is reached 359 | if epoch > epochs { 360 | // exit 361 | break 362 | } 363 | // increase number of epoch 364 | epoch++ 365 | 366 | } 367 | 368 | } 369 | 370 | // ElmanTrain train a mlp MultiLayerNetwork with BackPropagation algorithm for assisted learning. 371 | func ElmanTrain(mlp *MultiLayerNetwork, patterns []Pattern, epochs int) { 372 | 373 | epoch := 0 374 | 375 | // for fixed number of epochs 376 | for { 377 | 378 | rand.Seed(time.Now().UTC().UnixNano()) 379 | p_i_r := rand.Intn(len(patterns)) 380 | 381 | // for each pattern in training set 382 | for p_i, pattern := range patterns { 383 | 384 | // back propagation 385 | BackPropagate(mlp, &pattern, pattern.MultipleExpectation, 1) 386 | 387 | if (epoch % 100 == 0 && p_i == p_i_r) { 388 | 389 | // get output from network 390 | o_out := Execute(mlp, &pattern, 1) 391 | for o_out_i, o_out_v := range(o_out) { 392 | o_out[o_out_i] = mu.Round(o_out_v, .5, 0) 393 | } 394 | log.WithFields(log.Fields{ 395 | "SUM": " ==========================", 396 | }).Info() 397 | log.WithFields(log.Fields{ 398 | "a_n_1": mu.ConvertBinToInt(pattern.Features[0:int(len(pattern.Features)/2)]), 399 | "a_n_2": pattern.Features[0:int(len(pattern.Features)/2)], 400 | }).Info() 401 | log.WithFields(log.Fields{ 402 | "b_n_1": mu.ConvertBinToInt(pattern.Features[int(len(pattern.Features)/2):]), 403 | "b_n_2": pattern.Features[int(len(pattern.Features)/2):], 404 | }).Info() 405 | log.WithFields(log.Fields{ 406 | "sum_1": mu.ConvertBinToInt(pattern.MultipleExpectation), 407 | "sum_2": pattern.MultipleExpectation, 408 | }).Info() 409 | log.WithFields(log.Fields{ 410 | "sum_1": mu.ConvertBinToInt(o_out), 411 | "sum_2": o_out, 412 | }).Info() 413 | log.WithFields(log.Fields{ 414 | "END": " ==========================", 415 | }).Info() 416 | 417 | } 418 | 419 | } 420 | 421 | log.WithFields(log.Fields{ 422 | "level": "info", 423 | "place": "validation", 424 | "method": "ElmanTrain", 425 | "epoch": epoch, 426 | }).Debug("Training epoch completed.") 427 | 428 | // if max number of epochs is reached 429 | if epoch > epochs { 430 | // exit 431 | break 432 | } 433 | // increase number of epoch 434 | epoch++ 435 | 436 | } 437 | 438 | } -------------------------------------------------------------------------------- /model/neural/neuralLayer.go: -------------------------------------------------------------------------------- 1 | // Neural provides struct to represents most common neural networks model and algorithms to train / test them. 2 | package neural 3 | 4 | import ( 5 | 6 | // sys import 7 | "os" 8 | 9 | // third part import 10 | log "github.com/sirupsen/logrus" 11 | 12 | ) 13 | 14 | // Level struct represents a simple NeuronUnits network with a slice of n NeuronUnits. 15 | type NeuralLayer struct { 16 | 17 | // NeuronUnits represents NeuronUnits in layer 18 | NeuronUnits []NeuronUnit 19 | // Lrate represents number of NeuronUnit in layer 20 | Length int 21 | 22 | } 23 | 24 | // ####################################################################################### 25 | 26 | func init() { 27 | 28 | // Output to stdout instead of the default stderr 29 | log.SetOutput(os.Stdout) 30 | // Only log the warning severity or above. 31 | log.SetLevel(log.InfoLevel) 32 | 33 | } 34 | 35 | // PrepareLayer create a NeuralLayer with n NeuronUnits inside 36 | // [n:int] is an int that specifies the number of neurons in the NeuralLayer 37 | // [p:int] is an int that specifies the number of neurons in the previous NeuralLayer 38 | // It returns a NeuralLayer object 39 | func PrepareLayer(n int, p int) (l NeuralLayer) { 40 | 41 | l = NeuralLayer{NeuronUnits: make([]NeuronUnit, n), Length: n} 42 | 43 | for i := 0; i < n; i++ { 44 | RandomNeuronInit(&l.NeuronUnits[i], p) 45 | } 46 | 47 | log.WithFields(log.Fields{ 48 | "level": "info", 49 | "msg": "multilayer perceptron init completed", 50 | "neurons": len(l.NeuronUnits), 51 | "lengthPreviousLayer": l.Length, 52 | }).Info("Complete NeuralLayer init.") 53 | 54 | return 55 | 56 | } 57 | 58 | //make([]NeuronUnit, n) -------------------------------------------------------------------------------- /model/neural/neuronUnit.go: -------------------------------------------------------------------------------- 1 | // Neural provides struct to represents most common neural networks model and algorithms to train / test them. 2 | package neural 3 | 4 | import ( 5 | 6 | // sys import 7 | "math/rand" 8 | "os" 9 | 10 | // third part import 11 | log "github.com/sirupsen/logrus" 12 | 13 | // this repo internal import 14 | mu "github.com/made2591/go-perceptron-go/util" 15 | ) 16 | 17 | const ( 18 | 19 | SCALING_FACTOR = 0.0000000000001 20 | 21 | ) 22 | 23 | // NeuronUnit struct represents a simple NeuronUnit network with a slice of n weights. 24 | type NeuronUnit struct { 25 | 26 | // Weights represents NeuronUnit vector representation 27 | Weights []float64 28 | // Bias represents NeuronUnit natural propensity to spread signal 29 | Bias float64 30 | // Lrate represents learning rate of neuron 31 | Lrate float64 32 | 33 | // Value represents desired value when loading input into network in Multi NeuralLayer Perceptron 34 | Value float64 35 | // Delta represents delta error for unit 36 | Delta float64 37 | 38 | } 39 | 40 | // ####################################################################################### 41 | 42 | func init() { 43 | // Output to stdout instead of the default stderr 44 | log.SetOutput(os.Stdout) 45 | // Only log the warning severity or above. 46 | log.SetLevel(log.InfoLevel) 47 | } 48 | 49 | // RandomNeuronInit initialize neuron weight, bias and learning rate using NormFloat64 random value. 50 | func RandomNeuronInit(neuron *NeuronUnit, dim int) { 51 | 52 | neuron.Weights = make([]float64, dim) 53 | 54 | // init random weights 55 | for index, _ := range neuron.Weights { 56 | // init random threshold weight 57 | neuron.Weights[index] = rand.NormFloat64() * SCALING_FACTOR 58 | } 59 | 60 | // init random bias and lrate 61 | neuron.Bias = rand.NormFloat64() * SCALING_FACTOR 62 | neuron.Lrate = rand.NormFloat64() * SCALING_FACTOR 63 | neuron.Value = rand.NormFloat64() * SCALING_FACTOR 64 | neuron.Delta = rand.NormFloat64() * SCALING_FACTOR 65 | 66 | log.WithFields(log.Fields{ 67 | "level": "debug", 68 | "place": "neuron", 69 | "func": "RandomNeuronInit", 70 | "msg": "random neuron weights init", 71 | "weights": neuron.Weights, 72 | }).Debug() 73 | 74 | } 75 | 76 | // UpdateWeights performs update in neuron weights with respect to passed pattern. 77 | // It returns error of prediction before and after updating weights. 78 | func UpdateWeights(neuron *NeuronUnit, pattern *Pattern) (float64, float64) { 79 | 80 | // compute prediction value and error for pattern given neuron BEFORE update (actual state) 81 | var predictedValue, prevError, postError float64 = Predict(neuron, pattern), 0.0, 0.0 82 | prevError = pattern.SingleExpectation - predictedValue 83 | 84 | // performs weights update for neuron 85 | neuron.Bias = neuron.Bias + neuron.Lrate*prevError 86 | 87 | // performs weights update for neuron 88 | for index, _ := range neuron.Weights { 89 | neuron.Weights[index] = neuron.Weights[index] + neuron.Lrate*prevError*pattern.Features[index] 90 | } 91 | 92 | // compute prediction value and error for pattern given neuron AFTER update (actual state) 93 | predictedValue = Predict(neuron, pattern) 94 | postError = pattern.SingleExpectation - predictedValue 95 | 96 | log.WithFields(log.Fields{ 97 | "level": "debug", 98 | "place": "neuron", 99 | "func": "UpdateWeights", 100 | "msg": "updating weights of neuron", 101 | "weights": neuron.Weights, 102 | }).Debug() 103 | 104 | // return errors 105 | return prevError, postError 106 | 107 | } 108 | 109 | // TrainNeuron trains a passed neuron with patterns passed, for specified number of epoch. 110 | // If init is 0, leaves weights unchanged before training. 111 | // If init is 1, reset weights and bias of neuron before training. 112 | func TrainNeuron(neuron *NeuronUnit, patterns []Pattern, epochs int, init int) { 113 | 114 | // init weights if specified 115 | if init == 1 { 116 | neuron.Weights = make([]float64, len(patterns[0].Features)) 117 | neuron.Bias = 0.0 118 | } 119 | 120 | // init counter 121 | var epoch int = 0 122 | 123 | // accumulator errors prev and post weights updates 124 | var squaredPrevError, squaredPostError float64 = 0.0, 0.0 125 | 126 | // in each epoch 127 | for epoch < epochs { 128 | 129 | // update weight using each pattern in training set 130 | for _, pattern := range patterns { 131 | prevError, postError := UpdateWeights(neuron, &pattern) 132 | // NOTE: in each step, use weights already updated by previous 133 | squaredPrevError = squaredPrevError + (prevError * prevError) 134 | squaredPostError = squaredPostError + (postError * postError) 135 | } 136 | 137 | log.WithFields(log.Fields{ 138 | "level": "debug", 139 | "place": "error evolution in epoch", 140 | "method": "TrainNeuron", 141 | "msg": "epoch and squared errors reached before and after updating weights", 142 | "epochReached": epoch + 1, 143 | "squaredErrorPrev": squaredPrevError, 144 | "squaredErrorPost": squaredPostError, 145 | }).Debug() 146 | 147 | // increment epoch counter 148 | epoch++ 149 | 150 | } 151 | 152 | } 153 | 154 | // Predict performs a neuron prediction to passed pattern. 155 | // It returns a float64 binary predicted value. 156 | func Predict(neuron *NeuronUnit, pattern *Pattern) float64 { 157 | 158 | if mu.ScalarProduct(neuron.Weights, pattern.Features)+neuron.Bias < 0.0 { 159 | return 0.0 160 | } 161 | return 1.0 162 | 163 | } 164 | 165 | // Accuracy calculate percentage of equal values between two float64 based slices. 166 | // It returns int number and a float64 percentage value of corrected values. 167 | func Accuracy(actual []float64, predicted []float64) (int, float64) { 168 | 169 | // if slices have different number of elements 170 | if len(actual) != len(predicted) { 171 | log.WithFields(log.Fields{ 172 | "level": "error", 173 | "place": "neuron", 174 | "method": "Accuracy", 175 | "msg": "accuracy between actual and predicted slices of values", 176 | "actualLen": len(actual), 177 | "predictedLen": len(predicted), 178 | }).Error("Failed to compute accuracy between actual values and predictions: different length.") 179 | return -1, -1.0 180 | } 181 | 182 | // init result 183 | var correct int = 0 184 | 185 | for index, value := range actual { 186 | if value == predicted[index] { 187 | correct++ 188 | } 189 | } 190 | 191 | // return correct 192 | return correct, float64(correct) / float64(len(actual)) * 100.0 193 | 194 | } 195 | -------------------------------------------------------------------------------- /model/neural/pattern.go: -------------------------------------------------------------------------------- 1 | // Neural provides struct to represents most common neural networks model and algorithms to train / test them. 2 | package neural 3 | 4 | import ( 5 | // sys import 6 | "encoding/csv" 7 | "io" 8 | "io/ioutil" 9 | "os" 10 | "strings" 11 | 12 | // third part import 13 | log "github.com/sirupsen/logrus" 14 | 15 | // this repo internal import 16 | mu "github.com/made2591/go-perceptron-go/util" 17 | ) 18 | 19 | func init() { 20 | // Output to stdout instead of the default stderr 21 | log.SetOutput(os.Stdout) 22 | // Only log the warning severity or above. 23 | log.SetLevel(log.InfoLevel) 24 | } 25 | 26 | // Pattern struct represents one pattern with dimensions and desired value 27 | type Pattern struct { 28 | 29 | // Features that describe the pattern 30 | Features []float64 31 | // Raw (usually string) expected value 32 | SingleRawExpectation string 33 | // Numeric representation of expected value 34 | SingleExpectation float64 35 | // Numeric representation of expected value 36 | MultipleExpectation []float64 37 | 38 | } 39 | 40 | // ####################################################################################### 41 | 42 | // LoadPatternsFromCSVFile load a CSV dataset into an array of Pattern. 43 | func LoadPatternsFromCSVFile(filePath string) ([]Pattern, error, []string) { 44 | 45 | // init patterns 46 | var patterns []Pattern 47 | 48 | // read content ([]byte), check error (error) 49 | fileContent, error := ioutil.ReadFile(filePath) 50 | 51 | if error != nil { 52 | log.WithFields(log.Fields{ 53 | "level": "fatal", 54 | "place": "patterns", 55 | "method": "LoadPatternsFromCSVFile", 56 | "msg": "reading file in specific path", 57 | "filePath": filePath, 58 | "error": error, 59 | }).Fatal("Failed to read file in specified path.") 60 | return patterns, error, nil 61 | } 62 | 63 | // create pointer to read file 64 | pointer := csv.NewReader(strings.NewReader(string(fileContent))) 65 | 66 | var lineCounter int = 0 67 | // for each record in file 68 | for { 69 | 70 | // read line, check error 71 | line, error := pointer.Read() 72 | 73 | log.WithFields(log.Fields{ 74 | "level": "debug", 75 | "place": "patterns", 76 | "method": "LoadPatternsFromCSVFile", 77 | "line": line, 78 | }).Debug() 79 | 80 | // if end of file reached, exit loop 81 | if error == io.EOF { 82 | log.WithFields(log.Fields{ 83 | "level": "info", 84 | "place": "patterns", 85 | "method": "LoadPatternsFromCSVFile", 86 | "readData": len(patterns), 87 | "msg": "File reading completed.", 88 | }).Info("File reading completed.") 89 | break 90 | } 91 | 92 | // if another error encountered, exit program 93 | if error != nil { 94 | log.WithFields(log.Fields{ 95 | "level": "error", 96 | "place": "patterns", 97 | "method": "LoadPatternsFromCSVFile", 98 | "msg": "parsing file in specific line number", 99 | "lineCounter": lineCounter, 100 | "error": error, 101 | }).Error("Failed to parse line.") 102 | return patterns, error, nil 103 | } 104 | 105 | // line values cast to float64 106 | var floatingValues []float64 = mu.StringToFloat(line, 1, -1.0) 107 | 108 | // add casted pattern to training set 109 | patterns = append( 110 | patterns, 111 | Pattern{Features: floatingValues, SingleRawExpectation: line[len(line)-1]}) 112 | 113 | lineCounter = lineCounter + 1 114 | 115 | } 116 | 117 | // cast expected values to float64 numeric values 118 | mapped := RawExpectedConversion(patterns) 119 | 120 | // return patterns 121 | return patterns, nil, mapped 122 | 123 | } 124 | 125 | // RawExpectedConversion converts (string) raw expected values in patterns 126 | // training / testing sets to float64 values 127 | // It works on pattern struct (pointer) passed. It doens't returns nothing 128 | func RawExpectedConversion(patterns []Pattern) []string { 129 | 130 | // collect expected string values 131 | var rawExpectedValues []string 132 | 133 | // for each pattern in training set 134 | for _, pattern := range patterns { 135 | check, _ := mu.StringInSlice(pattern.SingleRawExpectation, rawExpectedValues) 136 | if !check { 137 | rawExpectedValues = append(rawExpectedValues, pattern.SingleRawExpectation) 138 | } 139 | log.WithFields(log.Fields{ 140 | "level": "debug", 141 | "place": "patterns", 142 | "msg": "raw class exctraction", 143 | "rawExpectedAdded": pattern.SingleRawExpectation, 144 | }).Debug() 145 | } 146 | 147 | log.WithFields(log.Fields{ 148 | "level": "info", 149 | "place": "patterns", 150 | "msg": "raw class exctraction completed", 151 | "numberOfRawUnique": len(rawExpectedValues), 152 | }).Info("Complete SingleRawExpectation value set filling.") 153 | 154 | // for each pattern in training set 155 | for index, _ := range patterns { 156 | // find mapped int value (using index of rawExpectedValues slice) 157 | for mapped, value := range rawExpectedValues { 158 | if strings.Compare(value, patterns[index].SingleRawExpectation) == 0 { 159 | // conversion to float64 value 160 | patterns[index].SingleExpectation = float64(mapped) 161 | } 162 | } 163 | } 164 | 165 | return rawExpectedValues 166 | 167 | } 168 | 169 | // CreateRandomPatternArray load a CSV dataset into an array of Pattern. 170 | func CreateRandomPatternArray(d int, k int) ([]Pattern) { 171 | 172 | // init patterns 173 | var patterns []Pattern; 174 | 175 | // for i times 176 | var i = 0 177 | for i < k { 178 | 179 | a := mu.GenerateRandomIntWithBinaryDim(d) 180 | b := mu.GenerateRandomIntWithBinaryDim(d) 181 | c := a+b 182 | 183 | log.WithFields(log.Fields{ 184 | "ai": a, 185 | "as": mu.ConvertIntToBinary(a, d), 186 | "bi": b, 187 | "bs": mu.ConvertIntToBinary(b, d), 188 | "ci": c, 189 | "cs": mu.ConvertIntToBinary(c, d+1), 190 | }).Debug() 191 | 192 | ab := mu.ConvertIntToBinary(a, d) 193 | bb := mu.ConvertIntToBinary(b, d) 194 | for _, v := range(bb) { 195 | ab = append(ab, v) 196 | } 197 | 198 | // add casted pattern to training set 199 | patterns = append( 200 | patterns, 201 | Pattern{Features: ab, 202 | MultipleExpectation: mu.ConvertIntToBinary(c, d+1)}) 203 | 204 | i = i + 1 205 | 206 | } 207 | 208 | // return patterns 209 | return patterns 210 | 211 | } -------------------------------------------------------------------------------- /model/neural/transferFunction.go: -------------------------------------------------------------------------------- 1 | // Neural provides struct to represents most common neural networks model and algorithms to train / test them. 2 | package neural 3 | 4 | import ( 5 | 6 | "math" 7 | 8 | ) 9 | 10 | // define transfer function desiderata 11 | type transferFunction func(float64) float64 12 | 13 | // different type of transfer function 14 | 15 | func HeavysideTransfer(d float64) float64 { 16 | 17 | if d >= 0.0 { 18 | return 1.0 19 | } 20 | return 0.0 21 | 22 | } 23 | 24 | func HeavysideTransferDerivate(d float64) float64 { 25 | 26 | return 1.0 27 | 28 | } 29 | 30 | func SigmoidalTransfer(d float64) float64 { 31 | 32 | return 1 / (1 + math.Pow(math.E, - d)) 33 | 34 | } 35 | 36 | func SigmoidalTransferDerivate(d float64) float64 { 37 | 38 | return 1.0 39 | 40 | } 41 | 42 | func HyperbolicTransfer(d float64) float64 { 43 | 44 | return math.Tanh(d) 45 | 46 | } 47 | 48 | func HyperbolicTransferDerivate(d float64) float64 { 49 | 50 | return 1 - math.Pow(d, 2) 51 | 52 | } -------------------------------------------------------------------------------- /res/iris.all_data.csv: -------------------------------------------------------------------------------- 1 | 5.1,3.4,1.5,0.2,Iris-setosa 2 | 6.3,2.5,5,1.9,Iris-virginica 3 | 5.9,3,4.2,1.5,Iris-versicolor 4 | 5.7,2.6,3.5,1,Iris-versicolor 5 | 7.7,2.8,6.7,2,Iris-virginica 6 | 6,3,4.8,1.8,Iris-virginica 7 | 6.7,3.3,5.7,2.5,Iris-virginica 8 | 5.8,2.7,4.1,1,Iris-versicolor 9 | 5.5,2.3,4,1.3,Iris-versicolor 10 | 6.6,3,4.4,1.4,Iris-versicolor 11 | 5.1,3.7,1.5,0.4,Iris-setosa 12 | 4.6,3.6,1,0.2,Iris-setosa 13 | 7.7,2.6,6.9,2.3,Iris-virginica 14 | 6.1,2.8,4,1.3,Iris-versicolor 15 | 4.9,2.4,3.3,1,Iris-versicolor 16 | 6.8,3,5.5,2.1,Iris-virginica 17 | 4.9,3.1,1.5,0.1,Iris-setosa 18 | 6.4,2.7,5.3,1.9,Iris-virginica 19 | 5.5,2.4,3.8,1.1,Iris-versicolor 20 | 5.6,3,4.5,1.5,Iris-versicolor 21 | 5.8,2.8,5.1,2.4,Iris-virginica 22 | 5.8,2.7,5.1,1.9,Iris-virginica 23 | 6.4,2.8,5.6,2.2,Iris-virginica 24 | 6.7,3,5.2,2.3,Iris-virginica 25 | 4.6,3.1,1.5,0.2,Iris-setosa 26 | 6.9,3.1,5.4,2.1,Iris-virginica 27 | 5.9,3,5.1,1.8,Iris-virginica 28 | 6.2,2.8,4.8,1.8,Iris-virginica 29 | 5.1,3.5,1.4,0.3,Iris-setosa 30 | 5.8,2.7,3.9,1.2,Iris-versicolor 31 | 6.7,3.1,4.7,1.5,Iris-versicolor 32 | 6.3,2.5,4.9,1.5,Iris-versicolor 33 | 6.8,2.8,4.8,1.4,Iris-versicolor 34 | 6.2,2.2,4.5,1.5,Iris-versicolor 35 | 4.9,3.1,1.5,0.1,Iris-setosa 36 | 5.2,4.1,1.5,0.1,Iris-setosa 37 | 6.4,3.2,5.3,2.3,Iris-virginica 38 | 5,2.3,3.3,1,Iris-versicolor 39 | 5.6,3,4.1,1.3,Iris-versicolor 40 | 6.3,3.4,5.6,2.4,Iris-virginica 41 | 6.3,2.3,4.4,1.3,Iris-versicolor 42 | 6.3,2.8,5.1,1.5,Iris-virginica 43 | 5.1,3.3,1.7,0.5,Iris-setosa 44 | 6.3,2.9,5.6,1.8,Iris-virginica 45 | 5,3.4,1.5,0.2,Iris-setosa 46 | 7.2,3,5.8,1.6,Iris-virginica 47 | 6.4,3.2,4.5,1.5,Iris-versicolor 48 | 5,2,3.5,1,Iris-versicolor 49 | 5.5,2.5,4,1.3,Iris-versicolor 50 | 5.1,3.8,1.6,0.2,Iris-setosa 51 | 6.1,2.6,5.6,1.4,Iris-virginica 52 | 6.2,2.9,4.3,1.3,Iris-versicolor 53 | 7.6,3,6.6,2.1,Iris-virginica 54 | 5.3,3.7,1.5,0.2,Iris-setosa 55 | 5.4,3.9,1.3,0.4,Iris-setosa 56 | 6,3.4,4.5,1.6,Iris-versicolor 57 | 5.2,3.5,1.5,0.2,Iris-setosa 58 | 4.6,3.4,1.4,0.3,Iris-setosa 59 | 4.9,2.5,4.5,1.7,Iris-virginica 60 | 6.1,3,4.6,1.4,Iris-versicolor 61 | 5.1,3.5,1.4,0.2,Iris-setosa 62 | 5.4,3.7,1.5,0.2,Iris-setosa 63 | 6.8,3.2,5.9,2.3,Iris-virginica 64 | 6.4,2.8,5.6,2.1,Iris-virginica 65 | 5,3.5,1.6,0.6,Iris-setosa 66 | 5,3,1.6,0.2,Iris-setosa 67 | 4.9,3,1.4,0.2,Iris-setosa 68 | 4.8,3.4,1.6,0.2,Iris-setosa 69 | 6.5,3,5.2,2,Iris-virginica 70 | 5.4,3.9,1.7,0.4,Iris-setosa 71 | 5,3.2,1.2,0.2,Iris-setosa 72 | 7.7,3.8,6.7,2.2,Iris-virginica 73 | 7.3,2.9,6.3,1.8,Iris-virginica 74 | 6.7,3.1,5.6,2.4,Iris-virginica 75 | 5.7,2.8,4.1,1.3,Iris-versicolor 76 | 5.5,2.4,3.7,1,Iris-versicolor 77 | 6.5,3,5.8,2.2,Iris-virginica 78 | 6.6,2.9,4.6,1.3,Iris-versicolor 79 | 5.7,3.8,1.7,0.3,Iris-setosa 80 | 4.8,3.1,1.6,0.2,Iris-setosa 81 | 5,3.3,1.4,0.2,Iris-setosa 82 | 5.7,2.9,4.2,1.3,Iris-versicolor 83 | 4.7,3.2,1.3,0.2,Iris-setosa 84 | 4.4,3.2,1.3,0.2,Iris-setosa 85 | 4.8,3,1.4,0.1,Iris-setosa 86 | 6.4,3.1,5.5,1.8,Iris-virginica 87 | 4.3,3,1.1,0.1,Iris-setosa 88 | 5.8,2.7,5.1,1.9,Iris-virginica 89 | 5.6,2.9,3.6,1.3,Iris-versicolor 90 | 7.2,3.6,6.1,2.5,Iris-virginica 91 | 5.7,2.5,5,2,Iris-virginica 92 | 6.9,3.1,4.9,1.5,Iris-versicolor 93 | 6.5,3.2,5.1,2,Iris-virginica 94 | 6,2.2,4,1,Iris-versicolor 95 | 4.9,3.1,1.5,0.1,Iris-setosa 96 | 5.1,3.8,1.9,0.4,Iris-setosa 97 | 5.5,4.2,1.4,0.2,Iris-setosa 98 | 5.8,2.6,4,1.2,Iris-versicolor 99 | 7,3.2,4.7,1.4,Iris-versicolor 100 | 5.2,3.4,1.4,0.2,Iris-setosa 101 | 5.2,2.7,3.9,1.4,Iris-versicolor 102 | 6.3,3.3,6,2.5,Iris-virginica 103 | 6.7,3,5,1.7,Iris-versicolor 104 | 6,2.7,5.1,1.6,Iris-versicolor 105 | 7.2,3.2,6,1.8,Iris-virginica 106 | 6.7,2.5,5.8,1.8,Iris-virginica 107 | 5.7,3,4.2,1.2,Iris-versicolor 108 | 5.6,2.7,4.2,1.3,Iris-versicolor 109 | 6.5,2.8,4.6,1.5,Iris-versicolor 110 | 6.1,2.9,4.7,1.4,Iris-versicolor 111 | 6,2.9,4.5,1.5,Iris-versicolor 112 | 6.9,3.2,5.7,2.3,Iris-virginica 113 | 5.8,4,1.2,0.2,Iris-setosa 114 | 5.9,3.2,4.8,1.8,Iris-versicolor 115 | 5.4,3.4,1.7,0.2,Iris-setosa 116 | 5.6,2.5,3.9,1.1,Iris-versicolor 117 | 6.4,2.9,4.3,1.3,Iris-versicolor 118 | 7.9,3.8,6.4,2,Iris-virginica 119 | 5.4,3,4.5,1.5,Iris-versicolor 120 | 5.5,3.5,1.3,0.2,Iris-setosa 121 | 5.7,4.4,1.5,0.4,Iris-setosa 122 | 5,3.4,1.6,0.4,Iris-setosa 123 | 6.1,2.8,4.7,1.2,Iris-versicolor 124 | 6.3,3.3,4.7,1.6,Iris-versicolor 125 | 5,3.6,1.4,0.2,Iris-setosa 126 | 4.4,3,1.3,0.2,Iris-setosa 127 | 4.7,3.2,1.6,0.2,Iris-setosa 128 | 5.1,2.5,3,1.1,Iris-versicolor 129 | 6.5,3,5.5,1.8,Iris-virginica 130 | 7.4,2.8,6.1,1.9,Iris-virginica 131 | 5.5,2.6,4.4,1.2,Iris-versicolor 132 | 6.2,3.4,5.4,2.3,Iris-virginica 133 | 5,3.5,1.3,0.3,Iris-setosa 134 | 7.7,3,6.1,2.3,Iris-virginica 135 | 4.6,3.2,1.4,0.2,Iris-setosa 136 | 4.8,3.4,1.9,0.2,Iris-setosa 137 | 5.7,2.8,4.5,1.3,Iris-versicolor 138 | 6.7,3.3,5.7,2.1,Iris-virginica 139 | 5.4,3.4,1.5,0.4,Iris-setosa 140 | 4.4,2.9,1.4,0.2,Iris-setosa 141 | 4.5,2.3,1.3,0.3,Iris-setosa 142 | 6,2.2,5,1.5,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 6.3,2.7,4.9,1.8,Iris-virginica 145 | 6.7,3.1,4.4,1.4,Iris-versicolor 146 | 5.6,2.8,4.9,2,Iris-virginica 147 | 5.1,3.8,1.5,0.3,Iris-setosa 148 | 4.8,3,1.4,0.3,Iris-setosa 149 | 7.1,3,5.9,2.1,Iris-virginica 150 | 6.1,3,4.9,1.8,Iris-virginica -------------------------------------------------------------------------------- /res/sonar.all_data.csv: -------------------------------------------------------------------------------- 1 | 0.0200,0.0371,0.0428,0.0207,0.0954,0.0986,0.1539,0.1601,0.3109,0.2111,0.1609,0.1582,0.2238,0.0645,0.0660,0.2273,0.3100,0.2999,0.5078,0.4797,0.5783,0.5071,0.4328,0.5550,0.6711,0.6415,0.7104,0.8080,0.6791,0.3857,0.1307,0.2604,0.5121,0.7547,0.8537,0.8507,0.6692,0.6097,0.4943,0.2744,0.0510,0.2834,0.2825,0.4256,0.2641,0.1386,0.1051,0.1343,0.0383,0.0324,0.0232,0.0027,0.0065,0.0159,0.0072,0.0167,0.0180,0.0084,0.0090,0.0032,R 2 | 0.0453,0.0523,0.0843,0.0689,0.1183,0.2583,0.2156,0.3481,0.3337,0.2872,0.4918,0.6552,0.6919,0.7797,0.7464,0.9444,1.0000,0.8874,0.8024,0.7818,0.5212,0.4052,0.3957,0.3914,0.3250,0.3200,0.3271,0.2767,0.4423,0.2028,0.3788,0.2947,0.1984,0.2341,0.1306,0.4182,0.3835,0.1057,0.1840,0.1970,0.1674,0.0583,0.1401,0.1628,0.0621,0.0203,0.0530,0.0742,0.0409,0.0061,0.0125,0.0084,0.0089,0.0048,0.0094,0.0191,0.0140,0.0049,0.0052,0.0044,R 3 | 0.0262,0.0582,0.1099,0.1083,0.0974,0.2280,0.2431,0.3771,0.5598,0.6194,0.6333,0.7060,0.5544,0.5320,0.6479,0.6931,0.6759,0.7551,0.8929,0.8619,0.7974,0.6737,0.4293,0.3648,0.5331,0.2413,0.5070,0.8533,0.6036,0.8514,0.8512,0.5045,0.1862,0.2709,0.4232,0.3043,0.6116,0.6756,0.5375,0.4719,0.4647,0.2587,0.2129,0.2222,0.2111,0.0176,0.1348,0.0744,0.0130,0.0106,0.0033,0.0232,0.0166,0.0095,0.0180,0.0244,0.0316,0.0164,0.0095,0.0078,R 4 | 0.0100,0.0171,0.0623,0.0205,0.0205,0.0368,0.1098,0.1276,0.0598,0.1264,0.0881,0.1992,0.0184,0.2261,0.1729,0.2131,0.0693,0.2281,0.4060,0.3973,0.2741,0.3690,0.5556,0.4846,0.3140,0.5334,0.5256,0.2520,0.2090,0.3559,0.6260,0.7340,0.6120,0.3497,0.3953,0.3012,0.5408,0.8814,0.9857,0.9167,0.6121,0.5006,0.3210,0.3202,0.4295,0.3654,0.2655,0.1576,0.0681,0.0294,0.0241,0.0121,0.0036,0.0150,0.0085,0.0073,0.0050,0.0044,0.0040,0.0117,R 5 | 0.0762,0.0666,0.0481,0.0394,0.0590,0.0649,0.1209,0.2467,0.3564,0.4459,0.4152,0.3952,0.4256,0.4135,0.4528,0.5326,0.7306,0.6193,0.2032,0.4636,0.4148,0.4292,0.5730,0.5399,0.3161,0.2285,0.6995,1.0000,0.7262,0.4724,0.5103,0.5459,0.2881,0.0981,0.1951,0.4181,0.4604,0.3217,0.2828,0.2430,0.1979,0.2444,0.1847,0.0841,0.0692,0.0528,0.0357,0.0085,0.0230,0.0046,0.0156,0.0031,0.0054,0.0105,0.0110,0.0015,0.0072,0.0048,0.0107,0.0094,R 6 | 0.0286,0.0453,0.0277,0.0174,0.0384,0.0990,0.1201,0.1833,0.2105,0.3039,0.2988,0.4250,0.6343,0.8198,1.0000,0.9988,0.9508,0.9025,0.7234,0.5122,0.2074,0.3985,0.5890,0.2872,0.2043,0.5782,0.5389,0.3750,0.3411,0.5067,0.5580,0.4778,0.3299,0.2198,0.1407,0.2856,0.3807,0.4158,0.4054,0.3296,0.2707,0.2650,0.0723,0.1238,0.1192,0.1089,0.0623,0.0494,0.0264,0.0081,0.0104,0.0045,0.0014,0.0038,0.0013,0.0089,0.0057,0.0027,0.0051,0.0062,R 7 | 0.0317,0.0956,0.1321,0.1408,0.1674,0.1710,0.0731,0.1401,0.2083,0.3513,0.1786,0.0658,0.0513,0.3752,0.5419,0.5440,0.5150,0.4262,0.2024,0.4233,0.7723,0.9735,0.9390,0.5559,0.5268,0.6826,0.5713,0.5429,0.2177,0.2149,0.5811,0.6323,0.2965,0.1873,0.2969,0.5163,0.6153,0.4283,0.5479,0.6133,0.5017,0.2377,0.1957,0.1749,0.1304,0.0597,0.1124,0.1047,0.0507,0.0159,0.0195,0.0201,0.0248,0.0131,0.0070,0.0138,0.0092,0.0143,0.0036,0.0103,R 8 | 0.0519,0.0548,0.0842,0.0319,0.1158,0.0922,0.1027,0.0613,0.1465,0.2838,0.2802,0.3086,0.2657,0.3801,0.5626,0.4376,0.2617,0.1199,0.6676,0.9402,0.7832,0.5352,0.6809,0.9174,0.7613,0.8220,0.8872,0.6091,0.2967,0.1103,0.1318,0.0624,0.0990,0.4006,0.3666,0.1050,0.1915,0.3930,0.4288,0.2546,0.1151,0.2196,0.1879,0.1437,0.2146,0.2360,0.1125,0.0254,0.0285,0.0178,0.0052,0.0081,0.0120,0.0045,0.0121,0.0097,0.0085,0.0047,0.0048,0.0053,R 9 | 0.0223,0.0375,0.0484,0.0475,0.0647,0.0591,0.0753,0.0098,0.0684,0.1487,0.1156,0.1654,0.3833,0.3598,0.1713,0.1136,0.0349,0.3796,0.7401,0.9925,0.9802,0.8890,0.6712,0.4286,0.3374,0.7366,0.9611,0.7353,0.4856,0.1594,0.3007,0.4096,0.3170,0.3305,0.3408,0.2186,0.2463,0.2726,0.1680,0.2792,0.2558,0.1740,0.2121,0.1099,0.0985,0.1271,0.1459,0.1164,0.0777,0.0439,0.0061,0.0145,0.0128,0.0145,0.0058,0.0049,0.0065,0.0093,0.0059,0.0022,R 10 | 0.0164,0.0173,0.0347,0.0070,0.0187,0.0671,0.1056,0.0697,0.0962,0.0251,0.0801,0.1056,0.1266,0.0890,0.0198,0.1133,0.2826,0.3234,0.3238,0.4333,0.6068,0.7652,0.9203,0.9719,0.9207,0.7545,0.8289,0.8907,0.7309,0.6896,0.5829,0.4935,0.3101,0.0306,0.0244,0.1108,0.1594,0.1371,0.0696,0.0452,0.0620,0.1421,0.1597,0.1384,0.0372,0.0688,0.0867,0.0513,0.0092,0.0198,0.0118,0.0090,0.0223,0.0179,0.0084,0.0068,0.0032,0.0035,0.0056,0.0040,R 11 | 0.0039,0.0063,0.0152,0.0336,0.0310,0.0284,0.0396,0.0272,0.0323,0.0452,0.0492,0.0996,0.1424,0.1194,0.0628,0.0907,0.1177,0.1429,0.1223,0.1104,0.1847,0.3715,0.4382,0.5707,0.6654,0.7476,0.7654,0.8555,0.9720,0.9221,0.7502,0.7209,0.7757,0.6055,0.5021,0.4499,0.3947,0.4281,0.4427,0.3749,0.1972,0.0511,0.0793,0.1269,0.1533,0.0690,0.0402,0.0534,0.0228,0.0073,0.0062,0.0062,0.0120,0.0052,0.0056,0.0093,0.0042,0.0003,0.0053,0.0036,R 12 | 0.0123,0.0309,0.0169,0.0313,0.0358,0.0102,0.0182,0.0579,0.1122,0.0835,0.0548,0.0847,0.2026,0.2557,0.1870,0.2032,0.1463,0.2849,0.5824,0.7728,0.7852,0.8515,0.5312,0.3653,0.5973,0.8275,1.0000,0.8673,0.6301,0.4591,0.3940,0.2576,0.2817,0.2641,0.2757,0.2698,0.3994,0.4576,0.3940,0.2522,0.1782,0.1354,0.0516,0.0337,0.0894,0.0861,0.0872,0.0445,0.0134,0.0217,0.0188,0.0133,0.0265,0.0224,0.0074,0.0118,0.0026,0.0092,0.0009,0.0044,R 13 | 0.0079,0.0086,0.0055,0.0250,0.0344,0.0546,0.0528,0.0958,0.1009,0.1240,0.1097,0.1215,0.1874,0.3383,0.3227,0.2723,0.3943,0.6432,0.7271,0.8673,0.9674,0.9847,0.9480,0.8036,0.6833,0.5136,0.3090,0.0832,0.4019,0.2344,0.1905,0.1235,0.1717,0.2351,0.2489,0.3649,0.3382,0.1589,0.0989,0.1089,0.1043,0.0839,0.1391,0.0819,0.0678,0.0663,0.1202,0.0692,0.0152,0.0266,0.0174,0.0176,0.0127,0.0088,0.0098,0.0019,0.0059,0.0058,0.0059,0.0032,R 14 | 0.0090,0.0062,0.0253,0.0489,0.1197,0.1589,0.1392,0.0987,0.0955,0.1895,0.1896,0.2547,0.4073,0.2988,0.2901,0.5326,0.4022,0.1571,0.3024,0.3907,0.3542,0.4438,0.6414,0.4601,0.6009,0.8690,0.8345,0.7669,0.5081,0.4620,0.5380,0.5375,0.3844,0.3601,0.7402,0.7761,0.3858,0.0667,0.3684,0.6114,0.3510,0.2312,0.2195,0.3051,0.1937,0.1570,0.0479,0.0538,0.0146,0.0068,0.0187,0.0059,0.0095,0.0194,0.0080,0.0152,0.0158,0.0053,0.0189,0.0102,R 15 | 0.0124,0.0433,0.0604,0.0449,0.0597,0.0355,0.0531,0.0343,0.1052,0.2120,0.1640,0.1901,0.3026,0.2019,0.0592,0.2390,0.3657,0.3809,0.5929,0.6299,0.5801,0.4574,0.4449,0.3691,0.6446,0.8940,0.8978,0.4980,0.3333,0.2350,0.1553,0.3666,0.4340,0.3082,0.3024,0.4109,0.5501,0.4129,0.5499,0.5018,0.3132,0.2802,0.2351,0.2298,0.1155,0.0724,0.0621,0.0318,0.0450,0.0167,0.0078,0.0083,0.0057,0.0174,0.0188,0.0054,0.0114,0.0196,0.0147,0.0062,R 16 | 0.0298,0.0615,0.0650,0.0921,0.1615,0.2294,0.2176,0.2033,0.1459,0.0852,0.2476,0.3645,0.2777,0.2826,0.3237,0.4335,0.5638,0.4555,0.4348,0.6433,0.3932,0.1989,0.3540,0.9165,0.9371,0.4620,0.2771,0.6613,0.8028,0.4200,0.5192,0.6962,0.5792,0.8889,0.7863,0.7133,0.7615,0.4401,0.3009,0.3163,0.2809,0.2898,0.0526,0.1867,0.1553,0.1633,0.1252,0.0748,0.0452,0.0064,0.0154,0.0031,0.0153,0.0071,0.0212,0.0076,0.0152,0.0049,0.0200,0.0073,R 17 | 0.0352,0.0116,0.0191,0.0469,0.0737,0.1185,0.1683,0.1541,0.1466,0.2912,0.2328,0.2237,0.2470,0.1560,0.3491,0.3308,0.2299,0.2203,0.2493,0.4128,0.3158,0.6191,0.5854,0.3395,0.2561,0.5599,0.8145,0.6941,0.6985,0.8660,0.5930,0.3664,0.6750,0.8697,0.7837,0.7552,0.5789,0.4713,0.1252,0.6087,0.7322,0.5977,0.3431,0.1803,0.2378,0.3424,0.2303,0.0689,0.0216,0.0469,0.0426,0.0346,0.0158,0.0154,0.0109,0.0048,0.0095,0.0015,0.0073,0.0067,R 18 | 0.0192,0.0607,0.0378,0.0774,0.1388,0.0809,0.0568,0.0219,0.1037,0.1186,0.1237,0.1601,0.3520,0.4479,0.3769,0.5761,0.6426,0.6790,0.7157,0.5466,0.5399,0.6362,0.7849,0.7756,0.5780,0.4862,0.4181,0.2457,0.0716,0.0613,0.1816,0.4493,0.5976,0.3785,0.2495,0.5771,0.8852,0.8409,0.3570,0.3133,0.6096,0.6378,0.2709,0.1419,0.1260,0.1288,0.0790,0.0829,0.0520,0.0216,0.0360,0.0331,0.0131,0.0120,0.0108,0.0024,0.0045,0.0037,0.0112,0.0075,R 19 | 0.0270,0.0092,0.0145,0.0278,0.0412,0.0757,0.1026,0.1138,0.0794,0.1520,0.1675,0.1370,0.1361,0.1345,0.2144,0.5354,0.6830,0.5600,0.3093,0.3226,0.4430,0.5573,0.5782,0.6173,0.8132,0.9819,0.9823,0.9166,0.7423,0.7736,0.8473,0.7352,0.6671,0.6083,0.6239,0.5972,0.5715,0.5242,0.2924,0.1536,0.2003,0.2031,0.2207,0.1778,0.1353,0.1373,0.0749,0.0472,0.0325,0.0179,0.0045,0.0084,0.0010,0.0018,0.0068,0.0039,0.0120,0.0132,0.0070,0.0088,R 20 | 0.0126,0.0149,0.0641,0.1732,0.2565,0.2559,0.2947,0.4110,0.4983,0.5920,0.5832,0.5419,0.5472,0.5314,0.4981,0.6985,0.8292,0.7839,0.8215,0.9363,1.0000,0.9224,0.7839,0.5470,0.4562,0.5922,0.5448,0.3971,0.0882,0.2385,0.2005,0.0587,0.2544,0.2009,0.0329,0.1547,0.1212,0.2446,0.3171,0.3195,0.3051,0.0836,0.1266,0.1381,0.1136,0.0516,0.0073,0.0278,0.0372,0.0121,0.0153,0.0092,0.0035,0.0098,0.0121,0.0006,0.0181,0.0094,0.0116,0.0063,R 21 | 0.0473,0.0509,0.0819,0.1252,0.1783,0.3070,0.3008,0.2362,0.3830,0.3759,0.3021,0.2909,0.2301,0.1411,0.1582,0.2430,0.4474,0.5964,0.6744,0.7969,0.8319,0.7813,0.8626,0.7369,0.4122,0.2596,0.3392,0.3788,0.4488,0.6281,0.7449,0.7328,0.7704,0.7870,0.6048,0.5860,0.6385,0.7279,0.6286,0.5316,0.4069,0.1791,0.1625,0.2527,0.1903,0.1643,0.0604,0.0209,0.0436,0.0175,0.0107,0.0193,0.0118,0.0064,0.0042,0.0054,0.0049,0.0082,0.0028,0.0027,R 22 | 0.0664,0.0575,0.0842,0.0372,0.0458,0.0771,0.0771,0.1130,0.2353,0.1838,0.2869,0.4129,0.3647,0.1984,0.2840,0.4039,0.5837,0.6792,0.6086,0.4858,0.3246,0.2013,0.2082,0.1686,0.2484,0.2736,0.2984,0.4655,0.6990,0.7474,0.7956,0.7981,0.6715,0.6942,0.7440,0.8169,0.8912,1.0000,0.8753,0.7061,0.6803,0.5898,0.4618,0.3639,0.1492,0.1216,0.1306,0.1198,0.0578,0.0235,0.0135,0.0141,0.0190,0.0043,0.0036,0.0026,0.0024,0.0162,0.0109,0.0079,R 23 | 0.0099,0.0484,0.0299,0.0297,0.0652,0.1077,0.2363,0.2385,0.0075,0.1882,0.1456,0.1892,0.3176,0.1340,0.2169,0.2458,0.2589,0.2786,0.2298,0.0656,0.1441,0.1179,0.1668,0.1783,0.2476,0.2570,0.1036,0.5356,0.7124,0.6291,0.4756,0.6015,0.7208,0.6234,0.5725,0.7523,0.8712,0.9252,0.9709,0.9297,0.8995,0.7911,0.5600,0.2838,0.4407,0.5507,0.4331,0.2905,0.1981,0.0779,0.0396,0.0173,0.0149,0.0115,0.0202,0.0139,0.0029,0.0160,0.0106,0.0134,R 24 | 0.0115,0.0150,0.0136,0.0076,0.0211,0.1058,0.1023,0.0440,0.0931,0.0734,0.0740,0.0622,0.1055,0.1183,0.1721,0.2584,0.3232,0.3817,0.4243,0.4217,0.4449,0.4075,0.3306,0.4012,0.4466,0.5218,0.7552,0.9503,1.0000,0.9084,0.8283,0.7571,0.7262,0.6152,0.5680,0.5757,0.5324,0.3672,0.1669,0.0866,0.0646,0.1891,0.2683,0.2887,0.2341,0.1668,0.1015,0.1195,0.0704,0.0167,0.0107,0.0091,0.0016,0.0084,0.0064,0.0026,0.0029,0.0037,0.0070,0.0041,R 25 | 0.0293,0.0644,0.0390,0.0173,0.0476,0.0816,0.0993,0.0315,0.0736,0.0860,0.0414,0.0472,0.0835,0.0938,0.1466,0.0809,0.1179,0.2179,0.3326,0.3258,0.2111,0.2302,0.3361,0.4259,0.4609,0.2606,0.0874,0.2862,0.5606,0.8344,0.8096,0.7250,0.8048,0.9435,1.0000,0.8960,0.5516,0.3037,0.2338,0.2382,0.3318,0.3821,0.1575,0.2228,0.1582,0.1433,0.1634,0.1133,0.0567,0.0133,0.0170,0.0035,0.0052,0.0083,0.0078,0.0075,0.0105,0.0160,0.0095,0.0011,R 26 | 0.0201,0.0026,0.0138,0.0062,0.0133,0.0151,0.0541,0.0210,0.0505,0.1097,0.0841,0.0942,0.1204,0.0420,0.0031,0.0162,0.0624,0.2127,0.3436,0.3813,0.3825,0.4764,0.6313,0.7523,0.8675,0.8788,0.7901,0.8357,0.9631,0.9619,0.9236,0.8903,0.9708,0.9647,0.7892,0.5307,0.2718,0.1953,0.1374,0.3105,0.3790,0.4105,0.3355,0.2998,0.2748,0.2024,0.1043,0.0453,0.0337,0.0122,0.0072,0.0108,0.0070,0.0063,0.0030,0.0011,0.0007,0.0024,0.0057,0.0044,R 27 | 0.0151,0.0320,0.0599,0.1050,0.1163,0.1734,0.1679,0.1119,0.0889,0.1205,0.0847,0.1518,0.2305,0.2793,0.3404,0.4527,0.6950,0.8807,0.9154,0.7542,0.6736,0.7146,0.8335,0.7701,0.6993,0.6543,0.5040,0.4926,0.4992,0.4161,0.1631,0.0404,0.0637,0.2962,0.3609,0.1866,0.0476,0.1497,0.2405,0.1980,0.3175,0.2379,0.1716,0.1559,0.1556,0.0422,0.0493,0.0476,0.0219,0.0059,0.0086,0.0061,0.0015,0.0084,0.0128,0.0054,0.0011,0.0019,0.0023,0.0062,R 28 | 0.0177,0.0300,0.0288,0.0394,0.0630,0.0526,0.0688,0.0633,0.0624,0.0613,0.1680,0.3476,0.4561,0.5188,0.6308,0.7201,0.5153,0.3818,0.2644,0.3345,0.4865,0.6628,0.7389,0.9213,1.0000,0.7750,0.5593,0.6172,0.8635,0.6592,0.4770,0.4983,0.3330,0.3076,0.2876,0.2226,0.0794,0.0603,0.1049,0.0606,0.1530,0.0983,0.1643,0.1901,0.1107,0.1917,0.1467,0.0392,0.0356,0.0270,0.0168,0.0102,0.0122,0.0044,0.0075,0.0124,0.0099,0.0057,0.0032,0.0019,R 29 | 0.0100,0.0275,0.0190,0.0371,0.0416,0.0201,0.0314,0.0651,0.1896,0.2668,0.3376,0.3282,0.2432,0.1268,0.1278,0.4441,0.6795,0.7051,0.7966,0.9401,0.9857,0.8193,0.5789,0.6394,0.7043,0.6875,0.4081,0.1811,0.2064,0.3917,0.3791,0.2042,0.2227,0.3341,0.3984,0.5077,0.5534,0.3352,0.2723,0.2278,0.2044,0.1986,0.0835,0.0908,0.1380,0.1948,0.1211,0.0843,0.0589,0.0247,0.0118,0.0088,0.0104,0.0036,0.0088,0.0047,0.0117,0.0020,0.0091,0.0058,R 30 | 0.0189,0.0308,0.0197,0.0622,0.0080,0.0789,0.1440,0.1451,0.1789,0.2522,0.2607,0.3710,0.3906,0.2672,0.2716,0.4183,0.6988,0.5733,0.2226,0.2631,0.7473,0.7263,0.3393,0.2824,0.6053,0.5897,0.4967,0.8616,0.8339,0.4084,0.2268,0.1745,0.0507,0.1588,0.3040,0.1369,0.1605,0.2061,0.0734,0.0202,0.1638,0.1583,0.1830,0.1886,0.1008,0.0663,0.0183,0.0404,0.0108,0.0143,0.0091,0.0038,0.0096,0.0142,0.0190,0.0140,0.0099,0.0092,0.0052,0.0075,R 31 | 0.0240,0.0218,0.0324,0.0569,0.0330,0.0513,0.0897,0.0713,0.0569,0.0389,0.1934,0.2434,0.2906,0.2606,0.3811,0.4997,0.3015,0.3655,0.6791,0.7307,0.5053,0.4441,0.6987,0.8133,0.7781,0.8943,0.8929,0.8913,0.8610,0.8063,0.5540,0.2446,0.3459,0.1615,0.2467,0.5564,0.4681,0.0979,0.1582,0.0751,0.3321,0.3745,0.2666,0.1078,0.1418,0.1687,0.0738,0.0634,0.0144,0.0226,0.0061,0.0162,0.0146,0.0093,0.0112,0.0094,0.0054,0.0019,0.0066,0.0023,R 32 | 0.0084,0.0153,0.0291,0.0432,0.0951,0.0752,0.0414,0.0259,0.0692,0.1753,0.1970,0.1167,0.1683,0.0814,0.2179,0.5121,0.7231,0.7776,0.6222,0.3501,0.3733,0.2622,0.3776,0.7361,0.8673,0.8223,0.7772,0.7862,0.5652,0.3635,0.3534,0.3865,0.3370,0.1693,0.2627,0.3195,0.1388,0.1048,0.1681,0.1910,0.1174,0.0933,0.0856,0.0951,0.0986,0.0956,0.0426,0.0407,0.0106,0.0179,0.0056,0.0236,0.0114,0.0136,0.0117,0.0060,0.0058,0.0031,0.0072,0.0045,R 33 | 0.0195,0.0213,0.0058,0.0190,0.0319,0.0571,0.1004,0.0668,0.0691,0.0242,0.0728,0.0639,0.3002,0.3854,0.4767,0.4602,0.3175,0.4160,0.6428,1.0000,0.8631,0.5212,0.3156,0.5952,0.7732,0.6042,0.4375,0.5487,0.4720,0.6235,0.3851,0.1590,0.3891,0.5294,0.3504,0.4480,0.4041,0.5031,0.6475,0.5493,0.3548,0.2028,0.1882,0.0845,0.1315,0.1590,0.0562,0.0617,0.0343,0.0370,0.0261,0.0157,0.0074,0.0271,0.0203,0.0089,0.0095,0.0095,0.0021,0.0053,R 34 | 0.0442,0.0477,0.0049,0.0581,0.0278,0.0678,0.1664,0.1490,0.0974,0.1268,0.1109,0.2375,0.2007,0.2140,0.1109,0.2036,0.2468,0.6682,0.8345,0.8252,0.8017,0.8982,0.9664,0.8515,0.6626,0.3241,0.2054,0.5669,0.5726,0.4877,0.7532,0.7600,0.5185,0.4120,0.5560,0.5569,0.1336,0.3831,0.4611,0.4330,0.2556,0.1466,0.3489,0.2659,0.0944,0.1370,0.1344,0.0416,0.0719,0.0637,0.0210,0.0204,0.0216,0.0135,0.0055,0.0073,0.0080,0.0105,0.0059,0.0105,R 35 | 0.0311,0.0491,0.0692,0.0831,0.0079,0.0200,0.0981,0.1016,0.2025,0.0767,0.1767,0.2555,0.2812,0.2722,0.3227,0.3463,0.5395,0.7911,0.9064,0.8701,0.7672,0.2957,0.4148,0.6043,0.3178,0.3482,0.6158,0.8049,0.6289,0.4999,0.5830,0.6660,0.4124,0.1260,0.2487,0.4676,0.5382,0.3150,0.2139,0.1848,0.1679,0.2328,0.1015,0.0713,0.0615,0.0779,0.0761,0.0845,0.0592,0.0068,0.0089,0.0087,0.0032,0.0130,0.0188,0.0101,0.0229,0.0182,0.0046,0.0038,R 36 | 0.0206,0.0132,0.0533,0.0569,0.0647,0.1432,0.1344,0.2041,0.1571,0.1573,0.2327,0.1785,0.1507,0.1916,0.2061,0.2307,0.2360,0.1299,0.3812,0.5858,0.4497,0.4876,1.0000,0.8675,0.4718,0.5341,0.6197,0.7143,0.5605,0.3728,0.2481,0.1921,0.1386,0.3325,0.2883,0.3228,0.2607,0.2040,0.2396,0.1319,0.0683,0.0334,0.0716,0.0976,0.0787,0.0522,0.0500,0.0231,0.0221,0.0144,0.0307,0.0386,0.0147,0.0018,0.0100,0.0096,0.0077,0.0180,0.0109,0.0070,R 37 | 0.0094,0.0166,0.0398,0.0359,0.0681,0.0706,0.1020,0.0893,0.0381,0.1328,0.1303,0.0273,0.0644,0.0712,0.1204,0.0717,0.1224,0.2349,0.3684,0.3918,0.4925,0.8793,0.9606,0.8786,0.6905,0.6937,0.5674,0.6540,0.7802,0.7575,0.5836,0.6316,0.8108,0.9039,0.8647,0.6695,0.4027,0.2370,0.2685,0.3662,0.3267,0.2200,0.2996,0.2205,0.1163,0.0635,0.0465,0.0422,0.0174,0.0172,0.0134,0.0141,0.0191,0.0145,0.0065,0.0129,0.0217,0.0087,0.0077,0.0122,R 38 | 0.0333,0.0221,0.0270,0.0481,0.0679,0.0981,0.0843,0.1172,0.0759,0.0920,0.1475,0.0522,0.1119,0.0970,0.1174,0.1678,0.1642,0.1205,0.0494,0.1544,0.3485,0.6146,0.9146,0.9364,0.8677,0.8772,0.8553,0.8833,1.0000,0.8296,0.6601,0.5499,0.5716,0.6859,0.6825,0.5142,0.2750,0.1358,0.1551,0.2646,0.1994,0.1883,0.2746,0.1651,0.0575,0.0695,0.0598,0.0456,0.0021,0.0068,0.0036,0.0022,0.0032,0.0060,0.0054,0.0063,0.0143,0.0132,0.0051,0.0041,R 39 | 0.0123,0.0022,0.0196,0.0206,0.0180,0.0492,0.0033,0.0398,0.0791,0.0475,0.1152,0.0520,0.1192,0.1943,0.1840,0.2077,0.1956,0.1630,0.1218,0.1017,0.1354,0.3157,0.4645,0.5906,0.6776,0.8119,0.8594,0.9228,0.8387,0.7238,0.6292,0.5181,0.4629,0.5255,0.5147,0.3929,0.1279,0.0411,0.0859,0.1131,0.1306,0.1757,0.2648,0.1955,0.0656,0.0580,0.0319,0.0301,0.0272,0.0074,0.0149,0.0125,0.0134,0.0026,0.0038,0.0018,0.0113,0.0058,0.0047,0.0071,R 40 | 0.0091,0.0213,0.0206,0.0505,0.0657,0.0795,0.0970,0.0872,0.0743,0.0837,0.1579,0.0898,0.0309,0.1856,0.2969,0.2032,0.1264,0.1655,0.1661,0.2091,0.2310,0.4460,0.6634,0.6933,0.7663,0.8206,0.7049,0.7560,0.7466,0.6387,0.4846,0.3328,0.5356,0.8741,0.8573,0.6718,0.3446,0.3150,0.2702,0.2598,0.2742,0.3594,0.4382,0.2460,0.0758,0.0187,0.0797,0.0748,0.0367,0.0155,0.0300,0.0112,0.0112,0.0102,0.0026,0.0097,0.0098,0.0043,0.0071,0.0108,R 41 | 0.0068,0.0232,0.0513,0.0444,0.0249,0.0637,0.0422,0.1130,0.1911,0.2475,0.1606,0.0922,0.2398,0.3220,0.4295,0.2652,0.0666,0.1442,0.2373,0.2595,0.2493,0.3903,0.6384,0.8037,0.7026,0.6874,0.6997,0.8558,1.0000,0.9621,0.8996,0.7575,0.6902,0.5686,0.4396,0.4546,0.2959,0.1587,0.1681,0.0842,0.1173,0.1754,0.2728,0.1705,0.0194,0.0213,0.0354,0.0420,0.0093,0.0204,0.0199,0.0173,0.0163,0.0055,0.0045,0.0068,0.0041,0.0052,0.0194,0.0105,R 42 | 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0.0093,0.0269,0.0217,0.0339,0.0305,0.1172,0.1450,0.0638,0.0740,0.1360,0.2132,0.3738,0.3738,0.2673,0.2333,0.5367,0.7312,0.7659,0.6271,0.4395,0.4330,0.4326,0.5544,0.7360,0.8589,0.8989,0.9420,0.9401,0.9379,0.8575,0.7284,0.6700,0.7547,0.8773,0.9919,0.9922,0.9419,0.8388,0.6605,0.4816,0.2917,0.1769,0.1136,0.0701,0.1578,0.1938,0.1106,0.0693,0.0176,0.0205,0.0309,0.0212,0.0091,0.0056,0.0086,0.0092,0.0070,0.0116,0.0060,0.0110,R 45 | 0.0257,0.0447,0.0388,0.0239,0.1315,0.1323,0.1608,0.2145,0.0847,0.0561,0.0891,0.0861,0.1531,0.1524,0.1849,0.2871,0.2009,0.2748,0.5017,0.2172,0.4978,0.5265,0.3647,0.5768,0.5161,0.5715,0.4006,0.3650,0.6685,0.8659,0.8052,0.4082,0.3379,0.5092,0.6776,0.7313,0.6062,0.7040,0.8849,0.8979,0.7751,0.7247,0.7733,0.7762,0.6009,0.4514,0.3096,0.1859,0.0956,0.0206,0.0206,0.0096,0.0153,0.0096,0.0131,0.0198,0.0025,0.0199,0.0255,0.0180,R 46 | 0.0408,0.0653,0.0397,0.0604,0.0496,0.1817,0.1178,0.1024,0.0583,0.2176,0.2459,0.3332,0.3087,0.2613,0.3232,0.3731,0.4203,0.5364,0.7062,0.8196,0.8835,0.8299,0.7609,0.7605,0.8367,0.8905,0.7652,0.5897,0.3037,0.0823,0.2787,0.7241,0.8032,0.8050,0.7676,0.7468,0.6253,0.1730,0.2916,0.5003,0.5220,0.4824,0.4004,0.3877,0.1651,0.0442,0.0663,0.0418,0.0475,0.0235,0.0066,0.0062,0.0129,0.0184,0.0069,0.0198,0.0199,0.0102,0.0070,0.0055,R 47 | 0.0308,0.0339,0.0202,0.0889,0.1570,0.1750,0.0920,0.1353,0.1593,0.2795,0.3336,0.2940,0.1608,0.3335,0.4985,0.7295,0.7350,0.8253,0.8793,0.9657,1.0000,0.8707,0.6471,0.5973,0.8218,0.7755,0.6111,0.4195,0.2990,0.1354,0.2438,0.5624,0.5555,0.6963,0.7298,0.7022,0.5468,0.1421,0.4738,0.6410,0.4375,0.3178,0.2377,0.2808,0.1374,0.1136,0.1034,0.0688,0.0422,0.0117,0.0070,0.0167,0.0127,0.0138,0.0090,0.0051,0.0029,0.0122,0.0056,0.0020,R 48 | 0.0373,0.0281,0.0232,0.0225,0.0179,0.0733,0.0841,0.1031,0.0993,0.0802,0.1564,0.2565,0.2624,0.1179,0.0597,0.1563,0.2241,0.3586,0.1792,0.3256,0.6079,0.6988,0.8391,0.8553,0.7710,0.6215,0.5736,0.4402,0.4056,0.4411,0.5130,0.5965,0.7272,0.6539,0.5902,0.5393,0.4897,0.4081,0.4145,0.6003,0.7196,0.6633,0.6287,0.4087,0.3212,0.2518,0.1482,0.0988,0.0317,0.0269,0.0066,0.0008,0.0045,0.0024,0.0006,0.0073,0.0096,0.0054,0.0085,0.0060,R 49 | 0.0190,0.0038,0.0642,0.0452,0.0333,0.0690,0.0901,0.1454,0.0740,0.0349,0.1459,0.3473,0.3197,0.2823,0.0166,0.0572,0.2164,0.4563,0.3819,0.5627,0.6484,0.7235,0.8242,0.8766,1.0000,0.8582,0.6563,0.5087,0.4817,0.4530,0.4521,0.4532,0.5385,0.5308,0.5356,0.5271,0.4260,0.2436,0.1205,0.3845,0.4107,0.5067,0.4216,0.2479,0.1586,0.1124,0.0651,0.0789,0.0325,0.0070,0.0026,0.0093,0.0118,0.0112,0.0094,0.0140,0.0072,0.0022,0.0055,0.0122,R 50 | 0.0119,0.0582,0.0623,0.0600,0.1397,0.1883,0.1422,0.1447,0.0487,0.0864,0.2143,0.3720,0.2665,0.2113,0.1103,0.1136,0.1934,0.4142,0.3279,0.6222,0.7468,0.7676,0.7867,0.8253,1.0000,0.9481,0.7539,0.6008,0.5437,0.5387,0.5619,0.5141,0.6084,0.5621,0.5956,0.6078,0.5025,0.2829,0.0477,0.2811,0.3422,0.5147,0.4372,0.2470,0.1708,0.1343,0.0838,0.0755,0.0304,0.0074,0.0069,0.0025,0.0103,0.0074,0.0123,0.0069,0.0076,0.0073,0.0030,0.0138,R 51 | 0.0353,0.0713,0.0326,0.0272,0.0370,0.0792,0.1083,0.0687,0.0298,0.0880,0.1078,0.0979,0.2250,0.2819,0.2099,0.1240,0.1699,0.0939,0.1091,0.1410,0.1268,0.3151,0.1430,0.2264,0.5756,0.7876,0.7158,0.5998,0.5583,0.6295,0.7659,0.8940,0.8436,0.6807,0.8380,1.0000,0.9497,0.7866,0.5647,0.3480,0.2585,0.2304,0.2948,0.3363,0.3017,0.2193,0.1316,0.1078,0.0559,0.0035,0.0098,0.0163,0.0242,0.0043,0.0202,0.0108,0.0037,0.0096,0.0093,0.0053,R 52 | 0.0131,0.0068,0.0308,0.0311,0.0085,0.0767,0.0771,0.0640,0.0726,0.0901,0.0750,0.0844,0.1226,0.1619,0.2317,0.2934,0.3526,0.3657,0.3221,0.3093,0.4084,0.4285,0.4663,0.5956,0.6948,0.8386,0.8875,0.6404,0.3308,0.3425,0.4920,0.4592,0.3034,0.4366,0.5175,0.5122,0.4746,0.4902,0.4603,0.4460,0.4196,0.2873,0.2296,0.0949,0.0095,0.0527,0.0383,0.0107,0.0108,0.0077,0.0109,0.0062,0.0028,0.0040,0.0075,0.0039,0.0053,0.0013,0.0052,0.0023,R 53 | 0.0087,0.0046,0.0081,0.0230,0.0586,0.0682,0.0993,0.0717,0.0576,0.0818,0.1315,0.1862,0.2789,0.2579,0.2240,0.2568,0.2933,0.2991,0.3924,0.4691,0.5665,0.6464,0.6774,0.7577,0.8856,0.9419,1.0000,0.8564,0.6790,0.5587,0.4147,0.2946,0.2025,0.0688,0.1171,0.2157,0.2216,0.2776,0.2309,0.1444,0.1513,0.1745,0.1756,0.1424,0.0908,0.0138,0.0469,0.0480,0.0159,0.0045,0.0015,0.0052,0.0038,0.0079,0.0114,0.0050,0.0030,0.0064,0.0058,0.0030,R 54 | 0.0293,0.0378,0.0257,0.0062,0.0130,0.0612,0.0895,0.1107,0.0973,0.0751,0.0528,0.1209,0.1763,0.2039,0.2727,0.2321,0.2676,0.2934,0.3295,0.4910,0.5402,0.6257,0.6826,0.7527,0.8504,0.8938,0.9928,0.9134,0.7080,0.6318,0.6126,0.4638,0.2797,0.1721,0.1665,0.2561,0.2735,0.3209,0.2724,0.1880,0.1552,0.2522,0.2121,0.1801,0.1473,0.0681,0.1091,0.0919,0.0397,0.0093,0.0076,0.0065,0.0072,0.0108,0.0051,0.0102,0.0041,0.0055,0.0050,0.0087,R 55 | 0.0132,0.0080,0.0188,0.0141,0.0436,0.0668,0.0609,0.0131,0.0899,0.0922,0.1445,0.1475,0.2087,0.2558,0.2603,0.1985,0.2394,0.3134,0.4077,0.4529,0.4893,0.5666,0.6234,0.6741,0.8282,0.8823,0.9196,0.8965,0.7549,0.6736,0.6463,0.5007,0.3663,0.2298,0.1362,0.2123,0.2395,0.2673,0.2865,0.2060,0.1659,0.2633,0.2552,0.1696,0.1467,0.1286,0.0926,0.0716,0.0325,0.0258,0.0136,0.0044,0.0028,0.0021,0.0022,0.0048,0.0138,0.0140,0.0028,0.0064,R 56 | 0.0201,0.0116,0.0123,0.0245,0.0547,0.0208,0.0891,0.0836,0.1335,0.1199,0.1742,0.1387,0.2042,0.2580,0.2616,0.2097,0.2532,0.3213,0.4327,0.4760,0.5328,0.6057,0.6696,0.7476,0.8930,0.9405,1.0000,0.9785,0.8473,0.7639,0.6701,0.4989,0.3718,0.2196,0.1416,0.2680,0.2630,0.3104,0.3392,0.2123,0.1170,0.2655,0.2203,0.1541,0.1464,0.1044,0.1225,0.0745,0.0490,0.0224,0.0032,0.0076,0.0045,0.0056,0.0075,0.0037,0.0045,0.0029,0.0008,0.0018,R 57 | 0.0152,0.0102,0.0113,0.0263,0.0097,0.0391,0.0857,0.0915,0.0949,0.1504,0.1911,0.2115,0.2249,0.2573,0.1701,0.2023,0.2538,0.3417,0.4026,0.4553,0.5525,0.5991,0.5854,0.7114,0.9500,0.9858,1.0000,0.9578,0.8642,0.7128,0.5893,0.4323,0.2897,0.1744,0.0770,0.2297,0.2459,0.3101,0.3312,0.2220,0.0871,0.2064,0.1808,0.1624,0.1120,0.0815,0.1117,0.0950,0.0412,0.0120,0.0048,0.0049,0.0041,0.0036,0.0013,0.0046,0.0037,0.0011,0.0034,0.0033,R 58 | 0.0216,0.0124,0.0174,0.0152,0.0608,0.1026,0.1139,0.0877,0.1160,0.0866,0.1564,0.0780,0.0997,0.0915,0.0662,0.1134,0.1740,0.2573,0.3294,0.3910,0.5438,0.6115,0.7022,0.7610,0.7973,0.9105,0.8807,0.7949,0.7990,0.7180,0.6407,0.6312,0.5929,0.6168,0.6498,0.6764,0.6253,0.5117,0.3890,0.3273,0.2509,0.1530,0.1323,0.1657,0.1215,0.0978,0.0452,0.0273,0.0179,0.0092,0.0018,0.0052,0.0049,0.0096,0.0134,0.0122,0.0047,0.0018,0.0006,0.0023,R 59 | 0.0225,0.0019,0.0075,0.0097,0.0445,0.0906,0.0889,0.0655,0.1624,0.1452,0.1442,0.0948,0.0618,0.1641,0.0708,0.0844,0.2590,0.2679,0.3094,0.4678,0.5958,0.7245,0.8773,0.9214,0.9282,0.9942,1.0000,0.9071,0.8545,0.7293,0.6499,0.6071,0.5588,0.5967,0.6275,0.5459,0.4786,0.3965,0.2087,0.1651,0.1836,0.0652,0.0758,0.0486,0.0353,0.0297,0.0241,0.0379,0.0119,0.0073,0.0051,0.0034,0.0129,0.0100,0.0044,0.0057,0.0030,0.0035,0.0021,0.0027,R 60 | 0.0125,0.0152,0.0218,0.0175,0.0362,0.0696,0.0873,0.0616,0.1252,0.1302,0.0888,0.0500,0.0628,0.1274,0.0801,0.0742,0.2048,0.2950,0.3193,0.4567,0.5959,0.7101,0.8225,0.8425,0.9065,0.9802,1.0000,0.8752,0.7583,0.6616,0.5786,0.5128,0.4776,0.4994,0.5197,0.5071,0.4577,0.3505,0.1845,0.1890,0.1967,0.1041,0.0550,0.0492,0.0622,0.0505,0.0247,0.0219,0.0102,0.0047,0.0019,0.0041,0.0074,0.0030,0.0050,0.0048,0.0017,0.0041,0.0086,0.0058,R 61 | 0.0130,0.0006,0.0088,0.0456,0.0525,0.0778,0.0931,0.0941,0.1711,0.1483,0.1532,0.1100,0.0890,0.1236,0.1197,0.1145,0.2137,0.2838,0.3640,0.5430,0.6673,0.7979,0.9273,0.9027,0.9192,1.0000,0.9821,0.9092,0.8184,0.6962,0.5900,0.5447,0.5142,0.5389,0.5531,0.5318,0.4826,0.3790,0.1831,0.1750,0.1679,0.0674,0.0609,0.0375,0.0533,0.0278,0.0179,0.0114,0.0073,0.0116,0.0092,0.0078,0.0041,0.0013,0.0011,0.0045,0.0039,0.0022,0.0023,0.0016,R 62 | 0.0135,0.0045,0.0051,0.0289,0.0561,0.0929,0.1031,0.0883,0.1596,0.1908,0.1576,0.1112,0.1197,0.1174,0.1415,0.2215,0.2658,0.2713,0.3862,0.5717,0.6797,0.8747,1.0000,0.8948,0.8420,0.9174,0.9307,0.9050,0.8228,0.6986,0.5831,0.4924,0.4563,0.5159,0.5670,0.5284,0.5144,0.3742,0.2282,0.1193,0.1088,0.0431,0.1070,0.0583,0.0046,0.0473,0.0408,0.0290,0.0192,0.0094,0.0025,0.0037,0.0084,0.0102,0.0096,0.0024,0.0037,0.0028,0.0030,0.0030,R 63 | 0.0086,0.0215,0.0242,0.0445,0.0667,0.0771,0.0499,0.0906,0.1229,0.1185,0.0775,0.1101,0.1042,0.0853,0.0456,0.1304,0.2690,0.2947,0.3669,0.4948,0.6275,0.8162,0.9237,0.8710,0.8052,0.8756,1.0000,0.9858,0.9427,0.8114,0.6987,0.6810,0.6591,0.6954,0.7290,0.6680,0.5917,0.4899,0.3439,0.2366,0.1716,0.1013,0.0766,0.0845,0.0260,0.0333,0.0205,0.0309,0.0101,0.0095,0.0047,0.0072,0.0054,0.0022,0.0016,0.0029,0.0058,0.0050,0.0024,0.0030,R 64 | 0.0067,0.0096,0.0024,0.0058,0.0197,0.0618,0.0432,0.0951,0.0836,0.1180,0.0978,0.0909,0.0656,0.0593,0.0832,0.1297,0.2038,0.3811,0.4451,0.5224,0.5911,0.6566,0.6308,0.5998,0.4958,0.5647,0.6906,0.8513,1.0000,0.9166,0.7676,0.6177,0.5468,0.5516,0.5463,0.5515,0.4561,0.3466,0.3384,0.2853,0.2502,0.1641,0.1605,0.1491,0.1326,0.0687,0.0602,0.0561,0.0306,0.0154,0.0029,0.0048,0.0023,0.0020,0.0040,0.0019,0.0034,0.0034,0.0051,0.0031,R 65 | 0.0071,0.0103,0.0135,0.0494,0.0253,0.0806,0.0701,0.0738,0.0117,0.0898,0.0289,0.1554,0.1437,0.1035,0.1424,0.1227,0.0892,0.2047,0.0827,0.1524,0.3031,0.1608,0.0667,0.1426,0.0395,0.1653,0.3399,0.4855,0.5206,0.5508,0.6102,0.5989,0.6764,0.8897,1.0000,0.9517,0.8459,0.7073,0.6697,0.6326,0.5102,0.4161,0.2816,0.1705,0.1421,0.0971,0.0879,0.0863,0.0355,0.0233,0.0252,0.0043,0.0048,0.0076,0.0124,0.0105,0.0054,0.0032,0.0073,0.0063,R 66 | 0.0176,0.0172,0.0501,0.0285,0.0262,0.0351,0.0362,0.0535,0.0258,0.0474,0.0526,0.1854,0.1040,0.0948,0.0912,0.1688,0.1568,0.0375,0.1316,0.2086,0.1976,0.0946,0.1965,0.1242,0.0616,0.2141,0.4642,0.6471,0.6340,0.6107,0.7046,0.5376,0.5934,0.8443,0.9481,0.9705,0.7766,0.6313,0.5760,0.6148,0.5450,0.4813,0.3406,0.1916,0.1134,0.0640,0.0911,0.0980,0.0563,0.0187,0.0088,0.0042,0.0175,0.0171,0.0079,0.0050,0.0112,0.0179,0.0294,0.0063,R 67 | 0.0265,0.0440,0.0137,0.0084,0.0305,0.0438,0.0341,0.0780,0.0844,0.0779,0.0327,0.2060,0.1908,0.1065,0.1457,0.2232,0.2070,0.1105,0.1078,0.1165,0.2224,0.0689,0.2060,0.2384,0.0904,0.2278,0.5872,0.8457,0.8467,0.7679,0.8055,0.6260,0.6545,0.8747,0.9885,0.9348,0.6960,0.5733,0.5872,0.6663,0.5651,0.5247,0.3684,0.1997,0.1512,0.0508,0.0931,0.0982,0.0524,0.0188,0.0100,0.0038,0.0187,0.0156,0.0068,0.0097,0.0073,0.0081,0.0086,0.0095,R 68 | 0.0368,0.0403,0.0317,0.0293,0.0820,0.1342,0.1161,0.0663,0.0155,0.0506,0.0906,0.2545,0.1464,0.1272,0.1223,0.1669,0.1424,0.1285,0.1857,0.1136,0.2069,0.0219,0.2400,0.2547,0.0240,0.1923,0.4753,0.7003,0.6825,0.6443,0.7063,0.5373,0.6601,0.8708,0.9518,0.9605,0.7712,0.6772,0.6431,0.6720,0.6035,0.5155,0.3802,0.2278,0.1522,0.0801,0.0804,0.0752,0.0566,0.0175,0.0058,0.0091,0.0160,0.0160,0.0081,0.0070,0.0135,0.0067,0.0078,0.0068,R 69 | 0.0195,0.0142,0.0181,0.0406,0.0391,0.0249,0.0892,0.0973,0.0840,0.1191,0.1522,0.1322,0.1434,0.1244,0.0653,0.0890,0.1226,0.1846,0.3880,0.3658,0.2297,0.2610,0.4193,0.5848,0.5643,0.5448,0.4772,0.6897,0.9797,1.0000,0.9546,0.8835,0.7662,0.6547,0.5447,0.4593,0.4679,0.1987,0.0699,0.1493,0.1713,0.1654,0.2600,0.3846,0.3754,0.2414,0.1077,0.0224,0.0155,0.0187,0.0125,0.0028,0.0067,0.0120,0.0012,0.0022,0.0058,0.0042,0.0067,0.0012,R 70 | 0.0216,0.0215,0.0273,0.0139,0.0357,0.0785,0.0906,0.0908,0.1151,0.0973,0.1203,0.1102,0.1192,0.1762,0.2390,0.2138,0.1929,0.1765,0.0746,0.1265,0.2005,0.1571,0.2605,0.5386,0.8440,1.0000,0.8684,0.6742,0.5537,0.4638,0.3609,0.2055,0.1620,0.2092,0.3100,0.2344,0.1058,0.0383,0.0528,0.1291,0.2241,0.1915,0.1587,0.0942,0.0840,0.0670,0.0342,0.0469,0.0357,0.0136,0.0082,0.0140,0.0044,0.0052,0.0073,0.0021,0.0047,0.0024,0.0009,0.0017,R 71 | 0.0065,0.0122,0.0068,0.0108,0.0217,0.0284,0.0527,0.0575,0.1054,0.1109,0.0937,0.0827,0.0920,0.0911,0.1487,0.1666,0.1268,0.1374,0.1095,0.1286,0.2146,0.2889,0.4238,0.6168,0.8167,0.9622,0.8280,0.5816,0.4667,0.3539,0.2727,0.1410,0.1863,0.2176,0.2360,0.1725,0.0589,0.0621,0.1847,0.2452,0.2984,0.3041,0.2275,0.1480,0.1102,0.1178,0.0608,0.0333,0.0276,0.0100,0.0023,0.0069,0.0025,0.0027,0.0052,0.0036,0.0026,0.0036,0.0006,0.0035,R 72 | 0.0036,0.0078,0.0092,0.0387,0.0530,0.1197,0.1243,0.1026,0.1239,0.0888,0.0937,0.1245,0.1599,0.1542,0.1846,0.1732,0.1477,0.1748,0.1455,0.1579,0.2257,0.1975,0.3368,0.5828,0.8505,1.0000,0.8457,0.6624,0.5564,0.3925,0.3233,0.2054,0.1920,0.2227,0.3147,0.2268,0.0795,0.0748,0.1166,0.1969,0.2619,0.2507,0.1983,0.0948,0.0931,0.0965,0.0381,0.0435,0.0336,0.0055,0.0079,0.0119,0.0055,0.0035,0.0036,0.0004,0.0018,0.0049,0.0024,0.0016,R 73 | 0.0208,0.0186,0.0131,0.0211,0.0610,0.0613,0.0612,0.0506,0.0989,0.1093,0.1063,0.1179,0.1291,0.1591,0.1680,0.1918,0.1615,0.1647,0.1397,0.1426,0.2429,0.2816,0.4290,0.6443,0.9061,1.0000,0.8087,0.6119,0.5260,0.3677,0.2746,0.1020,0.1339,0.1582,0.1952,0.1787,0.0429,0.1096,0.1762,0.2481,0.3150,0.2920,0.1902,0.0696,0.0758,0.0910,0.0441,0.0244,0.0265,0.0095,0.0140,0.0074,0.0063,0.0081,0.0087,0.0044,0.0028,0.0019,0.0049,0.0023,R 74 | 0.0139,0.0222,0.0089,0.0108,0.0215,0.0136,0.0659,0.0954,0.0786,0.1015,0.1261,0.0828,0.0493,0.0848,0.1514,0.1396,0.1066,0.1923,0.2991,0.3247,0.3797,0.5658,0.7483,0.8757,0.9048,0.7511,0.6858,0.7043,0.5864,0.3773,0.2206,0.2628,0.2672,0.2907,0.1982,0.2288,0.3186,0.2871,0.2921,0.2806,0.2682,0.2112,0.1513,0.1789,0.1850,0.1717,0.0898,0.0656,0.0445,0.0110,0.0024,0.0062,0.0072,0.0113,0.0012,0.0022,0.0025,0.0059,0.0039,0.0048,R 75 | 0.0109,0.0093,0.0121,0.0378,0.0679,0.0863,0.1004,0.0664,0.0941,0.1036,0.0972,0.0501,0.1546,0.3404,0.4804,0.6570,0.7738,0.7827,0.8152,0.8129,0.8297,0.8535,0.8870,0.8894,0.8980,0.9667,1.0000,0.9134,0.6762,0.4659,0.2895,0.2959,0.1746,0.2112,0.2569,0.2276,0.2149,0.1601,0.0371,0.0117,0.0488,0.0288,0.0597,0.0431,0.0369,0.0025,0.0327,0.0257,0.0182,0.0108,0.0124,0.0077,0.0023,0.0117,0.0053,0.0077,0.0076,0.0056,0.0055,0.0039,R 76 | 0.0202,0.0104,0.0325,0.0239,0.0807,0.1529,0.1154,0.0608,0.1317,0.1370,0.0843,0.0269,0.1254,0.3046,0.5584,0.7973,0.8341,0.8057,0.8616,0.8769,0.9413,0.9403,0.9409,1.0000,0.9725,0.9309,0.9351,0.7317,0.4421,0.3244,0.4161,0.4611,0.4031,0.3000,0.2459,0.1348,0.2541,0.2255,0.1598,0.1485,0.0845,0.0569,0.0855,0.1262,0.1153,0.0570,0.0426,0.0425,0.0235,0.0006,0.0188,0.0127,0.0081,0.0067,0.0043,0.0065,0.0049,0.0054,0.0073,0.0054,R 77 | 0.0239,0.0189,0.0466,0.0440,0.0657,0.0742,0.1380,0.1099,0.1384,0.1376,0.0938,0.0259,0.1499,0.2851,0.5743,0.8278,0.8669,0.8131,0.9045,0.9046,1.0000,0.9976,0.9872,0.9761,0.9009,0.9724,0.9675,0.7633,0.4434,0.3822,0.4727,0.4007,0.3381,0.3172,0.2222,0.0733,0.2692,0.1888,0.0712,0.1062,0.0694,0.0300,0.0893,0.1459,0.1348,0.0391,0.0546,0.0469,0.0201,0.0095,0.0155,0.0091,0.0151,0.0080,0.0018,0.0078,0.0045,0.0026,0.0036,0.0024,R 78 | 0.0336,0.0294,0.0476,0.0539,0.0794,0.0804,0.1136,0.1228,0.1235,0.0842,0.0357,0.0689,0.1705,0.3257,0.4602,0.6225,0.7327,0.7843,0.7988,0.8261,1.0000,0.9814,0.9620,0.9601,0.9118,0.9086,0.7931,0.5877,0.3474,0.4235,0.4633,0.3410,0.2849,0.2847,0.1742,0.0549,0.1192,0.1154,0.0855,0.1811,0.1264,0.0799,0.0378,0.1268,0.1125,0.0505,0.0949,0.0677,0.0259,0.0170,0.0033,0.0150,0.0111,0.0032,0.0035,0.0169,0.0137,0.0015,0.0069,0.0051,R 79 | 0.0231,0.0351,0.0030,0.0304,0.0339,0.0860,0.1738,0.1351,0.1063,0.0347,0.0575,0.1382,0.2274,0.4038,0.5223,0.6847,0.7521,0.7760,0.7708,0.8627,1.0000,0.8873,0.8057,0.8760,0.9066,0.9430,0.8846,0.6500,0.2970,0.2423,0.2992,0.2285,0.2277,0.1529,0.1037,0.0352,0.1073,0.1373,0.1331,0.1454,0.1115,0.0440,0.0762,0.1381,0.0831,0.0654,0.0844,0.0595,0.0497,0.0313,0.0154,0.0106,0.0097,0.0022,0.0052,0.0072,0.0056,0.0038,0.0043,0.0030,R 80 | 0.0108,0.0086,0.0058,0.0460,0.0752,0.0887,0.1015,0.0494,0.0472,0.0393,0.1106,0.1412,0.2202,0.2976,0.4116,0.4754,0.5390,0.6279,0.7060,0.7918,0.9493,1.0000,0.9645,0.9432,0.8658,0.7895,0.6501,0.4492,0.4739,0.6153,0.4929,0.3195,0.3735,0.3336,0.1052,0.0671,0.0379,0.0461,0.1694,0.2169,0.1677,0.0644,0.0159,0.0778,0.0653,0.0210,0.0509,0.0387,0.0262,0.0101,0.0161,0.0029,0.0078,0.0114,0.0083,0.0058,0.0003,0.0023,0.0026,0.0027,R 81 | 0.0229,0.0369,0.0040,0.0375,0.0455,0.1452,0.2211,0.1188,0.0750,0.1631,0.2709,0.3358,0.4091,0.4400,0.5485,0.7213,0.8137,0.9185,1.0000,0.9418,0.9116,0.9349,0.7484,0.5146,0.4106,0.3443,0.6981,0.8713,0.9013,0.8014,0.4380,0.1319,0.1709,0.2484,0.3044,0.2312,0.1338,0.2056,0.2474,0.2790,0.1610,0.0056,0.0351,0.1148,0.1331,0.0276,0.0763,0.0631,0.0309,0.0240,0.0115,0.0064,0.0022,0.0122,0.0151,0.0056,0.0026,0.0029,0.0104,0.0163,R 82 | 0.0100,0.0194,0.0155,0.0489,0.0839,0.1009,0.1627,0.2071,0.2696,0.2990,0.3242,0.3565,0.3951,0.5201,0.6953,0.8468,1.0000,0.9278,0.8510,0.8010,0.8142,0.8825,0.7302,0.6107,0.7159,0.8458,0.6319,0.4808,0.6291,0.7152,0.6005,0.4235,0.4106,0.3992,0.1730,0.1975,0.2370,0.1339,0.1583,0.3151,0.1968,0.2054,0.1272,0.1129,0.1946,0.2195,0.1930,0.1498,0.0773,0.0196,0.0122,0.0130,0.0073,0.0077,0.0075,0.0060,0.0080,0.0019,0.0053,0.0019,R 83 | 0.0409,0.0421,0.0573,0.0130,0.0183,0.1019,0.1054,0.1070,0.2302,0.2259,0.2373,0.3323,0.3827,0.4840,0.6812,0.7555,0.9522,0.9826,0.8871,0.8268,0.7561,0.8217,0.6967,0.6444,0.6948,0.8014,0.6053,0.6084,0.8877,0.8557,0.5563,0.2897,0.3638,0.4786,0.2908,0.0899,0.2043,0.1707,0.0407,0.1286,0.1581,0.2191,0.1701,0.0971,0.2217,0.2732,0.1874,0.1062,0.0665,0.0405,0.0113,0.0028,0.0036,0.0105,0.0120,0.0087,0.0061,0.0061,0.0030,0.0078,R 84 | 0.0217,0.0340,0.0392,0.0236,0.1081,0.1164,0.1398,0.1009,0.1147,0.1777,0.4079,0.4113,0.3973,0.5078,0.6509,0.8073,0.9819,1.0000,0.9407,0.8452,0.8106,0.8460,0.6212,0.5815,0.7745,0.8204,0.5601,0.2989,0.5009,0.6628,0.5753,0.4055,0.3746,0.3481,0.1580,0.1422,0.2130,0.1866,0.1003,0.2396,0.2241,0.2029,0.0710,0.1606,0.1669,0.1700,0.1829,0.1403,0.0506,0.0224,0.0095,0.0031,0.0103,0.0078,0.0077,0.0094,0.0031,0.0030,0.0013,0.0069,R 85 | 0.0378,0.0318,0.0423,0.0350,0.1787,0.1635,0.0887,0.0817,0.1779,0.2053,0.3135,0.3118,0.3686,0.3885,0.5850,0.7868,0.9739,1.0000,0.9843,0.8610,0.8443,0.9061,0.5847,0.4033,0.5946,0.6793,0.6389,0.5002,0.5578,0.4831,0.4729,0.3318,0.3969,0.3894,0.2314,0.1036,0.1312,0.0864,0.2569,0.3179,0.2649,0.2714,0.1713,0.0584,0.1230,0.2200,0.2198,0.1074,0.0423,0.0162,0.0093,0.0046,0.0044,0.0078,0.0102,0.0065,0.0061,0.0062,0.0043,0.0053,R 86 | 0.0365,0.1632,0.1636,0.1421,0.1130,0.1306,0.2112,0.2268,0.2992,0.3735,0.3042,0.0387,0.2679,0.5397,0.6204,0.7257,0.8350,0.6888,0.4450,0.3921,0.5605,0.7545,0.8311,1.0000,0.8762,0.7092,0.7009,0.5014,0.3942,0.4456,0.4072,0.0773,0.1423,0.0401,0.3597,0.6847,0.7076,0.3597,0.0612,0.3027,0.3966,0.3868,0.2380,0.2059,0.2288,0.1704,0.1587,0.1792,0.1022,0.0151,0.0223,0.0110,0.0071,0.0205,0.0164,0.0063,0.0078,0.0094,0.0110,0.0068,R 87 | 0.0188,0.0370,0.0953,0.0824,0.0249,0.0488,0.1424,0.1972,0.1873,0.1806,0.2139,0.1523,0.1975,0.4844,0.7298,0.7807,0.7906,0.6122,0.4200,0.2807,0.5148,0.7569,0.8596,1.0000,0.8457,0.6797,0.6971,0.5843,0.4772,0.5201,0.4241,0.1592,0.1668,0.0588,0.3967,0.7147,0.7319,0.3509,0.0589,0.2690,0.4200,0.3874,0.2440,0.2000,0.2307,0.1886,0.1960,0.1701,0.1366,0.0398,0.0143,0.0093,0.0033,0.0113,0.0030,0.0057,0.0090,0.0057,0.0068,0.0024,R 88 | 0.0856,0.0454,0.0382,0.0203,0.0385,0.0534,0.2140,0.3110,0.2837,0.2751,0.2707,0.0946,0.1020,0.4519,0.6737,0.6699,0.7066,0.5632,0.3785,0.2721,0.5297,0.7697,0.8643,0.9304,0.9372,0.6247,0.6024,0.6810,0.5047,0.5775,0.4754,0.2400,0.2779,0.1997,0.5305,0.7409,0.7775,0.4424,0.1416,0.3508,0.4482,0.4208,0.3054,0.2235,0.2611,0.2798,0.2392,0.2021,0.1326,0.0358,0.0128,0.0172,0.0138,0.0079,0.0037,0.0051,0.0258,0.0102,0.0037,0.0037,R 89 | 0.0274,0.0242,0.0621,0.0560,0.1129,0.0973,0.1823,0.1745,0.1440,0.1808,0.2366,0.0906,0.1749,0.4012,0.5187,0.7312,0.9062,0.9260,0.7434,0.4463,0.5103,0.6952,0.7755,0.8364,0.7283,0.6399,0.5759,0.4146,0.3495,0.4437,0.2665,0.2024,0.1942,0.0765,0.3725,0.5843,0.4827,0.2347,0.0999,0.3244,0.3990,0.2975,0.1684,0.1761,0.1683,0.0729,0.1190,0.1297,0.0748,0.0067,0.0255,0.0113,0.0108,0.0085,0.0047,0.0074,0.0104,0.0161,0.0220,0.0173,R 90 | 0.0235,0.0291,0.0749,0.0519,0.0227,0.0834,0.0677,0.2002,0.2876,0.3674,0.2974,0.0837,0.1912,0.5040,0.6352,0.6804,0.7505,0.6595,0.4509,0.2964,0.4019,0.6794,0.8297,1.0000,0.8240,0.7115,0.7726,0.6124,0.4936,0.5648,0.4906,0.1820,0.1811,0.1107,0.4603,0.6650,0.6423,0.2166,0.1951,0.4947,0.4925,0.4041,0.2402,0.1392,0.1779,0.1946,0.1723,0.1522,0.0929,0.0179,0.0242,0.0083,0.0037,0.0095,0.0105,0.0030,0.0132,0.0068,0.0108,0.0090,R 91 | 0.0126,0.0519,0.0621,0.0518,0.1072,0.2587,0.2304,0.2067,0.3416,0.4284,0.3015,0.1207,0.3299,0.5707,0.6962,0.9751,1.0000,0.9293,0.6210,0.4586,0.5001,0.5032,0.7082,0.8420,0.8109,0.7690,0.8105,0.6203,0.2356,0.2595,0.6299,0.6762,0.2903,0.4393,0.8529,0.7180,0.4801,0.5856,0.4993,0.2866,0.0601,0.1167,0.2737,0.2812,0.2078,0.0660,0.0491,0.0345,0.0172,0.0287,0.0027,0.0208,0.0048,0.0199,0.0126,0.0022,0.0037,0.0034,0.0114,0.0077,R 92 | 0.0253,0.0808,0.0507,0.0244,0.1724,0.3823,0.3729,0.3583,0.3429,0.2197,0.2653,0.3223,0.5582,0.6916,0.7943,0.7152,0.3512,0.2008,0.2676,0.4299,0.5280,0.3489,0.1430,0.5453,0.6338,0.7712,0.6838,0.8015,0.8073,0.8310,0.7792,0.5049,0.1413,0.2767,0.5084,0.4787,0.1356,0.2299,0.2789,0.3833,0.2933,0.1155,0.1705,0.1294,0.0909,0.0800,0.0567,0.0198,0.0114,0.0151,0.0085,0.0178,0.0073,0.0079,0.0038,0.0116,0.0033,0.0039,0.0081,0.0053,R 93 | 0.0260,0.0192,0.0254,0.0061,0.0352,0.0701,0.1263,0.1080,0.1523,0.1630,0.1030,0.2187,0.1542,0.2630,0.2940,0.2978,0.0699,0.1401,0.2990,0.3915,0.3598,0.2403,0.4208,0.5675,0.6094,0.6323,0.6549,0.7673,1.0000,0.8463,0.5509,0.4444,0.5169,0.4268,0.1802,0.0791,0.0535,0.1906,0.2561,0.2153,0.2769,0.2841,0.1733,0.0815,0.0335,0.0933,0.1018,0.0309,0.0208,0.0318,0.0132,0.0118,0.0120,0.0051,0.0070,0.0015,0.0035,0.0008,0.0044,0.0077,R 94 | 0.0459,0.0437,0.0347,0.0456,0.0067,0.0890,0.1798,0.1741,0.1598,0.1408,0.2693,0.3259,0.4545,0.5785,0.4471,0.2231,0.2164,0.3201,0.2915,0.4235,0.4460,0.2380,0.6415,0.8966,0.8918,0.7529,0.6838,0.8390,1.0000,0.8362,0.5427,0.4577,0.8067,0.6973,0.3915,0.1558,0.1598,0.2161,0.5178,0.4782,0.2344,0.3599,0.2785,0.1807,0.0352,0.0473,0.0322,0.0408,0.0163,0.0088,0.0121,0.0067,0.0032,0.0109,0.0164,0.0151,0.0070,0.0085,0.0117,0.0056,R 95 | 0.0025,0.0309,0.0171,0.0228,0.0434,0.1224,0.1947,0.1661,0.1368,0.1430,0.0994,0.2250,0.2444,0.3239,0.3039,0.2410,0.0367,0.1672,0.3038,0.4069,0.3613,0.1994,0.4611,0.6849,0.7272,0.7152,0.7102,0.8516,1.0000,0.7690,0.4841,0.3717,0.6096,0.5110,0.2586,0.0916,0.0947,0.2287,0.3480,0.2095,0.1901,0.2941,0.2211,0.1524,0.0746,0.0606,0.0692,0.0446,0.0344,0.0082,0.0108,0.0149,0.0077,0.0036,0.0114,0.0085,0.0101,0.0016,0.0028,0.0014,R 96 | 0.0291,0.0400,0.0771,0.0809,0.0521,0.1051,0.0145,0.0674,0.1294,0.1146,0.0942,0.0794,0.0252,0.1191,0.1045,0.2050,0.1556,0.2690,0.3784,0.4024,0.3470,0.1395,0.1208,0.2827,0.1500,0.2626,0.4468,0.7520,0.9036,0.7812,0.4766,0.2483,0.5372,0.6279,0.3647,0.4572,0.6359,0.6474,0.5520,0.3253,0.2292,0.0653,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0056,0.0237,0.0204,0.0050,0.0137,0.0164,0.0081,0.0139,0.0111,R 97 | 0.0181,0.0146,0.0026,0.0141,0.0421,0.0473,0.0361,0.0741,0.1398,0.1045,0.0904,0.0671,0.0997,0.1056,0.0346,0.1231,0.1626,0.3652,0.3262,0.2995,0.2109,0.2104,0.2085,0.2282,0.0747,0.1969,0.4086,0.6385,0.7970,0.7508,0.5517,0.2214,0.4672,0.4479,0.2297,0.3235,0.4480,0.5581,0.6520,0.5354,0.2478,0.2268,0.1788,0.0898,0.0536,0.0374,0.0990,0.0956,0.0317,0.0142,0.0076,0.0223,0.0255,0.0145,0.0233,0.0041,0.0018,0.0048,0.0089,0.0085,R 98 | 0.0491,0.0279,0.0592,0.1270,0.1772,0.1908,0.2217,0.0768,0.1246,0.2028,0.0947,0.2497,0.2209,0.3195,0.3340,0.3323,0.2780,0.2975,0.2948,0.1729,0.3264,0.3834,0.3523,0.5410,0.5228,0.4475,0.5340,0.5323,0.3907,0.3456,0.4091,0.4639,0.5580,0.5727,0.6355,0.7563,0.6903,0.6176,0.5379,0.5622,0.6508,0.4797,0.3736,0.2804,0.1982,0.2438,0.1789,0.1706,0.0762,0.0238,0.0268,0.0081,0.0129,0.0161,0.0063,0.0119,0.0194,0.0140,0.0332,0.0439,M 99 | 0.1313,0.2339,0.3059,0.4264,0.4010,0.1791,0.1853,0.0055,0.1929,0.2231,0.2907,0.2259,0.3136,0.3302,0.3660,0.3956,0.4386,0.4670,0.5255,0.3735,0.2243,0.1973,0.4337,0.6532,0.5070,0.2796,0.4163,0.5950,0.5242,0.4178,0.3714,0.2375,0.0863,0.1437,0.2896,0.4577,0.3725,0.3372,0.3803,0.4181,0.3603,0.2711,0.1653,0.1951,0.2811,0.2246,0.1921,0.1500,0.0665,0.0193,0.0156,0.0362,0.0210,0.0154,0.0180,0.0013,0.0106,0.0127,0.0178,0.0231,M 100 | 0.0201,0.0423,0.0554,0.0783,0.0620,0.0871,0.1201,0.2707,0.1206,0.0279,0.2251,0.2615,0.1770,0.3709,0.4533,0.5553,0.4616,0.3797,0.3450,0.2665,0.2395,0.1127,0.2556,0.5169,0.3779,0.4082,0.5353,0.5116,0.4544,0.4258,0.3869,0.3939,0.4661,0.3974,0.2194,0.1816,0.1023,0.2108,0.3253,0.3697,0.2912,0.3010,0.2563,0.1927,0.2062,0.1751,0.0841,0.1035,0.0641,0.0153,0.0081,0.0191,0.0182,0.0160,0.0290,0.0090,0.0242,0.0224,0.0190,0.0096,M 101 | 0.0629,0.1065,0.1526,0.1229,0.1437,0.1190,0.0884,0.0907,0.2107,0.3597,0.5466,0.5205,0.5127,0.5395,0.6558,0.8705,0.9786,0.9335,0.7917,0.7383,0.6908,0.3850,0.0671,0.0502,0.2717,0.2839,0.2234,0.1911,0.0408,0.2531,0.1979,0.1891,0.2433,0.1956,0.2667,0.1340,0.1073,0.2023,0.1794,0.0227,0.1313,0.1775,0.1549,0.1626,0.0708,0.0129,0.0795,0.0762,0.0117,0.0061,0.0257,0.0089,0.0262,0.0108,0.0138,0.0187,0.0230,0.0057,0.0113,0.0131,M 102 | 0.0335,0.0134,0.0696,0.1180,0.0348,0.1180,0.1948,0.1607,0.3036,0.4372,0.5533,0.5771,0.7022,0.7067,0.7367,0.7391,0.8622,0.9458,0.8782,0.7913,0.5760,0.3061,0.0563,0.0239,0.2554,0.4862,0.5027,0.4402,0.2847,0.1797,0.3560,0.3522,0.3321,0.3112,0.3638,0.0754,0.1834,0.1820,0.1815,0.1593,0.0576,0.0954,0.1086,0.0812,0.0784,0.0487,0.0439,0.0586,0.0370,0.0185,0.0302,0.0244,0.0232,0.0093,0.0159,0.0193,0.0032,0.0377,0.0126,0.0156,M 103 | 0.0587,0.1210,0.1268,0.1498,0.1436,0.0561,0.0832,0.0672,0.1372,0.2352,0.3208,0.4257,0.5201,0.4914,0.5950,0.7221,0.9039,0.9111,0.8723,0.7686,0.7326,0.5222,0.3097,0.3172,0.2270,0.1640,0.1746,0.1835,0.2048,0.1674,0.2767,0.3104,0.3399,0.4441,0.5046,0.2814,0.1681,0.2633,0.3198,0.1933,0.0934,0.0443,0.0780,0.0722,0.0405,0.0553,0.1081,0.1139,0.0767,0.0265,0.0215,0.0331,0.0111,0.0088,0.0158,0.0122,0.0038,0.0101,0.0228,0.0124,M 104 | 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0.0116,0.0179,0.0449,0.1096,0.1913,0.0924,0.0761,0.1092,0.0757,0.1006,0.2500,0.3988,0.3809,0.4753,0.6165,0.6464,0.8024,0.9208,0.9832,0.9634,0.8646,0.8325,0.8276,0.8007,0.6102,0.4853,0.4355,0.4307,0.4399,0.3833,0.3032,0.3035,0.3197,0.2292,0.2131,0.2347,0.3201,0.4455,0.3655,0.2715,0.1747,0.1781,0.2199,0.1056,0.0573,0.0307,0.0237,0.0470,0.0102,0.0057,0.0031,0.0163,0.0099,0.0084,0.0270,0.0277,0.0097,0.0054,0.0148,0.0092,M 107 | 0.0331,0.0423,0.0474,0.0818,0.0835,0.0756,0.0374,0.0961,0.0548,0.0193,0.0897,0.1734,0.1936,0.2803,0.3313,0.5020,0.6360,0.7096,0.8333,0.8730,0.8073,0.7507,0.7526,0.7298,0.6177,0.4946,0.4531,0.4099,0.4540,0.4124,0.3139,0.3194,0.3692,0.3776,0.4469,0.4777,0.4716,0.4664,0.3893,0.4255,0.4064,0.3712,0.3863,0.2802,0.1283,0.1117,0.1303,0.0787,0.0436,0.0224,0.0133,0.0078,0.0174,0.0176,0.0038,0.0129,0.0066,0.0044,0.0134,0.0092,M 108 | 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0.0260,0.0363,0.0136,0.0272,0.0214,0.0338,0.0655,0.1400,0.1843,0.2354,0.2720,0.2442,0.1665,0.0336,0.1302,0.1708,0.2177,0.3175,0.3714,0.4552,0.5700,0.7397,0.8062,0.8837,0.9432,1.0000,0.9375,0.7603,0.7123,0.8358,0.7622,0.4567,0.1715,0.1549,0.1641,0.1869,0.2655,0.1713,0.0959,0.0768,0.0847,0.2076,0.2505,0.1862,0.1439,0.1470,0.0991,0.0041,0.0154,0.0116,0.0181,0.0146,0.0129,0.0047,0.0039,0.0061,0.0040,0.0036,0.0061,0.0115,M 209 | -------------------------------------------------------------------------------- /util/util.go: -------------------------------------------------------------------------------- 1 | // Util provides util to handle common tasks: file and struct operations, string manipulation, etc. 2 | package util 3 | 4 | import ( 5 | 6 | // sys import 7 | "os" 8 | "time" 9 | "strconv" 10 | "math" 11 | "math/rand" 12 | 13 | // github import 14 | log "github.com/sirupsen/logrus" 15 | 16 | ) 17 | 18 | func init() { 19 | // Output to stdout instead of the default stderr 20 | log.SetOutput(os.Stdout) 21 | // Only log the warning severity or above. 22 | log.SetLevel(log.DebugLevel) 23 | } 24 | 25 | // Random return pseudo random number in [min, max] 26 | func Random(min, max int) int { 27 | max = max + 1 28 | rand.Seed(time.Now().Unix()) 29 | return rand.Intn(max-min) + min 30 | } 31 | 32 | // StringInSlice looks for a string in slice. 33 | // It returns true or false and position of string in slice (false, -1 if not found). 34 | func StringInSlice(element string, slice []string) (bool, int) { 35 | 36 | // for element in slice 37 | for index, value := range slice { 38 | if value == element { 39 | return true, index 40 | } 41 | } 42 | 43 | // return false, placeholder 44 | return false, -1 45 | 46 | } 47 | 48 | // StringToFloat cast a slice of string element to a slice of float64 element. 49 | // If passed mode is 0, for each error encountered in casting, passed default will be inserted 50 | // in slice. 51 | // If passed mode is 1, output slice will contain only correctly casted element. 52 | // It returns slice converted. 53 | func StringToFloat(slice []string, mode int, def float64) []float64 { 54 | 55 | // result declaration 56 | var result []float64 57 | 58 | // if mode is 0, pre-alloc struct 59 | if mode == 0 { 60 | // pre-alloc struct 61 | result = make([]float64, len(slice)) 62 | } 63 | 64 | for index, value := range slice { 65 | 66 | // cast and error 67 | casted, err := strconv.ParseFloat(value, 64) 68 | 69 | if mode == 0 { 70 | if err == nil { 71 | // add casted 72 | result[index] = casted 73 | } else { 74 | // add default 75 | result[index] = def 76 | } 77 | } else { 78 | if err == nil { 79 | // add casted 80 | result = append(result, casted) 81 | } 82 | } 83 | } 84 | 85 | // return result 86 | return result 87 | 88 | } 89 | 90 | // ScalarProduct compute scalar product between two float64 based slices. 91 | // It returns a float64 value. 92 | func ScalarProduct(a []float64, b []float64) float64 { 93 | 94 | // if slices have different number of elements 95 | if len(a) != len(b) { 96 | log.WithFields(log.Fields{ 97 | "level": "error", 98 | "place": "mixed", 99 | "method": "ScalarProduct", 100 | "msg": "scalar product between slices", 101 | "aLen": len(a), 102 | "bLen": len(b), 103 | }).Error("Failed to compute scalar product between slices: different length.") 104 | return -1.0 105 | } 106 | 107 | // init result 108 | var result float64 = 0.0 109 | 110 | // for each element compute product 111 | for index, value := range a { 112 | result = result + (value * b[index]) 113 | } 114 | 115 | // return value 116 | return result 117 | 118 | } 119 | 120 | // MaxInSlice return max value in float64 slice 121 | // It returns the max float64 value and index of max in slice. 122 | func MaxInSlice(v []float64) (float64, int) { 123 | mv := 0.0 124 | mi := 0 125 | for i, e := range v { 126 | if e > mv { 127 | mv = e 128 | mi = i 129 | } 130 | } 131 | return mv, mi 132 | } 133 | 134 | func GenerateRandomIntWithBinaryDim(d int) int64 { 135 | 136 | rand.Seed(time.Now().UTC().UnixNano()) 137 | return rand.Int63n(int64(2^d)) 138 | 139 | } 140 | 141 | func GenerateRandomBinaryInt(d int) []float64 { 142 | 143 | rand.Seed(time.Now().UTC().UnixNano()) 144 | bn := rand.Int63n(int64(2^d)) 145 | bi := make([]float64, d) 146 | bs := strconv.FormatInt(bn, 2) 147 | zn := d-len(bs) 148 | for z_index := 0; z_index < d; z_index++ { 149 | if z_index < zn { 150 | bi[z_index] = 0.0 151 | } else { 152 | bi[z_index], _ = strconv.ParseFloat(string(bs[z_index-zn]), 64) 153 | } 154 | } 155 | // log.Debug(bn) 156 | // log.Debug(bi) 157 | // log.Debug(bs) 158 | // log.Debug(zn) 159 | return bi 160 | 161 | } 162 | 163 | func ConvertIntToBinary(n int64, d int) []float64 { 164 | 165 | bs := strconv.FormatInt(n, 2) 166 | bi := make([]float64, d) 167 | zn := d-len(bs) 168 | if zn < 0 { 169 | log.Warning("Too small base") 170 | bi = make([]float64, len(bs)) 171 | zn = 0 172 | } 173 | for z_index := 0; z_index < d; z_index++ { 174 | if z_index < zn { 175 | bi[z_index] = 0.0 176 | } else { 177 | bi[z_index], _ = strconv.ParseFloat(string(bs[z_index-zn]), 64) 178 | } 179 | } 180 | return bi 181 | 182 | } 183 | 184 | func ConvertBinToInt(n []float64) int { 185 | 186 | bi := 0 187 | for i := len(n)-1; i >= 0; i-- { 188 | // log.Info(int(n[i]), " * ", 2, "^", ((len(n)-i)-1), "=", (int(n[i])*int(math.Pow(float64(2), float64(len(n)-i-1))))) 189 | bi = bi + (int(n[i])*int(math.Pow(float64(2), float64(len(n)-i-1)))) 190 | } 191 | // log.Info(n, " = ", bi) 192 | return bi 193 | 194 | } 195 | 196 | func Round(val float64, roundOn float64, places int ) (newVal float64) { 197 | var round float64 198 | pow := math.Pow(10, float64(places)) 199 | digit := pow * val 200 | _, div := math.Modf(digit) 201 | if div >= roundOn { 202 | round = math.Ceil(digit) 203 | } else { 204 | round = math.Floor(digit) 205 | } 206 | newVal = round / pow 207 | return 208 | } -------------------------------------------------------------------------------- /validation/validation.go: -------------------------------------------------------------------------------- 1 | package validation 2 | 3 | import ( 4 | "math/rand" 5 | "time" 6 | 7 | // third part import 8 | log "github.com/sirupsen/logrus" 9 | 10 | // internal import 11 | mn "github.com/made2591/go-perceptron-go/model/neural" 12 | mu "github.com/made2591/go-perceptron-go/util" 13 | 14 | //"fmt" 15 | ) 16 | 17 | // TrainTestPatternsSplit split an array of patterns in training and testing. 18 | // if shuffle is 0 the function takes the first percentage items as train and the other as test 19 | // otherwise the patterns array is shuffled before partitioning 20 | func TrainTestPatternsSplit(patterns []mn.Pattern, percentage float64, shuffle int) (train []mn.Pattern, test []mn.Pattern) { 21 | 22 | // create splitting pivot 23 | var splitPivot int = int(float64(len(patterns)) * percentage) 24 | train = make([]mn.Pattern, splitPivot) 25 | test = make([]mn.Pattern, len(patterns)-splitPivot) 26 | 27 | // if mixed mode, split with shuffling 28 | if shuffle == 1 { 29 | // create random indexes permutation 30 | rand.Seed(time.Now().UTC().UnixNano()) 31 | perm := rand.Perm(len(patterns)) 32 | 33 | // copy training data 34 | for i := 0; i < splitPivot; i++ { 35 | train[i] = patterns[perm[i]] 36 | } 37 | // copy test data 38 | for i := 0; i < len(patterns)-splitPivot; i++ { 39 | test[i] = patterns[perm[i]] 40 | } 41 | 42 | } else { 43 | // else, split without shuffle 44 | train = patterns[:splitPivot] 45 | test = patterns[splitPivot:] 46 | } 47 | 48 | log.WithFields(log.Fields{ 49 | "level": "info", 50 | "msg": "splitting completed", 51 | "trainSet": len(train), 52 | "testSet: ": len(test), 53 | }).Info("Complete splitting train/test set.") 54 | 55 | return train, test 56 | } 57 | 58 | // TrainTestPatternsSplit split an array of patterns in training and testing. 59 | // if shuffle is 0 the function takes the first percentage items as train and the other as test 60 | // otherwise the patterns array is shuffled before partitioning 61 | func TrainTestPatternSplit(patterns []mn.Pattern, percentage float64, shuffle int) (train []mn.Pattern, test []mn.Pattern) { 62 | 63 | // create splitting pivot 64 | var splitPivot int = int(float64(len(patterns)) * percentage) 65 | train = make([]mn.Pattern, splitPivot) 66 | test = make([]mn.Pattern, len(patterns)-splitPivot) 67 | 68 | // if mixed mode, split with shuffling 69 | if shuffle == 1 { 70 | // create random indexes permutation 71 | rand.Seed(time.Now().UTC().UnixNano()) 72 | perm := rand.Perm(len(patterns)) 73 | 74 | // copy training data 75 | for i := 0; i < splitPivot; i++ { 76 | train[i] = patterns[perm[i]] 77 | } 78 | // copy test data 79 | for i := 0; i < len(patterns)-splitPivot; i++ { 80 | test[i] = patterns[perm[i]] 81 | } 82 | 83 | } else { 84 | // else, split without shuffle 85 | train = patterns[:splitPivot] 86 | test = patterns[splitPivot:] 87 | } 88 | 89 | log.WithFields(log.Fields{ 90 | "level": "info", 91 | "msg": "splitting completed", 92 | "trainSet": len(train), 93 | "testSet: ": len(test), 94 | }).Info("Complete splitting train/test set.") 95 | 96 | return train, test 97 | } 98 | 99 | // KFoldPatternsSplit split an array of patterns in k subsets. 100 | // if shuffle is 0 the function partitions the items maintaining the order 101 | // otherwise the patterns array is shuffled before partitioning 102 | func KFoldPatternsSplit(patterns []mn.Pattern, k int, shuffle int) [][]mn.Pattern { 103 | 104 | // get the size of each fold 105 | var size = int(len(patterns) / k) 106 | var freeElements = int(len(patterns) % k) 107 | 108 | folds := make([][]mn.Pattern, k) 109 | 110 | var perm []int 111 | // if mixed mode, split with shuffling 112 | if shuffle == 1 { 113 | // create random indexes permutation 114 | rand.Seed(time.Now().UTC().UnixNano()) 115 | perm = rand.Perm(len(patterns)) 116 | } 117 | 118 | // start splitting 119 | currSize := 0 120 | foldStart := 0 121 | curr := 0 122 | for f := 0; f < k; f++ { 123 | curr = foldStart 124 | currSize = size 125 | if f < freeElements { 126 | // add another 127 | currSize++ 128 | } 129 | 130 | // create array 131 | folds[f] = make([]mn.Pattern, currSize) 132 | 133 | // copy elements 134 | 135 | for i := 0; i < currSize; i++ { 136 | if shuffle == 1 { 137 | folds[f][i] = patterns[perm[curr]] 138 | } else { 139 | folds[f][i] = patterns[curr] 140 | } 141 | curr++ 142 | } 143 | 144 | foldStart = curr 145 | 146 | } 147 | 148 | log.WithFields(log.Fields{ 149 | "level": "info", 150 | "msg": "splitting completed", 151 | "numberOfFolds": k, 152 | "meanFoldSize: ": size, 153 | "consideredElements": (size * k) + freeElements, 154 | }).Info("Complete folds splitting.") 155 | 156 | return folds 157 | } 158 | 159 | // RandomSubsamplingValidation perform evaluation on neuron algorithm. 160 | // It returns scores reached for each fold iteration. 161 | func RandomSubsamplingValidation(neuron *mn.NeuronUnit, patterns []mn.Pattern, percentage float64, epochs int, folds int, shuffle int) []float64 { 162 | 163 | // results and predictions vars init 164 | var scores, actual, predicted []float64 165 | var train, test []mn.Pattern 166 | 167 | scores = make([]float64, folds) 168 | 169 | for t := 0; t < folds; t++ { 170 | // split the dataset with shuffling 171 | train, test = TrainTestPatternsSplit(patterns, percentage, shuffle) 172 | 173 | // train neuron with set of patterns, for specified number of epochs 174 | mn.TrainNeuron(neuron, train, epochs, 1) 175 | 176 | // compute predictions for each pattern in testing set 177 | for _, pattern := range test { 178 | actual = append(actual, pattern.SingleExpectation) 179 | predicted = append(predicted, mn.Predict(neuron, &pattern)) 180 | } 181 | 182 | // compute score 183 | _, percentageCorrect := mn.Accuracy(actual, predicted) 184 | scores[t] = percentageCorrect 185 | 186 | log.WithFields(log.Fields{ 187 | "level": "info", 188 | "place": "validation", 189 | "method": "RandomSubsamplingValidation", 190 | "foldNumber": t, 191 | "trainSetLen": len(train), 192 | "testSetLen": len(test), 193 | "percentageCorrect": percentageCorrect, 194 | }).Info("Evaluation completed for current fold.") 195 | } 196 | 197 | // compute average score 198 | acc := 0.0 199 | for i := 0; i < len(scores); i++ { 200 | acc += scores[i] 201 | } 202 | 203 | mean := acc / float64(len(scores)) 204 | 205 | log.WithFields(log.Fields{ 206 | "level": "info", 207 | "place": "validation", 208 | "method": "RandomSubsamplingValidation", 209 | "folds": folds, 210 | "trainSetLen": len(train), 211 | "testSetLen": len(test), 212 | "meanScore": mean, 213 | }).Info("Evaluation completed for all folds.") 214 | 215 | return scores 216 | } 217 | 218 | // RandomSubsamplingValidation perform evaluation on neuron algorithm. 219 | // It returns scores reached for each fold iteration. 220 | func KFoldValidation(neuron *mn.NeuronUnit, patterns []mn.Pattern, epochs int, k int, shuffle int) []float64 { 221 | 222 | // results and predictions vars init 223 | var scores, actual, predicted []float64 224 | var train, test []mn.Pattern 225 | 226 | scores = make([]float64, k) 227 | 228 | // split the dataset with shuffling 229 | folds := KFoldPatternsSplit(patterns, k, shuffle) 230 | 231 | // the t-th fold is used as test 232 | for t := 0; t < k; t++ { 233 | // prepare train 234 | train = nil 235 | for i := 0; i < k; i++ { 236 | if i != t { 237 | train = append(train, folds[i]...) 238 | } 239 | } 240 | test = folds[t] 241 | 242 | // train neuron with set of patterns, for specified number of epochs 243 | mn.TrainNeuron(neuron, train, epochs, 1) 244 | 245 | // compute predictions for each pattern in testing set 246 | for _, pattern := range test { 247 | actual = append(actual, pattern.SingleExpectation) 248 | predicted = append(predicted, mn.Predict(neuron, &pattern)) 249 | } 250 | 251 | // compute score 252 | _, percentageCorrect := mn.Accuracy(actual, predicted) 253 | scores[t] = percentageCorrect 254 | 255 | log.WithFields(log.Fields{ 256 | "level": "info", 257 | "place": "validation", 258 | "method": "KFoldValidation", 259 | "foldNumber": t, 260 | "trainSetLen": len(train), 261 | "testSetLen": len(test), 262 | "percentageCorrect": percentageCorrect, 263 | }).Info("Evaluation completed for current fold.") 264 | } 265 | 266 | // compute average score 267 | acc := 0.0 268 | for i := 0; i < len(scores); i++ { 269 | acc += scores[i] 270 | } 271 | 272 | mean := acc / float64(len(scores)) 273 | 274 | log.WithFields(log.Fields{ 275 | "level": "info", 276 | "place": "validation", 277 | "method": "KFoldValidation", 278 | "folds": k, 279 | "trainSetLen": len(train), 280 | "testSetLen": len(test), 281 | "meanScore": mean, 282 | }).Info("Evaluation completed for all folds.") 283 | 284 | return scores 285 | 286 | } 287 | 288 | // It returns scores reached for each fold iteration. 289 | func MLPRandomSubsamplingValidation(mlp *mn.MultiLayerNetwork, patterns []mn.Pattern, percentage float64, epochs int, folds int, shuffle int, mapped []string) []float64 { 290 | 291 | // results and predictions vars init 292 | var scores, actual, predicted []float64 293 | var train, test []mn.Pattern 294 | 295 | scores = make([]float64, folds) 296 | 297 | for t := 0; t < folds; t++ { 298 | // split the dataset with shuffling 299 | train, test = TrainTestPatternsSplit(patterns, percentage, shuffle) 300 | 301 | // train mlp with set of patterns, for specified number of epochs 302 | mn.MLPTrain(mlp, patterns, mapped, epochs) 303 | 304 | // compute predictions for each pattern in testing set 305 | for _, pattern := range test { 306 | // get actual 307 | actual = append(actual, pattern.SingleExpectation) 308 | // get output from network 309 | o_out := mn.Execute(mlp, &pattern) 310 | // get index of max output 311 | _, indexMaxOut := mu.MaxInSlice(o_out) 312 | // add to predicted values 313 | predicted = append(predicted, float64(indexMaxOut)) 314 | } 315 | 316 | // compute score 317 | _, percentageCorrect := mn.Accuracy(actual, predicted) 318 | scores[t] = percentageCorrect 319 | 320 | log.WithFields(log.Fields{ 321 | "level": "info", 322 | "place": "validation", 323 | "method": "MLPRandomSubsamplingValidation", 324 | "foldNumber": t, 325 | "trainSetLen": len(train), 326 | "testSetLen": len(test), 327 | "percentageCorrect": percentageCorrect, 328 | }).Info("Evaluation completed for current fold.") 329 | } 330 | 331 | // compute average score 332 | acc := 0.0 333 | for i := 0; i < len(scores); i++ { 334 | acc += scores[i] 335 | } 336 | 337 | mean := acc / float64(len(scores)) 338 | 339 | log.WithFields(log.Fields{ 340 | "level": "info", 341 | "place": "validation", 342 | "method": "MLPRandomSubsamplingValidation", 343 | "folds": folds, 344 | "trainSetLen": len(train), 345 | "testSetLen": len(test), 346 | "meanScore": mean, 347 | }).Info("Evaluation completed for all folds.") 348 | 349 | return scores 350 | } 351 | 352 | // RandomSubsamplingValidation perform evaluation on neuron algorithm. 353 | // It returns scores reached for each fold iteration. 354 | func MLPKFoldValidation(mlp *mn.MultiLayerNetwork, patterns []mn.Pattern, epochs int, k int, shuffle int, mapped []string) []float64 { 355 | 356 | // results and predictions vars init 357 | var scores, actual, predicted []float64 358 | var train, test []mn.Pattern 359 | 360 | scores = make([]float64, k) 361 | 362 | // split the dataset with shuffling 363 | folds := KFoldPatternsSplit(patterns, k, shuffle) 364 | 365 | // the t-th fold is used as test 366 | for t := 0; t < k; t++ { 367 | // prepare train 368 | train = nil 369 | for i := 0; i < k; i++ { 370 | if i != t { 371 | train = append(train, folds[i]...) 372 | } 373 | } 374 | test = folds[t] 375 | 376 | // train mlp with set of patterns, for specified number of epochs 377 | mn.MLPTrain(mlp, patterns, mapped, epochs) 378 | 379 | // compute predictions for each pattern in testing set 380 | for _, pattern := range test { 381 | // get actual 382 | actual = append(actual, pattern.SingleExpectation) 383 | // get output from network 384 | o_out := mn.Execute(mlp, &pattern) 385 | // get index of max output 386 | _, indexMaxOut := mu.MaxInSlice(o_out) 387 | // add to predicted values 388 | predicted = append(predicted, float64(indexMaxOut)) 389 | } 390 | 391 | // compute score 392 | _, percentageCorrect := mn.Accuracy(actual, predicted) 393 | scores[t] = percentageCorrect 394 | 395 | log.WithFields(log.Fields{ 396 | "level": "info", 397 | "place": "validation", 398 | "method": "MLPKFoldValidation", 399 | "foldNumber": t, 400 | "trainSetLen": len(train), 401 | "testSetLen": len(test), 402 | "percentageCorrect": percentageCorrect, 403 | }).Info("Evaluation completed for current fold.") 404 | } 405 | 406 | // compute average score 407 | acc := 0.0 408 | for i := 0; i < len(scores); i++ { 409 | acc += scores[i] 410 | } 411 | 412 | mean := acc / float64(len(scores)) 413 | 414 | log.WithFields(log.Fields{ 415 | "level": "info", 416 | "place": "validation", 417 | "method": "MLPKFoldValidation", 418 | "folds": k, 419 | "trainSetLen": len(train), 420 | "testSetLen": len(test), 421 | "meanScore": mean, 422 | }).Info("Evaluation completed for all folds.") 423 | 424 | return scores 425 | 426 | } 427 | 428 | // RNNValidation perform evaluation on neuron algorithm. 429 | func RNNValidation(mlp *mn.MultiLayerNetwork, patterns []mn.Pattern, epochs int, shuffle int) (float64, []float64) { 430 | 431 | // results and predictions vars init 432 | var scores []float64 433 | scores = make([]float64, len(patterns)) 434 | 435 | // train mlp with set of patterns, for specified number of epochs 436 | mn.ElmanTrain(mlp, patterns, epochs) 437 | p_cor := 0.0 438 | 439 | // compute predictions for each pattern in testing set 440 | for p_i, pattern := range patterns { 441 | // get output from network 442 | o_out := mn.Execute(mlp, &pattern, 1) 443 | for o_out_i, o_out_v := range(o_out) { 444 | o_out[o_out_i] = mu.Round(o_out_v, .5, 0) 445 | } 446 | log.WithFields(log.Fields{ 447 | "a_p_b": pattern.Features, 448 | "rea_c": pattern.MultipleExpectation, 449 | "pre_c": o_out, 450 | }).Debug() 451 | 452 | // add to predicted values 453 | _, p_cor = mn.Accuracy(pattern.MultipleExpectation, o_out) 454 | // compute score 455 | scores[p_i] = p_cor; 456 | } 457 | 458 | // compute average score 459 | acc := 0.0 460 | for i := 0; i < len(scores); i++ { 461 | acc += scores[i] 462 | } 463 | 464 | mean := acc / float64(len(scores)) 465 | 466 | log.WithFields(log.Fields{ 467 | "level": "info", 468 | "place": "validation", 469 | "method": "RNNValidation", 470 | "trainSetLen": len(patterns), 471 | "testSetLen": len(patterns), 472 | "meanScore": mean, 473 | }).Info("Evaluation completed for all patterns.") 474 | 475 | return mean, scores 476 | 477 | } --------------------------------------------------------------------------------