/// <summary> /// Required method for Designer support - do not modify /// the contents of this method with the code editor. /// </summary> private void InitializeComponent() { ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH); var predictionResult = ConsumeModel.Predict(sampleData); this.label1 = new System.Windows.Forms.Label(); this.pictureBox1 = new System.Windows.Forms.PictureBox(); ((System.ComponentModel.ISupportInitialize)(this.pictureBox1)).BeginInit(); this.SuspendLayout(); // // label1 // this.label1.AutoSize = true; this.label1.Location = new System.Drawing.Point(33, 19); this.label1.Name = "label1"; this.label1.Size = new System.Drawing.Size(504, 17); this.label1.TabIndex = 7; if (predictionResult.Prediction == sampleData.Quote) { this.label1.Text = "MATCH: " + sampleData.Quote; } else { this.label1.Text = "MISS: " + sampleData.Quote; } // // pictureBox1 // if (predictionResult.Prediction == "Bargain") { this.pictureBox1.Image = global::FinalFinalcsc415.Properties.Resources.bargain; } if (predictionResult.Prediction == "Fish") { this.pictureBox1.Image = global::FinalFinalcsc415.Properties.Resources.fish; } if (predictionResult.Prediction == "Begins") { this.pictureBox1.Image = global::FinalFinalcsc415.Properties.Resources.begins; } if (predictionResult.Prediction == "Confusion") { this.pictureBox1.Image = global::FinalFinalcsc415.Properties.Resources.confusion; } this.pictureBox1.Location = new System.Drawing.Point(36, 72); this.pictureBox1.Name = "pictureBox1"; this.pictureBox1.Size = new System.Drawing.Size(731, 341); this.pictureBox1.SizeMode = System.Windows.Forms.PictureBoxSizeMode.CenterImage; this.pictureBox1.TabIndex = 8; this.pictureBox1.TabStop = false; // // Form1 // this.AutoScaleDimensions = new System.Drawing.SizeF(8F, 16F); this.AutoScaleMode = System.Windows.Forms.AutoScaleMode.Font; this.ClientSize = new System.Drawing.Size(800, 450); this.Controls.Add(this.pictureBox1); this.Controls.Add(this.label1); this.Name = "Form1"; this.Text = "Prequel Predictor"; ((System.ComponentModel.ISupportInitialize)(this.pictureBox1)).EndInit(); this.ResumeLayout(false); this.PerformLayout(); }
public ModelOutput AnalyzeText(ModelInput input) { var predEngine = CreatePredictionEngine(); return(predEngine.Predict(input)); }
private void Show_Click(object sender, EventArgs e) { treeView1.Nodes.Clear(); string gender; string actor; string year; gender = comboGender.Text; actor = comboCast.Text; year = "1900"; if (comboYear.Text != "") { year = comboYear.Text; } if (Implementation.Checked == true) { Dictionary <string, double> giniTree = arbol.giniTree(movie, gender, actor, year); List <Movies>[] buildTree = new List <Movies> [3]; buildTree[0] = arbol.buildTree(movie, giniTree, gender, actor, year)[0]; // raiz buildTree[1] = arbol.buildTree(movie, giniTree, gender, actor, year)[1]; // nodo1 buildTree[2] = arbol.buildTree(movie, giniTree, gender, actor, year)[2]; // nodo 2 foreach (string nodo in giniTree.Keys) { TreeNode newNode = new TreeNode(nodo); treeView1.Nodes.Add(newNode); } treeView1.Nodes.Add("movies"); TreeNode[] nodeClasification = treeView1.Nodes .Cast <TreeNode>() .Where(r => r.Text == "clasification") .ToArray(); TreeNode[] nodeCast = treeView1.Nodes .Cast <TreeNode>() .Where(r => r.Text == "cast") .ToArray(); TreeNode[] nodeYear = treeView1.Nodes .Cast <TreeNode>() .Where(r => r.Text == "year") .ToArray(); Dictionary <string, int> partCast = arbol.countCast(buildTree[nodeCast[0].Index]); Dictionary <string, int> partClas = arbol.countClasification(buildTree[nodeClasification[0].Index]); Dictionary <string, int> partYear = arbol.countYear(buildTree[nodeYear[0].Index]); foreach (string raiz in partCast.Keys) { nodeCast[0].Nodes.Add(raiz); } foreach (string raiz in partClas.Keys) { nodeClasification[0].Nodes.Add(raiz); } foreach (string raiz in partYear.Keys) { nodeYear[0].Nodes.Add(raiz); } if (buildTree[2] == null) { foreach (Movies nodo1 in buildTree[1]) { treeView1.Nodes[3].Nodes.Add(nodo1.Title); } } else { foreach (Movies nodo1 in buildTree[2]) { treeView1.Nodes[3].Nodes.Add(nodo1.Title); } } treeView1.EndUpdate(); } if (library.Checked == true) { var str = comboGender.SelectedItem; ModelInput sampleData = new ModelInput() { Listed_in = @"" + str, }; // Make a single prediction on the sample data and print results var predictionResult = ConsumeModel.Predict(sampleData); MessageBox.Show("Using model to make single prediction -- Comparing actual Type with predicted Type from sample data...\n\n" + "\n" + $"Listed_in: {sampleData.Listed_in}" + "\n" + $"\n\nPredicted Type value {predictionResult.Prediction} \nPredicted Type scores: [{String.Join(",", predictionResult.Score)}]\n\n" + "\n" + "=============== End of process ==============="); } //================ EXPERIMENTO ================== int repetitions = 0; string messages = ""; var stra = comboGender.SelectedItem; ModelInput sampleDatas = new ModelInput() { Listed_in = @"" + stra, }; var predictionResulte = ConsumeModel.Predict(sampleDatas); while (repetitions == 3) //number of repetitions established in the design { messages += $"Predicted Type scores: [{String.Join(",", predictionResulte.Score)}]\n\n"; repetitions++; } Console.WriteLine(messages); }
private void ImageBinaryClassificationAndDuplicateWorker(object sender, DoWorkEventArgs e) { //should probably check for inputdirecttext is validated _filesToProcess = Directory.GetFiles(InputDirText); //I need property and feild for data binding of the text box for model location //modelFilePath = _predictedResults = new ObservableCollection <CustomTwoClassificationImagePredictionResults>(); _targetOutputDirectoryPath = OutputDirText; //create output directories Console.WriteLine("-----------------------------------"); Console.WriteLine(_targetOutputDirectoryPath); var inventoryDir = System.IO.Path.Combine(_targetOutputDirectoryPath, "inventory"); Console.WriteLine(inventoryDir); Console.WriteLine(inventoryDir); var infrastructureDir = System.IO.Path.Combine(_targetOutputDirectoryPath, "infrastructure"); Console.WriteLine(infrastructureDir); if (!System.IO.Directory.Exists(inventoryDir)) { System.IO.Directory.CreateDirectory(inventoryDir); } if (!System.IO.Directory.Exists(infrastructureDir)) { System.IO.Directory.CreateDirectory(infrastructureDir); } MLContext mlContext = new MLContext(); // ModelOutput mop = ConsumeModel.Predict(mip, ConsumeModel.ClassicationModelEnum.classtwo); string modelPath = _modelInputFileText; //// Load model & create prediction engine ITransformer mlModel = mlContext.Model.Load(modelPath, out var modelInputSchema); foreach (var item in _filesToProcess) { Console.WriteLine(_imageClassificationCount.ToString()); _imageClassificationCount++; Console.WriteLine(_imageClassificationCount); //parse to model input ModelInput mip = new ModelInput(); mip.Label = "none"; //useful for evaluation section mip.ImageSource = item; //########################################################## var predEngine = mlContext.Model.CreatePredictionEngine <ModelInput, ModelOutput>(mlModel); Console.WriteLine($"number of columns is ======= {modelInputSchema.Count.ToString()}"); foreach (var inputItem in modelInputSchema) { Console.WriteLine($"name of column is ========={inputItem.Name}"); } // Console.WriteLine($"count is ======= {modelInputSchema.}"); // Use model to make prediction on input data ModelOutput result = predEngine.Predict(mip); string toprintDebugConsole = $"prediction class: {result.Prediction}|| score: {result.Score.FirstOrDefault()}"; //########################################################## Console.WriteLine(toprintDebugConsole); Console.WriteLine(result.Prediction.ToString()); Console.WriteLine(result.Score.FirstOrDefault()); Console.WriteLine("##########################"); Console.WriteLine(item); var filename = System.IO.Path.GetFileName(item); Console.WriteLine(filename); string disclass = System.IO.Path.Combine(_targetOutputDirectoryPath, result.Prediction); Console.WriteLine(disclass); var destfile = System.IO.Path.Combine(disclass, filename); Console.WriteLine(destfile); System.IO.File.Copy(item, destfile, true); //update ui with var newPredictionToUpdateOutputStatus = new CustomTwoClassificationImagePredictionResults(); newPredictionToUpdateOutputStatus.PredictionId = _imageClassificationCount.ToString(); newPredictionToUpdateOutputStatus.ImageOriginalPath = item.ToString(); Console.WriteLine(item.ToString()); newPredictionToUpdateOutputStatus.ModelOutputscore = result.Score.FirstOrDefault().ToString(); newPredictionToUpdateOutputStatus.ModelOutputPrediction = result.Prediction; Console.WriteLine("number of items in observable collection"); //from the backgroud thread in backgroudworker, I need to call the ui thread to update the listview uiContext.Send(x => PredictionResults.Add(newPredictionToUpdateOutputStatus), null); //PredictionResults.Add(newPredictionToUpdateOutputStatus); Console.WriteLine(PredictionResults.Count.ToString()); } }
static void Main(string[] args) { MLContext mlContext = new MLContext(); // Training code used by ML.NET CLI and AutoML to generate the model //ModelBuilder.CreateModel(); ITransformer mlModel = mlContext.Model.Load(GetAbsolutePath(MODEL_FILEPATH), out DataViewSchema inputSchema); var predEngine = mlContext.Model.CreatePredictionEngine <ModelInput, ModelOutput>(mlModel); Regex CSVParser = new Regex(",(?=(?:[^\"]*\"[^\"]*\")*(?![^\"]*\"))"); List <string> result_lines = new List <string>(); result_lines.Add("PassengerId,Survived"); using (StreamReader sr = new StreamReader(TEST_FILEPATH)) { Console.WriteLine("PassengerId,Survived"); String line; int i = 0; while ((line = sr.ReadLine()) != null) { string[] parts = CSVParser.Split(line); i++; if (i > 1) { string PassengerId = parts[0]; string Pclass = parts[1]; string Name = parts[2]; string Sex = parts[3]; string Age = parts[4]; string SibSp = parts[5]; string Parch = parts[6]; string Ticket = parts[7]; string Fare = parts[8]; string Cabin = parts[9]; string Embarked = parts[10]; var input = new ModelInput(); input.PassengerId = float.Parse(PassengerId, CultureInfo.InvariantCulture.NumberFormat); input.Name = Name; input.Sex = Sex; input.Ticket = Ticket; input.Cabin = Cabin; input.Embarked = Embarked; if (Age.Length > 0) { input.Age = float.Parse(Age, CultureInfo.InvariantCulture.NumberFormat); } else { input.Age = 0; } if (Fare.Length > 0) { input.Fare = float.Parse(Fare, CultureInfo.InvariantCulture.NumberFormat); } else { input.Fare = 0; } if (SibSp.Length > 0) { input.SibSp = float.Parse(SibSp, CultureInfo.InvariantCulture.NumberFormat); } else { input.SibSp = 0; } if (Parch.Length > 0) { input.Parch = float.Parse(Parch, CultureInfo.InvariantCulture.NumberFormat); } else { input.Parch = 0; } // Predict using input data. ModelOutput result = predEngine.Predict(input); Console.Write($"{PassengerId},"); string str = $"{PassengerId},"; if (result.Prediction) { Console.WriteLine("1"); str += "1"; } else { Console.WriteLine("0"); str += "0"; } result_lines.Add(str); } } } using (StreamWriter sw = new StreamWriter(OUTPUT_FILEPATH)) { foreach (string s in result_lines) { sw.WriteLine(s); } } Console.ReadKey(); }
static void Main(string[] args) { // Create single instance of sample data from first line of dataset for model input ModelInput sampleData = new ModelInput() { _0 = 0.82F, _1 = 0.86F, _2 = 0.86F, _3 = 0.9F, _4 = 0.9F, _5 = 0.9F, _6 = 0.92F, _7 = 0.96F, _8 = 0.96F, _9 = 1F, _10 = 1F, _11 = 0.96F, _12 = 1F, _13 = 0.96F, _14 = 0.96F, _15 = 0.92F, _16 = 0.96F, _17 = 0.92F, _18 = 0.92F, _19 = 0.96F, _20 = 0.92F, _21 = 0.92F, _22 = 0.92F, _23 = 0.92F, _24 = 0.9F, _25 = 0.9F, _26 = 0.86F, _27 = 0.86F, _28 = 0.86F, _29 = 0.9F, _30 = 0.9F, _31 = 0.9F, _32 = 0.9F, _33 = 0.92F, _34 = 0.96F, _35 = 0.96F, _36 = 0.92F, _37 = 0.86F, _38 = 0.82F, _39 = 0.86F, _40 = 0.86F, _41 = 0.9F, _42 = 0.86F, _43 = 0.82F, _44 = 0.86F, _45 = 0.92F, _46 = 0.92F, _47 = 0.9F, _48 = 0.86F, _49 = 0.86F, _50 = 0.9F, _51 = 0.9F, _52 = 0.9F, _53 = 0.86F, _54 = 0.86F, _55 = 0.86F, _56 = 0.9F, _57 = 0.92F, _58 = 0.92F, _59 = 0.92F, _60 = 0.9F, _61 = 0.86F, _62 = 0.9F, _63 = 0.9F, _64 = 0.9F, _65 = 0.86F, _66 = 0.86F, _67 = 0.86F, _68 = 0.86F, _69 = 0.86F, _70 = 0.86F, _71 = 0.86F, _72 = 0.86F, _73 = 0.86F, _74 = 0.86F, _75 = 0.86F, _76 = 0.86F, _77 = 0.82F, _78 = 0.86F, _79 = 0.86F, _80 = 0.86F, _81 = 0.86F, _82 = 0.9F, _83 = 0.9F, _84 = 0.9F, _85 = 0.9F, _86 = 0.92F, _87 = 0.92F, _88 = 0.9F, _89 = 0.9F, _90 = 0.9F, _91 = 0.9F, _92 = 0.9F, _93 = 0.9F, _94 = 0.86F, _95 = 0.86F, _96 = 0.86F, _97 = 0.86F, _98 = 0.86F, _99 = 0.9F, _100 = 0.86F, _101 = 0.86F, _102 = 0.86F, _103 = 0.9F, _104 = 0.9F, _105 = 0.9F, _106 = 0.9F, _107 = 0.86F, _108 = 0.9F, _109 = 0.86F, _110 = 0.86F, _111 = 0.86F, _112 = 0.82F, _113 = 0.82F, _114 = 0.82F, _115 = 0.82F, _116 = 0.82F, _117 = 0.82F, _118 = 0.82F, _119 = 0.82F, _120 = 0.86F, _121 = 0.86F, _122 = 0.86F, _123 = 0.86F, _124 = 0.9F, _125 = 0.9F, _126 = 0.9F, _127 = 0.92F, _128 = 0.92F, _129 = 0.92F, _130 = 0.96F, _131 = 0.96F, _132 = 0.96F, _133 = 0.96F, _134 = 0.96F, _135 = 0.96F, _136 = 0.96F, _137 = 0.96F, _138 = 0.96F, _139 = 0.96F, _140 = 0.96F, _141 = 0.96F, _142 = 0.92F, _143 = 0.92F, _144 = 0.92F, _145 = 0.9F, _146 = 0.86F, _147 = 0.82F, _148 = 0.86F, _149 = 0.9F, _150 = 0.86F, _151 = 0.86F, _152 = 0.82F, _153 = 0.82F, _154 = 0.86F, _155 = 0.86F, _156 = 0.86F, _157 = 0.82F, _158 = 0.82F, _159 = 0.82F, _160 = 0.86F, _161 = 0.86F, _162 = 0.86F, _163 = 0.82F, _164 = 0.86F, _165 = 0.86F, _166 = 0.9F, _167 = 0.86F, _168 = 0.86F, _169 = 0.86F, _170 = 0.9F, _171 = 0.92F, _172 = 0.96F, _173 = 0.96F, _174 = 0.96F, _175 = 0.92F, _176 = 0.92F, _177 = 0.92F, _178 = 0.9F, _179 = 0.86F, _180 = 0.86F, _181 = 0.86F, _182 = 0.86F, _183 = 0.86F, _184 = 0.86F, _185 = 0.86F, _186 = 0.82F, _187 = 0.82F, _188 = 0.82F, _189 = 0.82F, _190 = 0.86F, _191 = 0.86F, _192 = 0.86F, _193 = 0.86F, _194 = 0.86F, _195 = 0.9F, _196 = 0.86F, _197 = 0.86F, _198 = 0.86F, _199 = 0.86F, _200 = 0.86F, _201 = 0.86F, _202 = 0.86F, _203 = 0.86F, _204 = 0.86F, _205 = 0.82F, _206 = 0.82F, _207 = 0.8F, _208 = 0.8F, _209 = 0.8F, _210 = 0.8F, _211 = 0.76F, _212 = 0.72F, _213 = 0.66F, _214 = 0.62F, _215 = 0.62F, _216 = 0.62F, _217 = 0.6F, _218 = 0.56F, _219 = 0.16F, _220 = 0.16F, _221 = 0.16F, _222 = 0.16F, _223 = 0.18F, _224 = 0.18F, _225 = 0.16F, _226 = 0.12F, _227 = 0.12F, _228 = 0.04F, _229 = 0.04F, _230 = 0.04F, _231 = 0.04F, _232 = 0F, _233 = 0.04F, _234 = 0.28F, _235 = 0.32F, _236 = 0.34F, _237 = 0.34F, _238 = 0.38F, _239 = 0.38F, _240 = 0.38F, _241 = 0.38F, _242 = 0.4F, _243 = 0.4F, _244 = 0.4F, _245 = 0.44F, _246 = 0.44F, _247 = 0.4F, _248 = 0.4F, _249 = 0.4F, _250 = 0.38F, _251 = 0.38F, _252 = 0.38F, _253 = 0.34F, _254 = 0.38F, _255 = 0.38F, _256 = 0.4F, _257 = 0.38F, _258 = 0.38F, _259 = 0.38F, _260 = 0.38F, _261 = 0.34F, _262 = 0.32F, _263 = 0.28F, _264 = 0.72F, _265 = 0.72F, _266 = 0.76F, _267 = 0.76F, _268 = 0.76F, _269 = 0.8F, _270 = 0.8F, _271 = 0.8F, _272 = 0.8F, _273 = 0.8F, _274 = 0.76F, _275 = 0.76F, _276 = 0.8F, _277 = 0.8F, _278 = 0.8F, _279 = 0.82F, _280 = 0.82F, _281 = 0.82F, _282 = 0.82F, _283 = 0.82F, _284 = 0.8F, _285 = 0.8F, _286 = 0.8F, _287 = 0.8F, _288 = 0.8F, _289 = 0.8F, _290 = 0.8F, _291 = 0.8F, _292 = 0.82F, _293 = 0.82F, _294 = 0.82F, _295 = 0.8F, _296 = 0.8F, _297 = 0.76F, _298 = 0.76F, _299 = 0.76F, _300 = 0.72F, _301 = 0.72F, _302 = 0.72F, _303 = 0.72F, _304 = 0.72F, _305 = 0.72F, _306 = 0.72F, _307 = 0.72F, _308 = 0.72F, _309 = 0.72F, _310 = 0.7F, _311 = 0.7F, _312 = 0.72F, _313 = 0.72F, _314 = 0.72F, _315 = 0.72F, _316 = 0.72F, _317 = 0.72F, _318 = 0.7F, _319 = 0.7F, _320 = 0.7F, _321 = 0.7F, _322 = 0.7F, _323 = 0.7F, _324 = 0.72F, _325 = 0.76F, _326 = 0.76F, _327 = 0.76F, _328 = 0.76F, _329 = 0.72F, _330 = 0.72F, _331 = 0.72F, _332 = 0.72F, _333 = 0.72F, _334 = 0.72F, _335 = 0.76F, _336 = 0.72F, _337 = 0.72F, _338 = 0.72F, _339 = 0.72F, _340 = 0.7F, _341 = 0.7F, _342 = 0.7F, _343 = 0.7F, _344 = 0.7F, _345 = 0.7F, _346 = 0.7F, _347 = 0.7F, _348 = 0.7F, _349 = 0.7F, _350 = 0.7F, _351 = 0.66F, _352 = 0.66F, _353 = 0.7F, _354 = 0.7F, _355 = 0.66F, _356 = 0.66F, _357 = 0.66F, _358 = 0.66F, _359 = 0.62F, _360 = 0.62F, _361 = 0.66F, _362 = 0.66F, _363 = 0.66F, _364 = 0.7F, _365 = 0.66F, _366 = 0.7F, _367 = 0.7F, _368 = 0.7F, _369 = 0.66F, _370 = 0.66F, _371 = 0.66F, _372 = 0.66F, _373 = 0.66F, _374 = 0.62F, _375 = 0.62F, _376 = 0.62F, _377 = 0.62F, _378 = 0.62F, _379 = 0.62F, _380 = 0.62F, _381 = 0.62F, _382 = 0.62F, _383 = 0.66F, _384 = 0.66F, _385 = 0.7F, _386 = 0.7F, _387 = 0.7F, _388 = 0.66F, _389 = 0.62F, _390 = 0.66F, _391 = 0.6F, _392 = 0.6F, _393 = 0.62F, _394 = 0.56F, _395 = 0.6F, _396 = 0.6F, _397 = 0.6F, _398 = 0.62F, _399 = 0.62F, _400 = 0.62F, _401 = 0.6F, _402 = 0.62F, _403 = 0.6F, _404 = 0.62F, _405 = 0.66F, _406 = 0.7F, _407 = 0.72F, _408 = 0.7F, _409 = 0.7F, _410 = 0.62F, _411 = 0.7F, _412 = 0.66F, _413 = 0.66F, _414 = 0.62F, _415 = 0.62F, _416 = 0.62F, _417 = 0.62F, _418 = 0.62F, _419 = 0.62F, _420 = 0.6F, _421 = 0.62F, _422 = 0.62F, _423 = 0.62F, _424 = 0.6F, _425 = 0.6F, _426 = 0.62F, _427 = 0.6F, _428 = 0.6F, _429 = 0.6F, _430 = 0.6F, _431 = 0.62F, _432 = 0.6F, _433 = 0.62F, _434 = 0.66F, _435 = 0.66F, _436 = 0.6F, _437 = 0.6F, _438 = 0.56F, _439 = 0.6F, _440 = 0.56F, _441 = 0.54F, _442 = 0.54F, _443 = 0.54F, _444 = 0.54F, _445 = 0.56F, _446 = 0.56F, _447 = 0.56F, _448 = 0.54F, _449 = 0.54F, _450 = 0.54F, _451 = 0.54F, _452 = 0.56F, _453 = 0.56F, _454 = 0.56F, _455 = 0.56F, _456 = 0.6F, _457 = 0.6F, _458 = 0.6F, _459 = 0.56F, _460 = 0.56F, _461 = 0.56F, _462 = 0.56F, _463 = 0.54F, _464 = 0.54F, _465 = 0.56F, _466 = 0.54F, _467 = 0.54F, _468 = 0.54F, _469 = 0.54F, _470 = 0.56F, _471 = 0.56F, _472 = 0.56F, _473 = 0.56F, _474 = 0.54F, _475 = 0.54F, _476 = 0.54F, _477 = 0.54F, _478 = 0.54F, _479 = 0.5F, }; // Make a single prediction on the sample data and print results var predictionResult = ConsumeModel.Predict(sampleData); Console.WriteLine("Using model to make single prediction -- Comparing actual Target with predicted Target from sample data...\n\n"); Console.WriteLine($"_0: {sampleData._0}"); Console.WriteLine($"_1: {sampleData._1}"); Console.WriteLine($"_2: {sampleData._2}"); Console.WriteLine($"_3: {sampleData._3}"); Console.WriteLine($"_4: {sampleData._4}"); Console.WriteLine($"_5: {sampleData._5}"); Console.WriteLine($"_6: {sampleData._6}"); Console.WriteLine($"_7: {sampleData._7}"); Console.WriteLine($"_8: {sampleData._8}"); Console.WriteLine($"_9: {sampleData._9}"); Console.WriteLine($"_10: {sampleData._10}"); Console.WriteLine($"_11: {sampleData._11}"); Console.WriteLine($"_12: {sampleData._12}"); Console.WriteLine($"_13: {sampleData._13}"); Console.WriteLine($"_14: {sampleData._14}"); Console.WriteLine($"_15: {sampleData._15}"); Console.WriteLine($"_16: {sampleData._16}"); Console.WriteLine($"_17: {sampleData._17}"); Console.WriteLine($"_18: {sampleData._18}"); Console.WriteLine($"_19: {sampleData._19}"); Console.WriteLine($"_20: {sampleData._20}"); Console.WriteLine($"_21: {sampleData._21}"); Console.WriteLine($"_22: {sampleData._22}"); Console.WriteLine($"_23: {sampleData._23}"); Console.WriteLine($"_24: {sampleData._24}"); Console.WriteLine($"_25: {sampleData._25}"); Console.WriteLine($"_26: {sampleData._26}"); Console.WriteLine($"_27: {sampleData._27}"); Console.WriteLine($"_28: {sampleData._28}"); Console.WriteLine($"_29: {sampleData._29}"); Console.WriteLine($"_30: {sampleData._30}"); Console.WriteLine($"_31: {sampleData._31}"); Console.WriteLine($"_32: {sampleData._32}"); Console.WriteLine($"_33: {sampleData._33}"); Console.WriteLine($"_34: {sampleData._34}"); Console.WriteLine($"_35: {sampleData._35}"); Console.WriteLine($"_36: {sampleData._36}"); Console.WriteLine($"_37: {sampleData._37}"); Console.WriteLine($"_38: {sampleData._38}"); Console.WriteLine($"_39: {sampleData._39}"); Console.WriteLine($"_40: {sampleData._40}"); Console.WriteLine($"_41: {sampleData._41}"); Console.WriteLine($"_42: {sampleData._42}"); Console.WriteLine($"_43: {sampleData._43}"); Console.WriteLine($"_44: {sampleData._44}"); Console.WriteLine($"_45: {sampleData._45}"); Console.WriteLine($"_46: {sampleData._46}"); Console.WriteLine($"_47: {sampleData._47}"); Console.WriteLine($"_48: {sampleData._48}"); Console.WriteLine($"_49: {sampleData._49}"); Console.WriteLine($"_50: {sampleData._50}"); Console.WriteLine($"_51: {sampleData._51}"); Console.WriteLine($"_52: {sampleData._52}"); Console.WriteLine($"_53: {sampleData._53}"); Console.WriteLine($"_54: {sampleData._54}"); Console.WriteLine($"_55: {sampleData._55}"); Console.WriteLine($"_56: {sampleData._56}"); Console.WriteLine($"_57: {sampleData._57}"); Console.WriteLine($"_58: {sampleData._58}"); Console.WriteLine($"_59: {sampleData._59}"); Console.WriteLine($"_60: {sampleData._60}"); Console.WriteLine($"_61: {sampleData._61}"); Console.WriteLine($"_62: {sampleData._62}"); Console.WriteLine($"_63: {sampleData._63}"); Console.WriteLine($"_64: {sampleData._64}"); Console.WriteLine($"_65: {sampleData._65}"); Console.WriteLine($"_66: {sampleData._66}"); Console.WriteLine($"_67: {sampleData._67}"); Console.WriteLine($"_68: {sampleData._68}"); Console.WriteLine($"_69: {sampleData._69}"); Console.WriteLine($"_70: {sampleData._70}"); Console.WriteLine($"_71: {sampleData._71}"); Console.WriteLine($"_72: {sampleData._72}"); Console.WriteLine($"_73: {sampleData._73}"); Console.WriteLine($"_74: {sampleData._74}"); Console.WriteLine($"_75: {sampleData._75}"); Console.WriteLine($"_76: {sampleData._76}"); Console.WriteLine($"_77: {sampleData._77}"); Console.WriteLine($"_78: {sampleData._78}"); Console.WriteLine($"_79: {sampleData._79}"); Console.WriteLine($"_80: {sampleData._80}"); Console.WriteLine($"_81: {sampleData._81}"); Console.WriteLine($"_82: {sampleData._82}"); Console.WriteLine($"_83: {sampleData._83}"); Console.WriteLine($"_84: {sampleData._84}"); Console.WriteLine($"_85: {sampleData._85}"); Console.WriteLine($"_86: {sampleData._86}"); Console.WriteLine($"_87: {sampleData._87}"); Console.WriteLine($"_88: {sampleData._88}"); Console.WriteLine($"_89: {sampleData._89}"); Console.WriteLine($"_90: {sampleData._90}"); Console.WriteLine($"_91: {sampleData._91}"); Console.WriteLine($"_92: {sampleData._92}"); Console.WriteLine($"_93: {sampleData._93}"); Console.WriteLine($"_94: {sampleData._94}"); Console.WriteLine($"_95: {sampleData._95}"); Console.WriteLine($"_96: {sampleData._96}"); Console.WriteLine($"_97: {sampleData._97}"); Console.WriteLine($"_98: {sampleData._98}"); Console.WriteLine($"_99: {sampleData._99}"); Console.WriteLine($"_100: {sampleData._100}"); Console.WriteLine($"_101: {sampleData._101}"); Console.WriteLine($"_102: {sampleData._102}"); Console.WriteLine($"_103: {sampleData._103}"); Console.WriteLine($"_104: {sampleData._104}"); Console.WriteLine($"_105: {sampleData._105}"); Console.WriteLine($"_106: {sampleData._106}"); Console.WriteLine($"_107: {sampleData._107}"); Console.WriteLine($"_108: {sampleData._108}"); Console.WriteLine($"_109: {sampleData._109}"); Console.WriteLine($"_110: {sampleData._110}"); Console.WriteLine($"_111: {sampleData._111}"); Console.WriteLine($"_112: {sampleData._112}"); Console.WriteLine($"_113: {sampleData._113}"); Console.WriteLine($"_114: {sampleData._114}"); Console.WriteLine($"_115: {sampleData._115}"); Console.WriteLine($"_116: {sampleData._116}"); Console.WriteLine($"_117: {sampleData._117}"); Console.WriteLine($"_118: {sampleData._118}"); Console.WriteLine($"_119: {sampleData._119}"); Console.WriteLine($"_120: {sampleData._120}"); Console.WriteLine($"_121: {sampleData._121}"); Console.WriteLine($"_122: {sampleData._122}"); Console.WriteLine($"_123: {sampleData._123}"); Console.WriteLine($"_124: {sampleData._124}"); Console.WriteLine($"_125: {sampleData._125}"); Console.WriteLine($"_126: {sampleData._126}"); Console.WriteLine($"_127: {sampleData._127}"); Console.WriteLine($"_128: {sampleData._128}"); Console.WriteLine($"_129: {sampleData._129}"); Console.WriteLine($"_130: {sampleData._130}"); Console.WriteLine($"_131: {sampleData._131}"); Console.WriteLine($"_132: {sampleData._132}"); Console.WriteLine($"_133: {sampleData._133}"); Console.WriteLine($"_134: {sampleData._134}"); Console.WriteLine($"_135: {sampleData._135}"); Console.WriteLine($"_136: {sampleData._136}"); Console.WriteLine($"_137: {sampleData._137}"); Console.WriteLine($"_138: {sampleData._138}"); Console.WriteLine($"_139: {sampleData._139}"); Console.WriteLine($"_140: {sampleData._140}"); Console.WriteLine($"_141: {sampleData._141}"); Console.WriteLine($"_142: {sampleData._142}"); Console.WriteLine($"_143: {sampleData._143}"); Console.WriteLine($"_144: {sampleData._144}"); Console.WriteLine($"_145: {sampleData._145}"); Console.WriteLine($"_146: {sampleData._146}"); Console.WriteLine($"_147: {sampleData._147}"); Console.WriteLine($"_148: {sampleData._148}"); Console.WriteLine($"_149: {sampleData._149}"); Console.WriteLine($"_150: {sampleData._150}"); Console.WriteLine($"_151: {sampleData._151}"); Console.WriteLine($"_152: {sampleData._152}"); Console.WriteLine($"_153: {sampleData._153}"); Console.WriteLine($"_154: {sampleData._154}"); Console.WriteLine($"_155: {sampleData._155}"); Console.WriteLine($"_156: {sampleData._156}"); Console.WriteLine($"_157: {sampleData._157}"); Console.WriteLine($"_158: {sampleData._158}"); Console.WriteLine($"_159: {sampleData._159}"); Console.WriteLine($"_160: {sampleData._160}"); Console.WriteLine($"_161: {sampleData._161}"); Console.WriteLine($"_162: {sampleData._162}"); Console.WriteLine($"_163: {sampleData._163}"); Console.WriteLine($"_164: {sampleData._164}"); Console.WriteLine($"_165: {sampleData._165}"); Console.WriteLine($"_166: {sampleData._166}"); Console.WriteLine($"_167: {sampleData._167}"); Console.WriteLine($"_168: {sampleData._168}"); Console.WriteLine($"_169: {sampleData._169}"); Console.WriteLine($"_170: {sampleData._170}"); Console.WriteLine($"_171: {sampleData._171}"); Console.WriteLine($"_172: {sampleData._172}"); Console.WriteLine($"_173: {sampleData._173}"); Console.WriteLine($"_174: {sampleData._174}"); Console.WriteLine($"_175: {sampleData._175}"); Console.WriteLine($"_176: {sampleData._176}"); Console.WriteLine($"_177: {sampleData._177}"); Console.WriteLine($"_178: {sampleData._178}"); Console.WriteLine($"_179: {sampleData._179}"); Console.WriteLine($"_180: {sampleData._180}"); Console.WriteLine($"_181: {sampleData._181}"); Console.WriteLine($"_182: {sampleData._182}"); Console.WriteLine($"_183: {sampleData._183}"); Console.WriteLine($"_184: {sampleData._184}"); Console.WriteLine($"_185: {sampleData._185}"); Console.WriteLine($"_186: {sampleData._186}"); Console.WriteLine($"_187: {sampleData._187}"); Console.WriteLine($"_188: {sampleData._188}"); Console.WriteLine($"_189: {sampleData._189}"); Console.WriteLine($"_190: {sampleData._190}"); Console.WriteLine($"_191: {sampleData._191}"); Console.WriteLine($"_192: {sampleData._192}"); Console.WriteLine($"_193: {sampleData._193}"); Console.WriteLine($"_194: {sampleData._194}"); Console.WriteLine($"_195: {sampleData._195}"); Console.WriteLine($"_196: {sampleData._196}"); Console.WriteLine($"_197: {sampleData._197}"); Console.WriteLine($"_198: {sampleData._198}"); Console.WriteLine($"_199: {sampleData._199}"); Console.WriteLine($"_200: {sampleData._200}"); Console.WriteLine($"_201: {sampleData._201}"); Console.WriteLine($"_202: {sampleData._202}"); Console.WriteLine($"_203: {sampleData._203}"); Console.WriteLine($"_204: {sampleData._204}"); Console.WriteLine($"_205: {sampleData._205}"); Console.WriteLine($"_206: {sampleData._206}"); Console.WriteLine($"_207: {sampleData._207}"); Console.WriteLine($"_208: {sampleData._208}"); Console.WriteLine($"_209: {sampleData._209}"); Console.WriteLine($"_210: {sampleData._210}"); Console.WriteLine($"_211: {sampleData._211}"); Console.WriteLine($"_212: {sampleData._212}"); Console.WriteLine($"_213: {sampleData._213}"); Console.WriteLine($"_214: {sampleData._214}"); Console.WriteLine($"_215: {sampleData._215}"); Console.WriteLine($"_216: {sampleData._216}"); Console.WriteLine($"_217: {sampleData._217}"); Console.WriteLine($"_218: {sampleData._218}"); Console.WriteLine($"_219: {sampleData._219}"); Console.WriteLine($"_220: {sampleData._220}"); Console.WriteLine($"_221: {sampleData._221}"); Console.WriteLine($"_222: {sampleData._222}"); Console.WriteLine($"_223: {sampleData._223}"); Console.WriteLine($"_224: {sampleData._224}"); Console.WriteLine($"_225: {sampleData._225}"); Console.WriteLine($"_226: {sampleData._226}"); Console.WriteLine($"_227: {sampleData._227}"); Console.WriteLine($"_228: {sampleData._228}"); Console.WriteLine($"_229: {sampleData._229}"); Console.WriteLine($"_230: {sampleData._230}"); Console.WriteLine($"_231: {sampleData._231}"); Console.WriteLine($"_232: {sampleData._232}"); Console.WriteLine($"_233: {sampleData._233}"); Console.WriteLine($"_234: {sampleData._234}"); Console.WriteLine($"_235: {sampleData._235}"); Console.WriteLine($"_236: {sampleData._236}"); Console.WriteLine($"_237: {sampleData._237}"); Console.WriteLine($"_238: {sampleData._238}"); Console.WriteLine($"_239: {sampleData._239}"); Console.WriteLine($"_240: {sampleData._240}"); Console.WriteLine($"_241: {sampleData._241}"); Console.WriteLine($"_242: {sampleData._242}"); Console.WriteLine($"_243: {sampleData._243}"); Console.WriteLine($"_244: {sampleData._244}"); Console.WriteLine($"_245: {sampleData._245}"); Console.WriteLine($"_246: {sampleData._246}"); Console.WriteLine($"_247: {sampleData._247}"); Console.WriteLine($"_248: {sampleData._248}"); Console.WriteLine($"_249: {sampleData._249}"); Console.WriteLine($"_250: {sampleData._250}"); Console.WriteLine($"_251: {sampleData._251}"); Console.WriteLine($"_252: {sampleData._252}"); Console.WriteLine($"_253: {sampleData._253}"); Console.WriteLine($"_254: {sampleData._254}"); Console.WriteLine($"_255: {sampleData._255}"); Console.WriteLine($"_256: {sampleData._256}"); Console.WriteLine($"_257: {sampleData._257}"); Console.WriteLine($"_258: {sampleData._258}"); Console.WriteLine($"_259: {sampleData._259}"); Console.WriteLine($"_260: {sampleData._260}"); Console.WriteLine($"_261: {sampleData._261}"); Console.WriteLine($"_262: {sampleData._262}"); Console.WriteLine($"_263: {sampleData._263}"); Console.WriteLine($"_264: {sampleData._264}"); Console.WriteLine($"_265: {sampleData._265}"); Console.WriteLine($"_266: {sampleData._266}"); Console.WriteLine($"_267: {sampleData._267}"); Console.WriteLine($"_268: {sampleData._268}"); Console.WriteLine($"_269: {sampleData._269}"); Console.WriteLine($"_270: {sampleData._270}"); Console.WriteLine($"_271: {sampleData._271}"); Console.WriteLine($"_272: {sampleData._272}"); Console.WriteLine($"_273: {sampleData._273}"); Console.WriteLine($"_274: {sampleData._274}"); Console.WriteLine($"_275: {sampleData._275}"); Console.WriteLine($"_276: {sampleData._276}"); Console.WriteLine($"_277: {sampleData._277}"); Console.WriteLine($"_278: {sampleData._278}"); Console.WriteLine($"_279: {sampleData._279}"); Console.WriteLine($"_280: {sampleData._280}"); Console.WriteLine($"_281: {sampleData._281}"); Console.WriteLine($"_282: {sampleData._282}"); Console.WriteLine($"_283: {sampleData._283}"); Console.WriteLine($"_284: {sampleData._284}"); Console.WriteLine($"_285: {sampleData._285}"); Console.WriteLine($"_286: {sampleData._286}"); Console.WriteLine($"_287: {sampleData._287}"); Console.WriteLine($"_288: {sampleData._288}"); Console.WriteLine($"_289: {sampleData._289}"); Console.WriteLine($"_290: {sampleData._290}"); Console.WriteLine($"_291: {sampleData._291}"); Console.WriteLine($"_292: {sampleData._292}"); Console.WriteLine($"_293: {sampleData._293}"); Console.WriteLine($"_294: {sampleData._294}"); Console.WriteLine($"_295: {sampleData._295}"); Console.WriteLine($"_296: {sampleData._296}"); Console.WriteLine($"_297: {sampleData._297}"); Console.WriteLine($"_298: {sampleData._298}"); Console.WriteLine($"_299: {sampleData._299}"); Console.WriteLine($"_300: {sampleData._300}"); Console.WriteLine($"_301: {sampleData._301}"); Console.WriteLine($"_302: {sampleData._302}"); Console.WriteLine($"_303: {sampleData._303}"); Console.WriteLine($"_304: {sampleData._304}"); Console.WriteLine($"_305: {sampleData._305}"); Console.WriteLine($"_306: {sampleData._306}"); Console.WriteLine($"_307: {sampleData._307}"); Console.WriteLine($"_308: {sampleData._308}"); Console.WriteLine($"_309: {sampleData._309}"); Console.WriteLine($"_310: {sampleData._310}"); Console.WriteLine($"_311: {sampleData._311}"); Console.WriteLine($"_312: {sampleData._312}"); Console.WriteLine($"_313: {sampleData._313}"); Console.WriteLine($"_314: {sampleData._314}"); Console.WriteLine($"_315: {sampleData._315}"); Console.WriteLine($"_316: {sampleData._316}"); Console.WriteLine($"_317: {sampleData._317}"); Console.WriteLine($"_318: {sampleData._318}"); Console.WriteLine($"_319: {sampleData._319}"); Console.WriteLine($"_320: {sampleData._320}"); Console.WriteLine($"_321: {sampleData._321}"); Console.WriteLine($"_322: {sampleData._322}"); Console.WriteLine($"_323: {sampleData._323}"); Console.WriteLine($"_324: {sampleData._324}"); Console.WriteLine($"_325: {sampleData._325}"); Console.WriteLine($"_326: {sampleData._326}"); Console.WriteLine($"_327: {sampleData._327}"); Console.WriteLine($"_328: {sampleData._328}"); Console.WriteLine($"_329: {sampleData._329}"); Console.WriteLine($"_330: {sampleData._330}"); Console.WriteLine($"_331: {sampleData._331}"); Console.WriteLine($"_332: {sampleData._332}"); Console.WriteLine($"_333: {sampleData._333}"); Console.WriteLine($"_334: {sampleData._334}"); Console.WriteLine($"_335: {sampleData._335}"); Console.WriteLine($"_336: {sampleData._336}"); Console.WriteLine($"_337: {sampleData._337}"); Console.WriteLine($"_338: {sampleData._338}"); Console.WriteLine($"_339: {sampleData._339}"); Console.WriteLine($"_340: {sampleData._340}"); Console.WriteLine($"_341: {sampleData._341}"); Console.WriteLine($"_342: {sampleData._342}"); Console.WriteLine($"_343: {sampleData._343}"); Console.WriteLine($"_344: {sampleData._344}"); Console.WriteLine($"_345: {sampleData._345}"); Console.WriteLine($"_346: {sampleData._346}"); Console.WriteLine($"_347: {sampleData._347}"); Console.WriteLine($"_348: {sampleData._348}"); Console.WriteLine($"_349: {sampleData._349}"); Console.WriteLine($"_350: {sampleData._350}"); Console.WriteLine($"_351: {sampleData._351}"); Console.WriteLine($"_352: {sampleData._352}"); Console.WriteLine($"_353: {sampleData._353}"); Console.WriteLine($"_354: {sampleData._354}"); Console.WriteLine($"_355: {sampleData._355}"); Console.WriteLine($"_356: {sampleData._356}"); Console.WriteLine($"_357: {sampleData._357}"); Console.WriteLine($"_358: {sampleData._358}"); Console.WriteLine($"_359: {sampleData._359}"); Console.WriteLine($"_360: {sampleData._360}"); Console.WriteLine($"_361: {sampleData._361}"); Console.WriteLine($"_362: {sampleData._362}"); Console.WriteLine($"_363: {sampleData._363}"); Console.WriteLine($"_364: {sampleData._364}"); Console.WriteLine($"_365: {sampleData._365}"); Console.WriteLine($"_366: {sampleData._366}"); Console.WriteLine($"_367: {sampleData._367}"); Console.WriteLine($"_368: {sampleData._368}"); Console.WriteLine($"_369: {sampleData._369}"); Console.WriteLine($"_370: {sampleData._370}"); Console.WriteLine($"_371: {sampleData._371}"); Console.WriteLine($"_372: {sampleData._372}"); Console.WriteLine($"_373: {sampleData._373}"); Console.WriteLine($"_374: {sampleData._374}"); Console.WriteLine($"_375: {sampleData._375}"); Console.WriteLine($"_376: {sampleData._376}"); Console.WriteLine($"_377: {sampleData._377}"); Console.WriteLine($"_378: {sampleData._378}"); Console.WriteLine($"_379: {sampleData._379}"); Console.WriteLine($"_380: {sampleData._380}"); Console.WriteLine($"_381: {sampleData._381}"); Console.WriteLine($"_382: {sampleData._382}"); Console.WriteLine($"_383: {sampleData._383}"); Console.WriteLine($"_384: {sampleData._384}"); Console.WriteLine($"_385: {sampleData._385}"); Console.WriteLine($"_386: {sampleData._386}"); Console.WriteLine($"_387: {sampleData._387}"); Console.WriteLine($"_388: {sampleData._388}"); Console.WriteLine($"_389: {sampleData._389}"); Console.WriteLine($"_390: {sampleData._390}"); Console.WriteLine($"_391: {sampleData._391}"); Console.WriteLine($"_392: {sampleData._392}"); Console.WriteLine($"_393: {sampleData._393}"); Console.WriteLine($"_394: {sampleData._394}"); Console.WriteLine($"_395: {sampleData._395}"); Console.WriteLine($"_396: {sampleData._396}"); Console.WriteLine($"_397: {sampleData._397}"); Console.WriteLine($"_398: {sampleData._398}"); Console.WriteLine($"_399: {sampleData._399}"); Console.WriteLine($"_400: {sampleData._400}"); Console.WriteLine($"_401: {sampleData._401}"); Console.WriteLine($"_402: {sampleData._402}"); Console.WriteLine($"_403: {sampleData._403}"); Console.WriteLine($"_404: {sampleData._404}"); Console.WriteLine($"_405: {sampleData._405}"); Console.WriteLine($"_406: {sampleData._406}"); Console.WriteLine($"_407: {sampleData._407}"); Console.WriteLine($"_408: {sampleData._408}"); Console.WriteLine($"_409: {sampleData._409}"); Console.WriteLine($"_410: {sampleData._410}"); Console.WriteLine($"_411: {sampleData._411}"); Console.WriteLine($"_412: {sampleData._412}"); Console.WriteLine($"_413: {sampleData._413}"); Console.WriteLine($"_414: {sampleData._414}"); Console.WriteLine($"_415: {sampleData._415}"); Console.WriteLine($"_416: {sampleData._416}"); Console.WriteLine($"_417: {sampleData._417}"); Console.WriteLine($"_418: {sampleData._418}"); Console.WriteLine($"_419: {sampleData._419}"); Console.WriteLine($"_420: {sampleData._420}"); Console.WriteLine($"_421: {sampleData._421}"); Console.WriteLine($"_422: {sampleData._422}"); Console.WriteLine($"_423: {sampleData._423}"); Console.WriteLine($"_424: {sampleData._424}"); Console.WriteLine($"_425: {sampleData._425}"); Console.WriteLine($"_426: {sampleData._426}"); Console.WriteLine($"_427: {sampleData._427}"); Console.WriteLine($"_428: {sampleData._428}"); Console.WriteLine($"_429: {sampleData._429}"); Console.WriteLine($"_430: {sampleData._430}"); Console.WriteLine($"_431: {sampleData._431}"); Console.WriteLine($"_432: {sampleData._432}"); Console.WriteLine($"_433: {sampleData._433}"); Console.WriteLine($"_434: {sampleData._434}"); Console.WriteLine($"_435: {sampleData._435}"); Console.WriteLine($"_436: {sampleData._436}"); Console.WriteLine($"_437: {sampleData._437}"); Console.WriteLine($"_438: {sampleData._438}"); Console.WriteLine($"_439: {sampleData._439}"); Console.WriteLine($"_440: {sampleData._440}"); Console.WriteLine($"_441: {sampleData._441}"); Console.WriteLine($"_442: {sampleData._442}"); Console.WriteLine($"_443: {sampleData._443}"); Console.WriteLine($"_444: {sampleData._444}"); Console.WriteLine($"_445: {sampleData._445}"); Console.WriteLine($"_446: {sampleData._446}"); Console.WriteLine($"_447: {sampleData._447}"); Console.WriteLine($"_448: {sampleData._448}"); Console.WriteLine($"_449: {sampleData._449}"); Console.WriteLine($"_450: {sampleData._450}"); Console.WriteLine($"_451: {sampleData._451}"); Console.WriteLine($"_452: {sampleData._452}"); Console.WriteLine($"_453: {sampleData._453}"); Console.WriteLine($"_454: {sampleData._454}"); Console.WriteLine($"_455: {sampleData._455}"); Console.WriteLine($"_456: {sampleData._456}"); Console.WriteLine($"_457: {sampleData._457}"); Console.WriteLine($"_458: {sampleData._458}"); Console.WriteLine($"_459: {sampleData._459}"); Console.WriteLine($"_460: {sampleData._460}"); Console.WriteLine($"_461: {sampleData._461}"); Console.WriteLine($"_462: {sampleData._462}"); Console.WriteLine($"_463: {sampleData._463}"); Console.WriteLine($"_464: {sampleData._464}"); Console.WriteLine($"_465: {sampleData._465}"); Console.WriteLine($"_466: {sampleData._466}"); Console.WriteLine($"_467: {sampleData._467}"); Console.WriteLine($"_468: {sampleData._468}"); Console.WriteLine($"_469: {sampleData._469}"); Console.WriteLine($"_470: {sampleData._470}"); Console.WriteLine($"_471: {sampleData._471}"); Console.WriteLine($"_472: {sampleData._472}"); Console.WriteLine($"_473: {sampleData._473}"); Console.WriteLine($"_474: {sampleData._474}"); Console.WriteLine($"_475: {sampleData._475}"); Console.WriteLine($"_476: {sampleData._476}"); Console.WriteLine($"_477: {sampleData._477}"); Console.WriteLine($"_478: {sampleData._478}"); Console.WriteLine($"_479: {sampleData._479}"); Console.WriteLine($"\n\nPredicted Target value {predictionResult.Prediction} \nPredicted Target scores: [{String.Join(",", predictionResult.Score)}]\n\n"); Console.WriteLine("=============== End of process, hit any key to finish ==============="); Console.ReadKey(); }
public override void Init(ModelInput modelInput, ObjectProperties parentObjectProperties) { base.Init(modelInput, parentObjectProperties); _dropdown.onValueChanged.AddListener(x => OnUpdateValue()); }
static void Main(string[] args) { List <string> lines = new List <string>(); List <double[]> linesA = new List <double[]>(); _fileSystem = new FileSystemAccess(); _filePath = _fileSystem.GetCombinePath("AllLines.txt"); Console.WriteLine("Hello World!"); TestPowerSpectrogramCore.Class1 testClass; testClass = new TestPowerSpectrogramCore.Class1(); //public static string[] GetFiles (string path, string searchPattern, System.IO.SearchOption searchOption); int tæller = 0; string[] fileEntries = Directory.GetFiles(_fileSystem.GetCombinePath("Abnormal10Seks"), "*.wav"); string[] fileEntries2 = Directory.GetFiles(_fileSystem.GetCombinePath("Normal10Seks"), "*.wav"); string[] fileEntriesA = Directory.GetFiles(_fileSystem.GetCombinePath("\\NewTestData"), "*.wav"); Stopwatch sw = new Stopwatch(); Stopwatch sw2 = new Stopwatch(); sw.Start(); bool fast = false; if (fast) { for (int i = 0; i < 10; i++) { tæller++; Console.WriteLine(tæller); lines.Add(testClass.test2000(fileEntries[i])); tæller++; Console.WriteLine(tæller); lines.Add(testClass.test2000(fileEntries2[i])); } } else { Console.WriteLine("AbNormal"); foreach (string entry in fileEntries) { tæller++; sw2.Start(); lines.Add(testClass.test2000(entry)); Console.WriteLine("AbNormal Tæller: " + tæller + "\tTime: " + sw2.ElapsedMilliseconds + "ms\tTotal Time:" + ((double)sw.ElapsedMilliseconds / 1000.0) + "s"); sw2.Reset(); } tæller = 0; Console.WriteLine("Normal"); foreach (string entry in fileEntries2) { tæller++; sw2.Start(); lines.Add(testClass.test2000(entry)); Console.WriteLine("AbNormal Tæller: " + tæller + "\tTime: " + sw2.ElapsedMilliseconds + "ms\tTotal Time:" + ((double)sw.ElapsedMilliseconds / 1000.0) + "s"); sw2.Reset(); } } //lines.Add(testClass.test("AbNormal\\AbNormal_002_10s")); //lines.Add(testClass.test("AbNormal\\AbNormal_003_10s")); //lines.Add(testClass.test("AbNormal\\AbNormal_004_10s")); //lines.Add(testClass.test("AbNormal\\AbNormal_005_10s")); //lines.Add(testClass.test("Normal\\Normal_007_10s")); //lines.Add(testClass.test("Normal\\Normal_011_10s")); //lines.Add(testClass.test("Normal\\Normal_012_10s")); //lines.Add(testClass.test("Normal\\Normal_016_10s")); //lines.Add(testClass.test("Normal\\Normal_019_10s")); File.WriteAllLines(_filePath, lines); //using (TextWriter tw = new StreamWriter(_filePath)) //{ // foreach (String s in lines) // tw.WriteLine(s + "\n"); //} foreach (var VARIABLE in fileEntriesA) { double[] thenewdata = (testClass.test2000A(VARIABLE)); ModelInput sampleData = new ModelInput() { Col1 = (float)thenewdata[0], Col2 = (float)thenewdata[1], Col3 = (float)thenewdata[2], Col4 = (float)thenewdata[3], Col5 = (float)thenewdata[4], Col6 = (float)thenewdata[5], Col7 = (float)thenewdata[6], Col8 = (float)thenewdata[7], Col9 = (float)thenewdata[8], Col10 = (float)thenewdata[9], Col11 = (float)thenewdata[10], Col12 = (float)thenewdata[11], Col13 = (float)thenewdata[12], Col14 = (float)thenewdata[13], Col15 = (float)thenewdata[14], Col16 = (float)thenewdata[15], Col17 = (float)0, }; var predictionResult = ConsumeModel.Predict(sampleData); // Create single instance of sample data from first line of dataset for model input // Make a single prediction on the sample data and print results Console.WriteLine(VARIABLE); Console.WriteLine( "Using model to make single prediction -- Comparing actual Col0 with predicted Col0 from sample data...\n\n"); Console.WriteLine($"Col1: {sampleData.Col1}"); Console.WriteLine($"Col2: {sampleData.Col2}"); Console.WriteLine($"Col3: {sampleData.Col3}"); Console.WriteLine($"Col4: {sampleData.Col4}"); Console.WriteLine($"Col5: {sampleData.Col5}"); Console.WriteLine($"Col6: {sampleData.Col6}"); Console.WriteLine($"Col7: {sampleData.Col7}"); Console.WriteLine($"Col8: {sampleData.Col8}"); Console.WriteLine($"Col9: {sampleData.Col9}"); Console.WriteLine($"Col10: {sampleData.Col10}"); Console.WriteLine($"Col11: {sampleData.Col11}"); Console.WriteLine($"Col12: {sampleData.Col12}"); Console.WriteLine($"Col13: {sampleData.Col13}"); Console.WriteLine($"Col14: {sampleData.Col14}"); Console.WriteLine($"Col15: {sampleData.Col15}"); Console.WriteLine($"Col16: {sampleData.Col16}"); Console.WriteLine($"Col17: {sampleData.Col17}"); Console.WriteLine( $"\n\nPredicted Col0 value {predictionResult.Prediction} \nPredicted Col0 scores: [{String.Join(",", predictionResult.Score)}]\n\n"); Console.WriteLine("=============== End of process, hit any key to finish ==============="); Console.ReadKey(); } }
public ModelOutput Predict(ModelInput input) { var output = engine.Predict(input); return(output); }
public static void PredictionImage(this MLModelEngine <ModelInput, ModelOutput> mlEngine, Image img) { var solutionDirectory = Path.GetFullPath(Path.Combine(AppContext.BaseDirectory, "../../../../")); var assetsRelativePath = Path.Combine(solutionDirectory, "assets"); var undefinedRelativePath = Path.Combine(assetsRelativePath, "undefined"); BlockingCollection <dynamic> predictions = new BlockingCollection <dynamic>(); var imgRawFormat = img.RawFormat; var imgRepo = new ScreenRepository(img); var prefixFileName = DateTime.Now.ToString("yyyyMMddHHmmssfff"); System.Threading.Tasks.Parallel.ForEach(imgRepo.Parts, part => { ModelInput inputData = new ModelInput(); using (var ms = new MemoryStream()) { part.Source.Save(ms, imgRawFormat); inputData.Image = ms.ToArray(); } ModelOutput prediction = mlEngine.Predict(inputData); predictions.Add(new { PartImg = part, Prediction = prediction, }); var isPredictFail = false; if (part.PartType == PartScreenType.LifePool && !prediction.PredictedLabel.StartsWith("HP")) { isPredictFail = true; } else if (part.PartType == PartScreenType.ManaPool && !prediction.PredictedLabel.StartsWith("MP")) { isPredictFail = true; } else if (prediction.PredictedLabel.StartsWith("HP") && part.PartType != PartScreenType.LifePool) { isPredictFail = true; } else if (prediction.PredictedLabel.StartsWith("MP") && part.PartType != PartScreenType.ManaPool) { isPredictFail = true; } else if (part.PartType == PartScreenType.FlaskSlot1 && !prediction.PredictedLabel.StartsWith("LF") && !prediction.PredictedLabel.StartsWith("EF")) { isPredictFail = true; } else if (part.PartType == PartScreenType.FlaskSlot2 && !prediction.PredictedLabel.StartsWith("MF") && !prediction.PredictedLabel.StartsWith("EF")) { isPredictFail = true; } else if (part.PartType == PartScreenType.FlaskSlot3 && !prediction.PredictedLabel.StartsWith("MF") && !prediction.PredictedLabel.StartsWith("EF")) { isPredictFail = true; } else if (part.PartType == PartScreenType.FlaskSlot4 && !prediction.PredictedLabel.StartsWith("MF") && !prediction.PredictedLabel.StartsWith("EF")) { isPredictFail = true; } else if (part.PartType == PartScreenType.FlaskSlot5 && !prediction.PredictedLabel.StartsWith("MF") && !prediction.PredictedLabel.StartsWith("EF")) { isPredictFail = true; } if (isPredictFail || prediction.MaxScore < 0.5) { if (!UndefinedImageAllowSaveDate.HasValue || DateTime.Now > UndefinedImageAllowSaveDate.Value) { UndefinedImageAllowSaveDate = DateTime.Now.AddMinutes(1); var filePath = Path.Combine(undefinedRelativePath, $"{prefixFileName}_{part.PartType}.png"); part.Source.Save(filePath); } } }); foreach (var d in predictions) { PartScreen part = d.PartImg; ModelOutput prediction = d.Prediction; img.TagImage(part.Location, prediction); } }
static void Main(string[] args) { // Create single instance of sample data from first line of dataset for model input ModelInput sampleData = new ModelInput() { Fingerprint = @"", Minutia = @"", Nn15 = 0F, Nn30 = 0F, Nn45 = 0F, Nn60 = 0F, Nn75 = 0F, Nn90 = 0F, Nn105 = 0F, Nn120 = 0F, Nn135 = 0F, Nn150 = 0F, Nn165 = 0F, Nn180 = 0F, Nn195 = 0F, Nn210 = 0F, Nn225 = 0F, Nn240 = 0F, Nn255 = 0F, Nn270 = 0F, Nn285 = 0F, Nn300 = 0F, Nn315 = 0F, Nn330 = 0F, Nn345 = 0F, Nn360 = 0F, Nn375 = 0F, Nn390 = 0F, Nn405 = 0F, Nn420 = 0F, Nn435 = 0F, Nn450 = 0F, Nn465 = 0F, Nn480 = 0F, Nn495 = 0F, Nn510 = 0F, Nn525 = 0F, Nn540 = 0F, Nn555 = 0F, Nn570 = 0F, Nn585 = 0F, Nn600 = 0F, Nn607 = 0F, Nn15r = 0F, Nn30r = 0F, Nn45r = 0F, Nn60r = 0F, Nn75r = 0F, Nn90r = 0F, Nn105r = 0F, Nn120r = 0F, Nn135r = 0F, Nn150r = 0F, Nn165r = 0F, Nn180r = 0F, Nn195r = 0F, Nn210r = 0F, Nn225r = 0F, Nn240r = 0F, Nn255r = 0F, Nn270r = 0F, Nn285r = 0F, Nn300r = 0F, Nn315r = 0F, Nn330r = 0F, Nn345r = 0F, Nn360r = 0F, Nn375r = 0F, Nn390r = 0F, Nn405r = 0F, Nn420r = 0F, Nn435r = 0F, Nn450r = 0F, Nn465r = 0F, Nn480r = 0F, Nn495r = 0F, Nn510r = 0F, Nn525r = 0F, Nn540r = 0F, Nn555r = 0F, Nn570r = 0F, Nn585r = 0F, Nn600r = 0F, Nn607r = 0F, Nn30_nn15 = 0F, Nn45_nn30 = 0F, Nn60_nn45 = 0F, Nn75_nn60 = 0F, Nn90_nn75 = 0F, Nn105_nn90 = 0F, Nn120_nn105 = 0F, Nn135_nn120 = 0F, Nn150_nn135 = 0F, Nn165_nn150 = 0F, Nn180_nn165 = 0F, Nn195_nn180 = 0F, Nn210_nn195 = 0F, Nn225_nn210 = 0F, Nn240_nn225 = 0F, Nn255_nn240 = 0F, Nn270_nn255 = 0F, Nn285_nn270 = 0F, Nn300_nn285 = 0F, Nn315_nn300 = 0F, Nn330_nn315 = 0F, Nn345_nn330 = 0F, Nn360_nn345 = 0F, Nn375_nn360 = 0F, Nn390_nn375 = 0F, Nn405_nn390 = 0F, Nn420_nn405 = 0F, Nn435_nn420 = 0F, Nn450_nn435 = 0F, Nn465_nn450 = 0F, Nn480_nn465 = 0F, Nn495_nn480 = 0F, Nn510_nn495 = 0F, Nn525_nn510 = 0F, Nn540_nn525 = 0F, Nn555_nn540 = 0F, Nn570_nn555 = 0F, Nn585_nn570 = 0F, Nn600_nn585 = 0F, Nn607_nn600 = 0F, Nn30r_nn15r = 0F, Nn45r_nn30r = 0F, Nn60r_nn45r = 0F, Nn75r_nn60r = 0F, Nn90r_nn75r = 0F, Nn105r_nn90r = 0F, Nn120r_nn105r = 0F, Nn135r_nn120r = 0F, Nn150r_nn135r = 0F, Nn165r_nn150r = 0F, Nn180r_nn165r = 0F, Nn195r_nn180r = 0F, Nn210r_nn195r = 0F, Nn225r_nn210r = 0F, Nn240r_nn225r = 0F, Nn255r_nn240r = 0F, Nn270r_nn255r = 0F, Nn285r_nn270r = 0F, Nn300r_nn285r = 0F, Nn315r_nn300r = 0F, Nn330r_nn315r = 0F, Nn345r_nn330r = 0F, Nn360r_nn345r = 0F, Nn375r_nn360r = 0F, Nn390r_nn375r = 0F, Nn405r_nn390r = 0F, Nn420r_nn405r = 0F, Nn435r_nn420r = 0F, Nn450r_nn435r = 0F, Nn465r_nn450r = 0F, Nn480r_nn465r = 0F, Nn495r_nn480r = 0F, Nn510r_nn495r = 0F, Nn525r_nn510r = 0F, Nn540r_nn525r = 0F, Nn555r_nn540r = 0F, Nn570r_nn555r = 0F, Nn585r_nn570r = 0F, Nn600r_nn585r = 0F, Nn607r_nn600r = 0F, D1 = 0F, D2 = 0F, D3 = 0F, D4 = 0F, D5 = 0F, D6 = 0F, D7 = 0F, D8 = 0F, D9 = 0F, D10 = 0F, D11 = 0F, D12 = 0F, Df = 0F, D1r = 0F, D2r = 0F, D3r = 0F, D4r = 0F, D5r = 0F, D6r = 0F, D7r = 0F, D8r = 0F, D9r = 0F, D10r = 0F, D11r = 0F, D12r = 0F, Dfr = 0F, D2_d1 = 0F, D3_d2 = 0F, D4_d3 = 0F, D5_d4 = 0F, D6_d5 = 0F, D7_d6 = 0F, D8_d7 = 0F, D9_d8 = 0F, D10_d9 = 0F, D11_d10 = 0F, D12_d11 = 0F, Df_d12 = 0F, D2r_d1r = 0F, D3r_d2r = 0F, D4r_d3r = 0F, D5r_d4r = 0F, D6r_d5r = 0F, D7r_d6r = 0F, D8r_d7r = 0F, D9r_d8r = 0F, D10r_d9r = 0F, D11r_d10r = 0F, D12r_d11r = 0F, Dfr_d12r = 0F, Alpha1 = 0F, Alpha2 = 0F, Alpha3 = 0F, Alpha4 = 0F, Alpha5 = 0F, Alpha6 = 0F, Alpha7 = 0F, Alpha8 = 0F, Alpha9 = 0F, Alpha10 = 0F, Alpha11 = 0F, Alpha12 = 0F, Alphaf = 0F, Alphan1 = 0F, Alphan2 = 0F, Alphan3 = 0F, Alphan4 = 0F, Alphan5 = 0F, Alphan6 = 0F, Alphan7 = 0F, Alphan8 = 0F, Alphan9 = 0F, Alphan10 = 0F, Alphan11 = 0F, Alphan12 = 0F, Alphanf = 0F, Beta1 = 0F, Beta2 = 0F, Beta3 = 0F, Beta4 = 0F, Beta5 = 0F, Beta6 = 0F, Beta7 = 0F, Beta8 = 0F, Beta9 = 0F, Beta10 = 0F, Beta11 = 0F, Beta12 = 0F, Betaf = 0F, Alpha1_beta1 = 0F, Alpha2_beta2 = 0F, Alpha3_beta3 = 0F, Alpha4_beta4 = 0F, Alpha5_beta5 = 0F, Alpha6_beta6 = 0F, Alpha7_beta7 = 0F, Alpha8_beta8 = 0F, Alpha9_beta9 = 0F, Alpha10_beta10 = 0F, Alpha11_beta11 = 0F, Alpha12_beta12 = 0F, Alphaf_betaf = 0F, Type = @"", }; // Make a single prediction on the sample data and print results var predictionResult = ConsumeModel.Predict(sampleData); Console.WriteLine("Using model to make single prediction -- Comparing actual Label with predicted Label from sample data...\n\n"); Console.WriteLine($"Fingerprint: {sampleData.Fingerprint}"); Console.WriteLine($"Minutia: {sampleData.Minutia}"); Console.WriteLine($"Nn15: {sampleData.Nn15}"); Console.WriteLine($"Nn30: {sampleData.Nn30}"); Console.WriteLine($"Nn45: {sampleData.Nn45}"); Console.WriteLine($"Nn60: {sampleData.Nn60}"); Console.WriteLine($"Nn75: {sampleData.Nn75}"); Console.WriteLine($"Nn90: {sampleData.Nn90}"); Console.WriteLine($"Nn105: {sampleData.Nn105}"); Console.WriteLine($"Nn120: {sampleData.Nn120}"); Console.WriteLine($"Nn135: {sampleData.Nn135}"); Console.WriteLine($"Nn150: {sampleData.Nn150}"); Console.WriteLine($"Nn165: {sampleData.Nn165}"); Console.WriteLine($"Nn180: {sampleData.Nn180}"); Console.WriteLine($"Nn195: {sampleData.Nn195}"); Console.WriteLine($"Nn210: {sampleData.Nn210}"); Console.WriteLine($"Nn225: {sampleData.Nn225}"); Console.WriteLine($"Nn240: {sampleData.Nn240}"); Console.WriteLine($"Nn255: {sampleData.Nn255}"); Console.WriteLine($"Nn270: {sampleData.Nn270}"); Console.WriteLine($"Nn285: {sampleData.Nn285}"); Console.WriteLine($"Nn300: {sampleData.Nn300}"); Console.WriteLine($"Nn315: {sampleData.Nn315}"); Console.WriteLine($"Nn330: {sampleData.Nn330}"); Console.WriteLine($"Nn345: {sampleData.Nn345}"); Console.WriteLine($"Nn360: {sampleData.Nn360}"); Console.WriteLine($"Nn375: {sampleData.Nn375}"); Console.WriteLine($"Nn390: {sampleData.Nn390}"); Console.WriteLine($"Nn405: {sampleData.Nn405}"); Console.WriteLine($"Nn420: {sampleData.Nn420}"); Console.WriteLine($"Nn435: {sampleData.Nn435}"); Console.WriteLine($"Nn450: {sampleData.Nn450}"); Console.WriteLine($"Nn465: {sampleData.Nn465}"); Console.WriteLine($"Nn480: {sampleData.Nn480}"); Console.WriteLine($"Nn495: {sampleData.Nn495}"); Console.WriteLine($"Nn510: {sampleData.Nn510}"); Console.WriteLine($"Nn525: {sampleData.Nn525}"); Console.WriteLine($"Nn540: {sampleData.Nn540}"); Console.WriteLine($"Nn555: {sampleData.Nn555}"); Console.WriteLine($"Nn570: {sampleData.Nn570}"); Console.WriteLine($"Nn585: {sampleData.Nn585}"); Console.WriteLine($"Nn600: {sampleData.Nn600}"); Console.WriteLine($"Nn607: {sampleData.Nn607}"); Console.WriteLine($"Nn15r: {sampleData.Nn15r}"); Console.WriteLine($"Nn30r: {sampleData.Nn30r}"); Console.WriteLine($"Nn45r: {sampleData.Nn45r}"); Console.WriteLine($"Nn60r: {sampleData.Nn60r}"); Console.WriteLine($"Nn75r: {sampleData.Nn75r}"); Console.WriteLine($"Nn90r: {sampleData.Nn90r}"); Console.WriteLine($"Nn105r: {sampleData.Nn105r}"); Console.WriteLine($"Nn120r: {sampleData.Nn120r}"); Console.WriteLine($"Nn135r: {sampleData.Nn135r}"); Console.WriteLine($"Nn150r: {sampleData.Nn150r}"); Console.WriteLine($"Nn165r: {sampleData.Nn165r}"); Console.WriteLine($"Nn180r: {sampleData.Nn180r}"); Console.WriteLine($"Nn195r: {sampleData.Nn195r}"); Console.WriteLine($"Nn210r: {sampleData.Nn210r}"); Console.WriteLine($"Nn225r: {sampleData.Nn225r}"); Console.WriteLine($"Nn240r: {sampleData.Nn240r}"); Console.WriteLine($"Nn255r: {sampleData.Nn255r}"); Console.WriteLine($"Nn270r: {sampleData.Nn270r}"); Console.WriteLine($"Nn285r: {sampleData.Nn285r}"); Console.WriteLine($"Nn300r: {sampleData.Nn300r}"); Console.WriteLine($"Nn315r: {sampleData.Nn315r}"); Console.WriteLine($"Nn330r: {sampleData.Nn330r}"); Console.WriteLine($"Nn345r: {sampleData.Nn345r}"); Console.WriteLine($"Nn360r: {sampleData.Nn360r}"); Console.WriteLine($"Nn375r: {sampleData.Nn375r}"); Console.WriteLine($"Nn390r: {sampleData.Nn390r}"); Console.WriteLine($"Nn405r: {sampleData.Nn405r}"); Console.WriteLine($"Nn420r: {sampleData.Nn420r}"); Console.WriteLine($"Nn435r: {sampleData.Nn435r}"); Console.WriteLine($"Nn450r: {sampleData.Nn450r}"); Console.WriteLine($"Nn465r: {sampleData.Nn465r}"); Console.WriteLine($"Nn480r: {sampleData.Nn480r}"); Console.WriteLine($"Nn495r: {sampleData.Nn495r}"); Console.WriteLine($"Nn510r: {sampleData.Nn510r}"); Console.WriteLine($"Nn525r: {sampleData.Nn525r}"); Console.WriteLine($"Nn540r: {sampleData.Nn540r}"); Console.WriteLine($"Nn555r: {sampleData.Nn555r}"); Console.WriteLine($"Nn570r: {sampleData.Nn570r}"); Console.WriteLine($"Nn585r: {sampleData.Nn585r}"); Console.WriteLine($"Nn600r: {sampleData.Nn600r}"); Console.WriteLine($"Nn607r: {sampleData.Nn607r}"); Console.WriteLine($"Nn30_nn15: {sampleData.Nn30_nn15}"); Console.WriteLine($"Nn45_nn30: {sampleData.Nn45_nn30}"); Console.WriteLine($"Nn60_nn45: {sampleData.Nn60_nn45}"); Console.WriteLine($"Nn75_nn60: {sampleData.Nn75_nn60}"); Console.WriteLine($"Nn90_nn75: {sampleData.Nn90_nn75}"); Console.WriteLine($"Nn105_nn90: {sampleData.Nn105_nn90}"); Console.WriteLine($"Nn120_nn105: {sampleData.Nn120_nn105}"); Console.WriteLine($"Nn135_nn120: {sampleData.Nn135_nn120}"); Console.WriteLine($"Nn150_nn135: {sampleData.Nn150_nn135}"); Console.WriteLine($"Nn165_nn150: {sampleData.Nn165_nn150}"); Console.WriteLine($"Nn180_nn165: {sampleData.Nn180_nn165}"); Console.WriteLine($"Nn195_nn180: {sampleData.Nn195_nn180}"); Console.WriteLine($"Nn210_nn195: {sampleData.Nn210_nn195}"); Console.WriteLine($"Nn225_nn210: {sampleData.Nn225_nn210}"); Console.WriteLine($"Nn240_nn225: {sampleData.Nn240_nn225}"); Console.WriteLine($"Nn255_nn240: {sampleData.Nn255_nn240}"); Console.WriteLine($"Nn270_nn255: {sampleData.Nn270_nn255}"); Console.WriteLine($"Nn285_nn270: {sampleData.Nn285_nn270}"); Console.WriteLine($"Nn300_nn285: {sampleData.Nn300_nn285}"); Console.WriteLine($"Nn315_nn300: {sampleData.Nn315_nn300}"); Console.WriteLine($"Nn330_nn315: {sampleData.Nn330_nn315}"); Console.WriteLine($"Nn345_nn330: {sampleData.Nn345_nn330}"); Console.WriteLine($"Nn360_nn345: {sampleData.Nn360_nn345}"); Console.WriteLine($"Nn375_nn360: {sampleData.Nn375_nn360}"); Console.WriteLine($"Nn390_nn375: {sampleData.Nn390_nn375}"); Console.WriteLine($"Nn405_nn390: {sampleData.Nn405_nn390}"); Console.WriteLine($"Nn420_nn405: {sampleData.Nn420_nn405}"); Console.WriteLine($"Nn435_nn420: {sampleData.Nn435_nn420}"); Console.WriteLine($"Nn450_nn435: {sampleData.Nn450_nn435}"); Console.WriteLine($"Nn465_nn450: {sampleData.Nn465_nn450}"); Console.WriteLine($"Nn480_nn465: {sampleData.Nn480_nn465}"); Console.WriteLine($"Nn495_nn480: {sampleData.Nn495_nn480}"); Console.WriteLine($"Nn510_nn495: {sampleData.Nn510_nn495}"); Console.WriteLine($"Nn525_nn510: {sampleData.Nn525_nn510}"); Console.WriteLine($"Nn540_nn525: {sampleData.Nn540_nn525}"); Console.WriteLine($"Nn555_nn540: {sampleData.Nn555_nn540}"); Console.WriteLine($"Nn570_nn555: {sampleData.Nn570_nn555}"); Console.WriteLine($"Nn585_nn570: {sampleData.Nn585_nn570}"); Console.WriteLine($"Nn600_nn585: {sampleData.Nn600_nn585}"); Console.WriteLine($"Nn607_nn600: {sampleData.Nn607_nn600}"); Console.WriteLine($"Nn30r_nn15r: {sampleData.Nn30r_nn15r}"); Console.WriteLine($"Nn45r_nn30r: {sampleData.Nn45r_nn30r}"); Console.WriteLine($"Nn60r_nn45r: {sampleData.Nn60r_nn45r}"); Console.WriteLine($"Nn75r_nn60r: {sampleData.Nn75r_nn60r}"); Console.WriteLine($"Nn90r_nn75r: {sampleData.Nn90r_nn75r}"); Console.WriteLine($"Nn105r_nn90r: {sampleData.Nn105r_nn90r}"); Console.WriteLine($"Nn120r_nn105r: {sampleData.Nn120r_nn105r}"); Console.WriteLine($"Nn135r_nn120r: {sampleData.Nn135r_nn120r}"); Console.WriteLine($"Nn150r_nn135r: {sampleData.Nn150r_nn135r}"); Console.WriteLine($"Nn165r_nn150r: {sampleData.Nn165r_nn150r}"); Console.WriteLine($"Nn180r_nn165r: {sampleData.Nn180r_nn165r}"); Console.WriteLine($"Nn195r_nn180r: {sampleData.Nn195r_nn180r}"); Console.WriteLine($"Nn210r_nn195r: {sampleData.Nn210r_nn195r}"); Console.WriteLine($"Nn225r_nn210r: {sampleData.Nn225r_nn210r}"); Console.WriteLine($"Nn240r_nn225r: {sampleData.Nn240r_nn225r}"); Console.WriteLine($"Nn255r_nn240r: {sampleData.Nn255r_nn240r}"); Console.WriteLine($"Nn270r_nn255r: {sampleData.Nn270r_nn255r}"); Console.WriteLine($"Nn285r_nn270r: {sampleData.Nn285r_nn270r}"); Console.WriteLine($"Nn300r_nn285r: {sampleData.Nn300r_nn285r}"); Console.WriteLine($"Nn315r_nn300r: {sampleData.Nn315r_nn300r}"); Console.WriteLine($"Nn330r_nn315r: {sampleData.Nn330r_nn315r}"); Console.WriteLine($"Nn345r_nn330r: {sampleData.Nn345r_nn330r}"); Console.WriteLine($"Nn360r_nn345r: {sampleData.Nn360r_nn345r}"); Console.WriteLine($"Nn375r_nn360r: {sampleData.Nn375r_nn360r}"); Console.WriteLine($"Nn390r_nn375r: {sampleData.Nn390r_nn375r}"); Console.WriteLine($"Nn405r_nn390r: {sampleData.Nn405r_nn390r}"); Console.WriteLine($"Nn420r_nn405r: {sampleData.Nn420r_nn405r}"); Console.WriteLine($"Nn435r_nn420r: {sampleData.Nn435r_nn420r}"); Console.WriteLine($"Nn450r_nn435r: {sampleData.Nn450r_nn435r}"); Console.WriteLine($"Nn465r_nn450r: {sampleData.Nn465r_nn450r}"); Console.WriteLine($"Nn480r_nn465r: {sampleData.Nn480r_nn465r}"); Console.WriteLine($"Nn495r_nn480r: {sampleData.Nn495r_nn480r}"); Console.WriteLine($"Nn510r_nn495r: {sampleData.Nn510r_nn495r}"); Console.WriteLine($"Nn525r_nn510r: {sampleData.Nn525r_nn510r}"); Console.WriteLine($"Nn540r_nn525r: {sampleData.Nn540r_nn525r}"); Console.WriteLine($"Nn555r_nn540r: {sampleData.Nn555r_nn540r}"); Console.WriteLine($"Nn570r_nn555r: {sampleData.Nn570r_nn555r}"); Console.WriteLine($"Nn585r_nn570r: {sampleData.Nn585r_nn570r}"); Console.WriteLine($"Nn600r_nn585r: {sampleData.Nn600r_nn585r}"); Console.WriteLine($"Nn607r_nn600r: {sampleData.Nn607r_nn600r}"); Console.WriteLine($"D1: {sampleData.D1}"); Console.WriteLine($"D2: {sampleData.D2}"); Console.WriteLine($"D3: {sampleData.D3}"); Console.WriteLine($"D4: {sampleData.D4}"); Console.WriteLine($"D5: {sampleData.D5}"); Console.WriteLine($"D6: {sampleData.D6}"); Console.WriteLine($"D7: {sampleData.D7}"); Console.WriteLine($"D8: {sampleData.D8}"); Console.WriteLine($"D9: {sampleData.D9}"); Console.WriteLine($"D10: {sampleData.D10}"); Console.WriteLine($"D11: {sampleData.D11}"); Console.WriteLine($"D12: {sampleData.D12}"); Console.WriteLine($"Df: {sampleData.Df}"); Console.WriteLine($"D1r: {sampleData.D1r}"); Console.WriteLine($"D2r: {sampleData.D2r}"); Console.WriteLine($"D3r: {sampleData.D3r}"); Console.WriteLine($"D4r: {sampleData.D4r}"); Console.WriteLine($"D5r: {sampleData.D5r}"); Console.WriteLine($"D6r: {sampleData.D6r}"); Console.WriteLine($"D7r: {sampleData.D7r}"); Console.WriteLine($"D8r: {sampleData.D8r}"); Console.WriteLine($"D9r: {sampleData.D9r}"); Console.WriteLine($"D10r: {sampleData.D10r}"); Console.WriteLine($"D11r: {sampleData.D11r}"); Console.WriteLine($"D12r: {sampleData.D12r}"); Console.WriteLine($"Dfr: {sampleData.Dfr}"); Console.WriteLine($"D2_d1: {sampleData.D2_d1}"); Console.WriteLine($"D3_d2: {sampleData.D3_d2}"); Console.WriteLine($"D4_d3: {sampleData.D4_d3}"); Console.WriteLine($"D5_d4: {sampleData.D5_d4}"); Console.WriteLine($"D6_d5: {sampleData.D6_d5}"); Console.WriteLine($"D7_d6: {sampleData.D7_d6}"); Console.WriteLine($"D8_d7: {sampleData.D8_d7}"); Console.WriteLine($"D9_d8: {sampleData.D9_d8}"); Console.WriteLine($"D10_d9: {sampleData.D10_d9}"); Console.WriteLine($"D11_d10: {sampleData.D11_d10}"); Console.WriteLine($"D12_d11: {sampleData.D12_d11}"); Console.WriteLine($"Df_d12: {sampleData.Df_d12}"); Console.WriteLine($"D2r_d1r: {sampleData.D2r_d1r}"); Console.WriteLine($"D3r_d2r: {sampleData.D3r_d2r}"); Console.WriteLine($"D4r_d3r: {sampleData.D4r_d3r}"); Console.WriteLine($"D5r_d4r: {sampleData.D5r_d4r}"); Console.WriteLine($"D6r_d5r: {sampleData.D6r_d5r}"); Console.WriteLine($"D7r_d6r: {sampleData.D7r_d6r}"); Console.WriteLine($"D8r_d7r: {sampleData.D8r_d7r}"); Console.WriteLine($"D9r_d8r: {sampleData.D9r_d8r}"); Console.WriteLine($"D10r_d9r: {sampleData.D10r_d9r}"); Console.WriteLine($"D11r_d10r: {sampleData.D11r_d10r}"); Console.WriteLine($"D12r_d11r: {sampleData.D12r_d11r}"); Console.WriteLine($"Dfr_d12r: {sampleData.Dfr_d12r}"); Console.WriteLine($"Alpha1: {sampleData.Alpha1}"); Console.WriteLine($"Alpha2: {sampleData.Alpha2}"); Console.WriteLine($"Alpha3: {sampleData.Alpha3}"); Console.WriteLine($"Alpha4: {sampleData.Alpha4}"); Console.WriteLine($"Alpha5: {sampleData.Alpha5}"); Console.WriteLine($"Alpha6: {sampleData.Alpha6}"); Console.WriteLine($"Alpha7: {sampleData.Alpha7}"); Console.WriteLine($"Alpha8: {sampleData.Alpha8}"); Console.WriteLine($"Alpha9: {sampleData.Alpha9}"); Console.WriteLine($"Alpha10: {sampleData.Alpha10}"); Console.WriteLine($"Alpha11: {sampleData.Alpha11}"); Console.WriteLine($"Alpha12: {sampleData.Alpha12}"); Console.WriteLine($"Alphaf: {sampleData.Alphaf}"); Console.WriteLine($"Alphan1: {sampleData.Alphan1}"); Console.WriteLine($"Alphan2: {sampleData.Alphan2}"); Console.WriteLine($"Alphan3: {sampleData.Alphan3}"); Console.WriteLine($"Alphan4: {sampleData.Alphan4}"); Console.WriteLine($"Alphan5: {sampleData.Alphan5}"); Console.WriteLine($"Alphan6: {sampleData.Alphan6}"); Console.WriteLine($"Alphan7: {sampleData.Alphan7}"); Console.WriteLine($"Alphan8: {sampleData.Alphan8}"); Console.WriteLine($"Alphan9: {sampleData.Alphan9}"); Console.WriteLine($"Alphan10: {sampleData.Alphan10}"); Console.WriteLine($"Alphan11: {sampleData.Alphan11}"); Console.WriteLine($"Alphan12: {sampleData.Alphan12}"); Console.WriteLine($"Alphanf: {sampleData.Alphanf}"); Console.WriteLine($"Beta1: {sampleData.Beta1}"); Console.WriteLine($"Beta2: {sampleData.Beta2}"); Console.WriteLine($"Beta3: {sampleData.Beta3}"); Console.WriteLine($"Beta4: {sampleData.Beta4}"); Console.WriteLine($"Beta5: {sampleData.Beta5}"); Console.WriteLine($"Beta6: {sampleData.Beta6}"); Console.WriteLine($"Beta7: {sampleData.Beta7}"); Console.WriteLine($"Beta8: {sampleData.Beta8}"); Console.WriteLine($"Beta9: {sampleData.Beta9}"); Console.WriteLine($"Beta10: {sampleData.Beta10}"); Console.WriteLine($"Beta11: {sampleData.Beta11}"); Console.WriteLine($"Beta12: {sampleData.Beta12}"); Console.WriteLine($"Betaf: {sampleData.Betaf}"); Console.WriteLine($"Alpha1_beta1: {sampleData.Alpha1_beta1}"); Console.WriteLine($"Alpha2_beta2: {sampleData.Alpha2_beta2}"); Console.WriteLine($"Alpha3_beta3: {sampleData.Alpha3_beta3}"); Console.WriteLine($"Alpha4_beta4: {sampleData.Alpha4_beta4}"); Console.WriteLine($"Alpha5_beta5: {sampleData.Alpha5_beta5}"); Console.WriteLine($"Alpha6_beta6: {sampleData.Alpha6_beta6}"); Console.WriteLine($"Alpha7_beta7: {sampleData.Alpha7_beta7}"); Console.WriteLine($"Alpha8_beta8: {sampleData.Alpha8_beta8}"); Console.WriteLine($"Alpha9_beta9: {sampleData.Alpha9_beta9}"); Console.WriteLine($"Alpha10_beta10: {sampleData.Alpha10_beta10}"); Console.WriteLine($"Alpha11_beta11: {sampleData.Alpha11_beta11}"); Console.WriteLine($"Alpha12_beta12: {sampleData.Alpha12_beta12}"); Console.WriteLine($"Alphaf_betaf: {sampleData.Alphaf_betaf}"); Console.WriteLine($"Type: {sampleData.Type}"); Console.WriteLine($"\n\nPredicted Label value {predictionResult.Prediction} \nPredicted Label scores: [{String.Join(",", predictionResult.Score)}]\n\n"); Console.WriteLine("=============== End of process, hit any key to finish ==============="); Console.ReadKey(); }
public void CreatePdfForStoreRecomendations() { List <string> storeNames = getAllStoreNames(); List <Tuple <string, string> > tuples = getAllProductsTupleNameKey(); string text = ""; PdfPTable table = new PdfPTable(2); PdfPCell cell = new PdfPCell(new Phrase("Item Name")); cell.Colspan = 1; cell.HorizontalAlignment = 1; //0=Left, 1=Centre, 2=Right table.AddCell(cell); PdfPCell cell2 = new PdfPCell(new Phrase("Store Name")); cell2.Colspan = 1; cell2.HorizontalAlignment = 1; //0=Left, 1=Centre, 2=Right table.AddCell(cell2); foreach (var tuple in tuples) { float bestScore = 0; string storeName = ""; foreach (var store in storeNames) { ModelInput sampleData = new ModelInput() { Store_name = store, SerialKey = tuple.Item1, }; var predictionResult = ConsumeModel.Predict(sampleData); if (predictionResult.Score >= bestScore || bestScore == 0) { bestScore = predictionResult.Score; storeName = store; } } table.AddCell(tuple.Item2); table.AddCell(storeName); } Document doc = new Document(PageSize.A4, 7f, 5f, 5f, 0f); doc.AddTitle("Machine Learning results"); PdfWriter.GetInstance(doc, new FileStream(AppDomain.CurrentDomain.BaseDirectory + "Recommended Stores.pdf", FileMode.Create)); doc.Open(); // Paragraph p1 = new Paragraph(text); // doc.Add(p1); doc.Add(table); Font x = FontFactory.GetFont("nina fett"); x.Size = 19; x.SetStyle("Italic"); x.SetColor(0, 42, 255); Paragraph c2 = new Paragraph(@"Based on our recommendations for which products to buy in which store", x); c2.IndentationLeft = 30; doc.Add(c2); doc.Close(); }
public async Task <ActionResult> GetPredictionAsync([FromForm] string page, [FromForm] IFormFile audioFile) { try { var modelPath = ""; if (page == "Login") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelLogin.zip"); } else if (page == "Language") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelLanguage.zip"); } else if (page == "Home") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelHome.zip"); } else if (page == "cart") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelCartPage.zip"); } else if (page == "singleItem") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelSingleItemPage.zip"); } else if (page == "category") { modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelCategory.zip"); } else if (page == "pay") { //var v = @"C:\Users\Frank\source\repos\YorubaModelML\YorubaPredictionAPI\Models\MLModelPayPage.zip"; modelPath = Path.Combine(_env.ContentRootPath, "Models", "MLModelPayPage.zip"); } var model = new ConsumeModel(modelPath); var uploadPath = Path.Combine(_env.ContentRootPath, "uploads"); Directory.CreateDirectory(uploadPath); if (audioFile.Length > 0) { var audioFilePath = Path.Combine(uploadPath, audioFile.FileName); using (var fs = new FileStream(audioFilePath, FileMode.Create)) { await audioFile.CopyToAsync(fs); } double[] audio; int sampleRate; using (var audioFileReader = new AudioFileReader(audioFilePath)) { sampleRate = audioFileReader.WaveFormat.SampleRate; var wholeFile = new List <float>((int)(audioFileReader.Length / 4)); var readBuffer = new float[audioFileReader.WaveFormat.SampleRate * audioFileReader.WaveFormat.Channels]; int samplesRead = 0; while ((samplesRead = audioFileReader.Read(readBuffer, 0, readBuffer.Length)) > 0) { wholeFile.AddRange(readBuffer.Take(samplesRead)); } audio = Array.ConvertAll(wholeFile.ToArray(), x => (double)x); } int fftSize = 8192; var spec = new Spectrogram.Spectrogram(sampleRate, 4096, stepSize: 500, maxFreq: 3000, fixedWidth: 250); spec.Add(audio); var info = new FileInfo(audioFilePath); var imagepath = Path.Combine(uploadPath, info.Name + ".png"); spec.SaveImage(imagepath, intensity: 20_000); var md = new ModelInput { ImageSource = imagepath }; var result = model.Predict(md); Directory.Delete(uploadPath, true); return(Ok(new { class_id = result.Prediction, probability = result.Score.Max() })); //return Ok(_env.ContentRootPath); } _logger.LogError("File is Null"); return(BadRequest("File is null")); } catch (Exception ex) { _logger.LogError(ex.Message); return(BadRequest(ex.Message)); } }
private ModelOutput Predict(ModelInput sampleData) { var predictionResult = MyAutoML.LoadAndPrediction(sampleData); return(predictionResult); }
public ActionResult AddTechnicalCard(TeknikKart teknikKart, ModelInput input) { ViewBag.Result = ""; switch (input.Risk.ToLower()) { case "düşük": input.Risk = "dusuk"; break; case "orta": input.Risk = "orta"; break; case "yüksek": input.Risk = "yuksek"; break; case "çok yüksek": input.Risk = "cokyuksek"; break; default: break; } switch (input.TeknikUzman.ToLower()) { case "rafet": input.TeknikUzman = "rafet"; break; case "ilayda": input.TeknikUzman = "ilayda"; break; case "ali": input.TeknikUzman = "ali"; break; case "mucize": input.TeknikUzman = "mucize"; break; default: break; } var timePrediction = ConsumeModel.Predict(input); ViewBag.Result = timePrediction; teknikKart.TahminSüresi = ViewBag.Result.Score; context.teknikKarts.Add(teknikKart); context.SaveChanges(); int ID = Convert.ToInt32(teknikKart.MüsteriKartId); MüsteriKart müsteriKart = new MüsteriKart(); müsteriKart.TeknikST = context.teknikKarts.Where(x => x.MüsteriKartId == ID).Sum(x => x.TahminSüresi); var updateTotal = context.müsteriKarts.Find(ID); updateTotal.TeknikST = müsteriKart.TeknikST; context.SaveChanges(); return(RedirectToAction("TaskBoard", "HomeBoard")); }
private static ModelOutput PredecirCambioReserva(ModelInput bookingData) { return(ConsumeModel.Predict(bookingData)); }
public IActionResult Send() { /* * string reCaptchaVerification = Request.Form["g-recaptcha-response"]; * * if (await VerifyreCaptcha(reCaptchaVerification) != true) * { * return RedirectToAction("InvalidCaptcha"); * } */ string bodyText = Request.Form["body"]; string subject = Request.Form["subject"]; string fromAddress = Request.Form["emailaddress"]; var input = new ModelInput(); input.SentimentText = bodyText; ModelOutput result = ConsumeModel.Predict(input); if (result.Prediction) { return(RedirectToAction("SpamResponse")); } string[] bodyWords = bodyText.Split(" "); string[] subjectWords = subject.Split(" "); foreach (string word in bodyWords) { foreach (string badWord in badWords) { if (word.ToLower() == badWord) { return(RedirectToAction("InvalidMessage")); } } } foreach (string word in subjectWords) { foreach (string badWord in badWords) { if (word.ToLower() == badWord) { return(RedirectToAction("InvalidMessage")); } } } var message = new MimeMessage(); message.To.Add(new MailboxAddress("Inbox", _mailConfig.Value.Receiver)); message.From.Add(new MailboxAddress("Inbox", _mailConfig.Value.SmtpUser)); message.Subject = subject; message.Body = new TextPart(TextFormat.Html) { Text = bodyText + "<br /><br /> Sent from: " + fromAddress }; using (var client = new SmtpClient()) { client.Connect(_mailConfig.Value.SmtpServer, _mailConfig.Value.SmtpPort, true); client.Authenticate(_mailConfig.Value.SmtpUser, _mailConfig.Value.SmtpPassword); client.Send(message); client.Disconnect(true); } return(RedirectToAction("Index")); }
// Once all of the triangle pairs that need intersection testing have been determined, we can do the actual intersection testing // Get out the scissors !!! protected virtual void Snip(ModelInput in_a, ModelInput in_b, List <TrianglePair> pairs) { List <int> first_tris = new List <int>(); List <int> second_tris = new List <int>(); foreach (TrianglePair pair in pairs) { if (!first_tris.Contains(pair.a)) { first_tris.Add(pair.a); } if (!second_tris.Contains(pair.b)) { second_tris.Add(pair.b); } } WorkingModel a = in_a.ToWorkingModel(0, first_tris); WorkingModel b = in_b.ToWorkingModel(1, second_tris); WorkingModel.Intersect(a, b, pairs); List <BasicModelVert> first_notsafe_bmv = a.GetBMVList(first_in, first_xform); List <BasicModelVert> first_safe_bmv = new List <BasicModelVert>(); for (int i = 0; i < first_in.a_vert.Length; i++) { if (!first_tris.Contains(i)) { for (int j = 0; j < 3; j++) { int xyz = (int)((j == 0 ? first_in.a_vert : j == 1 ? first_in.b_vert : first_in.c_vert)[i]); int uv = (int)((j == 0 ? first_in.a_uv : j == 1 ? first_in.b_uv : first_in.c_uv)[i]); int norm = (int)((j == 0 ? first_in.a_norm : j == 1 ? first_in.b_norm : first_in.c_norm)[i]); first_safe_bmv.Add(new BasicModelVert { position = first_xform.TransformVec3(new Vec3 { x = first_in.x[xyz], y = first_in.y[xyz], z = first_in.z[xyz] }, 1.0), uv = new Vec2 { x = first_in.u[uv], y = first_in.v[uv] }, normal = Vec3.Normalize(first_xform.TransformVec3(new Vec3 { x = first_in.nx[norm], y = first_in.ny[norm], z = first_in.nz[norm] }, 0.0)) }); } } } List <BasicModelVert> second_notsafe_bmv = b.GetBMVList(second_in, second_xform); List <BasicModelVert> second_safe_bmv = new List <BasicModelVert>(); for (int i = 0; i < second_in.a_vert.Length; i++) { if (!second_tris.Contains(i)) { for (int j = 0; j < 3; j++) { int xyz = (int)((j == 0 ? second_in.a_vert : j == 1 ? second_in.b_vert : second_in.c_vert)[i]); int uv = (int)((j == 0 ? second_in.a_uv : j == 1 ? second_in.b_uv : second_in.c_uv)[i]); int norm = (int)((j == 0 ? second_in.a_norm : j == 1 ? second_in.b_norm : second_in.c_norm)[i]); second_safe_bmv.Add(new BasicModelVert { position = second_xform.TransformVec3(new Vec3 { x = second_in.x[xyz], y = second_in.y[xyz], z = second_in.z[xyz] }, 1.0), uv = new Vec2 { x = second_in.u[uv], y = second_in.v[uv] }, normal = Vec3.Normalize(second_xform.TransformVec3(new Vec3 { x = second_in.nx[norm], y = second_in.ny[norm], z = second_in.nz[norm] }, 0.0)) }); } } } cutEdges = new List <Vec3[]>(); cutEdges.AddRange(a.GetCutEdgesList()); cutEdges.AddRange(b.GetCutEdgesList()); cutPoints = new List <Vec3>(); foreach (Vec3[] edge in b.GetCutEdgesList()) { foreach (Vec3 vert in edge) { if (!cutPoints.Exists((v) => (v - vert).ComputeMagnitudeSquared() < 0.0000000000000000000001)) { cutPoints.Add(vert); } } } List <BasicModelVert> first_bmv_trimmed = ScrapTrimmedStuff(first_safe_bmv, first_notsafe_bmv, cutEdges, first_keep); List <BasicModelVert> second_bmv_trimmed = ScrapTrimmedStuff(second_safe_bmv, second_notsafe_bmv, cutEdges, second_keep); first_out = BMVListToModel(first_bmv_trimmed); second_out = BMVListToModel(second_bmv_trimmed); }
async public void extractFeatures(string _filepath, StorageFile sf) { op = new float[10]; tdVectors = new List <float[]>(); mfccVectors = new List <float[]>(); featureTimeList = new List <float[]>(); //NWaves FilePath = _filepath; PredictedLabel = "Ready!."; //player.Load(GetStreamFromFile(FilePath)); //player.Play(); mMedia.Source = MediaSource.CreateFromStorageFile(sf); bool test = player.IsPlaying; mMedia.AutoPlay = true; MusicProperties properties = await sf.Properties.GetMusicPropertiesAsync(); TimeSpan myTrackDuration = properties.Duration; duration = Convert.ToInt32(myTrackDuration.TotalSeconds); if (FilePath != null) { DiscreteSignal signal; // load var mfcc_no = 24; var samplingRate = 44100; var mfccOptions = new MfccOptions { SamplingRate = samplingRate, FeatureCount = mfcc_no, FrameDuration = 0.025 /*sec*/, HopDuration = 0.010 /*sec*/, PreEmphasis = 0.97, Window = WindowTypes.Hamming }; var opts = new MultiFeatureOptions { SamplingRate = samplingRate, FrameDuration = 0.025, HopDuration = 0.010 }; var tdExtractor = new TimeDomainFeaturesExtractor(opts); var mfccExtractor = new MfccExtractor(mfccOptions); // Read from file. featureString = String.Empty; featureString = $"green,"; //MFCC var mfccList = new List <List <double> >(); var tdList = new List <List <double> >(); //MFCC //TD Features //Spectral features for (var i = 0; i < mfcc_no; i++) { mfccList.Add(new List <double>()); } for (var i = 0; i < 4; i++) { tdList.Add(new List <double>()); } string specFeatures = String.Empty; Console.WriteLine($"{tag} Reading from file"); using (var stream = new FileStream(FilePath, FileMode.Open)) { var waveFile = new WaveFile(stream); signal = waveFile[channel : Channels.Left]; ////Compute MFCC float[] mfvfuck = new float[25]; var sig_sam = signal.Samples; mfccVectors = mfccExtractor.ComputeFrom(sig_sam); var fftSize = 1024; tdVectors = tdExtractor.ComputeFrom(signal.Samples); var fft = new Fft(fftSize); var resolution = (float)samplingRate / fftSize; var frequencies = Enumerable.Range(0, fftSize / 2 + 1) .Select(f => f * resolution) .ToArray(); var spectrum = new Fft(fftSize).MagnitudeSpectrum(signal).Samples; var centroid = Spectral.Centroid(spectrum, frequencies); var spread = Spectral.Spread(spectrum, frequencies); var flatness = Spectral.Flatness(spectrum, 0); var noiseness = Spectral.Noiseness(spectrum, frequencies, 3000); var rolloff = Spectral.Rolloff(spectrum, frequencies, 0.85f); var crest = Spectral.Crest(spectrum); var decrease = Spectral.Decrease(spectrum); var entropy = Spectral.Entropy(spectrum); specFeatures = $"{centroid},{spread},{flatness},{noiseness},{rolloff},{crest},{decrease},{entropy}"; //} Console.WriteLine($"{tag} All features ready"); for (int calibC = 0; calibC < mfccVectors.Count;) { featureString = String.Empty; var tmp = new ModelInput(); for (var j = 0; j < (mfccVectors.Count / duration) - 1 && calibC < mfccVectors.Count; j++) { for (var i = 0; i < mfcc_no; i++) { mfccList[i].Add(mfccVectors[calibC][i]); } for (var i = 0; i < 4; i++) { tdList[i].Add(tdVectors[calibC][i]); } calibC += 1; } var mfcc_statistics = new List <double>(); for (var i = 0; i < mfcc_no; i++) { //preheader += m + "_mean"; //preheader += m + "_min"; //preheader += m + "_var"; //preheader += m + "_sd"; //preheader += m + "_med"; //preheader += m + "_lq"; //preheader += m + "_uq"; //preheader += m + "_skew"; //preheader += m + "_kurt"; mfcc_statistics.Add(Statistics.Mean(mfccList[i])); mfcc_statistics.Add(Statistics.Minimum(mfccList[i])); mfcc_statistics.Add(Statistics.Variance(mfccList[i])); mfcc_statistics.Add(Statistics.StandardDeviation(mfccList[i])); mfcc_statistics.Add(Statistics.Median(mfccList[i])); mfcc_statistics.Add(Statistics.LowerQuartile(mfccList[i])); mfcc_statistics.Add(Statistics.UpperQuartile(mfccList[i])); mfcc_statistics.Add(Statistics.Skewness(mfccList[i])); mfcc_statistics.Add(Statistics.Kurtosis(mfccList[i])); } var td_statistics = new List <double>(); for (var i = 0; i < 4; i++) { td_statistics.Add(Statistics.Mean(tdList[i])); td_statistics.Add(Statistics.Minimum(tdList[i])); td_statistics.Add(Statistics.Variance(tdList[i])); td_statistics.Add(Statistics.StandardDeviation(tdList[i])); td_statistics.Add(Statistics.Median(tdList[i])); td_statistics.Add(Statistics.LowerQuartile(tdList[i])); td_statistics.Add(Statistics.UpperQuartile(tdList[i])); td_statistics.Add(Statistics.Skewness(tdList[i])); td_statistics.Add(Statistics.Kurtosis(tdList[i])); } // Write MFCCs featureString += String.Join(",", mfcc_statistics); featureString += ","; featureString += String.Join(",", td_statistics); //Write Spectral features as well featureString += ","; featureString += specFeatures; Console.WriteLine($"{tag} Feature String ready {featureString}"); if (File.Exists(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"))) { File.Delete(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp")); File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString); } else { File.WriteAllText(Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"), featureString); } MLContext mLContext = new MLContext(); string fileName = Path.Combine(Environment.GetFolderPath(Environment.SpecialFolder.LocalApplicationData), "temp"); IDataView dataView = mLContext.Data.LoadFromTextFile <ModelInput>( path: fileName, hasHeader: false, separatorChar: ',', allowQuoting: true, allowSparse: false); // Use first line of dataset as model input // You can replace this with new test data (hardcoded or from end-user application) ModelInput sampleForPrediction = mLContext.Data.CreateEnumerable <ModelInput>(dataView, false) .First(); ModelOutput opm = ConsumeModel.Predict(sampleForPrediction); featureTimeList.Add(opm.Score); Console.WriteLine($"{tag} Feature vs time list ready"); } //Console.WriteLine($"{tag} MFCC: {mfccVectors.Count}"); //Console.WriteLine($"{tag} TD: {tdVectors.Count}"); //Console.WriteLine($"{tag} featureTimeArray: {featureTimeList.Count} {featureString}"); } } playAudio(); }
public void Update(float absoluteTime, ModelInput input) { //TODO implement }
public override void Init(ModelInput modelInput, ObjectProperties parentObjectProperties) { dropdown.options.AddRange(MoveSyncData.instance.shapeData.shapesNameList); base.Init(modelInput, parentObjectProperties); }
public ModelOutput Predict(ModelInput modelInput) { return(this.predictionEnginePool.Predict(modelInput)); }
public LightGbmEx(string pathname /*= "creditcard.csv"*/, string modelname /* = "model.zip"*/) { MLContext mlContext = new MLContext(); IDataView trainingDataView = mlContext.Data.LoadFromTextFile <ModelInput>( path: pathname, hasHeader: true, separatorChar: ',', allowQuoting: true, allowSparse: false); var dataProcessPipeline = mlContext.Transforms.Concatenate("Features", new[] { "Time", "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "Amount" }); // Choosing algorithm var trainer = mlContext.BinaryClassification.Trainers.LightGbm(labelColumnName: "Class", featureColumnName: "Features"); // Appending algorithm to pipeline var trainingPipeline = dataProcessPipeline.Append(trainer); ITransformer model = trainingPipeline.Fit(trainingDataView); mlContext.Model.Save(model, trainingDataView.Schema, modelname); var crossValidationResults = mlContext.BinaryClassification.CrossValidateNonCalibrated(trainingDataView, trainingPipeline, numberOfFolds: 5, labelColumnName: "Class"); Console.WriteLine(crossValidationResults); var predEngine = mlContext.Model.CreatePredictionEngine <ModelInput, ModelOutput>(model); ModelInput sampleData = new ModelInput() { Time = 0, V1 = -2.076174782f, V2 = 2.142237995f, V3 = -2.522703577f, V4 = -1.888063034f, V5 = 1.98278475f, V6 = 3.732949553f, V7 = -1.217430393f, V8 = -0.536644267f, V9 = 0.272867038f, V10 = 0.300342205f, V11 = -0.451655998f, V12 = 0.566367644f, V13 = -0.317804444f, V14 = 0.855741736f, V15 = -0.041046986f, V16 = 0.046620056f, V17 = 0.01782216f, V18 = -0.772915626f, V19 = -0.354162802f, V20 = -0.308523004f, V21 = 2.016666112f, V22 = -1.588268798f, V23 = 0.588482263f, V24 = 0.632443919f, V25 = -0.201063916f, V26 = 0.199251167f, V27 = 0.43865731f, V28 = 0.172923188f, Amount = 8.95f, Class = false }; ModelOutput predictionResult = predEngine.Predict(sampleData); Console.WriteLine($"Actual value: {sampleData.Class} | Predicted value: {predictionResult.Prediction}"); }
public ClassifyRequest() { Input = new ModelInput(); }
static void Main(string[] args) { // Create single instance of sample data from first line of dataset for model input ModelInput sampleData = new ModelInput() { Age = 48F, Bp = 80F, Sg = 1.02F, Al = 1F, Su = 0F, Rbc = @"unknown", Pc = @"normal", Pcc = @"notpresent", Ba = @"notpresent", Bgr = 121F, Bu = 36F, Sc = 1.2F, Sod = 0F, Pot = 0F, Hemo = 15.4F, Pcv = @"44", Wbcc = @"7800", Rbcc = 5.2F, Htn = true, Dm = true, Cad = false, Appet = @"good", Pe = false, Ane = false, }; // Make a single prediction on the sample data and print results var predictionResult = ConsumeModel.Predict(sampleData); Console.WriteLine("Using model to make single prediction -- Comparing actual Class with predicted Class from sample data...\n\n"); Console.WriteLine($"Age: {sampleData.Age}"); Console.WriteLine($"Bp: {sampleData.Bp}"); Console.WriteLine($"Sg: {sampleData.Sg}"); Console.WriteLine($"Al: {sampleData.Al}"); Console.WriteLine($"Su: {sampleData.Su}"); Console.WriteLine($"Rbc: {sampleData.Rbc}"); Console.WriteLine($"Pc: {sampleData.Pc}"); Console.WriteLine($"Pcc: {sampleData.Pcc}"); Console.WriteLine($"Ba: {sampleData.Ba}"); Console.WriteLine($"Bgr: {sampleData.Bgr}"); Console.WriteLine($"Bu: {sampleData.Bu}"); Console.WriteLine($"Sc: {sampleData.Sc}"); Console.WriteLine($"Sod: {sampleData.Sod}"); Console.WriteLine($"Pot: {sampleData.Pot}"); Console.WriteLine($"Hemo: {sampleData.Hemo}"); Console.WriteLine($"Pcv: {sampleData.Pcv}"); Console.WriteLine($"Wbcc: {sampleData.Wbcc}"); Console.WriteLine($"Rbcc: {sampleData.Rbcc}"); Console.WriteLine($"Htn: {sampleData.Htn}"); Console.WriteLine($"Dm: {sampleData.Dm}"); Console.WriteLine($"Cad: {sampleData.Cad}"); Console.WriteLine($"Appet: {sampleData.Appet}"); Console.WriteLine($"Pe: {sampleData.Pe}"); Console.WriteLine($"Ane: {sampleData.Ane}"); Console.WriteLine($"\n\nPredicted Class: {predictionResult.Score}\n\n"); Console.WriteLine("=============== End of process, hit any key to finish ==============="); Console.ReadKey(); }
// For more info on consuming ML.NET models, visit https://aka.ms/mlnet-consume // Method for consuming model in your app public static ModelOutput Predict(ModelInput input) { ModelOutput result = PredictionEngine.Value.Predict(input); return(result); }
/// <summary> /// Use this method to predict on <see cref="ModelInput"/>. /// </summary> /// <param name="input">model input.</param> /// <returns><seealso cref=" ModelOutput"/></returns> public static ModelOutput Predict(ModelInput input) { var predEngine = PredictEngine.Value; return(predEngine.Predict(input)); }
public void CreateOrEditModel([FromBody] ModelInput input) { modelAppService.CreateOrEditModel(input); }
public ModelOutput PredictDistrict(ModelInput input) { return(_predictionEngine.Predict(input)); }
// For more info on consuming ML.NET models, visit https://aka.ms/model-builder-consume // Method for consuming model in your app public ModelOutput Predict(ModelInput input) => // Use model to make prediction on input data predEngine.Predict(input);