public void XORTest()
{
var range = Enumerable.Range(-10,16);
var log2gammas = range.Select(i => Math.Pow(2, i));
var log2Cs = range.Select(i => Math.Pow(2, i + 1));
var log2Rs = range.Select(i => Math.Pow(2, i + 1));
var prob = ProblemHelper.ReadProblem(XOR_TRAINING_FILE);
//Assert.IsTrue(prob.l == 4);
Tuple<double, double, double, int> best = Tuple.Create(0.0, 0.0 ,0.0, prob.l);
foreach (var g in log2gammas)
{
foreach (var c in log2Cs)
{
foreach (var r in log2Rs)
{
var svm = new C_SVC(prob, KernelHelper.SigmoidKernel(g,r), c);
var errorCout = 0;
for (int i = 0; i < prob.l; i++)
{
//var x = (prob.x[i].FirstOrDefault(xi => xi.index == 1) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 1).value;
//var y = (prob.x[i].FirstOrDefault(xi => xi.index == 2) == null) ? 0.0 : prob.x[i].FirstOrDefault(xi => xi.index == 2).value;
var z = svm.Predict(prob.x[i]);
var probabilities = svm.PredictProbabilities(prob.x[i]);
if (z != prob.y[i])
errorCout++;
//Debug.WriteLine(String.Format("x={0} & y={1} => z={2} -- {3}", x, y, z, z == prob.y[i]));
}
if (errorCout < best.Item4)
best = Tuple.Create(g, c, r, errorCout);
//Debug.WriteLine(String.Format("g={0} && C={1} && C={2} => Error rate = {3}%", g, c, r, (double)errorCout / prob.l * 100));
}
}
}
Debug.WriteLine(String.Format("BEST :: g={0} && C={1} && R={2} => Error rate = {3}%", best.Item1, best.Item2, best.Item3, (double)best.Item4 / (double)prob.l * 100));
}
static void Main(string[] args)
{
// reading the data
const string dataFilePath = @"E:\training data\testdata.manual.2009.06.14.csv";
var dataTable = DataTable.New.ReadCsv(dataFilePath);
var dataTablePhones = dataTable.Rows.Select(row => row).ToList(); //.Where(row => row["Tweet"].ToString().Contains("Phone")).ToList();
List<string> x = dataTablePhones.Select(row => row["Tweet"]).ToList();
double[] y = dataTablePhones.Select(row => double.Parse(row["Sentiment"])).ToArray();
// creating vocabulary
var vocabulary = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
// generating the problem
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y, vocabulary.ToList());
//creating and training SVM model
const int C = 1;
var model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
// prediction
string userInput;
var _predictionDictionary = new Dictionary<int, string> { { 0, "Bad" }, { 4, "Good" }, {2, "Neutral"} };
do
{
userInput = Console.ReadLine();
var newX = TextClassificationProblemBuilder.CreateNode(userInput, vocabulary);
var predictedY = model.Predict(newX);
Console.WriteLine("{0}", _predictionDictionary[(int)predictedY]);
Console.WriteLine(new string('=', 50));
} while (userInput != "quit");
}
/// <summary>
/// Show how to get the accuracy using cross validation method
/// Assert accurancy is greater than zero
///</summary>
//[TestMethod()]
public void DoCrossValidationTest()
{
var svm = new C_SVC(_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var cva = svm.GetCrossValidationAccuracy(5);
Assert.IsTrue(cva > 0);
}
public void DoCrossValidationTest()
{
var svm = new C_SVC(_prob, kernel, C);
var cva = svm.GetCrossValidationAccuracy(5);
Assert.IsTrue(cva > 0);
}
public void C_SVC_Should_predict_perfectly_XOR_dataset_with_polynomial_kernel()
{
// note : K(u; v) = (u v + 1)^2 kernel is able to feet exactly the xor function
// see http://www.doc.ic.ac.uk/~dfg/ProbabilisticInference/IDAPILecture18.pdf for more infos
var svm = new C_SVC(xor_problem, KernelHelper.PolynomialKernel(2, 1, 1), 1);
checkXOR(svm);
}
public IActionResult RecommendedPlaces()
{
// Load the predifined data for smv algorithm
var dataFilePath = "./wwwroot/svm/words.csv";
var dataTable = DataTable.New.ReadCsv(dataFilePath);
var data = dataTable.Rows.Select(row => row["Text"]).ToList();
// Load classes (-1 or +1)
var classes = dataTable.Rows.Select(row => double.Parse(row["IsRecommended"]))
.ToArray();
// Get words
var vocabulary = data.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
// Generate a svm problem
var problem = CreateProblem(data, classes, vocabulary.ToList());
// Create and train a smv model
const int C = 1;
var model = new libsvm.C_SVC(problem, KernelHelper.LinearKernel(), C);
var _predictionDictionary = new Dictionary <int, string> {
{ -1, "NotRecommended" }, { 1, "Recommended" }
};
// Get all reviews
var reviews = _context.Review.ToList();
// Get recommended reviews
foreach (var review in reviews)
{
if (review.Content != null)
{
var node = CreateNode(review.Content, vocabulary);
var prediction = model.Predict(node);
review.IsRecommended = _predictionDictionary[(int)prediction] == "Recommended";
}
else
{
review.IsRecommended = false;
}
}
var recommendedReviews = reviews.Where(p => p.IsRecommended == true);
foreach (var review in recommendedReviews)
{
review.Place = _context.Place.First(c => c.ID == review.PlaceID);
review.Comments = _context.Comment.Where(c => c.ReviewID == review.ID).ToList();
}
return(View(recommendedReviews.OrderByDescending(p => p.PublishDate)));
}
static void Main()
{
// STEP 4: Read the data
const string dataFilePath = @"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.csv";
var dataTable = DataTable.New.ReadCsv(dataFilePath);
List<string> x = dataTable.Rows.Select(row => row["Text"]).ToList();
double[] y = dataTable.Rows.Select(row => double.Parse(row["IsSunny"]))
.ToArray();
var vocabulary = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y, vocabulary.ToList());
// If you want you can save this problem with :
// ProblemHelper.WriteProblem(@"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.problem", problem);
// And then load it again using:
// var problem = ProblemHelper.ReadProblem(@"D:\MACHINE_LEARNING\SVM\Tutorial\sunnyData.problem");
const int C = 1;
var model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
var accuracy = model.GetCrossValidationAccuracy(10);
Console.Clear();
Console.WriteLine(new string('=', 50));
Console.WriteLine("Accuracy of the model is {0:P}", accuracy);
model.Export(string.Format(@"D:\MACHINE_LEARNING\SVM\Tutorial\model_{0}_accuracy.model", accuracy));
Console.WriteLine(new string('=', 50));
Console.WriteLine("The model is trained. \r\nEnter a sentence to make a prediction. (ex: sunny rainy sunny)");
Console.WriteLine(new string('=', 50));
string userInput;
_predictionDictionary = new Dictionary<int, string> { { -1, "Rainy" }, { 1, "Sunny" } };
do
{
userInput = Console.ReadLine();
var newX = TextClassificationProblemBuilder.CreateNode(userInput, vocabulary);
var predictedY = model.Predict(newX);
Console.WriteLine("The prediction is {0}", _predictionDictionary[(int)predictedY]);
Console.WriteLine(new string('=', 50));
} while (userInput != "quit");
Console.WriteLine("");
}
public static void SVMPredict()
{
var svm = new C_SVC(prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
double accuracy = svm.GetCrossValidationAccuracy(nr_fold);
for (int i = 0; i < test.l; i++)
{
svm_node[] x = test.x[i];
double y = test.y[i];
double predict = svm.Predict(x); // returns the predicted value 'y'
Dictionary<int, double> probabilities = svm.PredictProbabilities(x);
// returns the probabilities for each 'y' value
Console.WriteLine(predict + " :" + probabilities[1]);
}
Console.ReadKey();
}
/// <summary>
///Show how to predict probabilities for classification problems
///Verify that the prediction is always the most probable class
///</summary>
//[TestMethod()]
public void PredictTest()
{
var svm = new C_SVC(_prob, KernelHelper.RadialBasisFunctionKernel(gamma), C);
var nb_class = _prob.y.Distinct().Count();
for (int i = 0; i < _prob.l; i++)
{
var x = _prob.x[i];
var y = _prob.y[i];
var probabilities = svm.PredictProbabilities(x);
var predict = svm.Predict(x);
Assert.IsTrue(predict == probabilities.OrderByDescending(p => p.Value).First().Key);
Assert.IsNotNull(probabilities);
Assert.IsTrue(probabilities.Count == nb_class);
var sum = probabilities.Sum(e => e.Value) ;
}
}
public void C_SVC_should_always_return_the_same_cross_validation_accuracy_when_probability_is_false()
{
// Arrange
var problem = CreateSimpleProblem();
var model = new C_SVC(problem, KernelHelper.LinearKernel(), 1);
// Act
var results = new double[10];
for (int i = 0; i < 10; i++)
{
results[i] = model.GetCrossValidationAccuracy(10);
}
//Assert
for (int i = 1; i < 10; i++)
{
Assert.AreEqual(0.90909090909090906, results[i]);
}
}
public void C_SVC_should_enable_to_export_and_import_svm_models()
{
// note : K(u; v) = (u v + 1)^2 kernel is able to feet exactly the xor function
// see http://www.doc.ic.ac.uk/~dfg/ProbabilisticInference/IDAPILecture18.pdf for more infos
var svm = new C_SVC(xor_problem, KernelHelper.PolynomialKernel(2, 1, 1), 1);
var file_name = System.IO.Path.Combine(base_path, "test_export_temp.xml");
// make sure directory is clean
if (File.Exists(file_name))
File.Delete(file_name);
svm.Export(file_name);
Assert.IsTrue(File.Exists(file_name));
var new_svm = new C_SVC(file_name);
checkXOR(new_svm);
File.Delete(file_name); // cleanup
}
public void DoCrossValidationTest2()
{
var prob2 = ProblemHelper.ReadAndScaleProblem(SVMGUIDE1_TEST_FILE);
var svm = new C_SVC(prob2, KernelHelper.RadialBasisFunctionKernel(3.0), 2.0);
var cva = svm.GetCrossValidationAccuracy(5);
}
public void SVMConstructorTest1()
{
var svm = new C_SVC(LEU_TEST_FILE, kernel, C);
}
public void SVMConstructorTest()
{
var svm = new C_SVC(_prob, kernel, C);
}
static void Main()
{
const string dataFilePath = @"C:\Users\Rory\Desktop\Han.csv";
List<String> negwords = new List<String>();
List<String> poswords = new List<String>();
sentC = new List<String>();
//String negFile = "C:/Users/Rory/Desktop/negative-words.txt";
//String posFile = "C:/Users/Rory/Desktop/positive-words.txt";
//GetNeg(negwords, negFile);
//GetNeg(poswords, posFile);
String[] stopwords = new String[]{"hon.","gentleman","member","friend","lady","a", "about", "above", "above", "across", "after", "afterwards", "again", "against", "all", "almost", "alone", "along", "already", "also","although","always","am","among", "amongst", "amoungst", "amount", "an", "and", "another", "any","anyhow","anyone","anything","anyway", "anywhere", "are", "around", "as", "at", "back","be","became", "because","become","becomes", "becoming", "been", "before", "beforehand", "behind", "being", "below", "beside", "besides", "between", "beyond", "bill", "both", "bottom","but", "by", "call", "can", "cannot", "cant", "co", "con", "could", "couldnt", "cry", "de", "describe", "detail", "do", "done", "down", "due", "during", "each", "eg", "eight", "either", "eleven","else", "elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone", "everything", "everywhere", "except", "few", "fifteen", "fify", "fill", "find", "fire", "first", "five", "for", "former", "formerly", "forty", "found", "four", "from", "front", "full", "further", "get", "give", "go", "had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter", "hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his", "how", "however", "hundred", "i","ie", "if", "in", "inc", "indeed", "interest", "into", "is", "it", "its", "itself", "keep", "last", "latter", "latterly", "least", "less", "ltd", "made", "many", "may", "me", "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly", "move", "much", "must", "my", "myself", "name", "namely", "neither", "never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone", "nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on", "once", "one", "only", "onto", "or", "other", "others", "otherwise", "our", "ours", "ourselves", "out", "over", "own","part", "per", "perhaps", "please", "put", "rather", "re", "same", "see", "seem", "seemed", "seeming", "seems", "serious", "several", "she", "should", "show", "side", "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone", "something", "sometime", "sometimes", "somewhere", "still", "such", "system", "take", "ten", "than", "that", "the", "their", "them", "themselves", "then", "thence", "there", "thereafter", "thereby", "therefore", "therein", "thereupon", "these", "they", "thickv", "thin", "third", "this", "those", "though", "three", "through", "throughout", "thru", "thus", "to", "together", "too", "top", "toward", "towards", "twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us", "very", "via", "was", "we", "well", "were", "what", "whatever", "when", "whence", "whenever", "where", "whereafter", "whereas", "whereby", "wherein", "whereupon", "wherever", "whether", "which", "while", "whither", "who", "whoever", "whole", "whom", "whose", "why", "will", "with", "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves", "the"};
List<String> stop = stopwords.ToList<String>();
var dataTable = DataTable.New.ReadCsv(dataFilePath);
List<string> x = dataTable.Rows.Select(row => row["Text"]).ToList();
double[] y = dataTable.Rows.Select(row => double.Parse(row["IsPos"])).ToArray();
//var vocab = x.SelectMany(GetWords).Distinct().OrderBy(word => word).ToList();
Bigram b = new Bigram();
List<String> v = new List<string>();
String sent = "";
for(int i = 0; i < x.Count; i++)
{
String c = x[i].ToString();
c = c.Replace(",", "");
c = c.ToLower();
String[] sp = c.Split(' ');
for (int z = 0; z < sp.Length; z++)
{
String word = sp[z];
if(stop.Contains(word))
{
Debug.WriteLine("Stop Word");
}
else
{
sent += word;
sent += " ";
}
}
sent = sent.Trim();
v.AddRange(b.getNG(sent));
sent = "";
}
// int bound = v.Count();
//v.AddRange(negwords);
// int nBound = v.Count();
//v.AddRange(poswords);
// v = v.Distinct().ToList();
var problemBuilder = new TextClassificationProblemBuilder();
var problem = problemBuilder.CreateProblem(x, y, v);
ProblemHelper.WriteProblem(@"C:\Users\Rory\Desktop\hanData.problem", problem);
problem = ProblemHelper.ReadProblem(@"C:\Users\Rory\Desktop\hanData.problem");
const int C = 1;
var model = new C_SVC(problem, KernelHelper.LinearKernel(), C);
var accuracy = model.GetCrossValidationAccuracy(10);
Console.WriteLine("Accuracy of the model is {0:P}", accuracy);
string userInput;
_predictionDictionary = new Dictionary<int, string> { { -1, "Negative" }, { 1, "Positive" } };
getSent();
for (int i = 0; i < nText.Count; i++)
{
userInput = nText[i].ToString();
var newX = TextClassificationProblemBuilder.CreateNode(userInput, v);
var predictedY = model.Predict(newX);
Console.WriteLine("The prediction is {0}", _predictionDictionary[(int)predictedY]);
String pred = _predictionDictionary[(int)predictedY];
Console.WriteLine(new string('=', 50));
sentC.Add(pred);
}
addToO();
getNumbers();
forPeople();
using(StreamWriter file = new StreamWriter(@"C:\Users\Rory\Desktop\Peop.csv"))
{
file.WriteLine("PersID,Name,Positive,Negative,Party");
for(int i = 0; i < pl.Count; i++)
{
String line = pl[i].getID().ToString() + "," + pl[i].getName().ToString() + "," + pl[i].getPos().ToString() + "," + pl[i].getNeg().ToString() + "," + pl[i].getParty().ToString();
//var json = JsonConvert.SerializeObject(pl[i]);
file.WriteLine(line);
}
}
using (StreamWriter file = new StreamWriter(@"C:\Users\Rory\Desktop\PositiveSent.csv"))
{
file.WriteLine("SpeakerID,TargetID,Text,Sentiment");
for (int i = 0; i < ps.Count; i++)
{
String line = ps[i].getSID().ToString() + "," + ps[i].getAID().ToString() + "," + ps[i].getText().ToString() + "," + ps[i].getSent().ToString();
file.WriteLine(line);
}
}
using (StreamWriter file = new StreamWriter(@"C:\Users\Rory\Desktop\NegativeSent.csv"))
{
file.WriteLine("SpeakerID,TargetID,Text,Sentiment");
for (int i = 0; i < ns.Count; i++)
{
String line = ns[i].getSID().ToString() + "," + ns[i].getAID().ToString() + "," + ns[i].getText().ToString() + "," + ns[i].getSent().ToString();
file.WriteLine(line);
}
}
ns.AddRange(ps);
doSwap();
var json = "{\"nodes\":";
json += JsonConvert.SerializeObject(pl);
json += ",";
var edge = "\"edges\":";
edge += JsonConvert.SerializeObject(nss);
edge += "}";
var fullJSON = json + edge;
Debug.Write(fullJSON);
using (StreamWriter file = new StreamWriter(@"C:\Users\Rory\Desktop\Nodes2.JSON"))
{
file.Write(fullJSON);
}
getRels();
List<Relationship> sl = rl.OrderBy(o=>o.source).ToList();
var edges = "\"links\":";
edges += JsonConvert.SerializeObject(sl);
edges += "}";
var fullJ = json + edges;
using (StreamWriter file = new StreamWriter(@"C:\Users\Rory\Desktop\Rels2.JSON"))
{
file.Write(fullJ);
}
}
