├── .gitignore ├── LICENSE ├── controllers └── controller.go ├── datasets ├── dataset.csv ├── iris.csv ├── iris_train.csv ├── normalized.csv └── testing │ ├── dummyDataset.csv │ └── dummyDataset.json ├── dto └── dataTransfer.go ├── main.go ├── models ├── classNames.go ├── modelConfig.go ├── modelStats.go ├── nn.go ├── stats.json └── test.json ├── routes └── router.go └── utils └── utils.go /.gitignore: -------------------------------------------------------------------------------- 1 | /build 2 | -------------------------------------------------------------------------------- /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 | 635 | Copyright (C) 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 | Copyright (C) 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 | -------------------------------------------------------------------------------- /controllers/controller.go: -------------------------------------------------------------------------------- 1 | package controllers 2 | 3 | import ( 4 | "encoding/csv" 5 | "encoding/json" 6 | "fmt" 7 | "io/ioutil" 8 | "log" 9 | "net/http" 10 | "os" 11 | "reflect" 12 | "sort" 13 | "strconv" 14 | 15 | "github.com/libonomy/ai-engine/dto" 16 | "github.com/libonomy/ai-engine/models" 17 | "github.com/libonomy/ai-engine/utils" 18 | "github.com/libonomy/libonomy-gota/dataframe" 19 | "gonum.org/v1/gonum/floats" 20 | "gonum.org/v1/gonum/mat" 21 | ) 22 | 23 | type bodyVariables struct { 24 | ComputerPower float64 `json:"computerPower"` 25 | DownloadSpeed float64 `json:"downSpeed"` 26 | Ylabels string `json:"yLabels"` 27 | } 28 | 29 | type check1 struct { 30 | Name string 31 | } 32 | 33 | type check2 struct { 34 | Name string 35 | } 36 | 37 | type check3 struct { 38 | Name string 39 | } 40 | 41 | //Testing Function To Test its working 42 | func Testing(w http.ResponseWriter, r *http.Request) { 43 | s := "This is for testing function only" 44 | 45 | dto.SendResponse(w, r, http.StatusOK, "Success", map[string]interface{}{"testing": s}) 46 | } 47 | 48 | //GenerateCSV function to generate csv file from json data. 49 | func GenerateCSV(w http.ResponseWriter, r *http.Request) { 50 | variables := []bodyVariables{} 51 | fileVariables := []bodyVariables{} 52 | filename := "./datasets/testing/dummyDataset.json" 53 | csvFilename := "./datasets/testing/dummyDataset.csv" 54 | err := json.NewDecoder(r.Body).Decode(&variables) 55 | if err != nil { 56 | fmt.Println("There is some Error in Decoding Body Request", err.Error()) 57 | dto.SendResponse(w, r, http.StatusInternalServerError, "Bad", map[string]interface{}{"Error": err.Error()}) 58 | return 59 | } 60 | 61 | //fmt.Println(variables) 62 | _, err = os.Stat(filename) 63 | 64 | if err != nil { 65 | //checking if file does not exists 66 | fmt.Println("Error is os.stat", err.Error()) 67 | //fmt.Println("File Info ", fileInfo) 68 | 69 | jsonFile, _ := os.Create(filename) 70 | csvFile, _ := os.Create(csvFilename) 71 | 72 | defer jsonFile.Close() 73 | file, _ := json.MarshalIndent(variables, "", " ") 74 | _ = ioutil.WriteFile(filename, file, 0644) 75 | 76 | fileOpened, _ := os.Open(filename) 77 | dataFrame := dataframe.ReadJSON(fileOpened) 78 | csvWriter := csv.NewWriter(csvFile) 79 | csvWriter.WriteAll(dataFrame.Records()) 80 | // csvData := dataframe.ReadJSON(jsonFile) 81 | // csvData.WriteCSV(csvFile) 82 | dto.SendResponse(w, r, http.StatusOK, "Successfully Created CSV File From JSON", nil) 83 | return 84 | } 85 | 86 | file, err := os.Open(filename) 87 | if err != nil { 88 | dto.SendResponse(w, r, http.StatusBadRequest, "Cannot create file", nil) 89 | } 90 | fileBytes, _ := ioutil.ReadAll(file) 91 | fmt.Println("Code is Here 0") 92 | json.Unmarshal(fileBytes, &fileVariables) 93 | fmt.Println("Code is Here 1") 94 | 95 | fileVariables = append(fileVariables, variables[0]) 96 | writeF, _ := json.MarshalIndent(fileVariables, "", " ") 97 | _ = ioutil.WriteFile(filename, writeF, 0644) 98 | 99 | csvFile, _ := os.Create(csvFilename) 100 | fileOpened, _ := os.Open(filename) 101 | dataFrame := dataframe.ReadJSON(fileOpened) 102 | csvWriter := csv.NewWriter(csvFile) 103 | csvWriter.WriteAll(dataFrame.Records()) 104 | 105 | dto.SendResponse(w, r, http.StatusOK, "Successfully Created CSV File From JSON", nil) 106 | } 107 | 108 | //CleanData function to clean data and convert data to specific format 109 | func CleanData(w http.ResponseWriter, r *http.Request) { 110 | filename := "./datasets/testing/dummyDataset.csv" 111 | fmt.Println("code Here") 112 | f, err := os.Open(filename) 113 | newFile := f 114 | defer newFile.Close() 115 | if err != nil { 116 | fmt.Println("Error in opening file", f.Name(), err.Error()) 117 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in opening file "+filename+" Error is \n"+err.Error(), nil) 118 | return 119 | } 120 | fmt.Println("code Here") 121 | reader := csv.NewReader(f) 122 | rawData, err := reader.ReadAll() 123 | if err != nil { 124 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in opening file 2"+err.Error(), nil) 125 | return 126 | } 127 | 128 | data := dataframe.LoadRecords(rawData) 129 | // fmt.Println(data) 130 | 131 | // check := []int{1, 1, 1, 1, 1, 1} 132 | 133 | labelExtraction := []string{} 134 | 135 | labels := data.Col("yLabels").Records() 136 | 137 | var index int 138 | dataRecords := data.Records() 139 | for i, record := range dataRecords[0] { 140 | // fmt.Println("Index ", i, "Value", record) 141 | if record == "yLabels" { 142 | index = i 143 | } 144 | 145 | } 146 | 147 | for _, label := range labels { 148 | // fmt.Println("Index ", i, "Label", label) 149 | sort.Strings(labelExtraction) 150 | res := sort.SearchStrings(labelExtraction, label) 151 | 152 | if len(labelExtraction) == res { 153 | labelExtraction = append(labelExtraction, label) 154 | } 155 | // fmt.Println("Printing Result ", res) 156 | } 157 | // fmt.Println(rawData) 158 | 159 | var newRecords [][]string 160 | // fmt.Println(labelExtraction) 161 | length := len(rawData[0]) 162 | for i, record := range rawData { 163 | 164 | // fmt.Println("Length", length) 165 | if i == 0 { 166 | for _, label := range labelExtraction { 167 | record = append(record, label) 168 | } 169 | newRecords = append(newRecords, record) 170 | 171 | continue 172 | } 173 | for _, label := range labelExtraction { 174 | label = label 175 | // fmt.Println(label) 176 | record = append(record, "0.0") 177 | 178 | } 179 | 180 | for index, label := range labelExtraction { 181 | if record[length-1] == label { 182 | record[index+length] = "1.0" 183 | // fmt.Println("Yes in here") 184 | } 185 | 186 | // fmt.Println("Record", record[len(record)-(length)]) 187 | } 188 | newRecords = append(newRecords, record) 189 | 190 | } 191 | 192 | // fmt.Println(newRecords) 193 | var finalRecords [][]string 194 | for _, record := range newRecords { 195 | // fmt.Println(record[:length-1]) 196 | // fmt.Println(record[length:]) 197 | 198 | modified := append(record[:length-1], record[length:]...) 199 | finalRecords = append(finalRecords, modified) 200 | // finalRecords = append(finalRecords, record[length:]) 201 | } 202 | fmt.Println(finalRecords[0]) 203 | 204 | writer, _ := os.Create("./datasets/dataset.csv") 205 | wr := csv.NewWriter(writer) 206 | wr.WriteAll(finalRecords) 207 | wr.Flush() 208 | dto.SendResponse(w, r, http.StatusOK, "Success", map[string]interface{}{"raw csv data": finalRecords, "Index ": index, "Length": len(newRecords)}) 209 | } 210 | 211 | //NormalizeData function for data normalization from 0-1 212 | func NormalizeData(w http.ResponseWriter, r *http.Request) { 213 | f, err := os.Open("./datasets/dataset.csv") 214 | 215 | if err != nil { 216 | fmt.Println("Code Is Here") 217 | 218 | dto.SendResponse(w, r, http.StatusBadRequest, err.Error(), nil) 219 | return 220 | } 221 | fmt.Println("Code Is Here") 222 | 223 | csvReader := csv.NewReader(f) 224 | rawCSVdata, _ := csvReader.ReadAll() 225 | dataFrame := dataframe.LoadRecords(rawCSVdata) 226 | normalize := [][]string{} 227 | // fmt.Println("Data Name ", dataFrame.Col(string(rawCSVdata[0][0]))) 228 | for i, record := range rawCSVdata { 229 | if i == 0 { 230 | normalize = append(normalize, record) 231 | continue 232 | } 233 | noVal := []string{} 234 | for x, values := range record { 235 | val, _ := strconv.ParseFloat(values, 64) 236 | nVal := (val - dataFrame.Col(rawCSVdata[0][x]).Min()) / (dataFrame.Col(rawCSVdata[0][x]).Max() - dataFrame.Col(rawCSVdata[0][x]).Min()) 237 | sVal := strconv.FormatFloat(nVal, 'f', 6, 64) 238 | noVal = append(noVal, sVal) 239 | //fmt.Println(nVal) 240 | } 241 | normalize = append(normalize, noVal) 242 | } 243 | 244 | headers := rawCSVdata[0] 245 | wr, _ := os.Create("./datasets/normalized.csv") 246 | csvWriter := csv.NewWriter(wr) 247 | csvWriter.WriteAll(normalize) 248 | dto.SendResponse(w, r, http.StatusOK, "Everthings Fine", map[string]interface{}{"data": rawCSVdata, "summary": dataFrame.Describe().Records(), 249 | "Headers": headers, "Normalixe": normalize, "Original": rawCSVdata}) 250 | } 251 | 252 | //SplitAndShuffle to split and shuffle data 253 | func SplitAndShuffle(w http.ResponseWriter, r *http.Request) { 254 | percentage := r.FormValue("trainPercentage") 255 | percent, _ := strconv.ParseFloat(percentage, 64) 256 | 257 | trainPercent := percent / 100 258 | testPercent := ((100 - percent) / 100) / 2 259 | var validatePercent int 260 | var even bool 261 | 262 | f, _ := os.Open("./datasets/normalized.csv") 263 | reader := csv.NewReader(f) 264 | rawCSV, _ := reader.ReadAll() 265 | fmt.Println(len(rawCSV)) 266 | if int(float64(len(rawCSV))*testPercent)%2 == 0 { 267 | fmt.Println("Yes its even") 268 | even = true 269 | } else { 270 | fmt.Println("No ") 271 | even = false 272 | } 273 | 274 | fmt.Println(testPercent, even, validatePercent, trainPercent) 275 | dto.SendResponse(w, r, http.StatusOK, "Success", map[string]interface{}{}) 276 | } 277 | 278 | //Train funciton 279 | func Train(w http.ResponseWriter, r *http.Request) { 280 | rateString := r.URL.Query().Get("rate") 281 | epochsString := r.URL.Query().Get("epochs") 282 | hiddenString := r.URL.Query().Get("hidden") 283 | 284 | rate, _ := strconv.ParseFloat(rateString, 64) 285 | epochs, _ := strconv.ParseFloat(epochsString, 64) 286 | hidden, _ := strconv.ParseFloat(hiddenString, 64) 287 | 288 | fmt.Println("Rate is :\t", rate) 289 | fmt.Println("Type of Rate is :\t", reflect.TypeOf(rate)) 290 | 291 | fmt.Println("Epochs is :\t", epochs) 292 | fmt.Println("hidden", hidden) 293 | 294 | f, err := os.Open("./datasets/iris_train.csv") 295 | 296 | if err != nil { 297 | // fmt.Println("Error in Reading File ", err.Error()) 298 | return 299 | } 300 | reader := csv.NewReader(f) 301 | rawCSVdata, err := reader.ReadAll() 302 | inputsData := make([]float64, 4*len(rawCSVdata)) 303 | labelsData := make([]float64, 3*len(rawCSVdata)) 304 | // fmt.Println(len(rawCSVdata)) 305 | // fmt.Println(len(inputsData)) 306 | // fmt.Println(len(labelsData)) 307 | 308 | var inputIdx int 309 | var labelIdx int 310 | 311 | for idx, record := range rawCSVdata { 312 | if idx == 0 { 313 | continue 314 | } 315 | 316 | for i, val := range record { 317 | parsedVal, err := strconv.ParseFloat(val, 64) 318 | if err != nil { 319 | // fmt.Println("Error in Parsing Float Value", err.Error()) 320 | return 321 | } 322 | 323 | if i == 4 || i == 5 || i == 6 { 324 | labelsData[labelIdx] = parsedVal 325 | labelIdx++ 326 | } else { 327 | inputsData[inputIdx] = parsedVal 328 | inputIdx++ 329 | } 330 | } 331 | } 332 | 333 | inputs := mat.NewDense(len(rawCSVdata), 4, inputsData) 334 | labels := mat.NewDense(len(rawCSVdata), 3, labelsData) 335 | 336 | config := models.NeuralNetConfig{ 337 | InputNeurons: 4, 338 | OutputNeurons: 3, 339 | HiddenNeurons: 5, 340 | NumEpochs: int(epochs), 341 | LearningRate: rate, 342 | } 343 | 344 | network := utils.NewNetwork(config) 345 | // fmt.Println("Printing Network Before Training", network) 346 | 347 | trainOutput, err := utils.Train(inputs, labels, network) 348 | if err != nil { 349 | log.Fatal(err) 350 | } 351 | // fmt.Println("Printing Network After Training", network) 352 | 353 | // fmt.Println(trainOutput) 354 | // fmt.Println(network.BHidden.RawMatrix()) 355 | // fmt.Print("Printing B hidden \t") 356 | // fmt.Println(network.BHidden.Dims()) 357 | rowsBHidden, colsBHidden := network.BHidden.Dims() 358 | 359 | // fmt.Print("Printing W hidden \t") 360 | // fmt.Println(network.WHidden.Dims()) 361 | rowsWHidden, colsWHidden := network.WHidden.Dims() 362 | 363 | // fmt.Print("Printing W out \t") 364 | // fmt.Println(network.WOut.Dims()) 365 | rowsWOut, colsWOut := network.WOut.Dims() 366 | 367 | // fmt.Print("Printing B out \t") 368 | // fmt.Println(network.BOut.Dims()) 369 | rowsBOut, colsBOut := network.BOut.Dims() 370 | 371 | configuration := models.ModelConfig{} 372 | 373 | for i := 0; i < rowsBHidden; i++ { 374 | configuration.BHidden = append(configuration.BHidden, network.BHidden.RawRowView(i)) 375 | } 376 | for i := 0; i < rowsWHidden; i++ { 377 | configuration.WHidden = append(configuration.WHidden, network.WHidden.RawRowView(i)) 378 | } 379 | for i := 0; i < rowsWOut; i++ { 380 | configuration.WOut = append(configuration.WOut, network.WOut.RawRowView(i)) 381 | } 382 | for i := 0; i < rowsBOut; i++ { 383 | configuration.BOut = append(configuration.BOut, network.BOut.RawRowView(i)) 384 | } 385 | 386 | configuration.BHiddenDims = append(configuration.BHiddenDims, rowsBHidden) 387 | configuration.BHiddenDims = append(configuration.BHiddenDims, colsBHidden) 388 | configuration.WHiddenDims = append(configuration.WHiddenDims, rowsWHidden) 389 | configuration.WHiddenDims = append(configuration.WHiddenDims, colsWHidden) 390 | configuration.WOutDims = append(configuration.WOutDims, rowsWOut) 391 | configuration.WOutDims = append(configuration.WOutDims, colsWOut) 392 | configuration.BOutDims = append(configuration.BOutDims, rowsBOut) 393 | configuration.BOutDims = append(configuration.BOutDims, colsBOut) 394 | configuration.InputNeurons = network.Config.InputNeurons 395 | configuration.HiddenNeurons = network.Config.HiddenNeurons 396 | configuration.LearningRate = network.Config.LearningRate 397 | configuration.NumEpochs = network.Config.NumEpochs 398 | configuration.OutputNeurons = network.Config.OutputNeurons 399 | 400 | // fmt.Println("Configuration Values ", configuration) 401 | 402 | var truePosNeg int 403 | numPreds, _ := trainOutput.Dims() 404 | for i := 0; i < numPreds; i++ { 405 | // fmt.Println("Prediction Index ", i, "\t", trainOutput.RowView(i)) 406 | // Get the label. 407 | labelRow := mat.Row(nil, i, labels) 408 | var species int 409 | for idx, label := range labelRow { 410 | if label == 1.0 { 411 | species = idx 412 | break 413 | } 414 | } 415 | 416 | // Accumulate the true positive/negative count. 417 | if trainOutput.At(i, species) == floats.Max(mat.Row(nil, i, trainOutput)) { 418 | truePosNeg++ 419 | } 420 | } 421 | 422 | // Calculate the accuracy (subset accuracy). 423 | accuracy := float64(truePosNeg) / float64(numPreds) 424 | 425 | // Output the Accuracy value to standard out. 426 | fmt.Printf("\nAccuracy of Testing = %0.2f %%\n", accuracy*100) 427 | stats := models.ModelStats{} 428 | stats.TrainAccuracy = accuracy * 100 429 | 430 | file, _ := json.MarshalIndent(configuration, "", " ") 431 | 432 | _ = ioutil.WriteFile("./models/test.json", file, 0644) 433 | 434 | file, _ = json.MarshalIndent(stats, "", " ") 435 | 436 | _ = ioutil.WriteFile("./models/stats.json", file, 0644) 437 | 438 | dto.SendResponse(w, r, http.StatusOK, "Training Result ", map[string]interface{}{"output": configuration, "Accuracy": accuracy * 100}) 439 | 440 | } 441 | 442 | //Predict Function for prediction 443 | func Predict(w http.ResponseWriter, r *http.Request) { 444 | w.Header().Set("Content-Type", "application/x-www-form-urlencoded") 445 | stringf1 := r.FormValue("f1") 446 | stringf2 := r.FormValue("f2") 447 | stringf3 := r.FormValue("f3") 448 | stringf4 := r.FormValue("f4") 449 | 450 | f1, _ := strconv.ParseFloat(stringf1, 64) 451 | f2, _ := strconv.ParseFloat(stringf2, 64) 452 | f3, _ := strconv.ParseFloat(stringf3, 64) 453 | f4, _ := strconv.ParseFloat(stringf4, 64) 454 | 455 | input := []float64{f1, f2, f3, f4} 456 | configuration := models.ModelConfig{} 457 | file, _ := ioutil.ReadFile("./models/test.json") 458 | _ = json.Unmarshal([]byte(file), &configuration) 459 | 460 | // fmt.Println("Printing Configurations", configuration) 461 | config := models.NeuralNetConfig{ 462 | InputNeurons: configuration.InputNeurons, 463 | OutputNeurons: configuration.OutputNeurons, 464 | HiddenNeurons: configuration.HiddenNeurons, 465 | NumEpochs: configuration.NumEpochs, 466 | LearningRate: configuration.LearningRate, 467 | } 468 | 469 | network := utils.NewNetwork(config) 470 | 471 | network.BHidden = mat.NewDense(configuration.BHiddenDims[0], configuration.BHiddenDims[1], nil) 472 | for i := 0; i < configuration.BHiddenDims[0]; i++ { 473 | fmt.Println(i) 474 | network.BHidden.SetRow(i, configuration.BHidden[i]) 475 | } 476 | 477 | network.WHidden = mat.NewDense(configuration.WHiddenDims[0], configuration.WHiddenDims[1], nil) 478 | for i := 0; i < configuration.WHiddenDims[0]; i++ { 479 | network.WHidden.SetRow(i, configuration.WHidden[i]) 480 | } 481 | 482 | network.BOut = mat.NewDense(configuration.BOutDims[0], configuration.BOutDims[1], nil) 483 | for i := 0; i < configuration.BOutDims[0]; i++ { 484 | network.BOut.SetRow(i, configuration.BOut[i]) 485 | } 486 | 487 | network.WOut = mat.NewDense(configuration.WOutDims[0], configuration.WOutDims[1], nil) 488 | for i := 0; i < configuration.WOutDims[0]; i++ { 489 | network.WOut.SetRow(i, configuration.WOut[i]) 490 | } 491 | 492 | // fmt.Println("Printing Configurations", configuration) 493 | features := mat.NewDense(1, 4, input) 494 | predictions, err := utils.Predict(features, network) 495 | 496 | if err != nil { 497 | fmt.Println("Error in something", err.Error()) 498 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in Prediction", map[string]interface{}{"Error": err.Error()}) 499 | } 500 | 501 | fmt.Println(predictions) 502 | fmt.Println(floats.MaxIdx(mat.Row(nil, 0, predictions))) 503 | 504 | dto.SendResponse(w, r, http.StatusOK, "Success ", map[string]interface{}{"Class Name": models.ClassNames[floats.MaxIdx(mat.Row(nil, 0, predictions))], "Prediction": predictions.RawMatrix(), "Max Index": floats.MaxIdx(mat.Row(nil, 0, predictions)) + 1}) 505 | 506 | } 507 | -------------------------------------------------------------------------------- /datasets/dataset.csv: -------------------------------------------------------------------------------- 1 | computerPower,downSpeed,Useless,Useless1 2 | 122625.790000,26.680000,1.0,0.0 3 | 115044.080000,25.730000,1.0,0.0 4 | 124273.980000,29.630000,1.0,0.0 5 | 116362.640000,30.140000,0.0,1.0 6 | -------------------------------------------------------------------------------- /datasets/iris.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/libonomy/ai-engine/244a76f795c16ac031f42ea5d64dac888b764704/datasets/iris.csv -------------------------------------------------------------------------------- /datasets/iris_train.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,Iris-setosa,Iris-versicolor,Iris-virginica 2 | 0.583333,0.333333,0.779661,0.875000,0.000000,0.000000,1.000000 3 | 0.583333,0.500000,0.593220,0.583333,0.000000,1.000000,0.000000 4 | 0.194444,0.625000,0.101695,0.208333,1.000000,0.000000,0.000000 5 | 0.666667,0.458333,0.627119,0.583333,0.000000,1.000000,0.000000 6 | 0.194444,0.583333,0.084746,0.041667,1.000000,0.000000,0.000000 7 | 0.555556,0.125000,0.576271,0.500000,0.000000,1.000000,0.000000 8 | 0.333333,0.250000,0.576271,0.458333,0.000000,1.000000,0.000000 9 | 0.638889,0.416667,0.576271,0.541667,0.000000,1.000000,0.000000 10 | 0.222222,0.750000,0.084746,0.083333,1.000000,0.000000,0.000000 11 | 0.305556,0.583333,0.118644,0.041667,1.000000,0.000000,0.000000 12 | 0.222222,0.208333,0.338983,0.416667,0.000000,1.000000,0.000000 13 | 0.416667,0.291667,0.694915,0.750000,0.000000,0.000000,1.000000 14 | 0.194444,0.583333,0.101695,0.125000,1.000000,0.000000,0.000000 15 | 0.583333,0.458333,0.762712,0.708333,0.000000,0.000000,1.000000 16 | 0.666667,0.541667,0.796610,1.000000,0.000000,0.000000,1.000000 17 | 0.444444,0.500000,0.644068,0.708333,0.000000,1.000000,0.000000 18 | 0.694444,0.500000,0.830508,0.916667,0.000000,0.000000,1.000000 19 | 0.722222,0.458333,0.694915,0.916667,0.000000,0.000000,1.000000 20 | 0.166667,0.458333,0.084746,0.000000,1.000000,0.000000,0.000000 21 | 0.027778,0.416667,0.050847,0.041667,1.000000,0.000000,0.000000 22 | 0.416667,0.291667,0.525424,0.375000,0.000000,1.000000,0.000000 23 | 0.666667,0.208333,0.813559,0.708333,0.000000,0.000000,1.000000 24 | 0.444444,0.416667,0.542373,0.583333,0.000000,1.000000,0.000000 25 | 0.250000,0.583333,0.067797,0.041667,1.000000,0.000000,0.000000 26 | 0.777778,0.416667,0.830508,0.833333,0.000000,0.000000,1.000000 27 | 0.388889,0.333333,0.593220,0.500000,0.000000,1.000000,0.000000 28 | 0.194444,0.625000,0.050847,0.083333,1.000000,0.000000,0.000000 29 | 0.944444,0.416667,0.864407,0.916667,0.000000,0.000000,1.000000 30 | 0.944444,0.250000,1.000000,0.916667,0.000000,0.000000,1.000000 31 | 0.555556,0.208333,0.677966,0.750000,0.000000,0.000000,1.000000 32 | 0.222222,0.750000,0.152542,0.125000,1.000000,0.000000,0.000000 33 | 0.388889,0.333333,0.525424,0.500000,0.000000,1.000000,0.000000 34 | 0.500000,0.375000,0.627119,0.541667,0.000000,1.000000,0.000000 35 | 0.194444,0.125000,0.389831,0.375000,0.000000,1.000000,0.000000 36 | 0.638889,0.375000,0.610169,0.500000,0.000000,1.000000,0.000000 37 | 0.666667,0.541667,0.796610,0.833333,0.000000,0.000000,1.000000 38 | 0.361111,0.416667,0.525424,0.500000,0.000000,1.000000,0.000000 39 | 0.555556,0.583333,0.779661,0.958333,0.000000,0.000000,1.000000 40 | 0.666667,0.458333,0.576271,0.541667,0.000000,1.000000,0.000000 41 | 0.527778,0.583333,0.745763,0.916667,0.000000,0.000000,1.000000 42 | 0.361111,0.333333,0.661017,0.791667,0.000000,0.000000,1.000000 43 | 0.500000,0.333333,0.627119,0.458333,0.000000,1.000000,0.000000 44 | 0.083333,0.666667,0.000000,0.041667,1.000000,0.000000,0.000000 45 | 0.222222,0.708333,0.084746,0.125000,1.000000,0.000000,0.000000 46 | 0.166667,0.416667,0.067797,0.041667,1.000000,0.000000,0.000000 47 | 0.333333,0.125000,0.508475,0.500000,0.000000,1.000000,0.000000 48 | 0.472222,0.083333,0.508475,0.375000,0.000000,1.000000,0.000000 49 | 1.000000,0.750000,0.915254,0.791667,0.000000,0.000000,1.000000 50 | 0.388889,0.750000,0.118644,0.083333,1.000000,0.000000,0.000000 51 | 0.611111,0.333333,0.610169,0.583333,0.000000,1.000000,0.000000 52 | 0.583333,0.500000,0.728814,0.916667,0.000000,0.000000,1.000000 53 | 0.416667,0.333333,0.694915,0.958333,0.000000,0.000000,1.000000 54 | 0.250000,0.291667,0.491525,0.541667,0.000000,1.000000,0.000000 55 | 0.750000,0.500000,0.627119,0.541667,0.000000,1.000000,0.000000 56 | 0.250000,0.875000,0.084746,0.000000,1.000000,0.000000,0.000000 57 | 0.583333,0.333333,0.779661,0.833333,0.000000,0.000000,1.000000 58 | 0.194444,0.416667,0.101695,0.041667,1.000000,0.000000,0.000000 59 | 0.694444,0.416667,0.762712,0.833333,0.000000,0.000000,1.000000 60 | 0.194444,0.666667,0.067797,0.041667,1.000000,0.000000,0.000000 61 | 0.027778,0.375000,0.067797,0.041667,1.000000,0.000000,0.000000 62 | 0.416667,0.833333,0.033898,0.041667,1.000000,0.000000,0.000000 63 | 0.472222,0.083333,0.677966,0.583333,0.000000,0.000000,1.000000 64 | 0.555556,0.541667,0.627119,0.625000,0.000000,1.000000,0.000000 65 | 0.611111,0.416667,0.711864,0.791667,0.000000,0.000000,1.000000 66 | 0.472222,0.375000,0.593220,0.583333,0.000000,1.000000,0.000000 67 | 0.194444,0.000000,0.423729,0.375000,0.000000,1.000000,0.000000 68 | 0.555556,0.541667,0.847458,1.000000,0.000000,0.000000,1.000000 69 | 0.833333,0.375000,0.898305,0.708333,0.000000,0.000000,1.000000 70 | 0.388889,0.250000,0.423729,0.375000,0.000000,1.000000,0.000000 71 | 0.611111,0.500000,0.694915,0.791667,0.000000,0.000000,1.000000 72 | 0.527778,0.375000,0.559322,0.500000,0.000000,1.000000,0.000000 73 | 0.611111,0.416667,0.762712,0.708333,0.000000,0.000000,1.000000 74 | 0.333333,0.916667,0.067797,0.041667,1.000000,0.000000,0.000000 75 | 0.083333,0.500000,0.067797,0.041667,1.000000,0.000000,0.000000 76 | 0.416667,0.291667,0.491525,0.458333,0.000000,1.000000,0.000000 77 | 0.500000,0.250000,0.779661,0.541667,0.000000,0.000000,1.000000 78 | 0.527778,0.083333,0.593220,0.583333,0.000000,1.000000,0.000000 79 | 0.472222,0.291667,0.694915,0.625000,0.000000,1.000000,0.000000 80 | 0.361111,0.208333,0.491525,0.416667,0.000000,1.000000,0.000000 81 | 0.555556,0.375000,0.779661,0.708333,0.000000,0.000000,1.000000 82 | 0.805556,0.416667,0.813559,0.625000,0.000000,0.000000,1.000000 83 | 0.944444,0.750000,0.966102,0.875000,0.000000,0.000000,1.000000 84 | 0.000000,0.416667,0.016949,0.000000,1.000000,0.000000,0.000000 85 | 0.166667,0.458333,0.084746,0.000000,1.000000,0.000000,0.000000 86 | 0.805556,0.500000,0.847458,0.708333,0.000000,0.000000,1.000000 87 | 0.083333,0.583333,0.067797,0.083333,1.000000,0.000000,0.000000 88 | 0.194444,0.500000,0.033898,0.041667,1.000000,0.000000,0.000000 89 | 0.138889,0.458333,0.101695,0.041667,1.000000,0.000000,0.000000 90 | 0.222222,0.541667,0.118644,0.166667,1.000000,0.000000,0.000000 91 | 0.555556,0.333333,0.694915,0.583333,0.000000,0.000000,1.000000 92 | 0.083333,0.458333,0.084746,0.041667,1.000000,0.000000,0.000000 93 | 0.194444,0.541667,0.067797,0.041667,1.000000,0.000000,0.000000 94 | 0.111111,0.500000,0.101695,0.041667,1.000000,0.000000,0.000000 95 | 0.916667,0.416667,0.949153,0.833333,0.000000,0.000000,1.000000 96 | 0.666667,0.416667,0.711864,0.916667,0.000000,0.000000,1.000000 97 | 0.250000,0.625000,0.084746,0.041667,1.000000,0.000000,0.000000 98 | 0.388889,0.208333,0.677966,0.791667,0.000000,0.000000,1.000000 99 | 0.111111,0.500000,0.050847,0.041667,1.000000,0.000000,0.000000 100 | 0.722222,0.458333,0.745763,0.833333,0.000000,0.000000,1.000000 101 | 0.500000,0.333333,0.508475,0.500000,0.000000,1.000000,0.000000 102 | 0.222222,0.750000,0.101695,0.041667,1.000000,0.000000,0.000000 103 | 0.333333,0.166667,0.474576,0.416667,0.000000,1.000000,0.000000 104 | 0.944444,0.333333,0.966102,0.791667,0.000000,0.000000,1.000000 105 | 0.805556,0.666667,0.864407,1.000000,0.000000,0.000000,1.000000 106 | 0.305556,0.416667,0.593220,0.583333,0.000000,1.000000,0.000000 107 | 0.722222,0.500000,0.796610,0.916667,0.000000,0.000000,1.000000 108 | 0.027778,0.500000,0.050847,0.041667,1.000000,0.000000,0.000000 109 | 0.166667,0.166667,0.389831,0.375000,0.000000,1.000000,0.000000 110 | 0.222222,0.625000,0.067797,0.041667,1.000000,0.000000,0.000000 111 | 0.666667,0.458333,0.779661,0.958333,0.000000,0.000000,1.000000 112 | 0.583333,0.291667,0.728814,0.750000,0.000000,0.000000,1.000000 113 | 0.222222,0.625000,0.067797,0.083333,1.000000,0.000000,0.000000 114 | 0.472222,0.583333,0.593220,0.625000,0.000000,1.000000,0.000000 115 | 0.138889,0.583333,0.152542,0.041667,1.000000,0.000000,0.000000 116 | 0.555556,0.291667,0.661017,0.708333,0.000000,0.000000,1.000000 117 | 0.305556,0.583333,0.084746,0.125000,1.000000,0.000000,0.000000 118 | 0.333333,0.166667,0.457627,0.375000,0.000000,1.000000,0.000000 119 | 0.527778,0.333333,0.644068,0.708333,0.000000,0.000000,1.000000 120 | 0.138889,0.583333,0.101695,0.041667,1.000000,0.000000,0.000000 121 | 0.500000,0.416667,0.661017,0.708333,0.000000,0.000000,1.000000 122 | -------------------------------------------------------------------------------- /datasets/normalized.csv: -------------------------------------------------------------------------------- 1 | computerPower,downSpeed,Useless,Useless1 2 | 0.821429,0.215420,1.000000,0.000000 3 | 0.000000,0.000000,1.000000,0.000000 4 | 1.000000,0.884354,1.000000,0.000000 5 | 0.142857,1.000000,0.000000,1.000000 6 | -------------------------------------------------------------------------------- /datasets/testing/dummyDataset.csv: -------------------------------------------------------------------------------- 1 | computerPower,downSpeed,yLabels 2 | 122625.790000,26.680000,Useless 3 | 115044.080000,25.730000,Useless 4 | 124273.980000,29.630000,Useless 5 | 116362.640000,30.140000,Useless1 6 | -------------------------------------------------------------------------------- /datasets/testing/dummyDataset.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "computerPower": 122625.79, 4 | "downSpeed": 26.68, 5 | "yLabels": "Useless" 6 | }, 7 | { 8 | "computerPower": 115044.08, 9 | "downSpeed": 25.73, 10 | "yLabels": "Useless" 11 | }, 12 | { 13 | "computerPower": 124273.98, 14 | "downSpeed": 29.63, 15 | "yLabels": "Useless" 16 | }, 17 | { 18 | "computerPower": 116362.64, 19 | "downSpeed": 30.14, 20 | "yLabels": "Useless" 21 | } 22 | ] -------------------------------------------------------------------------------- /dto/dataTransfer.go: -------------------------------------------------------------------------------- 1 | package dto 2 | 3 | import ( 4 | "encoding/json" 5 | "net/http" 6 | ) 7 | 8 | type response struct { 9 | Msg string `json:"message"` 10 | Code int `json:"code"` 11 | Data interface{} `json:"data"` 12 | } 13 | 14 | //SendResponse function for sending response 15 | func SendResponse(w http.ResponseWriter, r *http.Request, code int, message string, data interface{}) { 16 | 17 | w.Header().Set("Content-Type", "application/json") 18 | w.WriteHeader(code) 19 | res := response{ 20 | Msg: message, 21 | Code: code, 22 | Data: data, 23 | } 24 | 25 | json.NewEncoder(w).Encode(res) 26 | 27 | } 28 | -------------------------------------------------------------------------------- /main.go: -------------------------------------------------------------------------------- 1 | package main 2 | 3 | import ( 4 | "fmt" 5 | 6 | "github.com/libonomy/ai-engine/routes" 7 | ) 8 | 9 | func main() { 10 | fmt.Println("AI Simulater is up !") 11 | routes.StartRoutes() 12 | } 13 | -------------------------------------------------------------------------------- /models/classNames.go: -------------------------------------------------------------------------------- 1 | package models 2 | 3 | //ClassNames to storeClass Names 4 | var ClassNames = []string{"CLASS 1", "CLASS 2", "CLASS 3"} 5 | -------------------------------------------------------------------------------- /models/modelConfig.go: -------------------------------------------------------------------------------- 1 | package models 2 | 3 | //ModelConfig structure for saving model details 4 | type ModelConfig struct { 5 | InputNeurons int `json:"inputNeurons"` 6 | OutputNeurons int `json:"outputNeurons"` 7 | HiddenNeurons int `json:"hiddenNeurons"` 8 | NumEpochs int `json:"numEpochs"` 9 | LearningRate float64 `json:"learningRate"` 10 | BHidden [][]float64 `json:"bHidden"` 11 | BHiddenDims []int `json:"bHiddenDims"` 12 | WHidden [][]float64 `json:"wHidden"` 13 | WHiddenDims []int `json:"wHiddenDims"` 14 | BOut [][]float64 `json:"bOut"` 15 | BOutDims []int `json:"bOutDims"` 16 | WOut [][]float64 `json:"wOut"` 17 | WOutDims []int `json:"wOutDims"` 18 | } 19 | -------------------------------------------------------------------------------- /models/modelStats.go: -------------------------------------------------------------------------------- 1 | package models 2 | 3 | //ModelStats structure for model statistics 4 | type ModelStats struct { 5 | TestAccuracy float64 `json:"testAccuracy"` 6 | TrainAccuracy float64 `json:"trainAccuracy"` 7 | PredictAccuracy float64 `json:"predictAccuracy"` 8 | } 9 | -------------------------------------------------------------------------------- /models/nn.go: -------------------------------------------------------------------------------- 1 | package models 2 | 3 | import "gonum.org/v1/gonum/mat" 4 | 5 | //NeuralNet structure 6 | type NeuralNet struct { 7 | Config NeuralNetConfig 8 | WHidden *mat.Dense 9 | BHidden *mat.Dense 10 | WOut *mat.Dense 11 | BOut *mat.Dense 12 | } 13 | 14 | //NeuralNetConfig structure 15 | type NeuralNetConfig struct { 16 | InputNeurons int 17 | OutputNeurons int 18 | HiddenNeurons int 19 | NumEpochs int 20 | LearningRate float64 21 | } 22 | -------------------------------------------------------------------------------- /models/stats.json: -------------------------------------------------------------------------------- 1 | { 2 | "testAccuracy": 0, 3 | "trainAccuracy": 98.34710743801654, 4 | "predictAccuracy": 0 5 | } -------------------------------------------------------------------------------- /models/test.json: -------------------------------------------------------------------------------- 1 | { 2 | "inputNeurons": 4, 3 | "outputNeurons": 3, 4 | "hiddenNeurons": 5, 5 | "numEpochs": 10000, 6 | "learningRate": 0.2, 7 | "bHidden": [ 8 | [ 9 | 1.4621366302962464, 10 | 1.24963101728865, 11 | 2.6204284465748486, 12 | -32.68509464806137, 13 | 7.7703057151006405 14 | ] 15 | ], 16 | "bHiddenDims": [ 17 | 1, 18 | 5 19 | ], 20 | "wHidden": [ 21 | [ 22 | -1.464263863474765, 23 | 1.6651367474050807, 24 | -2.7329984942170875, 25 | 14.355831570365929, 26 | -3.814203107288094 27 | ], 28 | [ 29 | -1.1368872362596791, 30 | -8.829071240679847, 31 | 3.8405959592941112, 32 | -7.455014933365406, 33 | 2.215300188420064 34 | ], 35 | [ 36 | -1.683579382550915, 37 | 4.087916900891488, 38 | -6.148645946519117, 39 | 23.41805877404477, 40 | -5.628368052942679 41 | ], 42 | [ 43 | -1.3524847224571435, 44 | 5.043377198450544, 45 | -6.246417785654093, 46 | 19.888949087650172, 47 | -4.820171907550262 48 | ] 49 | ], 50 | "wHiddenDims": [ 51 | 4, 52 | 5 53 | ], 54 | "bOut": [ 55 | [ 56 | 0.24972862069747273, 57 | 4.166242922727753, 58 | -7.385554960429287 59 | ] 60 | ], 61 | "bOutDims": [ 62 | 1, 63 | 3 64 | ], 65 | "wOut": [ 66 | [ 67 | -1.449353133211044, 68 | -1.7987902671275817, 69 | -3.6626158822913757 70 | ], 71 | [ 72 | -11.912125024870607, 73 | 6.289898722165389, 74 | 3.307603623312394 75 | ], 76 | [ 77 | 4.7597093702891415, 78 | -11.392161215855612, 79 | -4.530044285903677 80 | ], 81 | [ 82 | -2.3568395889775795, 83 | -18.21991700553699, 84 | 16.218497361867254 85 | ], 86 | [ 87 | 2.426632203127346, 88 | -0.2687512761093948, 89 | -9.153830490397482 90 | ] 91 | ], 92 | "wOutDims": [ 93 | 5, 94 | 3 95 | ] 96 | } -------------------------------------------------------------------------------- /routes/router.go: -------------------------------------------------------------------------------- 1 | package routes 2 | 3 | import ( 4 | "log" 5 | "net/http" 6 | 7 | "github.com/gorilla/mux" 8 | "github.com/libonomy/ai-engine/controllers" 9 | ) 10 | 11 | //StartRoutes listening 12 | func StartRoutes() { 13 | router := mux.NewRouter().StrictSlash(true) 14 | 15 | dev := router.PathPrefix("/api/dev").Subrouter() 16 | 17 | dev.HandleFunc("/testing", controllers.Testing).Methods("POST") 18 | dev.HandleFunc("/generateCSVfromJSON", controllers.GenerateCSV).Methods("POST") 19 | dev.HandleFunc("/cleanData", controllers.CleanData).Methods("GET") 20 | dev.HandleFunc("/normalizeCSVData", controllers.NormalizeData).Methods("GET") 21 | dev.HandleFunc("/splitData", controllers.SplitAndShuffle).Methods("POST") 22 | //dev.HandleFunc("/train", controllers.GenerateCSV).Methods("GET") 23 | //dev.HandleFunc("/test", controllers.GenerateCSV).Methods("GET") 24 | //dev.HandleFunc("/validate", controllers.GenerateCSV).Methods("GET") 25 | dev.HandleFunc("/predict", controllers.Predict).Methods("POST") 26 | dev.HandleFunc("/trainComplete", controllers.Train).Methods("GET") 27 | 28 | log.Fatal(http.ListenAndServe(":4400", router)) 29 | } 30 | -------------------------------------------------------------------------------- /utils/utils.go: -------------------------------------------------------------------------------- 1 | package utils 2 | 3 | import ( 4 | "bufio" 5 | "errors" 6 | "fmt" 7 | "log" 8 | "math" 9 | "math/rand" 10 | "os" 11 | "time" 12 | 13 | "github.com/libonomy/ai-engine/models" 14 | "github.com/libonomy/libonomy-gota/dataframe" 15 | "gonum.org/v1/gonum/floats" 16 | "gonum.org/v1/gonum/mat" 17 | ) 18 | 19 | //Sigmoid Activation Function 20 | func Sigmoid(x float64) float64 { 21 | return 1.0 / (1.0 + math.Exp(-x)) 22 | // if x < 0 { 23 | // return 0.0 24 | // } 25 | // return x 26 | } 27 | 28 | //SigmoidPrime Derivative of Sigmoid Function 29 | func SigmoidPrime(x float64) float64 { 30 | return x * (1.0 - x) 31 | // if x < 0 { 32 | // return 0.0 33 | // } 34 | 35 | // return 1.0 36 | } 37 | 38 | //NewNetwork for obtaining new network 39 | func NewNetwork(Config models.NeuralNetConfig) *models.NeuralNet { 40 | return &models.NeuralNet{Config: Config} 41 | } 42 | 43 | //SumAlongAxis function for sum 44 | func SumAlongAxis(axis int, m *mat.Dense) (*mat.Dense, error) { 45 | numRows, numCols := m.Dims() 46 | var output *mat.Dense 47 | switch axis { 48 | case 0: 49 | data := make([]float64, numCols) 50 | for i := 0; i < numCols; i++ { 51 | col := mat.Col(nil, i, m) 52 | data[i] = floats.Sum(col) 53 | } 54 | output = mat.NewDense(1, numCols, data) 55 | case 1: 56 | data := make([]float64, numRows) 57 | for i := 0; i < numRows; i++ { 58 | row := mat.Row(nil, i, m) 59 | data[i] = floats.Sum(row) 60 | } 61 | output = mat.NewDense(numRows, 1, data) 62 | default: 63 | return nil, errors.New("invalid axis, must be 0 or 1") 64 | } 65 | return output, nil 66 | } 67 | 68 | //SplitData function for spliting data set 69 | func SplitData(data dataframe.DataFrame) { 70 | 71 | trainingNum := (data.Nrow() * 4) / 5 72 | testNum := data.Nrow() / 5 73 | 74 | fmt.Println("Training ", trainingNum, "Testing", testNum) 75 | trainingIndex := make([]int, trainingNum) 76 | testingIndex := make([]int, testNum) 77 | 78 | for i := 0; i < trainingNum; i++ { 79 | trainingIndex[i] = i 80 | } 81 | for i := 0; i < testNum; i++ { 82 | testingIndex[i] = trainingNum + i 83 | } 84 | 85 | trainingDF := data.Subset(trainingIndex) 86 | testDF := data.Subset(testingIndex) 87 | 88 | setMap := map[int]dataframe.DataFrame{ 89 | 0: trainingDF, 90 | 1: testDF, 91 | } 92 | 93 | for i, nameDF := range []string{"iris_train.csv", "iris_test.csv"} { 94 | f, err := os.Create(nameDF) 95 | if err != nil { 96 | log.Fatal(err) 97 | } 98 | 99 | // Create a buffered writer. 100 | w := bufio.NewWriter(f) 101 | 102 | setMap[i].WriteCSV(w) 103 | } 104 | 105 | } 106 | 107 | //ShuffleRawCSVdata function for shuffling dataset 108 | func ShuffleRawCSVdata(data [][]string) [][]string { 109 | 110 | rand.Shuffle(len(data), func(i, j int) { 111 | data[i], data[j] = data[j], data[i] 112 | }) 113 | return data 114 | } 115 | 116 | //Train functions 117 | func Train(x, y *mat.Dense, nn *models.NeuralNet) (*mat.Dense, error) { 118 | randSource := rand.NewSource(time.Now().UnixNano()) 119 | randGen := rand.New(randSource) 120 | 121 | wHiddenRaw := make([]float64, nn.Config.HiddenNeurons*nn.Config.InputNeurons) 122 | bHiddenRaw := make([]float64, nn.Config.HiddenNeurons) 123 | wOutRaw := make([]float64, nn.Config.HiddenNeurons*nn.Config.OutputNeurons) 124 | bOutRaw := make([]float64, nn.Config.OutputNeurons) 125 | 126 | for _, param := range [][]float64{wHiddenRaw, bHiddenRaw, wOutRaw, bOutRaw} { 127 | for i := range param { 128 | param[i] = randGen.Float64() 129 | //fmt.Println(param[i]) 130 | } 131 | } 132 | 133 | wHidden := mat.NewDense(nn.Config.InputNeurons, nn.Config.HiddenNeurons, wHiddenRaw) 134 | bHidden := mat.NewDense(1, nn.Config.HiddenNeurons, bHiddenRaw) 135 | wOut := mat.NewDense(nn.Config.HiddenNeurons, nn.Config.OutputNeurons, wOutRaw) 136 | bOut := mat.NewDense(1, nn.Config.OutputNeurons, bOutRaw) 137 | // fmt.Println("Code Reached Here") 138 | var output mat.Dense 139 | 140 | for i := 0; i < nn.Config.NumEpochs; i++ { 141 | // Feed Forward Process 142 | var hiddenLayerInput mat.Dense 143 | //hiddenLayerInput := mat.NewDense(0, 0, nil) 144 | hiddenLayerInput.Mul(x, wHidden) 145 | addBHidden := func(_, col int, v float64) float64 { return v + bHidden.At(0, col) } 146 | hiddenLayerInput.Apply(addBHidden, &hiddenLayerInput) 147 | //fmt.Println("Code Reached Here", i) 148 | // hiddenLayerActivations := mat.NewDense(0, 0, nil) 149 | var hiddenLayerActivations mat.Dense 150 | applySigmoid := func(_, _ int, v float64) float64 { return Sigmoid(v) } 151 | hiddenLayerActivations.Apply(applySigmoid, &hiddenLayerInput) 152 | 153 | //outputLayerInput := mat.NewDense(0, 0, nil) 154 | var outputLayerInput mat.Dense 155 | outputLayerInput.Mul(&hiddenLayerActivations, wOut) 156 | addBOut := func(_, col int, v float64) float64 { return v + bOut.At(0, col) } 157 | outputLayerInput.Apply(addBOut, &outputLayerInput) 158 | output.Apply(applySigmoid, &outputLayerInput) 159 | 160 | // Back Propogation Process 161 | // networkError := mat.NewDense(0, 0, nil) 162 | var networkError mat.Dense 163 | networkError.Sub(y, &output) 164 | 165 | // slopeOutputLayer := mat.NewDense(0, 0, nil) 166 | var slopeOutputLayer mat.Dense 167 | applySigmoidPrime := func(_, _ int, v float64) float64 { return SigmoidPrime(v) } 168 | slopeOutputLayer.Apply(applySigmoidPrime, &output) 169 | // slopeHiddenLayer := mat.NewDense(0, 0, nil) 170 | var slopeHiddenLayer mat.Dense 171 | slopeHiddenLayer.Apply(applySigmoidPrime, &hiddenLayerActivations) 172 | 173 | // dOutput := mat.NewDense(0, 0, nil) 174 | var dOutput mat.Dense 175 | dOutput.MulElem(&networkError, &slopeOutputLayer) 176 | // errorAtHiddenLayer := mat.NewDense(0, 0, nil) 177 | var errorAtHiddenLayer mat.Dense 178 | errorAtHiddenLayer.Mul(&dOutput, wOut.T()) 179 | 180 | // dHiddenLayer := mat.NewDense(0, 0, nil) 181 | var dHiddenLayer mat.Dense 182 | dHiddenLayer.MulElem(&errorAtHiddenLayer, &slopeHiddenLayer) 183 | 184 | // Adjust The Parameters 185 | // wOutAdj := mat.NewDense(0, 0, nil) 186 | var wOutAdj mat.Dense 187 | wOutAdj.Mul(hiddenLayerActivations.T(), &dOutput) 188 | wOutAdj.Scale(nn.Config.LearningRate, &wOutAdj) 189 | wOut.Add(wOut, &wOutAdj) 190 | 191 | bOutAdj, err := SumAlongAxis(0, &dOutput) 192 | if err != nil { 193 | return nil, err 194 | } 195 | bOutAdj.Scale(nn.Config.LearningRate, bOutAdj) 196 | bOut.Add(bOut, bOutAdj) 197 | 198 | // wHiddenAdj := mat.NewDense(0, 0, nil) 199 | var wHiddenAdj mat.Dense 200 | wHiddenAdj.Mul(x.T(), &dHiddenLayer) 201 | wHiddenAdj.Scale(nn.Config.LearningRate, &wHiddenAdj) 202 | wHidden.Add(wHidden, &wHiddenAdj) 203 | 204 | bHiddenAdj, err := SumAlongAxis(0, &dHiddenLayer) 205 | if err != nil { 206 | return nil, err 207 | } 208 | bHiddenAdj.Scale(nn.Config.LearningRate, bHiddenAdj) 209 | bHidden.Add(bHidden, bHiddenAdj) 210 | 211 | } 212 | 213 | nn.WHidden = wHidden 214 | nn.BHidden = bHidden 215 | nn.WOut = wOut 216 | nn.BOut = bOut 217 | 218 | return &output, nil 219 | } 220 | 221 | //Predict for prediction 222 | func Predict(x *mat.Dense, nn *models.NeuralNet) (*mat.Dense, error) { 223 | 224 | // Check to make sure that our neuralNet value 225 | // represents a trained model. 226 | if nn.WHidden == nil || nn.WOut == nil || nn.BHidden == nil || nn.BOut == nil { 227 | return nil, errors.New("the supplied neurnal net weights and biases are empty") 228 | } 229 | 230 | // Define the output of the neural network. 231 | var output mat.Dense 232 | 233 | // Complete the feed forward process. 234 | var hiddenLayerInput mat.Dense 235 | hiddenLayerInput.Mul(x, nn.WHidden) 236 | addBHidden := func(_, col int, v float64) float64 { return v + nn.BHidden.At(0, col) } 237 | hiddenLayerInput.Apply(addBHidden, &hiddenLayerInput) 238 | 239 | var hiddenLayerActivations mat.Dense 240 | applySigmoid := func(_, _ int, v float64) float64 { return Sigmoid(v) } 241 | hiddenLayerActivations.Apply(applySigmoid, &hiddenLayerInput) 242 | 243 | var outputLayerInput mat.Dense 244 | outputLayerInput.Mul(&hiddenLayerActivations, nn.WOut) 245 | addBOut := func(_, col int, v float64) float64 { return v + nn.BOut.At(0, col) } 246 | outputLayerInput.Apply(addBOut, &outputLayerInput) 247 | output.Apply(applySigmoid, &outputLayerInput) 248 | 249 | return &output, nil 250 | } 251 | 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