public override void WriteJson(JsonWriter writer, object value, JsonSerializer serializer)
{
svm_model model = value as svm_model;
SVMModel smodel = new SVMModel(model);
serializer.Serialize(writer, smodel);
}
public svm_model initial(List <Dictionary <string, double> > docWordDicList, Dictionary <string, int> dictionary, List <int> trainingAnswer, double c, double gamma)
{
Console.WriteLine("==> Starting training...");
svm_problem prob = gen_svm_training_data(docWordDicList, dictionary, trainingAnswer);
svm_parameter param = new svm_parameter();
// set the default setting value
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.RBF;
param.degree = 3;
param.gamma = gamma;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = c;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
svm_model model = svm.svm_train(prob, param);
svm.svm_save_model(SVM_MODEL_FILE_NAME, model);
Console.WriteLine("==> Training done!!");
return(model);
}
SVMClassifier(Dictionary <string, int> identifiersMap, svm_model model)
{
/* for serialization */
svmNetwork = new ProbabilitySupportVectorMachine(model);
this.identifiersMap = identifiersMap;
this.model = model;
}
public SupportVectorMachineModel(svm_model model, RangeTransform rangeTransform, string targetVariable, IEnumerable <string> allowedInputVariables)
: base(targetVariable)
{
this.name = ItemName;
this.description = ItemDescription;
this.model = model;
this.rangeTransform = rangeTransform;
this.allowedInputVariables = allowedInputVariables.ToArray();
}
protected void Train(double C, double gamma, List <ISample> trainingSamples, string[] trainingIdentifiers)
{
// generate a numeric mapping of our string identifiers to unique numeric values
identifiersMap = trainingIdentifiers.Select((identifier, index) => new { identifier, index }).ToDictionary(id => id.identifier, id => id.index);
// don't include imposter samples
svmNetwork = TrainSVM(C, gamma, trainingSamples, (sample) => identifiersMap[sample.GetIdentifier()]);
model = svmNetwork.Model;
}
public static SVMModel Convert(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
return(null);
}
svm_model x = (svm_model)Marshal.PtrToStructure(ptr, typeof(svm_model));
return(SVMModel.Convert(x));
}
private SupportVectorMachineModel(SupportVectorMachineModel original, Cloner cloner)
: base(original, cloner)
{
// only using a shallow copy here! (gkronber)
this.model = original.model;
this.rangeTransform = original.rangeTransform;
this.allowedInputVariables = (string[])original.allowedInputVariables.Clone();
if (original.classValues != null)
{
this.classValues = (double[])original.classValues.Clone();
}
}
public static void Main(System.String[] argv)
{
int i, predict_probability = 0;
// parse options
for (i = 0; i < argv.Length; i++)
{
if (argv[i][0] != '-')
{
break;
}
++i;
switch (argv[i - 1][1])
{
case 'b':
predict_probability = atoi(argv[i]);
break;
default:
System.Console.Error.Write("unknown option\n");
exit_with_help();
break;
}
}
if (i >= argv.Length)
{
exit_with_help();
}
try
{
//UPGRADE_TODO: Expected value of parameters of constructor 'java.io.BufferedReader.BufferedReader' are different in the equivalent in .NET. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1092"'
System.IO.StreamReader input = new System.IO.StreamReader(argv[i]); // Input file
System.IO.BinaryWriter output = new System.IO.BinaryWriter(new System.IO.FileStream(argv[i + 2], System.IO.FileMode.Create)); // Output file
TextReader reader = File.OpenText(argv[i + 1]);
reader.ReadToEnd();
System.Text.UnicodeEncoding unicodeEncoding = new System.Text.UnicodeEncoding();
byte[] sBytes = unicodeEncoding.GetBytes(reader.ToString());
svm_model model = svm.svm_load_model(sBytes); // Model file
predict(input, output, model, predict_probability);
}
catch (System.IO.FileNotFoundException e)
{
string message = e.Message;
exit_with_help();
}
catch (System.IndexOutOfRangeException e)
{
string message = e.Message;
exit_with_help();
}
}
public static void Free(svm_model x)
{
Marshal.FreeHGlobal(x.rho);
x.rho = IntPtr.Zero;
Marshal.FreeHGlobal(x.probA);
x.probA = IntPtr.Zero;
Marshal.FreeHGlobal(x.probB);
x.probB = IntPtr.Zero;
Marshal.FreeHGlobal(x.sv_indices);
x.sv_indices = IntPtr.Zero;
Marshal.FreeHGlobal(x.label);
x.label = IntPtr.Zero;
Marshal.FreeHGlobal(x.nSV);
x.nSV = IntPtr.Zero;
SVMParameter.Free(x.param);
IntPtr i_ptr_sv = x.SV;
for (int i = 0; i < x.l; i++)
{
IntPtr ptr_nodes = (IntPtr)Marshal.PtrToStructure(i_ptr_sv, typeof(IntPtr));
SVMNode.Free(ptr_nodes);
i_ptr_sv = IntPtr.Add(i_ptr_sv, Marshal.SizeOf(typeof(IntPtr)));
}
Marshal.FreeHGlobal(x.SV);
x.SV = IntPtr.Zero;
IntPtr i_ptr_svcoef = x.sv_coef;
for (int i = 0; i < x.nr_class - 1; i++)
{
IntPtr temp = (IntPtr)Marshal.PtrToStructure(i_ptr_svcoef, typeof(IntPtr));
Marshal.FreeHGlobal(temp);
temp = IntPtr.Zero;
i_ptr_svcoef = IntPtr.Add(i_ptr_svcoef, Marshal.SizeOf(typeof(IntPtr)));
}
Marshal.FreeHGlobal(x.sv_coef);
x.sv_coef = IntPtr.Zero;
}
public static void Free(IntPtr ptr)
{
if (ptr == IntPtr.Zero)
{
return;
}
svm_model x = (svm_model)Marshal.PtrToStructure(ptr, typeof(svm_model));
SVMModel.Free(x);
Marshal.DestroyStructure(ptr, typeof(svm_model));
Marshal.FreeHGlobal(ptr);
ptr = IntPtr.Zero;
}
/// <summary>
/// Construct a SVM from a model.
/// </summary>
///
/// <param name="theModel">The model.</param>
public SupportVectorMachine(svm_model theModel)
{
_model = theModel;
_paras = _model.param;
_inputCount = 0;
// determine the input count
foreach (var element in _model.SV)
{
foreach (svm_node t in element)
{
_inputCount = Math.Max(t.index, _inputCount);
}
}
//
}
public MaltLibsvmModel(svm_model model, svm_problem problem)
{
param = model.param;
nr_class = model.nr_class;
l = model.l;
SV = model.SV;
sv_coef = model.sv_coef;
rho = model.rho;
label = model.label;
nSV = model.nSV;
start = new int[nr_class];
start[0] = 0;
for (int i = 1; i < nr_class; i++)
{
start[i] = start[i - 1] + nSV[i - 1];
}
}
public bool LoadFromFile(string fileName)
{
if (File.Exists(fileName))
{
FileStream fs = new FileStream(fileName, FileMode.Open);
using (BinaryReader r = new BinaryReader(fs))
{
this.model = new svm_model();
svm_parameter p = new svm_parameter();
p.C = r.ReadDouble();
p.cache_size = r.ReadDouble();
p.coef0 = r.ReadDouble();
p.degree = r.ReadDouble();
p.eps = r.ReadDouble();
p.gamma = r.ReadDouble();
p.kernel_type = r.ReadInt32();
p.nr_weight = r.ReadInt32();
p.nu = r.ReadDouble();
p.p = r.ReadDouble();
p.probability = r.ReadInt32();
p.shrinking = r.ReadInt32();
p.svm_type = r.ReadInt32();
p.weight = ReadDoubleArray(r);
p.weight_label = ReadIntArray(r);
this.model.param = p;
this.model.nr_class = r.ReadInt32();
this.model.l = r.ReadInt32();
this.model.SV = ReadSvmNodeArray(r);
this.model.sv_coef = ReadDouble2DArray(r);
this.model.rho = ReadDoubleArray(r);
this.model.probA = ReadDoubleArray(r);
this.model.probB = ReadDoubleArray(r);
this.model.label = ReadIntArray(r);
this.model.nSV = ReadIntArray(r);
return(true);
}
}
this.model = null;
return(false);
}
static void Main(string[] args)
{
List <Dictionary <string, double> > docWordDicList = new List <Dictionary <string, double> >();
Dictionary <string, int> dictionary = new Dictionary <string, int>();
List <int> trainingAnswer = new List <int>();
Dictionary <string, double> wordIDFDictionary = new Dictionary <string, double>();
Hashtable stopWordTable = genStopwordTable(STOP_WORD_PATH);
List <string> testFileNameList = new List <string>();
int dicSize = 100;
Console.WriteLine("==> Starting prepare data...");
NLPAdapter nlpAdapter = new NLPAdapter(NLP_MODEL_PATH);
trainModel(TRAINING_DATA_DIR,
LOG_DIR,
ref docWordDicList,
ref dictionary,
dicSize,
ref trainingAnswer,
ref wordIDFDictionary,
stopWordTable,
nlpAdapter
);
#if KNN_MODE
KNN knn = new KNN();
knn.set(dicSize, docWordDicList.Count());
knn.initial(docWordDicList, dictionary, trainingAnswer);
knn.train(3, 20);
knn.getAveDistance();
//knn.genLog(@"D:\work\KPMG\learning\classification\project1_0422\log");
List <KeyValuePair <int, int> > testAnswer = runKnnTest(knn, TEST_DATA_DIR, TEST_LOG_DIR, dictionary, wordIDFDictionary, stopWordTable, ref testFileNameList, nlpAdapter);
#else
Console.WriteLine("==> Starting get model...");
SVMAdapter svmAdapter = new SVMAdapter();
svm_model model = svmAdapter.getSVMModel(docWordDicList, dictionary, trainingAnswer, SVMAdapter.SVM_C_DEFAULT, SVMAdapter.SVM_GAMMA_DEFAULT);
Console.WriteLine("==> Starting SVM test...");
List <KeyValuePair <int, int> > testAnswer = runSVMTest(svmAdapter, TEST_DATA_DIR, TEST_LOG_DIR, dictionary, wordIDFDictionary, stopWordTable, ref testFileNameList, model, nlpAdapter);
Console.WriteLine("==> Starting SVM test done!!");
#endif
Console.WriteLine("==> Starting saving result...");
genStatistic(testAnswer, testFileNameList, LOG_DIR);
}
public void TrainModel(double[] labels, double[][] mlArray)
{
SvmProblemBuilder builder = new SvmProblemBuilder(labels, mlArray);
svm_problem problem = builder.CreateProblem();
svm_parameter param = new svm_parameter()
{
svm_type = 0,
kernel_type = 0,
cache_size = 512,
eps = 0.1,
C = 10,
nr_weight = 0,
weight_label = null,
weight = null
};
this.model = svm.svm_train(problem, param);
}
public override object ReadJson(JsonReader reader, Type objectType, object existingValue, JsonSerializer serializer)
{
SVMModel dmodel = serializer.Deserialize <SVMModel>(reader);
svm_model model = new svm_model()
{
SV = dmodel.SV
};
model.SV = dmodel.SV;
DataHelpers.SetFieldValue <int>(model, "l", dmodel.l);
DataHelpers.SetFieldValue <int[]>(model, "label", dmodel.label);
DataHelpers.SetFieldValue <int>(model, "nr_class", dmodel.nr_class);
DataHelpers.SetFieldValue <int[]>(model, "nSV", dmodel.nSV);
DataHelpers.SetFieldValue <double[]>(model, "probA", dmodel.probA);
DataHelpers.SetFieldValue <double[]>(model, "probB", dmodel.probB);
DataHelpers.SetFieldValue <double[]>(model, "rho", dmodel.rho);
DataHelpers.SetFieldValue <double[][]>(model, "sv_coef", dmodel.sv_coef);
DataHelpers.SetFieldValue <svm_parameter>(model, "param", dmodel.param);
return(model);
}
/// <summary>
/// Load the models.
/// </summary>
/// <param name="xmlin">Where to read the models from.</param>
/// <param name="network">Where the models are read into.</param>
private void HandleModels(ReadXML xmlin, SVMNetwork network)
{
int index = 0;
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODEL, true))
{
svm_parameter param = new svm_parameter();
svm_model model = new svm_model();
model.param = param;
network.Models[index] = model;
HandleModel(xmlin, network.Models[index]);
index++;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODELS, false))
{
break;
}
}
}
private void run(System.String[] argv)
{
parse_command_line(argv);
read_problem();
error_msg = svm.svm_check_parameter(prob, param);
if ((System.Object)error_msg != null)
{
System.Console.Error.Write("Error: " + error_msg + "\n");
System.Environment.Exit(1);
}
if (cross_validation != 0)
{
do_cross_validation();
}
else
{
model = svm.svm_train(prob, param);
svm.svm_save_model(model_file_name, model);
}
}
/// <summary>
/// Load the data from a model.
/// </summary>
/// <param name="xmlin">Where to read the data from.</param>
/// <param name="model">The model to load data into.</param>
private void HandleData(ReadXML xmlin, svm_model model)
{
int i = 0;
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = EngineArray.AllocateDouble2D(m, l);
model.SV = new svm_node[l][];
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_ROW, true))
{
String line = xmlin.ReadTextToTag();
String[] st = xmlin.ReadTextToTag().Split(',');
for (int k = 0; k < m; k++)
{
model.sv_coef[k][i] = Double.Parse(st[i]);
}
int n = st.Length / 2;
model.SV[i] = new svm_node[n];
int idx = 0;
for (int j = 0; j < n; j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = int.Parse(st[idx++]);
model.SV[i][j].value_Renamed = Double.Parse(st[idx++]);
}
i++;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, false))
{
break;
}
}
}
public static IntPtr Allocate(SVMModel x)
{
if (x == null || x.ClassCount < 1 || x.Labels == null || x.Labels.Length < 1 || x.Parameter == null ||
x.Rho == null || x.Rho.Length < 1 || x.SVCoefs == null || x.SVCoefs.Count < 1 || x.TotalSVCount < 1 ||
x.SVCounts == null || x.SVCounts.Length < 1)
{
return(IntPtr.Zero);
}
svm_model y = new svm_model();
y.nr_class = x.ClassCount;
y.l = x.TotalSVCount;
y.free_sv = (int)x.Creation;
// Allocate model.parameter
IntPtr ptr_param = SVMParameter.Allocate(x.Parameter);
y.param = (svm_parameter)Marshal.PtrToStructure(ptr_param, typeof(svm_parameter));
// Allocate model.rho
y.rho = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.Rho.Length);
Marshal.Copy(x.Rho, 0, y.rho, x.Rho.Length);
// Allocate model.probA
y.probA = IntPtr.Zero;
if (x.ProbabilityA != null)
{
y.probA = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.ProbabilityA.Length);
Marshal.Copy(x.ProbabilityA, 0, y.probA, x.ProbabilityA.Length);
}
// Allocate model.probB
y.probB = IntPtr.Zero;
if (x.ProbabilityB != null)
{
y.probB = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.ProbabilityB.Length);
Marshal.Copy(x.ProbabilityB, 0, y.probB, x.ProbabilityB.Length);
}
// Allocate model.label
y.label = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(int)) * x.Labels.Length);
Marshal.Copy(x.Labels, 0, y.label, x.Labels.Length);
// Allocate model.nSV
y.nSV = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(int)) * x.SVCounts.Length);
Marshal.Copy(x.SVCounts, 0, y.nSV, x.SVCounts.Length);
// Allocate model.sv_coef
y.sv_coef = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(IntPtr)) * x.SVCoefs.Count);
IntPtr i_ptr_svcoef = y.sv_coef;
for (int i = 0; i < x.SVCoefs.Count; i++)
{
IntPtr temp = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(double)) * x.SVCoefs[i].Length);
Marshal.Copy(x.SVCoefs[i], 0, temp, x.SVCoefs[i].Length);
Marshal.StructureToPtr(temp, i_ptr_svcoef, true);
i_ptr_svcoef = IntPtr.Add(i_ptr_svcoef, Marshal.SizeOf(typeof(IntPtr)));
}
// Allocate model.sv_indices
y.sv_indices = IntPtr.Zero;
if (x.SVIndices != null)
{
y.sv_indices = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(int)) * x.SVIndices.Length);
Marshal.Copy(x.SVIndices, 0, y.sv_indices, x.SVIndices.Length);
}
// Allocate model.SV
y.SV = IntPtr.Zero;
if (x.SV != null)
{
y.SV = Marshal.AllocHGlobal(Marshal.SizeOf(typeof(IntPtr)) * x.SV.Count);
IntPtr i_ptr_sv = y.SV;
for (int i = 0; i < x.SV.Count; i++)
{
// Prepare each node array
// 1) All nodes containing zero value is removed
// 2) A node which index is -1 is added to the end
List <SVMNode> temp = x.SV[i].Where(a => a.Value != 0).ToList();
temp.Add(new SVMNode(-1, 0));
SVMNode[] nodes = temp.ToArray();
// Allocate node array
IntPtr ptr_nodes = SVMNode.Allocate(nodes);
Marshal.StructureToPtr(ptr_nodes, i_ptr_sv, true);
i_ptr_sv = IntPtr.Add(i_ptr_sv, Marshal.SizeOf(typeof(IntPtr)));
}
}
// Allocate the model
int size = Marshal.SizeOf(y);
IntPtr ptr = Marshal.AllocHGlobal(size);
Marshal.StructureToPtr(y, ptr, true);
return(ptr);
}
/// <summary>
/// Handle a model.
/// </summary>
/// <param name="xmlin">Where to read the model from.</param>
/// <param name="model">Where to load the model into.</param>
private void HandleModel(ReadXML xmlin, svm_model model)
{
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_SVM, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.svm_type_table, xmlin.ReadTextToTag());
model.param.svm_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DEGREE, true))
{
model.param.degree = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_GAMMA, true))
{
model.param.gamma = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_COEF0, true))
{
model.param.coef0 = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NUMCLASS, true))
{
model.nr_class = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TOTALSV, true))
{
model.l = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_RHO, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.rho = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
{
model.rho[i] = double.Parse(st[i]);
}
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_LABEL, true))
{
int n = model.nr_class;
model.label = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
{
model.label[i] = int.Parse(st[i]);
}
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_A, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probA = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
{
model.probA[i] = Double.Parse(st[i]);
}
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_B, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probB = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
{
model.probB[i] = Double.Parse(st[i]);
}
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NSV, true))
{
int n = model.nr_class;
model.nSV = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
{
model.nSV[i] = int.Parse(st[i]);
}
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_KERNEL, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.kernel_type_table, xmlin
.ReadTextToTag());
model.param.kernel_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, true))
{
HandleData(xmlin, model);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODEL, false))
{
break;
}
}
}
private static List <KeyValuePair <int, int> > runSVMTest(SVMAdapter svmAdapter, string testPath, string logPath, Dictionary <string, int> dictionary, Dictionary <string, double> wordIDFDictionary, Hashtable stopWordTable, ref List <string> testFileNameList, svm_model model, NLPAdapter nlpAdapter)
{
string[] categories = Directory.GetDirectories(testPath);
List <KeyValuePair <int, int> > testAnswer = new List <KeyValuePair <int, int> >();
for (int i = 0; i < categories.Length; i++) //traverse Categories
{
Console.WriteLine(categories[i]);
string[] files = Directory.GetFiles(categories[i]);
for (int j = 0; j < files.Length; j++)
{
int testResult = -1;
testFileNameList.Add(Path.GetFileName(files[j]));
testResult = svmAdapter.runSVMTest(readDoc(files[j], stopWordTable, nlpAdapter), dictionary, wordIDFDictionary, model);
testAnswer.Add(new KeyValuePair <int, int>(testResult, i));
Console.WriteLine(testResult + "," + i);
}
}
return(testAnswer);
}
public static SVMModel Convert(svm_model x)
{
SVMModel y = new SVMModel();
y.Creation = (CreationType)x.free_sv;
y.ClassCount = x.nr_class;
y.TotalSVCount = x.l;
if (y.Creation == CreationType.LOAD_MODEL)
{
y.Parameter = new SVMParameter();
y.Parameter.Type = (SVMType)x.param.svm_type;
y.Parameter.Kernel = (SVMKernelType)x.param.kernel_type;
switch (y.Parameter.Kernel)
{
case SVMKernelType.LINEAR:
break;
case SVMKernelType.POLY:
y.Parameter.Gamma = x.param.gamma;
y.Parameter.Coef0 = x.param.coef0;
y.Parameter.Degree = x.param.degree;
break;
case SVMKernelType.RBF:
y.Parameter.Gamma = x.param.gamma;
break;
case SVMKernelType.SIGMOID:
y.Parameter.Gamma = x.param.gamma;
y.Parameter.Coef0 = x.param.coef0;
break;
}
}
else
{
y.Parameter = SVMParameter.Convert(x.param);
}
int problemCount = (int)(y.ClassCount * (y.ClassCount - 1) * 0.5);
y.Rho = new double[problemCount];
Marshal.Copy(x.rho, y.Rho, 0, y.Rho.Length);
y.ProbabilityA = null;
if (x.probA != IntPtr.Zero)
{
y.ProbabilityA = new double[problemCount];
Marshal.Copy(x.probA, y.ProbabilityA, 0, y.ProbabilityA.Length);
}
y.ProbabilityB = null;
if (x.probB != IntPtr.Zero)
{
y.ProbabilityB = new double[problemCount];
Marshal.Copy(x.probB, y.ProbabilityB, 0, y.ProbabilityB.Length);
}
y.SVCounts = new int[y.ClassCount];
Marshal.Copy(x.nSV, y.SVCounts, 0, y.SVCounts.Length);
y.Labels = new int[y.ClassCount];
Marshal.Copy(x.label, y.Labels, 0, y.Labels.Length);
y.SVCoefs = new List <double[]>(y.ClassCount - 1);
IntPtr i_ptr_svcoef = x.sv_coef;
for (int i = 0; i < y.ClassCount - 1; i++)
{
y.SVCoefs.Add(new double[y.TotalSVCount]);
IntPtr coef_ptr = (IntPtr)Marshal.PtrToStructure(i_ptr_svcoef, typeof(IntPtr));
Marshal.Copy(coef_ptr, y.SVCoefs[i], 0, y.SVCoefs[i].Length);
i_ptr_svcoef = IntPtr.Add(i_ptr_svcoef, Marshal.SizeOf(typeof(IntPtr)));
}
y.SVIndices = null;
if (x.sv_indices != IntPtr.Zero)
{
y.SVIndices = new int[y.TotalSVCount];
Marshal.Copy(x.sv_indices, y.SVIndices, 0, y.SVIndices.Length);
}
y.SV = new List <SVMNode[]>();
IntPtr i_ptr_sv = x.SV;
for (int i = 0; i < x.l; i++)
{
IntPtr ptr_nodes = (IntPtr)Marshal.PtrToStructure(i_ptr_sv, typeof(IntPtr));
SVMNode[] nodes = SVMNode.Convert(ptr_nodes);
y.SV.Add(nodes);
i_ptr_sv = IntPtr.Add(i_ptr_sv, Marshal.SizeOf(typeof(IntPtr)));
}
return(y);
}
private static void predict(System.IO.StreamReader input, System.IO.BinaryWriter output, svm_model model, int predict_probability)
{
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
int svm_type = svm.svm_get_svm_type(model);
int nr_class = svm.svm_get_nr_class(model);
int[] labels = new int[nr_class];
double[] prob_estimates = null;
if (predict_probability == 1)
{
if (svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR)
{
System.Console.Out.Write("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + svm.svm_get_svr_probability(model) + "\n");
}
else
{
svm.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write("labels");
for (int j = 0; j < nr_class; j++)
{
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write(" " + labels[j]);
}
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write("\n");
}
}
while (true)
{
System.String line = input.ReadLine();
if ((System.Object) line == null)
break;
SupportClass.Tokenizer st = new SupportClass.Tokenizer(line, " \t\n\r\f:");
double target = atof(st.NextToken());
int m = st.Count / 2;
svm_node[] x = new svm_node[m];
for (int j = 0; j < m; j++)
{
x[j] = new svm_node();
x[j].index = atoi(st.NextToken());
x[j].value = atof(st.NextToken());
}
double v;
if (predict_probability == 1 && (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.NU_SVC))
{
v = svm.svm_predict_probability(model, x, prob_estimates);
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write(v + " ");
for (int j = 0; j < nr_class; j++)
{
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write(prob_estimates[j] + " ");
}
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write("\n");
}
else
{
v = svm.svm_predict(model, x);
//UPGRADE_ISSUE: Method 'java.io.DataOutputStream.Write' was not converted. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1000_javaioDataOutputStreamWrite_javalangString"'
output.Write(v + "\n");
}
if (v == target)
++correct;
error += (v - target) * (v - target);
sumv += v;
sumy += target;
sumvv += v * v;
sumyy += target * target;
sumvy += v * target;
++total;
}
System.Console.Out.Write("Accuracy = " + (double) correct / total * 100 + "% (" + correct + "/" + total + ") (classification)\n");
System.Console.Out.Write("Mean squared error = " + error / total + " (regression)\n");
System.Console.Out.Write("Squared correlation coefficient = " + ((total * sumvy - sumv * sumy) * (total * sumvy - sumv * sumy)) / ((total * sumvv - sumv * sumv) * (total * sumyy - sumy * sumy)) + " (regression)\n");
}
/// <inheritdoc/>
public Object Read(Stream mask0)
{
var result = new SupportVectorMachine();
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <String, String> paras = section.ParseParams();
EngineArray.PutAll(paras, result.Properties);
}
if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("SVM-PARAM"))
{
IDictionary <String, String> p = section.ParseParams();
result.InputCount = EncogFileSection.ParseInt(p,
PersistConst.InputCount);
result.Params.C = EncogFileSection.ParseDouble(p,
ParamC);
result.Params.cache_size = EncogFileSection.ParseDouble(
p, ParamCacheSize);
result.Params.coef0 = EncogFileSection.ParseDouble(p,
ParamCoef0);
result.Params.degree = EncogFileSection.ParseDouble(p,
ParamDegree);
result.Params.eps = EncogFileSection.ParseDouble(p,
ParamEps);
result.Params.gamma = EncogFileSection.ParseDouble(p,
ParamGamma);
result.Params.kernel_type = EncogFileSection.ParseInt(
p, ParamKernelType);
result.Params.nr_weight = EncogFileSection.ParseInt(
p, ParamNumWeight);
result.Params.nu = EncogFileSection.ParseDouble(p,
ParamNu);
result.Params.p = EncogFileSection.ParseDouble(p,
ParamP);
result.Params.probability = EncogFileSection.ParseInt(
p, ParamProbability);
result.Params.shrinking = EncogFileSection.ParseInt(
p, ParamShrinking);
/*result.Params.statIterations = Encog.Persist.EncogFileSection.ParseInt(
* params_0, PersistSVM.PARAM_START_ITERATIONS);*/
result.Params.svm_type = EncogFileSection.ParseInt(p,
ParamSVMType);
result.Params.weight = EncogFileSection.ParseDoubleArray(
p, ParamWeight);
result.Params.weight_label = EncogFileSection
.ParseIntArray(p, ParamWeightLabel);
}
else if (section.SectionName.Equals("SVM") &&
section.SubSectionName.Equals("SVM-MODEL"))
{
try
{
#if !SILVERLIGHT
var rdr = new StringReader(
section.LinesAsString);
TextReader br = rdr;
svm_model model = svm.svm_load_model(rdr);
result.Model = model;
br.Close();
rdr.Close();
#endif
}
catch (IOException ex)
{
throw new PersistError(ex);
}
}
}
return(result);
}
/// <summary>
/// Save a model.
/// </summary>
/// <param name="xmlout">Where to save a model to.</param>
/// <param name="model">The model to save to.</param>
public static void SaveModel(WriteXML xmlout, svm_model model)
{
if (model != null)
{
xmlout.BeginTag(SVMNetworkPersistor.TAG_MODEL);
svm_parameter param = model.param;
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_SVM,
svm_type_table[param.svm_type]);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_KERNEL,
kernel_type_table[param.kernel_type]);
if (param.kernel_type == svm_parameter.POLY)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_DEGREE, param.degree);
}
if (param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.RBF ||
param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_GAMMA, param.gamma);
}
if (param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_COEF0, param.coef0);
}
int nr_class = model.nr_class;
int l = model.l;
xmlout.AddProperty(SVMNetworkPersistor.TAG_NUMCLASS, nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TOTALSV, l);
xmlout.AddProperty(SVMNetworkPersistor.TAG_RHO, model.rho, nr_class
* (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_LABEL, model.label,
nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_A, model.probA,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_B, model.probB,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_NSV, model.nSV, nr_class);
xmlout.BeginTag(SVMNetworkPersistor.TAG_DATA);
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
StringBuilder line = new StringBuilder();
for (int i = 0; i < l; i++)
{
line.Length = 0;
for (int j = 0; j < nr_class - 1; j++)
{
line.Append(sv_coef[j][i] + " ");
}
svm_node[] p = SV[i];
//if (param.kernel_type == svm_parameter.PRECOMPUTED)
//{
// line.Append("0:" + (int) (p[0].value));
//}
//else
for (int j = 0; j < p.Length; j++)
{
line.Append(p[j].index + ":" + p[j].value_Renamed + " ");
}
xmlout.AddProperty(SVMNetworkPersistor.TAG_ROW, line.ToString());
}
xmlout.EndTag();
xmlout.EndTag();
}
}
public int runSVMTest(Dictionary <string, double> docWord, Dictionary <string, int> dictionary, Dictionary <string, double> wordIDFDictionary, svm_model model)
{
List <KeyValuePair <int, int> > result = new List <KeyValuePair <int, int> >();
// generate svm nodes
List <KeyValuePair <string, string> > nodeList = new List <KeyValuePair <string, string> >();
List <svm_node> nodes = new List <svm_node>();
foreach (string word in docWord.Keys)
{
if (dictionary.ContainsKey(word))
{
int index = dictionary[word];
double TF = docWord[word];
double IDF = wordIDFDictionary[word];
double value = TF * IDF;
nodes.Add(new svm_node() // svm node
{
index = index,
value = value,
});
nodeList.Add(new KeyValuePair <string, string>(System.Convert.ToString(index), System.Convert.ToString(value)));
//trainStr = trainStr + "\t" + index + ":" + value;
}
}
if (nodeList.Count > 0)
{
// sort the list by feature index at first
nodeList.Sort(
delegate(KeyValuePair <string, string> firstPair,
KeyValuePair <string, string> nextPair)
{
int a = int.Parse(firstPair.Key);
int b = int.Parse(nextPair.Key);
return(a.CompareTo(b));
}
);
for (int k = 0; k < nodeList.Count; k++)
{
KeyValuePair <string, string> node = nodeList[k];
String index = node.Key;
String value = node.Value;
}
}
int resValue = (int)svm.svm_predict(model, nodes.ToArray());
return(resValue);
}
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: protected void trainInternal(java.util.LinkedHashMap<String, String> libOptions) throws org.maltparser.core.exception.MaltChainedException
protected internal override void trainInternal(LinkedHashMap <string, string> libOptions)
{
try
{
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final libsvm.svm_problem prob = readProblem(getInstanceInputStreamReader(".ins"), libOptions);
svm_problem prob = readProblem(getInstanceInputStreamReader(".ins"), libOptions);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final libsvm.svm_parameter param = getLibSvmParameters(libOptions);
svm_parameter param = getLibSvmParameters(libOptions);
if (svm.svm_check_parameter(prob, param) != null)
{
throw new LibException(svm.svm_check_parameter(prob, param));
}
Configuration config = Configuration;
if (config.LoggerInfoEnabled)
{
config.logInfoMessage("Creating LIBSVM model " + getFile(".moo").Name + "\n");
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final java.io.PrintStream out = System.out;
PrintStream @out = System.out;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final java.io.PrintStream err = System.err;
PrintStream err = System.err;
System.Out = NoPrintStream.NO_PRINTSTREAM;
System.Err = NoPrintStream.NO_PRINTSTREAM;
svm_model model = svm.svm_train(prob, param);
System.Out = err;
System.Out = @out;
ObjectOutputStream output = new ObjectOutputStream(new BufferedOutputStream(new FileStream(getFile(".moo").AbsolutePath, FileMode.Create, FileAccess.Write)));
try
{
output.writeObject(new MaltLibsvmModel(model, prob));
}
finally
{
output.close();
}
bool saveInstanceFiles = ((bool?)Configuration.getOptionValue("lib", "save_instance_files")).Value;
if (!saveInstanceFiles)
{
getFile(".ins").delete();
}
}
catch (OutOfMemoryException e)
{
throw new LibException("Out of memory. Please increase the Java heap size (-Xmx<size>). ", e);
}
catch (ArgumentException e)
{
throw new LibException("The LIBSVM learner was not able to redirect Standard Error stream. ", e);
}
catch (SecurityException e)
{
throw new LibException("The LIBSVM learner cannot remove the instance file. ", e);
}
catch (IOException e)
{
throw new LibException("The LIBSVM learner cannot save the model file '" + getFile(".mod").AbsolutePath + "'. ", e);
}
}
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: protected void trainExternal(String pathExternalTrain, java.util.LinkedHashMap<String, String> libOptions) throws org.maltparser.core.exception.MaltChainedException
protected internal override void trainExternal(string pathExternalTrain, LinkedHashMap <string, string> libOptions)
{
try
{
binariesInstances2SVMFileFormat(getInstanceInputStreamReader(".ins"), getInstanceOutputStreamWriter(".ins.tmp"));
Configuration config = Configuration;
if (config.LoggerInfoEnabled)
{
config.logInfoMessage("Creating learner model (external) " + getFile(".mod").Name);
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final libsvm.svm_problem prob = readProblem(getInstanceInputStreamReader(".ins"), libOptions);
svm_problem prob = readProblem(getInstanceInputStreamReader(".ins"), libOptions);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final String[] params = getLibParamStringArray(libOptions);
string[] @params = getLibParamStringArray(libOptions);
string[] arrayCommands = new string[@params.Length + 3];
int i = 0;
arrayCommands[i++] = pathExternalTrain;
for (; i <= @params.Length; i++)
{
arrayCommands[i] = @params[i - 1];
}
arrayCommands[i++] = getFile(".ins.tmp").AbsolutePath;
arrayCommands[i++] = getFile(".mod").AbsolutePath;
if (verbosity == Verbostity.ALL)
{
config.logInfoMessage('\n');
}
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final Process child = Runtime.getRuntime().exec(arrayCommands);
Process child = Runtime.Runtime.exec(arrayCommands);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final java.io.InputStream in = child.getInputStream();
Stream @in = child.InputStream;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final java.io.InputStream err = child.getErrorStream();
Stream err = child.ErrorStream;
int c;
while ((c = @in.Read()) != -1)
{
if (verbosity == Verbostity.ALL)
{
config.logInfoMessage((char)c);
}
}
while ((c = err.Read()) != -1)
{
if (verbosity == Verbostity.ALL || verbosity == Verbostity.ERROR)
{
config.logInfoMessage((char)c);
}
}
if (child.waitFor() != 0)
{
config.logErrorMessage(" FAILED (" + child.exitValue() + ")");
}
@in.Close();
err.Close();
svm_model model = svm.svm_load_model(getFile(".mod").AbsolutePath);
MaltLibsvmModel xmodel = new MaltLibsvmModel(model, prob);
ObjectOutputStream output = new ObjectOutputStream(new BufferedOutputStream(new FileStream(getFile(".moo").AbsolutePath, FileMode.Create, FileAccess.Write)));
try
{
output.writeObject(xmodel);
}
finally
{
output.close();
}
bool saveInstanceFiles = ((bool?)Configuration.getOptionValue("lib", "save_instance_files")).Value;
if (!saveInstanceFiles)
{
getFile(".ins").delete();
getFile(".mod").delete();
getFile(".ins.tmp").delete();
}
if (config.LoggerInfoEnabled)
{
config.logInfoMessage('\n');
}
}
catch (InterruptedException e)
{
throw new LibException("Learner is interrupted. ", e);
}
catch (ArgumentException e)
{
throw new LibException("The learner was not able to redirect Standard Error stream. ", e);
}
catch (SecurityException e)
{
throw new LibException("The learner cannot remove the instance file. ", e);
}
catch (IOException e)
{
throw new LibException("The learner cannot save the model file '" + getFile(".mod").AbsolutePath + "'. ", e);
}
catch (OutOfMemoryException e)
{
throw new LibException("Out of memory. Please increase the Java heap size (-Xmx<size>). ", e);
}
}
public SupportVectorMachineModel(svm_model model, RangeTransform rangeTransform, string targetVariable, IEnumerable <string> allowedInputVariables, IEnumerable <double> classValues)
: this(model, rangeTransform, targetVariable, allowedInputVariables)
{
this.classValues = classValues.ToArray();
}