protected override async Task OnMessageActivityAsync(ITurnContext <IMessageActivity> turnContext, CancellationToken cancellationToken)
{
// Add input data
var input = new ModelInput()
{
SentimentText = turnContext.Activity.Text
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
var replyText = result.Prediction ? "Toxic" : "Non-Toxic";
await turnContext.SendActivityAsync(MessageFactory.Text(replyText, replyText), cancellationToken);
}
static void Main(string[] args)
{
Console.WriteLine("Sentiment Prediction");
Console.WriteLine("Insert your text to evaluate...");
// Add input data
var input = new ModelInput();
input.SentimentText = Console.ReadLine();
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine("The result was a " + (!result.Prediction ? "Good" : "Bad") + " Sentiment");
Console.ReadLine();
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput()
{
Col0 = "This restaurant was wonderful."
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
// If Prediction is 1, sentiment is "Positive"; otherwise, sentiment is "Negative"
string sentiment = result.Prediction == "1" ? "Positive" : "Negative";
Console.WriteLine($"Text: {input.Col0}\nSentiment: {sentiment}");
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual IsPositive with predicted IsPositive from sample data...\n\n");
Console.WriteLine($"word: {sampleData.Word}");
Console.WriteLine($"isNegative: {sampleData.IsNegative}");
Console.WriteLine($"\n\nActual IsPositive: {sampleData.IsPositive} \nPredicted IsPositive: {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\racha\Downloads\Machine Learning\images\Auto\Auto1.jpg";
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Debug.WriteLine(result.Prediction);
Console.WriteLine(result.Prediction);
}
static void Main(string[] args)
{
string text;
do
{
var input = new ModelInput();
text = Console.ReadLine();
input.SentimentText = text;
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}");
}while (text != "q");
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Open with predicted Open from sample data...\n\n");
Console.WriteLine($"timestamp: {sampleData.Timestamp}");
Console.WriteLine($"polarity: {sampleData.Polarity}");
Console.WriteLine($"\n\nActual Open: {sampleData.Open} \nPredicted Open: {predictionResult.Score}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public async Task <IActionResult> Create(string postId, string parentId, string content)
{
var mi = new ModelInput();
mi.SentimentText = content;
var a = ConsumeModel.Predict(mi);
bool isRude = a.Prediction == "1";
var posterId = this.userManager.GetUserId(this.User);
await this.commentsService.CreateComment(posterId, parentId, postId, content, isRude);
return(this.RedirectToAction("ById", "JobPosts", new { id = postId }));
}
public override void FillSentiment()
{
int totalSentiment = 0;
ModelOutput result;
var input = new ModelInput();
foreach (string line in _article.Text)
{
input.Comment = line;
result = ConsumeModel.Predict(input);
totalSentiment += result.Prediction ? 1 : 0;
}
_article.Sentiment = totalSentiment / _article.Text.Count;
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput();
Console.Write("Enter Distance: ");
input.Trip_distance = Convert.ToInt32(Console.ReadLine());
Console.Write("Enter Time: ");
input.Trip_time_in_secs = Convert.ToInt32(Console.ReadLine());
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine("The estimated fare is: " + result.Score);
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput();
input.Comment = "This is heaven";
// Load model and predict output of sample data
string path = @"../SampleClassification/SampleClassification.Model/MLModel.zip";
ConsumeModel.MLNetModelPath = Path.GetFullPath(path);
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.Comment}\nIs Toxic: {result.Prediction}");
}
public void Comment(string commnet)
{
// Add input data
var input = new ModelInput()
{
Col0 = commnet
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
// If Prediction is 1, sentiment is "Positive"; otherwise, sentiment is "Negative"
string sentiment = result.Prediction == "1" ? "Positive" : "Negative";
Console.WriteLine($"Thank you For Your {sentiment} Comment");
}
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 Product_category with predicted Product_category from sample data...\n\n");
Console.WriteLine($"birth_year: {sampleData.Birth_year}");
Console.WriteLine($"user_gender: {sampleData.User_gender}");
Console.WriteLine($"\n\nActual Product_category: {sampleData.Product_category} \nPredicted Product_category value {predictionResult.Prediction} \nPredicted Product_category scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
//This line is for creating model
//ModelBuilder.CreateModel();
ModelInput input = new ModelInput()
{
Date = DateTime.Now,
WeightedPrice = 18245.52f
};
var result = ConsumeModel.Predict(input, horizon: 3);
Console.WriteLine($"Predicted Bitcoin actual price for is {result.ForecastedPrice[0]:#$}");
Console.WriteLine($"Predicted Bitcoin lowerbound price for is {result.LowerBoundPrice[0]:#$}");
Console.WriteLine($"Predicted Bitcoin upperbound price for is {result.UpperBoundPrice[0]:#$}");
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual DirectionChanged_ with predicted DirectionChanged_ from sample data...\n\n");
Console.WriteLine($"previousHit: {sampleData.PreviousHit}");
Console.WriteLine($"newHit: {sampleData.NewHit}");
Console.WriteLine($"\n\nActual DirectionChanged_: {sampleData.DirectionChanged_} \nPredicted DirectionChanged_: {predictionResult.Prediction}\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public async Task <IActionResult> AjaxComment(int ProjectId, string theComment)
{
//Find User Id
var _userId = _userManager.GetUserId(User);
var ProjectValidation = await _context.Projectdb.FindAsync(ProjectId);
var UserValidation = await _context.appUserdb.FindAsync(_userId);
//if user or project dosent exist at the database
if (UserValidation == null || ProjectValidation == null)
{
return(NotFound());
}
Comment comment = new Comment
{
project = await _context.Projectdb.FindAsync(ProjectId),
owner = await _context.appUserdb.FindAsync(_userId),
OwnerId = _userId,
theComment = theComment,
dateSubmit = DateTime.Now
};
var input = new ModelInput();
input.Comment = theComment;
ModelOutput result = ConsumeModel.Predict(input);
if (result.Prediction)
{
comment.RudeComment = true;
}
else
{
comment.RudeComment = false;
}
//If The Comments State Is Not Vaild.
if (!ModelState.IsValid)
{
return(View(comment));
}
Project project = comment.project;
project.comments.Add(comment);
_context.Add(comment);
//_context.Add(project);
await _context.SaveChangesAsync();
return(Json("OK!"));
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput();
while (true)
{
Console.Write("Enter line # in data file (ENTER to quit): ");
int lineNumber;
var success = int.TryParse(Console.ReadLine(), out lineNumber);
if (!success)
{
break;
}
var dataPath = @"C:\GitHub\Misc-csharp\MLPricePrediction\taxi-fare-train.txt";
string line = "";
using (var f = new StreamReader(dataPath))
{
for (int i = 0; i < lineNumber; ++i)
{
line = f.ReadLine();
}
}
var items = line.Split(',');
input.Vendor_id = items[0];
input.Rate_code = int.Parse(items[1]);
input.Passenger_count = int.Parse(items[2]);
input.Trip_time_in_secs = int.Parse(items[3]);
input.Trip_distance = float.Parse(items[4]);
input.Payment_type = items[5];
/*input.Vendor_id = "CMT"; // CMT, VTS
* input.Rate_code = 1;
* input.Passenger_count = 1;
* input.Trip_time_in_secs = 386;
* input.Trip_distance = 1.3f;
* input.Payment_type = "CRD"; // CSH, CRD*/
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Vendor:{input.Vendor_id} RateCode:{input.Rate_code} Passengers:{input.Passenger_count} TripTime:{input.Trip_time_in_secs} Distance:{input.Trip_distance} PaymentType:{input.Payment_type}");
Console.WriteLine($"Fare: {result.Score:N2}\n");
}
}
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 PARSEME_MWE with predicted PARSEME_MWE from sample data...\n\n");
Console.WriteLine($"FORMm3: {sampleData.FORMm3}");
Console.WriteLine($"LEMMAm3: {sampleData.LEMMAm3}");
Console.WriteLine($"UPOSm3: {sampleData.UPOSm3}");
Console.WriteLine($"HEADm3: {sampleData.HEADm3}");
Console.WriteLine($"DEPRELm3: {sampleData.DEPRELm3}");
Console.WriteLine($"FORMm2: {sampleData.FORMm2}");
Console.WriteLine($"LEMMAm2: {sampleData.LEMMAm2}");
Console.WriteLine($"UPOSm2: {sampleData.UPOSm2}");
Console.WriteLine($"HEADm2: {sampleData.HEADm2}");
Console.WriteLine($"DEPRELm2: {sampleData.DEPRELm2}");
Console.WriteLine($"FORMm1: {sampleData.FORMm1}");
Console.WriteLine($"LEMMAm1: {sampleData.LEMMAm1}");
Console.WriteLine($"UPOSm1: {sampleData.UPOSm1}");
Console.WriteLine($"HEADm1: {sampleData.HEADm1}");
Console.WriteLine($"DEPRELm1: {sampleData.DEPRELm1}");
Console.WriteLine($"FORM0: {sampleData.FORM0}");
Console.WriteLine($"LEMMA0: {sampleData.LEMMA0}");
Console.WriteLine($"UPOS0: {sampleData.UPOS0}");
Console.WriteLine($"HEAD0: {sampleData.HEAD0}");
Console.WriteLine($"DEPREL0: {sampleData.DEPREL0}");
Console.WriteLine($"FORMp1: {sampleData.FORMp1}");
Console.WriteLine($"LEMMAp1: {sampleData.LEMMAp1}");
Console.WriteLine($"UPOSp1: {sampleData.UPOSp1}");
Console.WriteLine($"HEADp1: {sampleData.HEADp1}");
Console.WriteLine($"DEPRELp1: {sampleData.DEPRELp1}");
Console.WriteLine($"FORMp2: {sampleData.FORMp2}");
Console.WriteLine($"LEMMAp2: {sampleData.LEMMAp2}");
Console.WriteLine($"UPOSp2: {sampleData.UPOSp2}");
Console.WriteLine($"HEADp2: {sampleData.HEADp2}");
Console.WriteLine($"DEPRELp2: {sampleData.DEPRELp2}");
Console.WriteLine($"FORMp3: {sampleData.FORMp3}");
Console.WriteLine($"LEMMAp3: {sampleData.LEMMAp3}");
Console.WriteLine($"UPOSp3: {sampleData.UPOSp3}");
Console.WriteLine($"HEADp3: {sampleData.HEADp3}");
Console.WriteLine($"DEPRELp3: {sampleData.DEPRELp3}");
Console.WriteLine($"\n\nActual PARSEME_MWE: {sampleData.PARSEME_MWE} \nPredicted PARSEME_MWE value {predictionResult.Prediction} \nPredicted PARSEME_MWE scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
public async Task <SendCommentDTO> Handle(Command request, CancellationToken cancellationToken)
{
var sender = await dataContext.Users.FirstOrDefaultAsync(x => x.Id == currentUser.UserId);
if (sender is null)
{
throw new HttpContextException(HttpStatusCode.NotFound, new { User = "******" });
}
var advertise = await dataContext.Advertise.Where(x => x.UniqueId == request.AdvertiseId)
.FirstOrDefaultAsync();
ModelInput sampleData = new ModelInput()
{
Comment = request.Comment,
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
var userComment = new UserComments
{
Advertise = advertise,
Commenter = sender,
CommentedAt = DateTime.UtcNow,
Comment = request.Comment,
PositiveAccuracy = predictionResult.Score[0],
NegativeAccuracy = predictionResult.Score[1]
};
await dataContext.UserComments.AddAsync(userComment);
var success = await dataContext.SaveChangesAsync() > 0;
if (success)
{
return(new SendCommentDTO
{
Comment = userComment.Comment,
CommentedAt = userComment.CommentedAt,
DisplayName = $"{userComment.Commenter.FirstName} {userComment.Commenter.LastName}"
});
}
else
{
throw new Exception("ERROR WHILE SENDING Comment");
}
}
static void Main(string[] args)
{
// Modelimizi eğittik. Artık ona örnek yorumlar yollayıp bunun zararlı olup olmadığını tahmin etmesini isteyebiliriz.
var input = new ModelInput();
input.SentimentText = "It was a very disgusting article, man.";
// Tahminlemeyi yaptırıyoruz
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Girilen Yorum: '{input.SentimentText}'\nZehirli mi?: {result.Prediction}");
input.SentimentText = "Great narration. I took great advantage. Thank you.";
result = ConsumeModel.Predict(input);
Console.WriteLine($"Girilen Yorum: '{input.SentimentText}'\nZehirli mi?: {result.Prediction}");
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = CreateSingleDataSample(DATA_FILEPATH);
// Make a single prediction on the sample data and print results
ModelOutput predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Area with predicted Area from sample data...\n\n");
Console.WriteLine($"ID: {sampleData.ID}");
Console.WriteLine($"Title: {sampleData.Title}");
Console.WriteLine($"Description: {sampleData.Description}");
Console.WriteLine($"\n\nActual Area: {sampleData.Area} \nPredicted Area value {predictionResult.Prediction} \nPredicted Area scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput();
input.Passenger_count = 1f;
input.Payment_type = "CRD";
input.Trip_distance = 5.3f;
input.Trip_time_in_secs = 850;
input.Vendor_id = "CMT";
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"The predicted fare is: {result.Score}");
}
protected override async Task OnMessageActivityAsync(ITurnContext <IMessageActivity> turnContext, CancellationToken cancellationToken)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Sentiment_Text = turnContext.Activity.Text,
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
var replyText = $"Echo: {turnContext.Activity.Text}";
await turnContext.SendActivityAsync(MessageFactory.Text(replyText, replyText), cancellationToken);
await turnContext.SendActivityAsync($"You are {predictionResult.Prediction}", null, null, cancellationToken);
}
static void Main(string[] args)
{
Console.WriteLine("Leave a Comment,Please :");
string Coment;
Coment = Console.ReadLine();//Get a comment from the user.
var input = new ModelInput();
input.SentimentText = Coment;//Give the comment left as input
// Load model and predict output from the comment left
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Text: {input.SentimentText}\nIs Toxic: {result.Prediction}\n");
}
static void Main(string[] args)
{
List <ModelInput> sampleData = CreateSingleDataSample(DATA_FILEPATH);
foreach (var data in sampleData)
{
var predictionResult = ConsumeModel.Predict(data);
Console.WriteLine("================================================================================================");
Console.WriteLine($"Title: {data.Title}");
//Console.WriteLine($"Author: {data.Author}");
//Console.WriteLine($"Description: {data.Description}");
//Console.WriteLine($"NumberOfViews: {data.NumberOfViews}");
Console.WriteLine($"Actual Like: {data.Like} \nPredicted Like: {predictionResult.Prediction}");
}
}
static void Main(string[] args)
{
// Add input data
var input = new ModelInput
{
Passenger_count = 2,
Trip_distance = 4,
Trip_time_in_secs = 1150,
Payment_type = "CRD"
};
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine($"Predicted fare: ${result.Score}");
}
static void Main(string[] args)
{
Console.WriteLine("AI Prediction of age of vin numbers");
Console.WriteLine("Training data: 10,000 rows spanish data");
// Add input data
var input = new ModelInput();
Console.WriteLine("Please enter a VIN:");
input.VIN = Console.ReadLine();
// Load model and predict output of sample data
ModelOutput result = ConsumeModel.Predict(input);
Console.WriteLine("The year is predicted to be: " + result.Prediction);
Console.ReadLine();
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Col1 = 13868.85F,
Col2 = 11063.38F,
Col3 = 5452.781F,
Col4 = 3019.123F,
Col5 = 2338.009F,
Col6 = 894.0856F,
Col7 = 479.2575F,
Col8 = 358.4319F,
Col9 = 283.7598F,
Col10 = 253.8049F,
Col11 = 256.5529F,
Col12 = 257.3511F,
Col13 = 268.1667F,
Col14 = 288.2473F,
Col15 = 289.4342F,
Col16 = 0F,
};
// Make a single prediction on the sample data and print results
var predictionResult = ConsumeModel.Predict(sampleData);
Console.WriteLine("Using model to make single prediction -- Comparing actual Col0 with predicted Col0 from sample data...\n\n");
Console.WriteLine($"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($"Col16: {sampleData.Col16}");
Console.WriteLine($"\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();
}
static void Main(string[] args)
{
// Create single instance of sample data from first line of dataset for model input
ModelInput sampleData = new ModelInput()
{
Listed_in = @"International TV Shows, TV Dramas, TV Sci-Fi & Fantasy",
};
// 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 Type with predicted Type from sample data...\n\n");
Console.WriteLine($"Listed_in: {sampleData.Listed_in}");
Console.WriteLine($"\n\nPredicted Type value {predictionResult.Prediction} \nPredicted Type 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()
{
Col0 = @"Wow... Loved this place.",
};
// 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 Col1 with predicted Col1 from sample data...\n\n");
Console.WriteLine($"Col0: {sampleData.Col0}");
Console.WriteLine($"\n\nPredicted Col1 value {predictionResult.Prediction} \nPredicted Col1 scores: [{String.Join(",", predictionResult.Score)}]\n\n");
Console.WriteLine("=============== End of process, hit any key to finish ===============");
Console.ReadKey();
}