static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Age = 63F,
Sex = 1F,
Trestbps = 145F,
Chol = 233F,
Fbs = 1F,
Restecg = 0F,
Thalach = 150F,
Exang = 0F,
};
// 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 Cp with predicted Cp from sample data...\n\n");
Console.WriteLine($"Age: {sampleData.Age}");
Console.WriteLine($"Sex: {sampleData.Sex}");
Console.WriteLine($"Trestbps: {sampleData.Trestbps}");
Console.WriteLine($"Chol: {sampleData.Chol}");
Console.WriteLine($"Fbs: {sampleData.Fbs}");
Console.WriteLine($"Restecg: {sampleData.Restecg}");
Console.WriteLine($"Thalach: {sampleData.Thalach}");
Console.WriteLine($"Exang: {sampleData.Exang}");
Console.WriteLine($"\n\nPredicted Cp value {predictionResult.Prediction} \nPredicted Cp scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
private void btnPredict_Click(object sender, RoutedEventArgs e)
{
List <SupplierData> predictedSuppliers = new List <SupplierData>();
predictedSuppliers = ConsumeModel.Predict((cmbCommodity.SelectedIndex + 1).ToString(),
txtVolume.Text, cmbMPProcess.SelectedItem.ToString());
}
public bool CompareAttemptWithActual()
{
List <int> numOfDirectionChangesForPassword = new List <int>();
List <int> numOfDirectionChangesForAttempt = new List <int>();
var savedOutput = GetEnumerableSamples("/Source/SavedPassword.csv");
var savedAttempt = GetEnumerableSamples("/Source/PasswordAttempt.csv");
foreach (ModelInput mi in savedOutput)
{
ModelOutput predictionResult = ConsumeModel.Predict(mi);
if (predictionResult.Prediction == true)
{
numOfDirectionChangesForPassword.Add(1);
}
}
foreach (ModelInput mi in savedAttempt)
{
ModelOutput predictionResult = ConsumeModel.Predict(mi);
if (predictionResult.Prediction == true)
{
numOfDirectionChangesForAttempt.Add(1);
}
}
Console.WriteLine(numOfDirectionChangesForPassword.Count.ToString());
Console.WriteLine(numOfDirectionChangesForAttempt.Count.ToString());
return((numOfDirectionChangesForAttempt.Count) < (numOfDirectionChangesForPassword.Count * 1.2) &&
(numOfDirectionChangesForAttempt.Count) > (numOfDirectionChangesForPassword.Count * 0.8));
}
static void Main(string[] args)
{
Console.WriteLine("Machine Learning - My picture!");
Console.WriteLine("Import photos from folder...");
string[] fileArray = Directory.GetFiles(@"C:\Users\lenovo\Desktop\Scan\");
for (int i = 0; i < fileArray.Length; i++)
{
string filename = null;
// using the method
filename = Path.GetFileName(fileArray[i]);
// Add input data
// Console.ForegroundColor = ConsoleColor.Yellow;
var input = new ModelInput();
input.ImageSource = fileArray[i];
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
string datac = String.Join(",", result.Score);
string[] words = datac.Split(',');
Console.WriteLine($"-------------- {result.Prediction} - {filename} Diem: " + datac);
string destinationFile = @"C:\Users\lenovo\Desktop\Result\" + result.Prediction + @"\" + filename;
// To move a file or folder to a new location:
System.IO.File.Copy(fileArray[i], destinationFile);
Console.WriteLine("--------------------- Copy a image to " + result.Prediction + @" - C:\Users\lenovo\Desktop\Result\" + result.Prediction);
// Console.WriteLine(@"C:\Users\lenovo\Desktop\Result\" + result.Prediction);
Console.WriteLine("------>Next Scan<------------------");
}
Console.WriteLine("Scanned!");
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()
{
Make = @"Mercedes-Benz",
Model = @"E 350",
Year = 2013F,
Power = 251F,
Mileage = 200000F,
Eurostandard = @"EURO 6",
Gearbox = @"Автоматични скорости",
};
// 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 Price with predicted Price from sample data...\n\n");
Console.WriteLine($"Make: {sampleData.Make}");
Console.WriteLine($"Model: {sampleData.Model}");
Console.WriteLine($"Year: {sampleData.Year}");
Console.WriteLine($"Power: {sampleData.Power}");
Console.WriteLine($"Mileage: {sampleData.Mileage}");
Console.WriteLine($"Eurostandard: {sampleData.Eurostandard}");
Console.WriteLine($"Gearbox: {sampleData.Gearbox}");
Console.WriteLine($"\n\nPredicted Price: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
private static void PrintIrisDataFromModelBuilder()
{
Console.WriteLine(@"----------------------------------------------------------------------");
Console.WriteLine(@"--- 2. Iris data from ModelBuilder. ---");
Console.WriteLine(@"----------------------------------------------------------------------");
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Col0 = 5.1F,
Col1 = 3.5F,
Col2 = 1.4F,
Col3 = 0.2F,
};
// 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 Col4 with predicted Col4 from sample data...");
Console.WriteLine($"Col0: {sampleData.Col0}");
Console.WriteLine($"Col1: {sampleData.Col1}");
Console.WriteLine($"Col2: {sampleData.Col2}");
Console.WriteLine($"Col3: {sampleData.Col3}");
Console.WriteLine($"Predicted Col4 value {predictionResult.Prediction}");
Console.WriteLine($"Predicted Col4 scores: [{string.Join(",", predictionResult.Score)}]");
}
private static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Sentiment with predicted Sentiment from sample data...\n\n");
Console.WriteLine($"SentimentText: {sampleData.SentimentText}");
Console.WriteLine($"\n\nActual Sentiment: {sampleData.Sentiment} \nPredicted Sentiment: {predictionResult.Prediction}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
Console.WriteLine("That was dull, right?");
MLContext mlContext = new MLContext();
var input = new ModelInput();
bool running = true;
while (running)
{
Console.Write("Enter your own value to predict or 'q' to quit: ");
input.SentimentText = Console.ReadLine();
if (input.SentimentText.ToLower() == "q")
{
running = false;
}
else
{
predictionResult = ConsumeModel.Predict(input);
Console.WriteLine($"Predicted: {predictionResult.Prediction} with a score {predictionResult.Score}");
}
}
}
static void Main(string[] args)
{
begin:
Console.WriteLine("Enter text to predict: ");
string aux = Console.ReadLine();
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Col1 = aux,
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Col0 with predicted Col0 from sample data...\n\n");
Console.WriteLine($"Col1: {sampleData.Col1}");
Console.WriteLine($"\n\nPredicted Col0 value {predictionResult.Prediction} \nPredicted Col0 scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
while (aux != "stop")
{
Console.Clear();
goto begin;
}
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Longitude = -122.23F,
Latitude = 37.88F,
Housing_median_age = 41F,
Total_rooms = 880F,
Total_bedrooms = 129F,
Population = 322F,
Households = 126F,
Median_income = 8.3252F,
Ocean_proximity = @"NEAR BAY",
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Median_house_value with predicted Median_house_value from sample data...\n\n");
Console.WriteLine($"Longitude: {sampleData.Longitude}");
Console.WriteLine($"Latitude: {sampleData.Latitude}");
Console.WriteLine($"Housing_median_age: {sampleData.Housing_median_age}");
Console.WriteLine($"Total_rooms: {sampleData.Total_rooms}");
Console.WriteLine($"Total_bedrooms: {sampleData.Total_bedrooms}");
Console.WriteLine($"Population: {sampleData.Population}");
Console.WriteLine($"Households: {sampleData.Households}");
Console.WriteLine($"Median_income: {sampleData.Median_income}");
Console.WriteLine($"Ocean_proximity: {sampleData.Ocean_proximity}");
Console.WriteLine($"\n\nPredicted Median_house_value: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public IActionResult Predict(ModelInput input)
{
var prediction = ConsumeModel.Predict(input);
ViewBag.Result = prediction;
return(View());
}
static void Main(string[] args)
{
ModelInput data = new ModelInput();
var features = "rock,-1.13947,0.17516886,-1.5538154,0.41143337,-0.57987577,0.3658361,-0.8737631,0.31099418,-0.6114717,0.22522274,-0.36270136,0.21625315,-0.19452207,0.19064106,-0.058592424,0.03778343,-0.21190715,0.09087317,-0.16246094,0.050544135,-0.08509372,0.008931654,-0.12052887,0.08522095,0.01979195,0.12628011,0.08306938,0.90629965,1544.9125,1601.7399,0.3005298,0.17325823,3328.125,129.86523,0.033054575,0.8297714";
var feature_list = features.Split(",");
FieldInfo[] fi = typeof(ModelInput).GetFields(BindingFlags.Public | BindingFlags.Instance | BindingFlags.NonPublic);
int ind = 0;
foreach (FieldInfo info in fi)
{
if (ind == 0)
{
info.SetValue(data, feature_list[ind]);
}
else
{
info.SetValue(data, float.Parse(feature_list[ind]));
}
ind += 1;
}
ModelOutput prediction = ConsumeModel.Predict(data);
Console.WriteLine($"Prediction: {prediction.Prediction}");
}
static void Main(string[] args)
{
bool terminate = false;
do
{
ModelInput sampleData = new ModelInput();
Console.Write("Enter ticket class (1, 2, 3):");
sampleData.Pclass = Convert.ToInt32(Console.ReadLine());
Console.Write("Enter gender (1 - male, 2 - female):");
sampleData.Gender_Normalized = Convert.ToInt32(Console.ReadLine());
Console.Write("Enter age range (1-10):");
sampleData.Age_Notmalized = Convert.ToInt32(Console.ReadLine());
Console.Write("Enter fare range (1-10):");
sampleData.Fare_Notmalized = Convert.ToInt32(Console.ReadLine());
ModelOutput predictedResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("The trained model predicts you " + (predictedResult.Prediction == true?"survived":"did not survice"));
Console.WriteLine("Press any key to make another prediction or <return> to terminate:");
string response = Console.ReadLine();
terminate = String.IsNullOrEmpty(response);
}while (!terminate);
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Star_rating with predicted Star_rating from sample data...\n\n");
Console.WriteLine($"marketplace: {sampleData.Marketplace}");
Console.WriteLine($"customer_id: {sampleData.Customer_id}");
Console.WriteLine($"review_id: {sampleData.Review_id}");
Console.WriteLine($"product_id: {sampleData.Product_id}");
Console.WriteLine($"product_parent: {sampleData.Product_parent}");
Console.WriteLine($"product_title: {sampleData.Product_title}");
Console.WriteLine($"product_category: {sampleData.Product_category}");
Console.WriteLine($"helpful_votes: {sampleData.Helpful_votes}");
Console.WriteLine($"total_votes: {sampleData.Total_votes}");
Console.WriteLine($"vine: {sampleData.Vine}");
Console.WriteLine($"verified_purchase: {sampleData.Verified_purchase}");
Console.WriteLine($"review_headline: {sampleData.Review_headline}");
Console.WriteLine($"review_body: {sampleData.Review_body}");
Console.WriteLine($"review_date: {sampleData.Review_date}");
Console.WriteLine($"\n\nActual Star_rating: {sampleData.Star_rating} \nPredicted Star_rating: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Askprice with predicted Askprice from sample data...\n\n");
Console.WriteLine($"num_bed: {sampleData.Num_bed}");
Console.WriteLine($"year_built: {sampleData.Year_built}");
Console.WriteLine($"longitude: {sampleData.Longitude}");
Console.WriteLine($"latitude: {sampleData.Latitude}");
Console.WriteLine($"num_room: {sampleData.Num_room}");
Console.WriteLine($"num_bath: {sampleData.Num_bath}");
Console.WriteLine($"living_area: {sampleData.Living_area}");
Console.WriteLine($"property_type: {sampleData.Property_type}");
Console.WriteLine($"num_parking: {sampleData.Num_parking}");
Console.WriteLine($"accessible_buildings: {sampleData.Accessible_buildings}");
Console.WriteLine($"family_quality: {sampleData.Family_quality}");
Console.WriteLine($"art_expos: {sampleData.Art_expos}");
Console.WriteLine($"emergency_shelters: {sampleData.Emergency_shelters}");
Console.WriteLine($"emergency_water: {sampleData.Emergency_water}");
Console.WriteLine($"Facilities: {sampleData.Facilities}");
Console.WriteLine($"fire_stations: {sampleData.Fire_stations}");
Console.WriteLine($"Cultural: {sampleData.Cultural}");
Console.WriteLine($"Monuments: {sampleData.Monuments}");
Console.WriteLine($"police_stations: {sampleData.Police_stations}");
Console.WriteLine($"Vacant: {sampleData.Vacant}");
Console.WriteLine($"Free_Parking: {sampleData.Free_Parking}");
Console.WriteLine($"\n\nActual Askprice: {sampleData.Askprice} \nPredicted Askprice: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Rev_id = 6.666748E+08F,
Comment = @" ==He is a Rapist!!!!!== Please edit the article to include this important fact. Thank You. — Preceding unsigned comment added by • ",
Year = 2015F,
Logged_in = @"True",
Ns = @"article",
Sample = @"blocked",
Split = @"train",
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Label with predicted Label from sample data...\n\n");
Console.WriteLine($"Rev_id: {sampleData.Rev_id}");
Console.WriteLine($"Comment: {sampleData.Comment}");
Console.WriteLine($"Year: {sampleData.Year}");
Console.WriteLine($"Logged_in: {sampleData.Logged_in}");
Console.WriteLine($"Ns: {sampleData.Ns}");
Console.WriteLine($"Sample: {sampleData.Sample}");
Console.WriteLine($"Split: {sampleData.Split}");
Console.WriteLine($"\n\nPredicted Label value {predictionResult.Prediction} \nPredicted Label scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
UDI = 1F,
Air_temperature_K = 298.1F,
Process_temperature_K = 308.6F,
Rotational_speed_rpm = 1551F,
Torque_Nm = 42.8F,
Tool_wearmin = 0F,
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Machine_failure with predicted Machine_failure from sample data...\n\n");
Console.WriteLine($"UDI: {sampleData.UDI}");
Console.WriteLine($"Air_temperature_K: {sampleData.Air_temperature_K}");
Console.WriteLine($"Process_temperature_K: {sampleData.Process_temperature_K}");
Console.WriteLine($"Rotational_speed_rpm: {sampleData.Rotational_speed_rpm}");
Console.WriteLine($"Torque_Nm: {sampleData.Torque_Nm}");
Console.WriteLine($"Tool_wearmin: {sampleData.Tool_wearmin}");
Console.WriteLine($"\n\nPredicted Machine_failure value {predictionResult.Prediction} \nPredicted Machine_failure scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
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()
{
Task = @"A",
Session_index = 1F,
Trial_index = 1F,
Trial_time = 146.911F,
Min_acc = 54.15718F,
Sd_acc = 2.104323F,
};
// 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 Trials_to_pass with predicted Trials_to_pass from sample data...\n\n");
Console.WriteLine($"Task: {sampleData.Task}");
Console.WriteLine($"Session_index: {sampleData.Session_index}");
Console.WriteLine($"Trial_index: {sampleData.Trial_index}");
Console.WriteLine($"Trial_time: {sampleData.Trial_time}");
Console.WriteLine($"Min_acc: {sampleData.Min_acc}");
Console.WriteLine($"Sd_acc: {sampleData.Sd_acc}");
Console.WriteLine($"\n\nPredicted Trials_to_pass: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
private float PredictPrice(string CPU,
double GHz,
string GPU,
string RAMType,
double RAMAmount,
double screenSize,
double storage,
bool isSSD,
double weight)
{
ModelInput input = new ModelInput()
{
CPU = CPU,
GHz = (float)GHz,
GPU = GPU,
RAMType = RAMType,
RAM = (float)RAMAmount,
Screen = (float)screenSize,
Storage = (float)storage,
SSD = isSSD,
Weight = (float)weight
};
ModelOutput result = ConsumeModel.Predict(input);
return(result.Score);
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Open_Price with predicted Open_Price from sample data...\n\n");
Console.WriteLine($"Date: {sampleData.Date}");
Console.WriteLine($"High Price: {sampleData.High_Price}");
Console.WriteLine($"Low Price: {sampleData.Low_Price}");
Console.WriteLine($"Close Price: {sampleData.Close_Price}");
Console.WriteLine($"WAP: {sampleData.WAP}");
Console.WriteLine($"No.of Shares: {sampleData.No_of_Shares}");
Console.WriteLine($"No. of Trades: {sampleData.No__of_Trades}");
Console.WriteLine($"Total Turnover (Rs.): {sampleData.Total_Turnover__Rs__}");
Console.WriteLine($"Deliverable Quantity: {sampleData.Deliverable_Quantity}");
Console.WriteLine($"% Deli. Qty to Traded Qty: {sampleData.__Deli__Qty_to_Traded_Qty}");
Console.WriteLine($"Spread High-Low: {sampleData.Spread_High_Low}");
Console.WriteLine($"Spread Close-Open: {sampleData.Spread_Close_Open}");
Console.WriteLine($"\n\nActual Open_Price: {sampleData.Open_Price} \nPredicted Open_Price: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput();
input.SentimentText = "That is rude.";
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}\n\n");
input.SentimentText = "Why hello there.";
// Load model and predict output of sample data
result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}\n\n");
input.SentimentText = "Screw you!!!";
// Load model and predict output of sample data
result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}\n\n");
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Vendor_id = @"CMT",
Rate_code = 1F,
Passenger_count = 2F,
Trip_time_in_secs = 1271F,
Trip_distance = 2.8F,
Payment_type = @"CSH",
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Fare_amount with predicted Fare_amount from sample data...\n\n");
Console.WriteLine($"Vendor_id: {sampleData.Vendor_id}");
Console.WriteLine($"Rate_code: {sampleData.Rate_code}");
Console.WriteLine($"Passenger_count: {sampleData.Passenger_count}");
Console.WriteLine($"Trip_time_in_secs: {sampleData.Trip_time_in_secs}");
Console.WriteLine($"Trip_distance: {sampleData.Trip_distance}");
Console.WriteLine($"Payment_type: {sampleData.Payment_type}");
Console.WriteLine($"\n\nPredicted Fare_amount: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public async IAsyncEnumerable <byte[]> GetStream()
{
var mp = _config.Value.ModelPath;
Initialize();
_capture.StartCameraCapture();
while (true)
{
var mat = _capture.Current;
if (mat == default || mat.Empty())
{
continue;
}
var bytes = mat.ToBytes();
if (Recognition)
{
var predictionResult = ConsumeModel.Predict(mp,
new ModelInput
{
Image = bytes
});
Console.WriteLine(
$"{predictionResult.Prediction} : {predictionResult.Score}");
DetectionResult = predictionResult;
}
yield return(bytes);
await Task.Delay(100, _token);
}
}
public string PredictDisorder(Questionnaire questionnaire)
{
ModelInput sampleData = new ModelInput()
{
Feeling_nervous = questionnaire.Nervous,
Panic = questionnaire.Panic,
Breathing_rapidly = questionnaire.BreathingRapidly,
Sweating = questionnaire.Sweating,
Trouble_in_concentration = questionnaire.TroublesInConcentration,
Having_trouble_in_sleeping = questionnaire.TroublesInSleeping,
Having_trouble_with_work = questionnaire.TroublesWithWork,
Hopelessness = questionnaire.Hopeless,
Anger = questionnaire.Angry,
Over_react = questionnaire.OverReacting,
Change_in_eating = questionnaire.ChangesInEating,
Suicidal_thought = questionnaire.SuicidalThoughts,
Feeling_tired = questionnaire.Tired,
Close_friend = questionnaire.CloseFriend,
Social_media_addiction = questionnaire.SocialMediaAddiction,
Weight_gain = questionnaire.WeightGain,
Material_possessions = questionnaire.MaterialPossession,
Introvert = questionnaire.Shy,
Popping_up_stressful_memory = questionnaire.StressfulMemories,
Having_nightmares = questionnaire.Nightmares,
Avoids_people_or_activities = questionnaire.AvoidingPeople,
Feeling_negative = questionnaire.NegativeThoughts,
Trouble_concentrating = questionnaire.TroublesInConcentration,
Blamming_yourself = questionnaire.BlamingYourself
};
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
return(predictionResult.Prediction);
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Name with predicted Name from sample data...\n\n");
Console.WriteLine($"car_id: {sampleData.Car_id}");
Console.WriteLine($"year: {sampleData.Year}");
Console.WriteLine($"price: {sampleData.Price}");
Console.WriteLine($"overall_score: {sampleData.Overall_score}");
Console.WriteLine($"driving_score: {sampleData.Driving_score}");
Console.WriteLine($"comfort_score: {sampleData.Comfort_score}");
Console.WriteLine($"interior_score: {sampleData.Interior_score}");
Console.WriteLine($"tech_score: {sampleData.Tech_score}");
Console.WriteLine($"storage_score: {sampleData.Storage_score}");
Console.WriteLine($"economical_score: {sampleData.Economical_score}");
Console.WriteLine($"good_value_score: {sampleData.Good_value_score}");
Console.WriteLine($"litres_on_100km: {sampleData.Litres_on_100km}");
Console.WriteLine($"seats: {sampleData.Seats}");
Console.WriteLine($"transmission: {sampleData.Transmission}");
Console.WriteLine($"horsepower: {sampleData.Horsepower}");
Console.WriteLine($"fuel: {sampleData.Fuel}");
Console.WriteLine($"\n\nActual Name: {sampleData.Name} \nPredicted Name value: {predictionResult.Prediction} \nPrediction accuracy: {predictionResult.Score.Max() * 100}%\nTotal number of records: {predictionResult.Score.Count()} \nPredicted Name scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public IActionResult Index(ModelInput input)
{
ModelOutput prediction = ConsumeModel.Predict(input);
ViewBag.Result = prediction;
return(View());
}
private void txtInput_KeyDown(object sender, KeyEventArgs e)
{
if (e.Key == Key.Enter)
{
dgResults.Items.Clear();
var text = txtInput.Text;
if (!string.IsNullOrEmpty(text))
{
var input = new ModelInput()
{
Content = text
};
var output = ConsumeModel.Predict(input);
foreach (var item in output)
{
dgResults.Items.Add(new DataGridResultColumn()
{
Label = item.Key,
Score = Math.Round(item.Value * 100, 2)
});
}
}
}
}
public JsonResult AnalyzeFeedback(string feedback)
{
try
{
ModelInput _data = new();
_data.Col0 = feedback;
var predictionResult = ConsumeModel.Predict(_data);
return(Json(new
{
Prediction = predictionResult.Prediction,
Score = predictionResult.Score,
Message = predictionResult.Prediction == "0"?"We are sorry for inconvience":"That's Great! Thank you for feedback"
}));
}
catch (Exception ex)
{
return(Json(new
{
Error = "Unable to Consume Model"
}));
}
}
public ResultDialog(iThrivTask task, int sessionIndex, int trialIndex, TimeSpan runtime, IEnumerable <double> accMagnitudes)
{
InitializeComponent();
label5.Text = task.ToString();
label6.Text = trialIndex.ToString();
label7.Text = runtime.ToString(@"mm\:ss");
try
{
var mean_acc = accMagnitudes.Average();
var var_acc = accMagnitudes.Select(a => Math.Pow(a - mean_acc, 2)).Average();
var input = new ModelInput
{
Task = task.ToString(),
Session_index = sessionIndex,
Trial_index = trialIndex,
Trial_time = (float)runtime.TotalSeconds,
Min_acc = (float)accMagnitudes.Min(),
Sd_acc = (float)Math.Sqrt(var_acc)
};
ModelOutput result = ConsumeModel.Predict(input);
label8.Text = Convert.ToInt32(result.Score).ToString();
}
catch
{
label8.Text = "An error occured";
}
}
private static void Main()
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Creditworthiness with predicted Creditworthiness from sample data...\n\n");
Console.WriteLine($"Id: {sampleData.Id}");
Console.WriteLine($"FrequencyOfIssuanceOfStatements: {sampleData.FrequencyOfIssuanceOfStatements}");
Console.WriteLine($"HasCreditCard: {sampleData.HasCreditCard}");
Console.WriteLine($"NumberOfInhabitantsInDistrict: {sampleData.NumberOfInhabitantsInDistrict}");
Console.WriteLine($"NumberOfCitiesInDistrict: {sampleData.NumberOfCitiesInDistrict}");
Console.WriteLine($"AverageSalaryInDistrict: {sampleData.AverageSalaryInDistrict}");
Console.WriteLine($"NumberOfPermanentOrders: {sampleData.NumberOfPermanentOrders}");
Console.WriteLine($"DebitedAmount: {sampleData.DebitedAmount}");
Console.WriteLine($"NumberOfCreditTransactions: {sampleData.NumberOfCreditTransactions}");
Console.WriteLine($"NumberOfWithdrawalTransactions: {sampleData.NumberOfWithdrawalTransactions}");
Console.WriteLine($"ActualAccountBalance: {sampleData.ActualAccountBalance}");
Console.WriteLine($"LoanAmount: {sampleData.LoanAmount}");
Console.WriteLine($"LoanDuration: {sampleData.LoanDuration}");
Console.WriteLine($"LoanMonthlyPayments: {sampleData.LoanMonthlyPayments}");
Console.WriteLine($"\n\nActual Creditworthiness: {sampleData.Creditworthiness} \nPredicted Creditworthiness: {predictionResult.Prediction}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
Debug.WriteLine("Object is not valid for this category");
// Add input data
var input = new ModelInput();
input.ImageSource = @"C:\Users\Eran Heymann\Desktop\Eran Sint-Maartenscollege\6v\Car models for inf2 project\car 91.png";
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Debug.WriteLine(result.Prediction);
Console.WriteLine(result.Prediction)
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
ImageSource = @"C:\Users\Eran Heymann\Desktop\Eran Sint-Maartenscollege\6v\Car models for inf2 project\car 91.png",
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Label with predicted Label from sample data...\n\n");
Console.WriteLine($"ImageSource: {sampleData.ImageSource}");
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();
}
