/// <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);
