├── .gitignore
├── LICENSE
├── README.md
├── constants
└── constants.go
├── controllers
└── controller.go
├── datasets
├── dataset.csv
├── iris.csv
├── iris_train.csv
├── normalized.csv
├── test_normalized.csv
├── testing
│ ├── dataStats.json
│ ├── dummyDataset.csv
│ └── dummyDataset.json
└── train_normalized.csv
├── dto
└── dataTransfer.go
├── go.mod
├── go.sum
├── main.go
├── models
├── classNames.go
├── modelConfig.go
├── modelStats.go
├── nn.go
├── stats.json
└── test.json
├── routes
└── router.go
└── utils
├── helper
├── csvFileWriter.go
└── helpers.go
└── utils.go
/.gitignore:
--------------------------------------------------------------------------------
1 | /build
2 |
--------------------------------------------------------------------------------
/LICENSE:
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563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. 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 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 |
2 |
3 | This repo contains the implementation of the data extraction module of libonomy daemon.
4 | The implementation covers the specifications that enables the daemon to extract the data from the node and feed it into the classification agent.
5 | >Note: The initial release covers the module release which is under heavy development and has not reached the beta release in this repository
6 |
7 | > Warning: The current module will be submerged with the daemon release of libonomy fully node implementation on goLang.
8 |
9 | ## Pre-requisites
10 | Ensure that `$GOPATH` is set correctly and that the `$GOPATH/bin` directory appears in `$PATH`.
11 | > OS X, Ubuntu , Windows Support included
12 | - [git](https://git-scm.com/)
13 | - [go lang v1.14+](https://golang.org/)
14 | - Docker. (Next Release)
15 |
16 |
17 | ## Developers Community ?
18 | - Libonomy [discord community](https://libonomy.com/discord)
19 | #### Upcoming Release
20 | - We will be releasing the AI neural network agent for our mainnet daemon
21 | - Support of agent with libonomy Software for macOS,Linux and windows
22 | ---
23 |
24 |
--------------------------------------------------------------------------------
/constants/constants.go:
--------------------------------------------------------------------------------
1 | package constants
2 |
3 | import "crypto/rsa"
4 |
5 | // PublicKey comment
6 | var PublicKey rsa.PublicKey
7 |
8 | // PrivateKey comment
9 | var PrivateKey *rsa.PrivateKey
10 |
--------------------------------------------------------------------------------
/controllers/controller.go:
--------------------------------------------------------------------------------
1 | package controllers
2 |
3 | import (
4 | "crypto/rsa"
5 | "encoding/csv"
6 | "encoding/json"
7 | "fmt"
8 | "io/ioutil"
9 | "log"
10 | "math"
11 | "net/http"
12 | "os"
13 | "sort"
14 | "strconv"
15 |
16 | "github.com/libonomy/node-extract/constants"
17 | "github.com/libonomy/node-extract/utils/helper"
18 |
19 | "github.com/libonomy/libonomy-gota/dataframe"
20 | "github.com/libonomy/node-extract/dto"
21 | "github.com/libonomy/node-extract/models"
22 | "github.com/libonomy/node-extract/utils"
23 | "gonum.org/v1/gonum/floats"
24 | "gonum.org/v1/gonum/mat"
25 | )
26 |
27 | type bodyVariables struct {
28 | MachineID string `json:"machineId"`
29 | ComputerPower float64 `json:"computerPower"`
30 | DownloadSpeed float64 `json:"downSpeed"`
31 | Ylabels string `json:"yLabels"`
32 | }
33 |
34 | type check1 struct {
35 | Name string
36 | }
37 |
38 | type check2 struct {
39 | Name string
40 | }
41 |
42 | type check3 struct {
43 | Name string
44 | }
45 |
46 | type fileStats struct {
47 | LabelIndex int `json:"labelIndex"`
48 | }
49 |
50 | //Testing Function To Test its working
51 | func Testing(w http.ResponseWriter, r *http.Request) {
52 | s := "This is for testing function only"
53 |
54 | dto.SendResponse(w, r, http.StatusOK, "Success", map[string]interface{}{"testing": s})
55 | }
56 |
57 | //GenerateCSV function to generate csv file from json data.
58 | func GenerateCSV(w http.ResponseWriter, r *http.Request) {
59 | body, _ := ioutil.ReadAll(r.Body)
60 | data := []byte(body)
61 | fmt.Println(string(body))
62 | decData := helper.DecryptDatafromClient(data)
63 |
64 | //variables := []bodyVariables{}
65 | fileVariables := []bodyVariables{}
66 | var variables []bodyVariables
67 | json.Unmarshal(decData, &variables)
68 | filename := "./datasets/testing/dummyDataset.json"
69 | csvFilename := "./datasets/testing/dummyDataset.csv"
70 |
71 | /* err := json.NewDecoder(r.Body).Decode(&variables)
72 | if err != nil {
73 | fmt.Println("There is some Error in Decoding Body Request", err.Error())
74 | dto.SendResponse(w, r, http.StatusInternalServerError, "Bad", map[string]interface{}{"Error": err.Error()})
75 | return
76 | } */
77 |
78 | //fmt.Println(variables)
79 | _, err := os.Stat(filename)
80 |
81 | if err != nil {
82 | //checking if file does not exists
83 | fmt.Println("Error is os.stat", err.Error())
84 | //fmt.Println("File Info ", fileInfo)
85 |
86 | jsonFile, _ := os.Create(filename)
87 | csvFile, _ := os.Create(csvFilename)
88 |
89 | defer jsonFile.Close()
90 | file, _ := json.MarshalIndent(variables, "", " ")
91 | _ = ioutil.WriteFile(filename, file, 0644)
92 |
93 | fileOpened, _ := os.Open(filename)
94 | dataFrame := dataframe.ReadJSON(fileOpened)
95 | csvWriter := csv.NewWriter(csvFile)
96 | csvWriter.WriteAll(dataFrame.Records())
97 | // csvData := dataframe.ReadJSON(jsonFile)
98 | // csvData.WriteCSV(csvFile)
99 | dto.SendResponse(w, r, http.StatusOK, "Successfully Created CSV File From JSON", nil)
100 | return
101 | }
102 |
103 | file, err := os.Open(filename)
104 | if err != nil {
105 | dto.SendResponse(w, r, http.StatusBadRequest, "Cannot create file", nil)
106 | }
107 | fileBytes, _ := ioutil.ReadAll(file)
108 | json.Unmarshal(fileBytes, &fileVariables)
109 | var alreadyExistsCheck bool = false
110 | // fmt.Println("Already Exists Check ", alreadyExistsCheck)
111 |
112 | for index, element := range fileVariables {
113 | if element.MachineID == variables[0].MachineID {
114 | alreadyExistsCheck = true
115 | // fmt.Println("Machine Id Already Exists")
116 | fileVariables[index].ComputerPower = variables[0].ComputerPower
117 | fileVariables[index].DownloadSpeed = variables[0].DownloadSpeed
118 | fileVariables[index].Ylabels = variables[0].Ylabels
119 | }
120 | // fmt.Println("Index", index, "Data", element)
121 | }
122 |
123 | if alreadyExistsCheck == true {
124 | alreadyExistsCheck = false
125 | writeF, _ := json.MarshalIndent(fileVariables, "", " ")
126 | _ = ioutil.WriteFile(filename, writeF, 0644)
127 |
128 | csvFile, _ := os.Create(csvFilename)
129 | fileOpened, _ := os.Open(filename)
130 | dataFrame := dataframe.ReadJSON(fileOpened)
131 | csvWriter := csv.NewWriter(csvFile)
132 | csvWriter.WriteAll(dataFrame.Records())
133 |
134 | dto.SendResponse(w, r, http.StatusOK, "Successfully Updated CSV File", nil)
135 | return
136 |
137 | }
138 | fileVariables = append(fileVariables, variables[0])
139 | writeF, _ := json.MarshalIndent(fileVariables, "", " ")
140 | _ = ioutil.WriteFile(filename, writeF, 0644)
141 |
142 | csvFile, _ := os.Create(csvFilename)
143 | fileOpened, _ := os.Open(filename)
144 | dataFrame := dataframe.ReadJSON(fileOpened)
145 | csvWriter := csv.NewWriter(csvFile)
146 | csvWriter.WriteAll(dataFrame.Records())
147 |
148 | dto.SendResponse(w, r, http.StatusOK, "Successfully Created CSV File From JSON", nil)
149 | }
150 |
151 | //CleanData function to clean data and convert data to specific format
152 | func CleanData(w http.ResponseWriter, r *http.Request) {
153 | filename := "./datasets/testing/dummyDataset.csv"
154 | fileSetStat := "./datasets/testing/dataStats.json"
155 |
156 | f, err := os.Open(filename)
157 | newFile := f
158 | defer newFile.Close()
159 | if err != nil {
160 | fmt.Println("Error in opening file", f.Name(), err.Error())
161 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in opening file "+filename+" Error is \n"+err.Error(), nil)
162 | return
163 | }
164 | reader := csv.NewReader(f)
165 | rawData, err := reader.ReadAll()
166 | if err != nil {
167 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in opening file 2"+err.Error(), nil)
168 | return
169 | }
170 | var dataWithoutID [][]string
171 | var machineID int
172 | // fmt.Println(dataWithoutID, machineID)
173 | for index, row := range rawData {
174 | var rowRawData []string
175 | if index == 0 {
176 | for rowIndex, rowValues := range row {
177 | if rowValues != "machineId" {
178 | rowRawData = append(rowRawData, rowValues)
179 | } else {
180 | machineID = rowIndex
181 | }
182 | }
183 | dataWithoutID = append(dataWithoutID, rowRawData)
184 | continue
185 | }
186 | for rowIndex, rowValues := range row {
187 | if rowIndex != machineID {
188 | rowRawData = append(rowRawData, rowValues)
189 | }
190 | }
191 | dataWithoutID = append(dataWithoutID, rowRawData)
192 | }
193 | rawData = dataWithoutID
194 | // fmt.Println("Data Without Machine Id", dataWithoutID)
195 |
196 | data := dataframe.LoadRecords(dataWithoutID)
197 | // fmt.Println("Printing Data Before Cleaning", data.String())
198 | // fmt.Println(data)
199 |
200 | // check := []int{1, 1, 1, 1, 1, 1}
201 |
202 | labelExtraction := []string{}
203 |
204 | labels := data.Col("yLabels").Records()
205 |
206 | var index int
207 | dataRecords := data.Records()
208 | for i, record := range dataRecords[0] {
209 | // fmt.Println("Index ", i, "Value", record)
210 | if record == "yLabels" {
211 | index = i
212 | }
213 | }
214 | // fmt.Println(fileSetStat)
215 | stats := fileStats{
216 | LabelIndex: index,
217 | }
218 | statsBytes, err := json.MarshalIndent(stats, "", "")
219 | if err != nil {
220 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error": err.Error()})
221 | return
222 | }
223 | err = ioutil.WriteFile(fileSetStat, statsBytes, 0644)
224 | if err != nil {
225 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error": err.Error()})
226 | return
227 | }
228 | // fmt.Println("Index Is", index)
229 |
230 | // fmt.Println("Labels From Dataset", labels)
231 | //Loop for extracting Labels
232 | for _, label := range labels {
233 | result := helper.StringContains(labelExtraction, label)
234 | if result == false {
235 | labelExtraction = append(labelExtraction, label)
236 | }
237 | // fmt.Println("String Contains", result)
238 | }
239 | // fmt.Println("Extracted Labels", labelExtraction)
240 | // fmt.Println(rawData)
241 | sort.Strings(labelExtraction)
242 |
243 | var newRecords [][]string
244 | // fmt.Println(labelExtraction)
245 | length := len(rawData[0])
246 | for i, record := range rawData {
247 |
248 | // fmt.Println("Length", length)
249 | if i == 0 {
250 | for _, label := range labelExtraction {
251 | record = append(record, label)
252 | }
253 | newRecords = append(newRecords, record)
254 |
255 | continue
256 | }
257 | for _, label := range labelExtraction {
258 | _ = label
259 | // fmt.Println(label)
260 | record = append(record, "0.0")
261 |
262 | }
263 |
264 | for index, label := range labelExtraction {
265 | if record[length-1] == label {
266 | record[index+length] = "1.0"
267 | }
268 |
269 | // fmt.Println("Record", record[len(record)-(length)])
270 | }
271 | newRecords = append(newRecords, record)
272 |
273 | }
274 |
275 | // fmt.Println(newRecords)
276 | var finalRecords [][]string
277 | for _, record := range newRecords {
278 | // fmt.Println(record[:length-1])
279 | // fmt.Println(record[length:])
280 |
281 | modified := append(record[:length-1], record[length:]...)
282 | finalRecords = append(finalRecords, modified)
283 | // finalRecords = append(finalRecords, record[length:])
284 | }
285 |
286 | // fmt.Println("After Cleaning Dataset", finalRecords)
287 | writer, _ := os.Create("./datasets/dataset.csv")
288 | wr := csv.NewWriter(writer)
289 | wr.WriteAll(finalRecords)
290 | wr.Flush()
291 | dto.SendResponse(w, r, http.StatusOK, "Success", map[string]interface{}{"raw csv data": finalRecords, "Index ": index, "Length": len(newRecords)})
292 | }
293 |
294 | //NormalizeData function for data normalization from 0-1
295 | func NormalizeData(w http.ResponseWriter, r *http.Request) {
296 | f, err := os.Open("./datasets/dataset.csv")
297 |
298 | if err != nil {
299 |
300 | dto.SendResponse(w, r, http.StatusBadRequest, err.Error(), nil)
301 | return
302 | }
303 |
304 | csvReader := csv.NewReader(f)
305 | rawCSVdata, err := csvReader.ReadAll()
306 | if err != nil {
307 | dto.SendResponse(w, r, http.StatusBadRequest, err.Error(), nil)
308 | return
309 | }
310 | dataFrame := dataframe.LoadRecords(rawCSVdata)
311 | normalize := [][]string{}
312 | // fmt.Println("Data Name ", dataFrame.Col(string(rawCSVdata[0][0])))
313 | for i, record := range rawCSVdata {
314 | if i == 0 {
315 | normalize = append(normalize, record)
316 | continue
317 | }
318 | noVal := []string{}
319 | for x, values := range record {
320 | // fmt.Println("Type ", reflect.TypeOf(values))
321 |
322 | val, err := strconv.ParseFloat(values, 64)
323 | if err != nil {
324 | dto.SendResponse(w, r, http.StatusBadRequest, err.Error(), nil)
325 | return
326 | }
327 | // fmt.Println("x", x, "Values", values)
328 | // fmt.Println("val", val)
329 | // fmt.Println("Min", dataFrame.Col(rawCSVdata[0][x]).Min())
330 | // fmt.Println("Max", dataFrame.Col(rawCSVdata[0][x]).Max())
331 | nVal := (val - dataFrame.Col(rawCSVdata[0][x]).Min()) / (dataFrame.Col(rawCSVdata[0][x]).Max() - dataFrame.Col(rawCSVdata[0][x]).Min())
332 | // fmt.Println("Number Value", nVal)
333 | sVal := strconv.FormatFloat(nVal, 'f', 6, 64)
334 | noVal = append(noVal, sVal)
335 | // fmt.Println(nVal)
336 | }
337 | normalize = append(normalize, noVal)
338 | }
339 |
340 | headers := rawCSVdata[0]
341 | wr, err := os.Create("./datasets/normalized.csv")
342 | if err != nil {
343 | dto.SendResponse(w, r, http.StatusBadRequest, err.Error(), nil)
344 | return
345 | }
346 | csvWriter := csv.NewWriter(wr)
347 | csvWriter.WriteAll(normalize)
348 | dto.SendResponse(w, r, http.StatusOK, "Everthings Fine", map[string]interface{}{"data": rawCSVdata, "summary": dataFrame.Describe().Records(),
349 | "Headers": headers, "Normalixe": normalize, "Original": rawCSVdata})
350 | }
351 |
352 | //SplitAndShuffle to split and shuffle data
353 | func SplitAndShuffle(w http.ResponseWriter, r *http.Request) {
354 | percentage := r.FormValue("trainPercentage")
355 | percent, err := strconv.ParseFloat(percentage, 64)
356 | if err != nil {
357 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
358 | return
359 | }
360 | filePath := "./datasets/normalized.csv"
361 | trainFilePath := "./datasets/train_normalized.csv"
362 | testFilePath := "./datasets/test_normalized.csv"
363 |
364 | percent = percent / 100
365 | fmt.Println("Percent", percent)
366 | normalizedFile, err := os.Open(filePath)
367 | if err != nil {
368 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
369 | return
370 | }
371 |
372 | normalizedFileReader := csv.NewReader(normalizedFile)
373 | normalizedFileData, err := normalizedFileReader.ReadAll()
374 | if err != nil {
375 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
376 | return
377 | }
378 |
379 | headers := normalizedFileData[0]
380 | fmt.Println("Headers Of Normalized Data", headers)
381 |
382 | splittingLength := int(math.Floor(float64(len(normalizedFileData)) * percent))
383 | fmt.Println("Splitting Length", splittingLength)
384 |
385 | var trainRawData [][]string
386 | var testRawData [][]string
387 | trainRawData = append(trainRawData, headers)
388 | testRawData = append(testRawData, headers)
389 | for i := 1; i < splittingLength; i++ {
390 | trainRawData = append(trainRawData, normalizedFileData[i])
391 | }
392 | for i := splittingLength; i < len(normalizedFileData); i++ {
393 | testRawData = append(testRawData, normalizedFileData[i])
394 | }
395 | // fmt.Println("Train Raw Data", trainRawData)
396 | // fmt.Println("Test Raw Data", testRawData)
397 |
398 | err = helper.WriteCSVFile(trainFilePath, trainRawData)
399 | if err != nil {
400 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
401 | return
402 | }
403 |
404 | err = helper.WriteCSVFile(testFilePath, testRawData)
405 | if err != nil {
406 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
407 | return
408 | }
409 | // trainPercent := percent / 100
410 | // testPercent := ((100 - percent) / 100) / 2
411 | // var validatePercent int
412 | // var even bool
413 | // fmt.Println("Train Percent", trainPercent)
414 | // fmt.Println("Test Percent", testPercent)
415 |
416 | // f, _ := os.Open("./datasets/normalized.csv")
417 | // reader := csv.NewReader(f)
418 | // rawCSV, _ := reader.ReadAll()
419 | // headers := rawCSV[0]
420 | // // length := len(rawCSV) / 2
421 | // fmt.Println("Train Data Length With Mat", math.Ceil(float64(len(rawCSV))*testPercent))
422 | // fmt.Println("Train Data Length", float64(len(rawCSV))*testPercent)
423 | // // fmt.Println("Length", length)
424 | // fmt.Println("Headers", headers)
425 | // fmt.Println(len(rawCSV))
426 | // if int(float64(len(rawCSV))*testPercent)%2 == 0 {
427 | // fmt.Println("Yes its even")
428 | // even = true
429 | // } else {
430 | // fmt.Println("No ")
431 | // even = false
432 | // }
433 |
434 | // csvPath := "./datasets/train_normalized.csv"
435 | // fmt.Println(csvPath)
436 | // if even == true {
437 | // // for i := 0; i < length; i++ {
438 | // // fmt.Println(csvPath)
439 | // // }
440 | // }
441 |
442 | // fmt.Println(testPercent, even, validatePercent, trainPercent)
443 | dto.SendResponse(w, r, http.StatusOK, "Successfully Splitted Data", map[string]interface{}{})
444 | }
445 |
446 | //Train funciton for training Neural Network
447 | func Train(w http.ResponseWriter, r *http.Request) {
448 | rateString := r.URL.Query().Get("rate")
449 | epochsString := r.URL.Query().Get("epochs")
450 | hiddenString := r.URL.Query().Get("hidden")
451 |
452 | rate, _ := strconv.ParseFloat(rateString, 64)
453 | epochs, _ := strconv.ParseFloat(epochsString, 64)
454 | hidden, _ := strconv.ParseFloat(hiddenString, 64)
455 |
456 | // if rate == 0 || epochs == 0 || hidden == 0 {
457 | // dto.SendResponse(w, r, http.StatusBadGateway, "Rate,Epochs & hidden Cannot be Empty", nil)
458 | // return
459 | // }
460 | if rate == 0 {
461 | rate = 0.2
462 | }
463 | if epochs == 0 {
464 | epochs = 10000
465 | }
466 | if hidden == 0 {
467 | hidden = 5
468 | }
469 | // fmt.Println("Rate is :\t", rate)
470 | // fmt.Println("Type of Rate is :\t", reflect.TypeOf(rate))
471 |
472 | // fmt.Println("Epochs is :\t", epochs)
473 | // fmt.Println("hidden", hidden)
474 |
475 | // f, err := os.Open("./datasets/normalized.csv")
476 | labelIndexStruct := fileStats{}
477 | labelIndexFile, err := os.Open("./datasets/testing/dataStats.json")
478 | if err != nil {
479 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
480 | return
481 | }
482 | labelIndexBytes, err := ioutil.ReadAll(labelIndexFile)
483 | if err != nil {
484 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
485 | return
486 | }
487 | err = json.Unmarshal(labelIndexBytes, &labelIndexStruct)
488 | fmt.Println("Label Structure", labelIndexStruct)
489 | if err != nil {
490 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
491 | return
492 | }
493 |
494 | // f, err := os.Open("./datasets/iris_train.csv")
495 | if err != nil {
496 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
497 | return
498 | }
499 |
500 | //normalized train file for training
501 | normalizedFile, err := os.Open("./datasets/train_normalized.csv")
502 | if err != nil {
503 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
504 | return
505 | }
506 | fileReader := csv.NewReader(normalizedFile)
507 | rawNormalizedData, err := fileReader.ReadAll()
508 | if err != nil {
509 | dto.SendResponse(w, r, http.StatusBadRequest, "Contact Support", map[string]interface{}{"Error Message": err.Error()})
510 | return
511 | }
512 |
513 | _ = rawNormalizedData
514 | // reader := csv.NewReader(f)
515 | // rawCSVdata, err := reader.ReadAll()
516 | featuresLength := len(rawNormalizedData[0]) - labelIndexStruct.LabelIndex
517 | // fmt.Println("Features Length", featuresLength, "Labels Length", labelIndexStruct.LabelIndex)
518 | inputsData := make([]float64, featuresLength*len(rawNormalizedData))
519 | labelsData := make([]float64, labelIndexStruct.LabelIndex*len(rawNormalizedData))
520 | fmt.Println("Features Data Length", len(inputsData), "Labels Data Length", len(labelsData))
521 |
522 | // inputsData := make([]float64, 4*len(rawCSVdata))
523 | // labelsData := make([]float64, 3*len(rawCSVdata))
524 |
525 | var inputIdx int
526 | var labelIdx int
527 |
528 | for idx, record := range rawNormalizedData {
529 | if idx == 0 {
530 | continue
531 | }
532 |
533 | for i, val := range record {
534 | parsedVal, err := strconv.ParseFloat(val, 64)
535 | if err != nil {
536 | // fmt.Println("Error in Parsing Float Value", err.Error())
537 | return
538 | }
539 |
540 | //if i >= labelIndex. This Condition Is Should be Implemented
541 | if i >= labelIndexStruct.LabelIndex {
542 | labelsData[labelIdx] = parsedVal
543 | labelIdx++
544 | } else {
545 | inputsData[inputIdx] = parsedVal
546 | inputIdx++
547 | }
548 | }
549 | }
550 |
551 | // inputs := mat.NewDense(len(rawCSVdata), 4, inputsData)
552 | // labels := mat.NewDense(len(rawCSVdata), 3, labelsData)
553 |
554 | inputs := mat.NewDense(len(rawNormalizedData), featuresLength, inputsData)
555 | labels := mat.NewDense(len(rawNormalizedData), labelIndexStruct.LabelIndex, labelsData)
556 |
557 | // config := models.NeuralNetConfig{
558 | // InputNeurons: 4,
559 | // OutputNeurons: 3,
560 | // HiddenNeurons: 5,
561 | // NumEpochs: int(epochs),
562 | // LearningRate: rate,
563 | // }
564 | config := models.NeuralNetConfig{
565 | InputNeurons: featuresLength,
566 | OutputNeurons: labelIndexStruct.LabelIndex,
567 | HiddenNeurons: 5,
568 | NumEpochs: int(epochs),
569 | LearningRate: rate,
570 | }
571 |
572 | network := utils.NewNetwork(config)
573 | // fmt.Println("Printing Network Before Training", network)
574 |
575 | trainOutput, err := utils.Train(inputs, labels, network)
576 | if err != nil {
577 | log.Fatal(err)
578 | }
579 | // fmt.Println("Printing Network After Training", network)
580 |
581 | // fmt.Println(trainOutput)
582 | // fmt.Println(network.BHidden.RawMatrix())
583 | // fmt.Print("Printing B hidden \t")
584 | // fmt.Println(network.BHidden.Dims())
585 | rowsBHidden, colsBHidden := network.BHidden.Dims()
586 |
587 | // fmt.Print("Printing W hidden \t")
588 | // fmt.Println(network.WHidden.Dims())
589 | rowsWHidden, colsWHidden := network.WHidden.Dims()
590 |
591 | // fmt.Print("Printing W out \t")
592 | // fmt.Println(network.WOut.Dims())
593 | rowsWOut, colsWOut := network.WOut.Dims()
594 |
595 | // fmt.Print("Printing B out \t")
596 | // fmt.Println(network.BOut.Dims())
597 | rowsBOut, colsBOut := network.BOut.Dims()
598 |
599 | configuration := models.ModelConfig{}
600 |
601 | for i := 0; i < rowsBHidden; i++ {
602 | configuration.BHidden = append(configuration.BHidden, network.BHidden.RawRowView(i))
603 | }
604 | for i := 0; i < rowsWHidden; i++ {
605 | configuration.WHidden = append(configuration.WHidden, network.WHidden.RawRowView(i))
606 | }
607 | for i := 0; i < rowsWOut; i++ {
608 | configuration.WOut = append(configuration.WOut, network.WOut.RawRowView(i))
609 | }
610 | for i := 0; i < rowsBOut; i++ {
611 | configuration.BOut = append(configuration.BOut, network.BOut.RawRowView(i))
612 | }
613 |
614 | configuration.BHiddenDims = append(configuration.BHiddenDims, rowsBHidden)
615 | configuration.BHiddenDims = append(configuration.BHiddenDims, colsBHidden)
616 | configuration.WHiddenDims = append(configuration.WHiddenDims, rowsWHidden)
617 | configuration.WHiddenDims = append(configuration.WHiddenDims, colsWHidden)
618 | configuration.WOutDims = append(configuration.WOutDims, rowsWOut)
619 | configuration.WOutDims = append(configuration.WOutDims, colsWOut)
620 | configuration.BOutDims = append(configuration.BOutDims, rowsBOut)
621 | configuration.BOutDims = append(configuration.BOutDims, colsBOut)
622 | configuration.InputNeurons = network.Config.InputNeurons
623 | configuration.HiddenNeurons = network.Config.HiddenNeurons
624 | configuration.LearningRate = network.Config.LearningRate
625 | configuration.NumEpochs = network.Config.NumEpochs
626 | configuration.OutputNeurons = network.Config.OutputNeurons
627 |
628 | // fmt.Println("Configuration Values ", configuration)
629 |
630 | var truePosNeg int
631 | numPreds, _ := trainOutput.Dims()
632 | for i := 0; i < numPreds; i++ {
633 | // fmt.Println("Prediction Index ", i, "\t", trainOutput.RowView(i))
634 | // Get the label.
635 | labelRow := mat.Row(nil, i, labels)
636 | var species int
637 | for idx, label := range labelRow {
638 | if label == 1.0 {
639 | species = idx
640 | break
641 | }
642 | }
643 |
644 | // Accumulate the true positive/negative count.
645 | if trainOutput.At(i, species) == floats.Max(mat.Row(nil, i, trainOutput)) {
646 | truePosNeg++
647 | }
648 | }
649 |
650 | // Calculate the accuracy (subset accuracy).
651 | accuracy := float64(truePosNeg) / float64(numPreds)
652 |
653 | // Output the Accuracy value to standard out.
654 | fmt.Printf("\nAccuracy of Testing = %0.2f %%\n", accuracy*100)
655 | stats := models.ModelStats{}
656 | stats.TrainAccuracy = accuracy * 100
657 |
658 | file, _ := json.MarshalIndent(configuration, "", " ")
659 |
660 | _ = ioutil.WriteFile("./models/test.json", file, 0644)
661 |
662 | file, _ = json.MarshalIndent(stats, "", " ")
663 |
664 | _ = ioutil.WriteFile("./models/stats.json", file, 0644)
665 |
666 | dto.SendResponse(w, r, http.StatusOK, "Training Result ", map[string]interface{}{"output": configuration, "Accuracy": accuracy * 100})
667 |
668 | }
669 |
670 | //Predict Function for prediction
671 | func Predict(w http.ResponseWriter, r *http.Request) {
672 | w.Header().Set("Content-Type", "application/x-www-form-urlencoded")
673 | stringf1 := r.FormValue("f1")
674 | stringf2 := r.FormValue("f2")
675 | stringf3 := r.FormValue("f3")
676 | stringf4 := r.FormValue("f4")
677 |
678 | f1, _ := strconv.ParseFloat(stringf1, 64)
679 | f2, _ := strconv.ParseFloat(stringf2, 64)
680 | f3, _ := strconv.ParseFloat(stringf3, 64)
681 | f4, _ := strconv.ParseFloat(stringf4, 64)
682 |
683 | input := []float64{f1, f2, f3, f4}
684 | configuration := models.ModelConfig{}
685 | file, _ := ioutil.ReadFile("./models/test.json")
686 | _ = json.Unmarshal([]byte(file), &configuration)
687 |
688 | // fmt.Println("Printing Configurations", configuration)
689 | config := models.NeuralNetConfig{
690 | InputNeurons: configuration.InputNeurons,
691 | OutputNeurons: configuration.OutputNeurons,
692 | HiddenNeurons: configuration.HiddenNeurons,
693 | NumEpochs: configuration.NumEpochs,
694 | LearningRate: configuration.LearningRate,
695 | }
696 |
697 | network := utils.NewNetwork(config)
698 |
699 | network.BHidden = mat.NewDense(configuration.BHiddenDims[0], configuration.BHiddenDims[1], nil)
700 | for i := 0; i < configuration.BHiddenDims[0]; i++ {
701 | fmt.Println(i)
702 | network.BHidden.SetRow(i, configuration.BHidden[i])
703 | }
704 |
705 | network.WHidden = mat.NewDense(configuration.WHiddenDims[0], configuration.WHiddenDims[1], nil)
706 | for i := 0; i < configuration.WHiddenDims[0]; i++ {
707 | network.WHidden.SetRow(i, configuration.WHidden[i])
708 | }
709 |
710 | network.BOut = mat.NewDense(configuration.BOutDims[0], configuration.BOutDims[1], nil)
711 | for i := 0; i < configuration.BOutDims[0]; i++ {
712 | network.BOut.SetRow(i, configuration.BOut[i])
713 | }
714 |
715 | network.WOut = mat.NewDense(configuration.WOutDims[0], configuration.WOutDims[1], nil)
716 | for i := 0; i < configuration.WOutDims[0]; i++ {
717 | network.WOut.SetRow(i, configuration.WOut[i])
718 | }
719 |
720 | // fmt.Println("Printing Configurations", configuration)
721 | features := mat.NewDense(1, 4, input)
722 | predictions, err := utils.Predict(features, network)
723 |
724 | if err != nil {
725 | fmt.Println("Error in something", err.Error())
726 | dto.SendResponse(w, r, http.StatusInternalServerError, "Error in Prediction", map[string]interface{}{"Error": err.Error()})
727 | }
728 |
729 | fmt.Println(predictions)
730 | fmt.Println(floats.MaxIdx(mat.Row(nil, 0, predictions)))
731 |
732 | 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})
733 |
734 | }
735 |
736 | type proResponse struct {
737 | Code int `json:"code"`
738 | Msg string `json:"message"`
739 | Key rsa.PublicKey `json:"key"`
740 | }
741 |
742 | //GettingPublicKey server
743 | func GettingPublicKey(w http.ResponseWriter, r *http.Request) {
744 |
745 | publicKey := constants.PublicKey
746 | fmt.Println("showing key", publicKey)
747 | objResp := proResponse{
748 | Msg: "Success",
749 | Code: 200,
750 | Key: publicKey,
751 | }
752 | userJSON, _ := json.Marshal(objResp)
753 | w.Header().Set("Content-Type", "application/json")
754 | w.WriteHeader(http.StatusOK)
755 | w.Write(userJSON)
756 | return
757 | }
758 |
--------------------------------------------------------------------------------
/datasets/dataset.csv:
--------------------------------------------------------------------------------
1 | computerPower,downSpeed,Label1,Label2
2 | 30.240000,4.240000,1.0,0.0
3 | 33.240000,44.240000,1.0,0.0
4 | 33.240000,44.240000,0.0,1.0
5 |
--------------------------------------------------------------------------------
/datasets/iris.csv:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/libonomy/node-extract/d22bfdd87bf5084116f51abbd765dfeeccf513cd/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,Label1,Label2
2 | 0.000000,0.000000,1.000000,0.000000
3 | 1.000000,1.000000,1.000000,0.000000
4 | 1.000000,1.000000,0.000000,1.000000
5 |
--------------------------------------------------------------------------------
/datasets/test_normalized.csv:
--------------------------------------------------------------------------------
1 | computerPower,downSpeed,Label1,Label2
2 | 1.000000,1.000000,0.000000,1.000000
3 |
--------------------------------------------------------------------------------
/datasets/testing/dataStats.json:
--------------------------------------------------------------------------------
1 | {
2 | "labelIndex": 2
3 | }
--------------------------------------------------------------------------------
/datasets/testing/dummyDataset.csv:
--------------------------------------------------------------------------------
1 | computerPower,downSpeed,machineId,yLabels
2 | 30.240000,4.240000,1,Label1
3 | 33.240000,44.240000,2,Label1
4 | 33.240000,44.240000,3,Label2
5 | 13223.142378,1.560000,3bb1b72aec1a44e1802ed42fd8b0e287,Useless
6 | 332276.536437,4.470000,09d5d877da7c4c28a56b59d7e1efb0d1,Useless
7 |
--------------------------------------------------------------------------------
/datasets/testing/dummyDataset.json:
--------------------------------------------------------------------------------
1 | [
2 | {
3 | "machineId": "1",
4 | "computerPower": 30.24,
5 | "downSpeed": 4.24,
6 | "yLabels": "Label1"
7 | },
8 | {
9 | "machineId": "2",
10 | "computerPower": 33.24,
11 | "downSpeed": 44.24,
12 | "yLabels": "Label1"
13 | },
14 | {
15 | "machineId": "3",
16 | "computerPower": 33.24,
17 | "downSpeed": 44.24,
18 | "yLabels": "Label2"
19 | },
20 | {
21 | "machineId": "3bb1b72aec1a44e1802ed42fd8b0e287",
22 | "computerPower": 13223.142377722432,
23 | "downSpeed": 1.56,
24 | "yLabels": "Useless"
25 | },
26 | {
27 | "machineId": "09d5d877da7c4c28a56b59d7e1efb0d1",
28 | "computerPower": 332276.5364368132,
29 | "downSpeed": 4.47,
30 | "yLabels": "Useless"
31 | }
32 | ]
--------------------------------------------------------------------------------
/datasets/train_normalized.csv:
--------------------------------------------------------------------------------
1 | computerPower,downSpeed,Label1,Label2
2 | 0.000000,0.000000,1.000000,0.000000
3 | 1.000000,1.000000,1.000000,0.000000
4 | 0.000000,0.000000,1.000000,0.000000
5 | 1.000000,1.000000,1.000000,0.000000
6 | 0.000000,0.000000,1.000000,0.000000
7 | 1.000000,1.000000,1.000000,0.000000
8 | 0.000000,0.000000,1.000000,0.000000
9 | 1.000000,1.000000,1.000000,0.000000
10 | 0.000000,0.000000,1.000000,0.000000
11 | 1.000000,1.000000,1.000000,0.000000
12 | 0.000000,0.000000,1.000000,0.000000
13 | 1.000000,1.000000,1.000000,0.000000
14 | 0.000000,0.000000,1.000000,0.000000
15 | 1.000000,1.000000,1.000000,0.000000
16 | 0.000000,0.000000,1.000000,0.000000
17 | 1.000000,1.000000,1.000000,0.000000
18 | 0.000000,0.000000,1.000000,0.000000
19 | 1.000000,1.000000,1.000000,0.000000
20 | 0.000000,0.000000,1.000000,0.000000
21 | 1.000000,1.000000,1.000000,0.000000
22 | 0.000000,0.000000,1.000000,0.000000
23 | 1.000000,1.000000,1.000000,0.000000
24 | 0.000000,0.000000,1.000000,0.000000
25 | 1.000000,1.000000,1.000000,0.000000
26 | 0.000000,0.000000,1.000000,0.000000
27 | 1.000000,1.000000,1.000000,0.000000
28 | 0.000000,0.000000,1.000000,0.000000
29 | 1.000000,1.000000,1.000000,0.000000
30 | 0.000000,0.000000,1.000000,0.000000
31 | 1.000000,1.000000,1.000000,0.000000
32 | 0.000000,0.000000,1.000000,0.000000
33 | 1.000000,1.000000,1.000000,0.000000
34 | 0.000000,0.000000,1.000000,0.000000
35 | 1.000000,1.000000,1.000000,0.000000
36 | 0.000000,0.000000,1.000000,0.000000
37 | 1.000000,1.000000,1.000000,0.000000
38 | 0.000000,0.000000,1.000000,0.000000
39 | 1.000000,1.000000,1.000000,0.000000
40 | 0.000000,0.000000,1.000000,0.000000
41 | 1.000000,1.000000,0.000000,1.000000
42 | 0.000000,0.000000,0.000000,1.000000
43 | 1.000000,1.000000,0.000000,1.000000
44 | 0.000000,0.000000,0.000000,1.000000
45 | 1.000000,1.000000,0.000000,1.000000
46 | 0.000000,0.000000,0.000000,1.000000
47 | 1.000000,1.000000,1.000000,0.000000
48 | 0.000000,0.000000,1.000000,0.000000
49 | 1.000000,1.000000,1.000000,0.000000
50 |
51 |
--------------------------------------------------------------------------------
/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 |
--------------------------------------------------------------------------------
/go.mod:
--------------------------------------------------------------------------------
1 | module github.com/libonomy/node-extract
2 |
3 | go 1.14
4 |
5 | require (
6 | github.com/Workiva/go-datastructures v1.0.52
7 | github.com/gorilla/mux v1.8.0
8 | github.com/libonomy/libonomy-gota v0.0.0-20200608155116-2b8b0b965097
9 | gonum.org/v1/gonum v0.8.1
10 | )
11 |
--------------------------------------------------------------------------------
/go.sum:
--------------------------------------------------------------------------------
1 | github.com/Workiva/go-datastructures v1.0.52 h1:PLSK6pwn8mYdaoaCZEMsXBpBotr4HHn9abU0yMQt0NI=
2 | github.com/Workiva/go-datastructures v1.0.52/go.mod h1:Z+F2Rca0qCsVYDS8z7bAGm8f3UkzuWYS/oBZz5a7VVA=
3 | github.com/ajstarks/svgo v0.0.0-20180226025133-644b8db467af/go.mod h1:K08gAheRH3/J6wwsYMMT4xOr94bZjxIelGM0+d/wbFw=
4 | github.com/fogleman/gg v1.2.1-0.20190220221249-0403632d5b90/go.mod h1:R/bRT+9gY/C5z7JzPU0zXsXHKM4/ayA+zqcVNZzPa1k=
5 | github.com/golang/freetype v0.0.0-20170609003504-e2365dfdc4a0/go.mod h1:E/TSTwGwJL78qG/PmXZO1EjYhfJinVAhrmmHX6Z8B9k=
6 | github.com/gorilla/mux v1.8.0 h1:i40aqfkR1h2SlN9hojwV5ZA91wcXFOvkdNIeFDP5koI=
7 | github.com/gorilla/mux v1.8.0/go.mod h1:DVbg23sWSpFRCP0SfiEN6jmj59UnW/n46BH5rLB71So=
8 | github.com/jung-kurt/gofpdf v1.0.3-0.20190309125859-24315acbbda5/go.mod h1:7Id9E/uU8ce6rXgefFLlgrJj/GYY22cpxn+r32jIOes=
9 | github.com/libonomy/libonomy-gota v0.0.0-20200608144345-72810edc40bb h1:bG0i60odD9sKNwieJbzgKGj7JeKmqkJ4ZOJHfZliAS4=
10 | github.com/libonomy/libonomy-gota v0.0.0-20200608144345-72810edc40bb/go.mod h1:pvTr7apSnCvzXMxHbbgyzUtq0rZXrXcXoMPzQZlZv+E=
11 | github.com/libonomy/libonomy-gota v0.0.0-20200608155116-2b8b0b965097 h1:/GpRT+uQ17xALnLJg6eao6LFLeo4e3Eq+2CHhlQ0E3E=
12 | github.com/libonomy/libonomy-gota v0.0.0-20200608155116-2b8b0b965097/go.mod h1:fgyDbO2k3ZpH0yCNlIddDsYwGGDq/mLRwF0hqYNWNr0=
13 | golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
14 | golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
15 | golang.org/x/exp v0.0.0-20190125153040-c74c464bbbf2 h1:y102fOLFqhV41b+4GPiJoa0k/x+pJcEi2/HB1Y5T6fU=
16 | golang.org/x/exp v0.0.0-20190125153040-c74c464bbbf2/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
17 | golang.org/x/image v0.0.0-20180708004352-c73c2afc3b81/go.mod h1:ux5Hcp/YLpHSI86hEcLt0YII63i6oz57MZXIpbrjZUs=
18 | golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
19 | golang.org/x/tools v0.0.0-20190206041539-40960b6deb8e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
20 | gonum.org/v1/gonum v0.0.0-20180816165407-929014505bf4/go.mod h1:Y+Yx5eoAFn32cQvJDxZx5Dpnq+c3wtXuadVZAcxbbBo=
21 | gonum.org/v1/gonum v0.8.1 h1:wGtP3yGpc5mCLOLeTeBdjeui9oZSz5De0eOjMLC/QuQ=
22 | gonum.org/v1/gonum v0.8.1/go.mod h1:oe/vMfY3deqTw+1EZJhuvEW2iwGF1bW9wwu7XCu0+v0=
23 | gonum.org/v1/netlib v0.0.0-20190313105609-8cb42192e0e0 h1:OE9mWmgKkjJyEmDAAtGMPjXu+YNeGvK9VTSHY6+Qihc=
24 | gonum.org/v1/netlib v0.0.0-20190313105609-8cb42192e0e0/go.mod h1:wa6Ws7BG/ESfp6dHfk7C6KdzKA7wR7u/rKwOGE66zvw=
25 | gonum.org/v1/plot v0.0.0-20190515093506-e2840ee46a6b/go.mod h1:Wt8AAjI+ypCyYX3nZBvf6cAIx93T+c/OS2HFAYskSZc=
26 | rsc.io/pdf v0.1.1/go.mod h1:n8OzWcQ6Sp37PL01nO98y4iUCRdTGarVfzxY20ICaU4=
27 |
--------------------------------------------------------------------------------
/main.go:
--------------------------------------------------------------------------------
1 | package main
2 |
3 | import (
4 | "fmt"
5 |
6 | "github.com/libonomy/node-extract/routes"
7 | )
8 |
9 | func main() {
10 | fmt.Println("AI training Server Is Running...")
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": 87.75510204081633,
4 | "predictAccuracy": 0
5 | }
--------------------------------------------------------------------------------
/models/test.json:
--------------------------------------------------------------------------------
1 | {
2 | "inputNeurons": 2,
3 | "outputNeurons": 2,
4 | "hiddenNeurons": 5,
5 | "numEpochs": 5000,
6 | "learningRate": 0.02,
7 | "bHidden": [
8 | [
9 | 0.9353095973436067,
10 | 0.9900387261784357,
11 | 0.850923029763354,
12 | 0.9315110226193309,
13 | 0.4042517334833132
14 | ]
15 | ],
16 | "bHiddenDims": [
17 | 1,
18 | 5
19 | ],
20 | "wHidden": [
21 | [
22 | -0.08474319763195065,
23 | 0.3100130201168171,
24 | 0.5974656601590075,
25 | 0.49839105073530965,
26 | 0.20263733856116947
27 | ],
28 | [
29 | -0.27255062967165533,
30 | 0.08550870365683617,
31 | 0.44630281189411847,
32 | 0.2769921496356301,
33 | 0.18023206730734556
34 | ]
35 | ],
36 | "wHiddenDims": [
37 | 2,
38 | 5
39 | ],
40 | "bOut": [
41 | [
42 | 0.2850400910788978,
43 | -0.5867013233136081
44 | ]
45 | ],
46 | "bOutDims": [
47 | 1,
48 | 2
49 | ],
50 | "wOut": [
51 | [
52 | -0.15882854248545028,
53 | -0.840530234053065
54 | ],
55 | [
56 | 0.7510319695104988,
57 | -0.49457111842021495
58 | ],
59 | [
60 | 0.529992472925581,
61 | -0.06325522625286654
62 | ],
63 | [
64 | 0.4071973452446177,
65 | -0.3262052558866998
66 | ],
67 | [
68 | 0.48680075386398747,
69 | -0.19917666555617833
70 | ]
71 | ],
72 | "wOutDims": [
73 | 5,
74 | 2
75 | ]
76 | }
--------------------------------------------------------------------------------
/routes/router.go:
--------------------------------------------------------------------------------
1 | package routes
2 |
3 | import (
4 | "log"
5 | "net/http"
6 |
7 | "github.com/libonomy/node-extract/utils/helper"
8 |
9 | "github.com/gorilla/mux"
10 | "github.com/libonomy/node-extract/controllers"
11 | )
12 |
13 | //StartRoutes listening
14 | func StartRoutes() {
15 | helper.ServerGenerateKey()
16 | router := mux.NewRouter().StrictSlash(true)
17 |
18 | dev := router.PathPrefix("/api/dev").Subrouter()
19 |
20 | dev.HandleFunc("/testing", controllers.Testing).Methods("POST")
21 | dev.HandleFunc("/generateCSVfromJSON", controllers.GenerateCSV).Methods("POST")
22 | dev.HandleFunc("/cleanData", controllers.CleanData).Methods("GET")
23 | dev.HandleFunc("/normalizeCSVData", controllers.NormalizeData).Methods("GET")
24 | dev.HandleFunc("/splitData", controllers.SplitAndShuffle).Methods("POST")
25 | //dev.HandleFunc("/train", controllers.GenerateCSV).Methods("GET")
26 | //dev.HandleFunc("/test", controllers.GenerateCSV).Methods("GET")
27 | //dev.HandleFunc("/validate", controllers.GenerateCSV).Methods("GET")
28 | dev.HandleFunc("/predict", controllers.Predict).Methods("POST")
29 | dev.HandleFunc("/trainComplete", controllers.Train).Methods("GET")
30 | dev.HandleFunc("/getKey", controllers.GettingPublicKey).Methods("GET")
31 | //dev.HandleFunc("/gettingData", controllers.GettingData).Methods("POST")
32 |
33 | log.Fatal(http.ListenAndServe(":4400", router))
34 | }
35 |
--------------------------------------------------------------------------------
/utils/helper/csvFileWriter.go:
--------------------------------------------------------------------------------
1 | package helper
2 |
3 | import (
4 | "encoding/csv"
5 | "os"
6 | )
7 |
8 | //WriteCSVFile function for writing CSV Files
9 | //It will check if file exists then Write to File
10 | //If not then it will create file and then Write To File
11 | func WriteCSVFile(path string, data [][]string) error {
12 |
13 | fileDescriptor, err := os.Create(path)
14 | if err != nil {
15 | return err
16 | }
17 |
18 | csvWriter := csv.NewWriter(fileDescriptor)
19 | csvWriter.WriteAll(data)
20 | csvWriter.Flush()
21 | return nil
22 | }
23 |
--------------------------------------------------------------------------------
/utils/helper/helpers.go:
--------------------------------------------------------------------------------
1 | package helper
2 |
3 | import (
4 | "crypto"
5 | "crypto/rand"
6 | "crypto/rsa"
7 | "fmt"
8 |
9 | "github.com/libonomy/node-extract/constants"
10 | )
11 |
12 | //StringContains Funtion for Checking if slice of array (a) contains string (x)
13 | func StringContains(a []string, x string) bool {
14 | for _, n := range a {
15 | if x == n {
16 | return true
17 | }
18 | }
19 | return false
20 | }
21 |
22 | //DecryptDatafromClient decryption of data coming from client
23 | func DecryptDatafromClient(encryptedBytes []byte) []byte {
24 |
25 | decryptedBytes, err := constants.PrivateKey.Decrypt(nil, encryptedBytes, &rsa.OAEPOptions{Hash: crypto.SHA256})
26 | if err != nil {
27 | panic(err)
28 | }
29 |
30 | fmt.Println("decrypted message: ", string(decryptedBytes))
31 | return decryptedBytes
32 | }
33 |
34 | // ServerGenerateKey server keys
35 | func ServerGenerateKey() {
36 | privateKey, err := rsa.GenerateKey(rand.Reader, 2048)
37 | if err != nil {
38 | panic(err)
39 | }
40 |
41 | /* modulusBytes := base64.StdEncoding.EncodeToString(privateKey.N.Bytes())
42 | privateExponentBytes := base64.StdEncoding.EncodeToString(privateKey.D.Bytes()) */
43 | /* fmt.Println("showing modules", modulusBytes)
44 | fmt.Println("showing private key", privateExponentBytes) */
45 | constants.PublicKey = privateKey.PublicKey
46 | constants.PrivateKey = privateKey
47 | fmt.Println("keys Registered")
48 | }
49 |
--------------------------------------------------------------------------------
/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/libonomy-gota/dataframe"
14 | "github.com/libonomy/node-extract/models"
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 | var output mat.Dense
138 |
139 | for i := 0; i < nn.Config.NumEpochs; i++ {
140 | // Feed Forward Process
141 | var hiddenLayerInput mat.Dense
142 | //hiddenLayerInput := mat.NewDense(0, 0, nil)
143 | hiddenLayerInput.Mul(x, wHidden)
144 | addBHidden := func(_, col int, v float64) float64 { return v + bHidden.At(0, col) }
145 | hiddenLayerInput.Apply(addBHidden, &hiddenLayerInput)
146 | // hiddenLayerActivations := mat.NewDense(0, 0, nil)
147 | var hiddenLayerActivations mat.Dense
148 | applySigmoid := func(_, _ int, v float64) float64 { return Sigmoid(v) }
149 | hiddenLayerActivations.Apply(applySigmoid, &hiddenLayerInput)
150 |
151 | //outputLayerInput := mat.NewDense(0, 0, nil)
152 | var outputLayerInput mat.Dense
153 | outputLayerInput.Mul(&hiddenLayerActivations, wOut)
154 | addBOut := func(_, col int, v float64) float64 { return v + bOut.At(0, col) }
155 | outputLayerInput.Apply(addBOut, &outputLayerInput)
156 | output.Apply(applySigmoid, &outputLayerInput)
157 |
158 | // Back Propogation Process
159 | // networkError := mat.NewDense(0, 0, nil)
160 | var networkError mat.Dense
161 | networkError.Sub(y, &output)
162 |
163 | // slopeOutputLayer := mat.NewDense(0, 0, nil)
164 | var slopeOutputLayer mat.Dense
165 | applySigmoidPrime := func(_, _ int, v float64) float64 { return SigmoidPrime(v) }
166 | slopeOutputLayer.Apply(applySigmoidPrime, &output)
167 | // slopeHiddenLayer := mat.NewDense(0, 0, nil)
168 | var slopeHiddenLayer mat.Dense
169 | slopeHiddenLayer.Apply(applySigmoidPrime, &hiddenLayerActivations)
170 |
171 | // dOutput := mat.NewDense(0, 0, nil)
172 | var dOutput mat.Dense
173 | dOutput.MulElem(&networkError, &slopeOutputLayer)
174 | // errorAtHiddenLayer := mat.NewDense(0, 0, nil)
175 | var errorAtHiddenLayer mat.Dense
176 | errorAtHiddenLayer.Mul(&dOutput, wOut.T())
177 |
178 | // dHiddenLayer := mat.NewDense(0, 0, nil)
179 | var dHiddenLayer mat.Dense
180 | dHiddenLayer.MulElem(&errorAtHiddenLayer, &slopeHiddenLayer)
181 |
182 | // Adjust The Parameters
183 | // wOutAdj := mat.NewDense(0, 0, nil)
184 | var wOutAdj mat.Dense
185 | wOutAdj.Mul(hiddenLayerActivations.T(), &dOutput)
186 | wOutAdj.Scale(nn.Config.LearningRate, &wOutAdj)
187 | wOut.Add(wOut, &wOutAdj)
188 |
189 | bOutAdj, err := SumAlongAxis(0, &dOutput)
190 | if err != nil {
191 | return nil, err
192 | }
193 | bOutAdj.Scale(nn.Config.LearningRate, bOutAdj)
194 | bOut.Add(bOut, bOutAdj)
195 |
196 | // wHiddenAdj := mat.NewDense(0, 0, nil)
197 | var wHiddenAdj mat.Dense
198 | wHiddenAdj.Mul(x.T(), &dHiddenLayer)
199 | wHiddenAdj.Scale(nn.Config.LearningRate, &wHiddenAdj)
200 | wHidden.Add(wHidden, &wHiddenAdj)
201 |
202 | bHiddenAdj, err := SumAlongAxis(0, &dHiddenLayer)
203 | if err != nil {
204 | return nil, err
205 | }
206 | bHiddenAdj.Scale(nn.Config.LearningRate, bHiddenAdj)
207 | bHidden.Add(bHidden, bHiddenAdj)
208 |
209 | }
210 |
211 | nn.WHidden = wHidden
212 | nn.BHidden = bHidden
213 | nn.WOut = wOut
214 | nn.BOut = bOut
215 |
216 | return &output, nil
217 | }
218 |
219 | //Predict for prediction
220 | func Predict(x *mat.Dense, nn *models.NeuralNet) (*mat.Dense, error) {
221 |
222 | // Check to make sure that our neuralNet value
223 | // represents a trained model.
224 | if nn.WHidden == nil || nn.WOut == nil || nn.BHidden == nil || nn.BOut == nil {
225 | return nil, errors.New("the supplied neurnal net weights and biases are empty")
226 | }
227 |
228 | // Define the output of the neural network.
229 | var output mat.Dense
230 |
231 | // Complete the feed forward process.
232 | var hiddenLayerInput mat.Dense
233 | hiddenLayerInput.Mul(x, nn.WHidden)
234 | addBHidden := func(_, col int, v float64) float64 { return v + nn.BHidden.At(0, col) }
235 | hiddenLayerInput.Apply(addBHidden, &hiddenLayerInput)
236 |
237 | var hiddenLayerActivations mat.Dense
238 | applySigmoid := func(_, _ int, v float64) float64 { return Sigmoid(v) }
239 | hiddenLayerActivations.Apply(applySigmoid, &hiddenLayerInput)
240 |
241 | var outputLayerInput mat.Dense
242 | outputLayerInput.Mul(&hiddenLayerActivations, nn.WOut)
243 | addBOut := func(_, col int, v float64) float64 { return v + nn.BOut.At(0, col) }
244 | outputLayerInput.Apply(addBOut, &outputLayerInput)
245 | output.Apply(applySigmoid, &outputLayerInput)
246 |
247 | return &output, nil
248 | }
249 |
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