public static double svm_predict(Model model, Node[] x)
{
if (model.Parameter.SvmType == SvmType.ONE_CLASS ||
model.Parameter.SvmType == SvmType.EPSILON_SVR ||
model.Parameter.SvmType == SvmType.NU_SVR)
{
double[] res = new double[1];
svm_predict_values(model, x, res);
if (model.Parameter.SvmType == SvmType.ONE_CLASS)
return (res[0] > 0) ? 1 : -1;
else
return res[0];
}
else
{
int i;
int nr_class = model.NumberOfClasses;
double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
svm_predict_values(model, x, dec_values);
int[] vote = new int[nr_class];
for (i = 0; i < nr_class; i++)
vote[i] = 0;
int pos = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
if (dec_values[pos++] > 0)
++vote[i];
else
++vote[j];
}
int vote_Max_idx = 0;
for (i = 1; i < nr_class; i++)
if (vote[i] > vote[vote_Max_idx])
vote_Max_idx = i;
return model.ClassLabels[vote_Max_idx];
}
}
public static double svm_predict_probability(Model model, Node[] x, double[] prob_estimates)
{
if ((model.Parameter.SvmType == SvmType.C_SVC || model.Parameter.SvmType == SvmType.NU_SVC) &&
model.PairwiseProbabilityA != null && model.PairwiseProbabilityB != null)
{
int i;
int nr_class = model.NumberOfClasses;
double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
svm_predict_values(model, x, dec_values);
double Min_prob = 1e-7;
double[,] pairwise_prob = new double[nr_class, nr_class];
int k = 0;
for (i = 0; i < nr_class; i++)
{
for (int j = i + 1; j < nr_class; j++)
{
pairwise_prob[i, j] = Math.Min(Math.Max(sigmoid_predict(dec_values[k], model.PairwiseProbabilityA[k], model.PairwiseProbabilityB[k]), Min_prob), 1 - Min_prob);
pairwise_prob[j, i] = 1 - pairwise_prob[i, j];
k++;
}
}
multiclass_probability(nr_class, pairwise_prob, prob_estimates);
int prob_Max_idx = 0;
for (i = 1; i < nr_class; i++)
if (prob_estimates[i] > prob_estimates[prob_Max_idx])
prob_Max_idx = i;
return model.ClassLabels[prob_Max_idx];
}
else
return svm_predict(model, x);
}
public static SvmType svm_get_svm_type(Model model)
{
return model.Parameter.SvmType;
}
public static double svm_get_svr_probability(Model model)
{
if ((model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR) &&
model.PairwiseProbabilityA != null)
return model.PairwiseProbabilityA[0];
else
{
Console.Error.WriteLine("Model doesn't contain information for SVR probability inference");
return 0;
}
}
public static void svm_get_labels(Model model, int[] label)
{
if (model.ClassLabels != null)
for (int i = 0; i < model.NumberOfClasses; i++)
label[i] = model.ClassLabels[i];
}
public static int svm_get_nr_class(Model model)
{
return model.NumberOfClasses;
}
/// <summary>
/// Predicts the class memberships of all the vectors in the problem.
/// </summary>
/// <param name="problem">The SVM Problem to solve</param>
/// <param name="outputFile">File for result output</param>
/// <param name="model">The Model to use</param>
/// <param name="predict_probability">Whether to output a distribution over the classes</param>
/// <returns>Percentage correctly labelled</returns>
public static double Predict(
Problem problem,
string outputFile,
Model model,
bool predict_probability)
{
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
StreamWriter output = outputFile != null ? new StreamWriter(outputFile) : null;
SvmType svm_type = Procedures.svm_get_svm_type(model);
int nr_class = Procedures.svm_get_nr_class(model);
int[] labels = new int[nr_class];
double[] prob_estimates = null;
if (predict_probability)
{
if (svm_type == SvmType.EPSILON_SVR || svm_type == SvmType.NU_SVR)
{
Console.WriteLine("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + Procedures.svm_get_svr_probability(model));
}
else
{
Procedures.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
if (output != null)
{
output.Write("labels");
for (int j = 0; j < nr_class; j++)
{
output.Write(" " + labels[j]);
}
output.Write("\n");
}
}
}
for (int i = 0; i < problem.Count; i++)
{
double target = problem.Y[i];
Node[] x = problem.X[i];
double v;
if (predict_probability && (svm_type == SvmType.C_SVC || svm_type == SvmType.NU_SVC))
{
v = Procedures.svm_predict_probability(model, x, prob_estimates);
if (output != null)
{
output.Write(v + " ");
for (int j = 0; j < nr_class; j++)
{
output.Write(prob_estimates[j] + " ");
}
output.Write("\n");
}
}
else
{
v = Procedures.svm_predict(model, x);
if(output != null)
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;
}
if(output != null)
output.Close();
return (double)correct / total;
}
public static double PredictRaw(Model model, Node[] x)
{
var d = new double[1];
Procedures.svm_predict_values(model, x, d);
return d[0];
}
/// <summary>
/// Predict the class for a single input vector.
/// </summary>
/// <param name="model">The Model to use for prediction</param>
/// <param name="x">The vector for which to predict class</param>
/// <returns>The result</returns>
public static double Predict(Model model, Node[] x)
{
return Procedures.svm_predict(model, x);
}
/// <summary>
/// Predicts a class distribution for the single input vector.
/// </summary>
/// <param name="model">Model to use for prediction</param>
/// <param name="x">The vector for which to predict the class distribution</param>
/// <returns>A probability distribtion over classes</returns>
public static double[] PredictProbability(Model model, Node[] x)
{
SvmType svm_type = Procedures.svm_get_svm_type(model);
if (svm_type != SvmType.C_SVC && svm_type != SvmType.NU_SVC)
throw new Exception("Model type " + svm_type + " unable to predict probabilities.");
int nr_class = Procedures.svm_get_nr_class(model);
double[] probEstimates = new double[nr_class];
Procedures.svm_predict_probability(model, x, probEstimates);
return probEstimates;
}
/// <summary>
/// Writes a model to the provided stream.
/// </summary>
/// <param name="stream">The output stream</param>
/// <param name="model">The model to write</param>
public static void Write(Stream stream, Model model)
{
TemporaryCulture.Start();
StreamWriter output = new StreamWriter(stream);
Parameter param = model.Parameter;
output.Write("svm_type " + param.SvmType + "\n");
output.Write("kernel_type " + param.KernelType + "\n");
if (param.KernelType == KernelType.POLY)
output.Write("degree " + param.Degree + "\n");
if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.RBF || param.KernelType == KernelType.SIGMOID)
output.Write("gamma " + param.Gamma + "\n");
if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.SIGMOID)
output.Write("coef0 " + param.Coefficient0 + "\n");
int nr_class = model.NumberOfClasses;
int l = model.SupportVectorCount;
output.Write("nr_class " + nr_class + "\n");
output.Write("total_sv " + l + "\n");
{
output.Write("rho");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
output.Write(" " + model.Rho[i]);
output.Write("\n");
}
if (model.ClassLabels != null)
{
output.Write("label");
for (int i = 0; i < nr_class; i++)
output.Write(" " + model.ClassLabels[i]);
output.Write("\n");
}
if (model.PairwiseProbabilityA != null)
// regression has probA only
{
output.Write("probA");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
output.Write(" " + model.PairwiseProbabilityA[i]);
output.Write("\n");
}
if (model.PairwiseProbabilityB != null)
{
output.Write("probB");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
output.Write(" " + model.PairwiseProbabilityB[i]);
output.Write("\n");
}
if (model.NumberOfSVPerClass != null)
{
output.Write("nr_sv");
for (int i = 0; i < nr_class; i++)
output.Write(" " + model.NumberOfSVPerClass[i]);
output.Write("\n");
}
output.Write("SV\n");
double[][] sv_coef = model.SupportVectorCoefficients;
Node[][] SV = model.SupportVectors;
for (int i = 0; i < l; i++)
{
for (int j = 0; j < nr_class - 1; j++)
output.Write(sv_coef[j][i] + " ");
Node[] p = SV[i];
if (p.Length == 0)
{
output.WriteLine();
continue;
}
if (param.KernelType == KernelType.PRECOMPUTED)
output.Write("0:{0}", (int)p[0].Value);
else
{
output.Write("{0}:{1}", p[0].Index, p[0].Value);
for (int j = 1; j < p.Length; j++)
output.Write(" {0}:{1}", p[j].Index, p[j].Value);
}
output.WriteLine();
}
output.Flush();
TemporaryCulture.Stop();
}
/// <summary>
/// Writes a model to the provided filename. This will overwrite any previous data in the file.
/// </summary>
/// <param name="filename">The desired file</param>
/// <param name="model">The Model to write</param>
public static void Write(string filename, Model model)
{
FileStream stream = File.Open(filename, FileMode.Create);
try
{
Write(stream, model);
}
finally
{
stream.Close();
}
}
/// <summary>
/// Reads a Model from the provided stream.
/// </summary>
/// <param name="stream">The stream from which to read the Model.</param>
/// <returns>the Model</returns>
public static Model Read(Stream stream)
{
TemporaryCulture.Start();
StreamReader input = new StreamReader(stream);
// read parameters
Model model = new Model();
Parameter param = new Parameter();
model.Parameter = param;
model.Rho = null;
model.PairwiseProbabilityA = null;
model.PairwiseProbabilityB = null;
model.ClassLabels = null;
model.NumberOfSVPerClass = null;
bool headerFinished = false;
while (!headerFinished)
{
string line = input.ReadLine();
string cmd, arg;
int splitIndex = line.IndexOf(' ');
if (splitIndex >= 0)
{
cmd = line.Substring(0, splitIndex);
arg = line.Substring(splitIndex + 1);
}
else
{
cmd = line;
arg = "";
}
arg = arg.ToLower();
int i, n;
switch (cmd)
{
case "svm_type":
param.SvmType = (SvmType)Enum.Parse(typeof(SvmType), arg.ToUpper());
break;
case "kernel_type":
param.KernelType = (KernelType)Enum.Parse(typeof(KernelType), arg.ToUpper());
break;
case "degree":
param.Degree = int.Parse(arg);
break;
case "gamma":
param.Gamma = double.Parse(arg);
break;
case "coef0":
param.Coefficient0 = double.Parse(arg);
break;
case "nr_class":
model.NumberOfClasses = int.Parse(arg);
break;
case "total_sv":
model.SupportVectorCount = int.Parse(arg);
break;
case "rho":
n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
model.Rho = new double[n];
string[] rhoParts = arg.Split();
for (i = 0; i < n; i++)
model.Rho[i] = double.Parse(rhoParts[i]);
break;
case "label":
n = model.NumberOfClasses;
model.ClassLabels = new int[n];
string[] labelParts = arg.Split();
for (i = 0; i < n; i++)
model.ClassLabels[i] = int.Parse(labelParts[i]);
break;
case "probA":
n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
model.PairwiseProbabilityA = new double[n];
string[] probAParts = arg.Split();
for (i = 0; i < n; i++)
model.PairwiseProbabilityA[i] = double.Parse(probAParts[i]);
break;
case "probB":
n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
model.PairwiseProbabilityB = new double[n];
string[] probBParts = arg.Split();
for (i = 0; i < n; i++)
model.PairwiseProbabilityB[i] = double.Parse(probBParts[i]);
break;
case "nr_sv":
n = model.NumberOfClasses;
model.NumberOfSVPerClass = new int[n];
string[] nrsvParts = arg.Split();
for (i = 0; i < n; i++)
model.NumberOfSVPerClass[i] = int.Parse(nrsvParts[i]);
break;
case "SV":
headerFinished = true;
break;
default:
throw new Exception("Unknown text in model file");
}
}
// read sv_coef and SV
int m = model.NumberOfClasses - 1;
int l = model.SupportVectorCount;
model.SupportVectorCoefficients = new double[m][];
for (int i = 0; i < m; i++)
{
model.SupportVectorCoefficients[i] = new double[l];
}
model.SupportVectors = new Node[l][];
for (int i = 0; i < l; i++)
{
string[] parts = input.ReadLine().Trim().Split();
for (int k = 0; k < m; k++)
model.SupportVectorCoefficients[k][i] = double.Parse(parts[k]);
int n = parts.Length - m;
model.SupportVectors[i] = new Node[n];
for (int j = 0; j < n; j++)
{
string[] nodeParts = parts[m + j].Split(':');
model.SupportVectors[i][j] = new Node();
model.SupportVectors[i][j].Index = int.Parse(nodeParts[0]);
model.SupportVectors[i][j].Value = double.Parse(nodeParts[1]);
}
}
TemporaryCulture.Stop();
return model;
}
public static void svm_predict_values(Model model, Node[] x, double[] dec_values)
{
if (model.Parameter.SvmType == SvmType.ONE_CLASS ||
model.Parameter.SvmType == SvmType.EPSILON_SVR ||
model.Parameter.SvmType == SvmType.NU_SVR)
{
double[] sv_coef = model.SupportVectorCoefficients[0];
double sum = 0;
for (int i = 0; i < model.SupportVectorCount; i++)
sum += sv_coef[i] * Kernel.KernelFunction(x, model.SupportVectors[i], model.Parameter);
sum -= model.Rho[0];
dec_values[0] = sum;
}
else
{
int i;
int nr_class = model.NumberOfClasses;
int l = model.SupportVectorCount;
double[] kvalue = new double[l];
for (i = 0; i < l; i++)
kvalue[i] = Kernel.KernelFunction(x, model.SupportVectors[i], model.Parameter);
int[] start = new int[nr_class];
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + model.NumberOfSVPerClass[i - 1];
int p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
double sum = 0;
int si = start[i];
int sj = start[j];
int ci = model.NumberOfSVPerClass[i];
int cj = model.NumberOfSVPerClass[j];
int k;
double[] coef1 = model.SupportVectorCoefficients[j - 1];
double[] coef2 = model.SupportVectorCoefficients[i];
for (k = 0; k < ci; k++)
sum += coef1[si + k] * kvalue[si + k];
for (k = 0; k < cj; k++)
sum += coef2[sj + k] * kvalue[sj + k];
sum -= model.Rho[p];
dec_values[p] = sum;
p++;
}
}
}
public static int svm_check_probability_model(Model model)
{
if (((model.Parameter.SvmType == SvmType.C_SVC || model.Parameter.SvmType == SvmType.NU_SVC) &&
model.PairwiseProbabilityA != null && model.PairwiseProbabilityB != null) ||
((model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR) &&
model.PairwiseProbabilityA != null))
return 1;
else
return 0;
}
//
// Interface functions
//
public static Model svm_train(Problem prob, Parameter param)
{
Model model = new Model();
model.Parameter = param;
if (param.SvmType == SvmType.ONE_CLASS ||
param.SvmType == SvmType.EPSILON_SVR ||
param.SvmType == SvmType.NU_SVR)
{
// regression or one-class-svm
model.NumberOfClasses = 2;
model.ClassLabels = null;
model.NumberOfSVPerClass = null;
model.PairwiseProbabilityA = null; model.PairwiseProbabilityB = null;
model.SupportVectorCoefficients = new double[1][];
if (param.Probability &&
(param.SvmType == SvmType.EPSILON_SVR ||
param.SvmType == SvmType.NU_SVR))
{
model.PairwiseProbabilityA = new double[1];
model.PairwiseProbabilityA[0] = svm_svr_probability(prob, param);
}
decision_function f = svm_train_one(prob, param, 0, 0);
model.Rho = new double[1];
model.Rho[0] = f.rho;
int nSV = 0;
int i;
for (i = 0; i < prob.Count; i++)
if (Math.Abs(f.alpha[i]) > 0) ++nSV;
model.SupportVectorCount = nSV;
model.SupportVectors = new Node[nSV][];
model.SupportVectorCoefficients[0] = new double[nSV];
int j = 0;
for (i = 0; i < prob.Count; i++)
if (Math.Abs(f.alpha[i]) > 0)
{
model.SupportVectors[j] = prob.X[i];
model.SupportVectorCoefficients[0][j] = f.alpha[i];
++j;
}
}
else
{
// classification
int l = prob.Count;
int[] tmp_nr_class = new int[1];
int[][] tmp_label = new int[1][];
int[][] tmp_start = new int[1][];
int[][] tmp_count = new int[1][];
int[] perm = new int[l];
// group training data of the same class
svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm);
int nr_class = tmp_nr_class[0];
int[] label = tmp_label[0];
int[] start = tmp_start[0];
int[] count = tmp_count[0];
Node[][] x = new Node[l][];
int i;
for (i = 0; i < l; i++)
x[i] = prob.X[perm[i]];
// calculate weighted C
double[] weighted_C = new double[nr_class];
for (i = 0; i < nr_class; i++)
weighted_C[i] = param.C;
foreach (int weightedLabel in param.Weights.Keys)
{
int index = Array.IndexOf<int>(label, weightedLabel);
if (index < 0)
Console.Error.WriteLine("warning: class label " + weightedLabel + " specified in weight is not found");
else weighted_C[index] *= param.Weights[weightedLabel];
}
// train k*(k-1)/2 models
bool[] nonzero = new bool[l];
for (i = 0; i < l; i++)
nonzero[i] = false;
decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2];
double[] probA = null, probB = null;
if (param.Probability)
{
probA = new double[nr_class * (nr_class - 1) / 2];
probB = new double[nr_class * (nr_class - 1) / 2];
}
int p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
Problem sub_prob = new Problem();
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
sub_prob.Count = ci + cj;
sub_prob.X = new Node[sub_prob.Count][];
sub_prob.Y = new double[sub_prob.Count];
int k;
for (k = 0; k < ci; k++)
{
sub_prob.X[k] = x[si + k];
sub_prob.Y[k] = +1;
}
for (k = 0; k < cj; k++)
{
sub_prob.X[ci + k] = x[sj + k];
sub_prob.Y[ci + k] = -1;
}
if (param.Probability)
{
double[] probAB = new double[2];
svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB);
probA[p] = probAB[0];
probB[p] = probAB[1];
}
f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]);
for (k = 0; k < ci; k++)
if (!nonzero[si + k] && Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0)
nonzero[sj + k] = true;
++p;
}
// build output
model.NumberOfClasses = nr_class;
model.ClassLabels = new int[nr_class];
for (i = 0; i < nr_class; i++)
model.ClassLabels[i] = label[i];
model.Rho = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
model.Rho[i] = f[i].rho;
if (param.Probability)
{
model.PairwiseProbabilityA = new double[nr_class * (nr_class - 1) / 2];
model.PairwiseProbabilityB = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
{
model.PairwiseProbabilityA[i] = probA[i];
model.PairwiseProbabilityB[i] = probB[i];
}
}
else
{
model.PairwiseProbabilityA = null;
model.PairwiseProbabilityB = null;
}
int nnz = 0;
int[] nz_count = new int[nr_class];
model.NumberOfSVPerClass = new int[nr_class];
for (i = 0; i < nr_class; i++)
{
int nSV = 0;
for (int j = 0; j < count[i]; j++)
if (nonzero[start[i] + j])
{
++nSV;
++nnz;
}
model.NumberOfSVPerClass[i] = nSV;
nz_count[i] = nSV;
}
Procedures.info("Total nSV = " + nnz + "\n");
model.SupportVectorCount = nnz;
model.SupportVectors = new Node[nnz][];
p = 0;
for (i = 0; i < l; i++)
if (nonzero[i]) model.SupportVectors[p++] = x[i];
int[] nz_start = new int[nr_class];
nz_start[0] = 0;
for (i = 1; i < nr_class; i++)
nz_start[i] = nz_start[i - 1] + nz_count[i - 1];
model.SupportVectorCoefficients = new double[nr_class - 1][];
for (i = 0; i < nr_class - 1; i++)
model.SupportVectorCoefficients[i] = new double[nnz];
p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nz_start[i];
int k;
for (k = 0; k < ci; k++)
if (nonzero[si + k])
model.SupportVectorCoefficients[j - 1][q++] = f[p].alpha[k];
q = nz_start[j];
for (k = 0; k < cj; k++)
if (nonzero[sj + k])
model.SupportVectorCoefficients[i][q++] = f[p].alpha[ci + k];
++p;
}
}
return model;
}
public override ClassifierResult Build()
{
//For this example (and indeed, many scenarios), the default
//parameters will suffice.
Parameter parameters = new Parameter();
double C;
double Gamma;
//This will do a grid optimization to find the best parameters
//and store them in C and Gamma, outputting the entire
//search to params.txt.
ParameterSelection.Grid(_trainProblem, parameters, "params.txt", out C, out Gamma);
parameters.C = C;
parameters.Gamma = Gamma;
//Train the model using the optimal parameters.
_model = Training.Train(_trainProblem, parameters);
// пробегаем по всем клиентски данным и сохраняем результат
var probDictList = new Dictionary<string, Dictionary<int, double>>();
foreach (string id in _testDataDict.Keys)
{
if (!probDictList.ContainsKey(id))
probDictList.Add(id, new Dictionary<int, double>());
foreach (var sarr in _testDataDict[id])
{
var y = PredictProba(sarr);
double prob = y.Probs[1];
int kmax = probDictList[id].Keys.Count == 0 ? 0 : probDictList[id].Keys.Max() + 1;
probDictList[id].Add(kmax, prob);
}
}
// вероятность дефолта определяется как среднее по записям для клиента
var probDict = new Dictionary<string, double>();
foreach (var id in probDictList.Keys)
{
int cnt = probDictList[id].Keys.Count();
double prob = 0;
foreach (var d in probDictList[id].Keys)
{
prob += probDictList[id][d];
}
if (!probDict.ContainsKey(id))
probDict.Add(id, prob / cnt);
}
// находим статистики классификации
var rlist = new RocItem[_resultDict.Count]; // массив для оценки результата
int idx = 0;
foreach (string id in probDict.Keys)
{
if (rlist[idx] == null) rlist[idx] = new RocItem();
rlist[idx].Prob = probDict[id]; // среднее по наблюдениям
rlist[idx].Target = _resultDict[id];
rlist[idx].Predicted = 0;
idx++;
}
Array.Sort(rlist, (o1, o2) => (1 - o1.Prob).CompareTo(1 - o2.Prob));
var cres = ResultCalc.GetResult(rlist, 0.05);
var clsRes = new ClassifierResult();
clsRes.BestResult = cres;
clsRes.LastResult = cres;
clsRes.ResDict.Add(0, cres);
return clsRes;
}
