91 lines
2.7 KiB
C#
91 lines
2.7 KiB
C#
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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using System.Threading;
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using BotSharp.Algorithm.HiddenMarkovModel.Helpers;
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using BotSharp.Algorithm.HiddenMarkovModel.Learning.Unsupervised;
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namespace BotSharp.Algorithm.HiddenMarkovModel.Learning.Supervised
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{
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public partial class HiddenMarkovClassifierLearning
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{
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public double ComputeError(double[][] inputs, int[] outputs)
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{
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int errors = 0;
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Parallel.For(0, inputs.Length, i =>
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{
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int expectedOutput = outputs[i];
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int actualOutput = mClassifier.Compute(inputs[i]);
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if (expectedOutput != actualOutput)
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{
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Interlocked.Increment(ref errors);
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}
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});
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return errors / (double)inputs.Length;
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}
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public double Run(double[][] observations_db, int[] class_labels)
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{
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int class_count = mClassifier.ClassCount;
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double[] logLikelihood = new double[class_count];
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int K=class_labels.Length;
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DiagnosticsHelper.Assert(observations_db.Length==K);
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int[] class_label_counts = new int[class_count];
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Parallel.For(0, class_count, i =>
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{
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IUnsupervisedLearning teacher = mAlgorithmEntity(i);
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List<int> match_record_index_set = new List<int>();
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for (int k = 0; k < K; ++k)
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{
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if (class_labels[k] == i)
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{
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match_record_index_set.Add(k);
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}
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}
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int K2 = match_record_index_set.Count;
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class_label_counts[i] = K2;
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if (K2 != 0)
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{
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double[][] observations_subdb = new double[K2][];
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for (int k = 0; k < K2; ++k)
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{
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int record_index = match_record_index_set[k];
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observations_subdb[k] = observations_db[record_index];
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}
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logLikelihood[i] = teacher.Run(observations_subdb);
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}
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});
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if (mEmpirical)
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{
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for (int i = 0; i < class_count; i++)
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{
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mClassifier.Priors[i] = (double)class_label_counts[i] / K;
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}
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}
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//if (mRejection)
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//{
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// mClassifier.Threshold = Threshold();
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//}
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return logLikelihood.Sum();
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}
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}
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}
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