public void read_test()
{
string basePath = NUnit.Framework.TestContext.CurrentContext.TestDirectory;
MemoryStream file = new MemoryStream(Encoding.Default.GetBytes(Resources.L1R_LR_a9a));
TextReader reader = new StreamReader(file);
File.WriteAllText(Path.Combine(basePath, "svm.txt"), reader.ReadToEnd());
#region doc_read
// Let's say we have used LIBLINEAR to learn a linear SVM model that has
// been stored in a text file named "svm.txt". We would like to load this
// same model in .NET and use it to make predictions using C#.
//
// First, we use LibSvmModel.Load to load the LIBLINEAR model from disk:
LibSvmModel model = LibSvmModel.Load(Path.Combine(basePath, "svm.txt"));
// Now, we can use the model class to create the equivalent Accord.NET SVM:
SupportVectorMachine svm = model.CreateMachine();
// Now, we can use this machine normally, like as shown in the
// examples in the Support Vector Machine documentation page.
#endregion
Assert.AreEqual(2, svm.NumberOfClasses);
Assert.AreEqual(122, svm.NumberOfInputs);
Assert.AreEqual(1, svm.Weights.Length);
Assert.AreEqual(1, svm.SupportVectors.Length);
}
public void ReadLinearMachineTest()
{
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.L1R_LR_a9a));
LibSvmModel reader = LibSvmModel.Load(file);
Assert.AreEqual(-1, reader.Bias);
Assert.AreEqual(2, reader.Classes);
Assert.AreEqual(123, reader.Dimension);
Assert.AreEqual(2, reader.Labels.Length);
Assert.AreEqual(+1, reader.Labels[0]);
Assert.AreEqual(-1, reader.Labels[1]);
Assert.AreEqual(LibSvmSolverType.L1RegularizedLogisticRegression, reader.Solver);
Assert.AreEqual(null, reader.Vectors);
Assert.AreEqual(123, reader.Weights.Length);
for (int i = 0; i < a9a_weights.Length; i++)
{
Assert.AreEqual(a9a_weights[i], reader.Weights[i]);
}
var machine = reader.CreateMachine();
Assert.IsNull(machine.SupportVectors);
Assert.AreEqual(machine.Threshold, a9a_weights[0]);
for (int i = 0; i < machine.Weights.Length; i++)
{
Assert.AreEqual(machine.Weights[i], a9a_weights[i + 1]);
}
}
public void ReadWriteTest_a9a()
{
string basePath = NUnit.Framework.TestContext.CurrentContext.TestDirectory;
MemoryStream file = new MemoryStream(Encoding.Default.GetBytes(Resources.L1R_LR_a9a));
LibSvmModel model1 = LibSvmModel.Load(file);
string savePath = Path.Combine(basePath, "svm.txt");
var svm1 = model1.CreateMachine();
model1.Save(savePath);
LibSvmModel model2 = LibSvmModel.Load(savePath);
var svm2 = model2.CreateMachine();
LibSvmModel model3 = LibSvmModel.FromMachine(svm1);
var svm3 = model3.CreateMachine();
model3.Solver = LibSvmSolverType.L1RegularizedLogisticRegression;
string aPath = Path.Combine(basePath, "a.txt");
string bPath = Path.Combine(basePath, "b.txt");
string cPath = Path.Combine(basePath, "c.txt");
model1.Save(aPath);
model2.Save(bPath);
model3.Save(cPath);
string a = File.ReadAllText(aPath);
string b = File.ReadAllText(bPath);
string c = File.ReadAllText(cPath);
Assert.AreEqual(a, b);
Assert.AreEqual(a, c);
Assert.AreEqual(svm1.Weights, svm2.Weights);
Assert.AreEqual(svm1.SupportVectors, svm2.SupportVectors);
Assert.AreEqual(svm1.Threshold, svm2.Threshold);
Assert.AreEqual(svm1.Weights, svm3.Weights);
Assert.AreEqual(svm1.SupportVectors, svm3.SupportVectors);
Assert.AreEqual(svm1.Threshold, svm3.Threshold);
}
public void WriteLinearMachineTest_ExactCopy()
{
MemoryStream file = new MemoryStream(
Encoding.Default.GetBytes(Resources.L1R_LR_a9a));
LibSvmModel reader = LibSvmModel.Load(file);
MemoryStream destination = new MemoryStream();
reader.Save(destination);
destination.Seek(0, SeekOrigin.Begin);
TextReader textReader = new StreamReader(destination);
string[] actual = textReader.ReadToEnd()
.Split(new[] { Environment.NewLine }, StringSplitOptions.None);
string[] expected = Resources.L1R_LR_a9a
.Split(new[] { "\r\n" }, StringSplitOptions.None);
Assert.AreEqual(expected.Length, actual.Length);
for (int i = 0; i < 7; i++)
{
Assert.AreEqual(expected[i], actual[i]);
}
for (int i = 6; i < expected.Length; i++)
{
if (expected[i] == actual[i])
{
continue;
}
double a = Double.Parse(expected[i], CultureInfo.InvariantCulture);
double b = Double.Parse(actual[i], CultureInfo.InvariantCulture);
Assert.AreEqual(a, b);
}
Assert.AreEqual(expected[expected.Length - 1], String.Empty);
}
static void Main()
{
// Path to the folder with classifiers models
var jarRoot = @"\Users\devir\OneDrive\Documents\Visual Studio 2015\Projects\ner";
var classifiersDirecrory = jarRoot + @"\classifiers";
// Loading 3 class classifier model
var classifier = CRFClassifier.getClassifierNoExceptions(
classifiersDirecrory + @"\english.muc.7class.distsim.crf.ser.gz");
var s1 = " She got up this morning at 9:00 am and went to a shop to spend five dollars to buy a 50% off toothbrush.";
var s2 = "Tell the latest on olympics from the New York.";
Console.WriteLine("{0}\n", classifier.classifyToCharacterOffsets(s1));
Console.WriteLine("{0}\n", classifier.classifyWithInlineXML(s1));
//MUNCULIN NER SATU SATU
string result = classifier.classifyWithInlineXML(s1);
String substr1 = "TIME";
String substr2 = "LOCATION";
String substr3 = "PERSON";
String substr4 = "ORGANIZATION";
String substr5 = "MONEY";
String substr6 = "Percent";
String substr7 = "Date";
string total1, total2, total3, total4, total5, total6, total7;
//if (result.Contains(substr1))
//{
// string[] hasiltime = GetStringInBetween("<TIME>", "</TIME>", result, false, false);
// string output_time = hasiltime[0];
// string next_time = hasiltime[1];
// total1 = output_time;
// // Console.WriteLine(output_time);
//}
//if (result.Contains(substr2))
//{
// string[] hasillocation = GetStringInBetween("<LOCATION>", "</LOCATION>", result, false, false);
// string output_location = hasillocation[0];
// string next_loc = hasillocation[1];
// //Console.WriteLine(output_location);
// total2 = output_location;
//}
//if (result.Contains(substr3))
//{
// string[] hasilperson = GetStringInBetween("<PERSON>", "</PERSON>", result, false, false);
// string output_person = hasilperson[0];
// string next_person = hasilperson[1];
// //Console.WriteLine(hasilperson);
// total3 = output_person;
//}
//if (result.Contains(substr4))
//{
// string[] hasilORGANIZATION = GetStringInBetween("<ORGANIZATION>", "</ORGANIZATION>", result, false, false);
// string output_ORGANIZATION = hasilORGANIZATION[0];
// string next_ORGANIZATION = hasilORGANIZATION[1];
// //Console.WriteLine(output_ORGANIZATION);
// total4 = output_ORGANIZATION;
//}
//if (result.Contains(substr5))
//{
// string[] hasilMONEY = GetStringInBetween("<MONEY>", "</MONEY>", result, false, false);
// string output_MONEY = hasilMONEY[0];
// string next_MONEY = hasilMONEY[1];
// // Console.WriteLine(output_MONEY);
// total5 = output_MONEY;
//}
//if (result.Contains(substr6))
//{
// string[] hasilPercent = GetStringInBetween("<Percent>", "</Percent>", result, false, false);
// string output_Percent = hasilPercent[0];
// string next_Percent = hasilPercent[1];
// //Console.WriteLine(output_Percent);
// total6 = output_Percent;
//}
//if (result.Contains(substr7))
//{
// string[] hasilDate = GetStringInBetween("<Date>", "</Date>", result, false, false);
// string output_Date = hasilDate[0];
// string next_Date = hasilDate[1];
// //Console.WriteLine(output_Date);
// total7 = output_Date;
//}
string[] hasiltime = GetStringInBetween("<TIME>", "</TIME>", result, false, false);
string output_time = hasiltime[0];
string next_time = hasiltime[1];
total1 = output_time;
//Console.WriteLine(output_time);
string[] hasillocation = GetStringInBetween("<LOCATION>", "</LOCATION>", result, false, false);
string output_location = hasillocation[0];
string next_loc = hasillocation[1];
//Console.WriteLine(output_location);
total2 = output_location;
string[] hasilperson = GetStringInBetween("<PERSON>", "</PERSON>", result, false, false);
string output_person = hasilperson[0];
string next_person = hasilperson[1];
//Console.WriteLine(hasilperson);
total3 = output_person;
string[] hasilORGANIZATION = GetStringInBetween("<ORGANIZATION>", "</ORGANIZATION>", result, false, false);
string output_ORGANIZATION = hasilORGANIZATION[0];
string next_ORGANIZATION = hasilORGANIZATION[1];
//Console.WriteLine(output_ORGANIZATION);
total4 = output_ORGANIZATION;
string[] hasilMONEY = GetStringInBetween("<MONEY>", "</MONEY>", result, false, false);
string output_MONEY = hasilMONEY[0];
string next_MONEY = hasilMONEY[1];
// Console.WriteLine(output_MONEY);
total5 = output_MONEY;
string[] hasilPercent = GetStringInBetween("<Percent>", "</Percent>", result, false, false);
string output_Percent = hasilPercent[0];
string next_Percent = hasilPercent[1];
//Console.WriteLine(output_Percent);
total6 = output_Percent;
string[] hasilDate = GetStringInBetween("<Date>", "</Date>", result, false, false);
string output_Date = hasilDate[0];
string next_Date = hasilDate[1];
//Console.WriteLine(output_Date);
total7 = output_Date;
//BOW
string semua = total1 + ";" + total2 + ";" + total3 + ";" + total4 + ";" + total5 + ";" + total6 + ";" + total7 + ";";
Console.WriteLine(semua);
string[] gabungan = { total1, total2, total3, total4, total5, total6, total7 };
foreach (var a in gabungan)
{
Console.WriteLine(a);
}
string[][] words = gabungan.Tokenize();
//var codebook = new TFIDF()
//{
// Tf = TermFrequency.Log,
// Idf = InverseDocumentFrequency.Default
//};
var codebook = new BagOfWords()
{
MaximumOccurance = 1 // the resulting vector will have only 0's and 1's
};
codebook.Learn(words);
double[] bow1 = codebook.Transform(words[0]);
double[] bow2 = codebook.Transform(words[1]);
double[] bow3 = codebook.Transform(words[2]);
double[] bow4 = codebook.Transform(words[3]);
double[] bow5 = codebook.Transform(words[4]);
double[] bow6 = codebook.Transform(words[5]);
double[] bow7 = codebook.Transform(words[6]);
double[][] keseluruhanBOW1 = { bow1, bow2, bow3, bow4, bow5, bow6, bow7 };
//coba
bool quitNow = false;
while (!quitNow)
{
string val;
Console.Write("Enter question: ");
val = Console.ReadLine();
string[] textss =
{
val,
};
string[][] wordss = textss.Tokenize();
//var codebook2 = new TFIDF()
//{
// Tf = TermFrequency.Log,
// Idf = InverseDocumentFrequency.Default
//};
var codebook2 = new BagOfWords()
{
MaximumOccurance = 1 // the resulting vector will have only 0's and 1's
};
codebook2.Learn(wordss);
double[] c1 = codebook2.Transform(wordss[0]);
string path = @"C:\Users\devir\OneDrive\Documents\Visual Studio 2015\Projects\ner";
//var load_svm_model = Serializer.Load<MulticlassClassifierBase>(Path.Combine(path, "pelatihanSVMbayardanpergi.bin"));
//LibSvmModel modela = LibSvmModel.Load(Path.Combine(path, "pelatihanSVMbayardanpergi.bint"));
//int jawaban = load_svm_model.Decide( c1); // answer will be 2.
// Now, we can use the model class to create the equivalent Accord.NET SVM:
//Console.WriteLine(jawaban);
LibSvmModel model = LibSvmModel.Load(Path.Combine(path, "pelatihanSVMbayardanpergi.txt"));
// Now, we can use the model class to create the equivalent Accord.NET SVM:
SupportVectorMachine svm = model.CreateMachine();
// Compute classification error
bool predicted = svm.Decide(c1);
// var machine = teacher.Learn(inputs, outputs);
if (predicted == false)
{
Console.WriteLine("BAYAR");
}
;
if (predicted == true)
{
Console.WriteLine("PERGI");
}
;
Console.ReadLine();
}
// In order to convert any 2d array to jagged one
// let's use a generic implementation
}
