Github user geobmx540 commented on a diff in the pull request:
https://github.com/apache/lucenenet/pull/26#discussion_r21422060
--- Diff: src/Lucene.Net.Classification/KNearesteighborClassifier.cs ---
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+namespace Lucene.Net.Classification
+{
+ using Lucene.Net.Analysis;
+ using Lucene.Net.Index;
+ using Lucene.Net.Queries.Mlt;
+ using Lucene.Net.Search;
+ using Lucene.Net.Util;
+ using System;
+ using System.Collections.Generic;
+ using System.IO;
+
+ /// <summary>
+ /// A k-Nearest Neighbor classifier (see
<code>http://en.wikipedia.org/wiki/K-nearest_neighbors</code>) based
+ /// on {@link MoreLikeThis}
+ ///
+ /// @lucene.experimental
+ /// </summary>
+ public class KNearestNeighborClassifier : Classifier<BytesRef>
+ {
+
+ private MoreLikeThis _mlt;
+ private String[] _textFieldNames;
+ private String _classFieldName;
+ private IndexSearcher _indexSearcher;
+ private readonly int _k;
+ private Query _query;
+
+ private int _minDocsFreq;
+ private int _minTermFreq;
+
+ /// <summary>Create a {@link Classifier} using kNN
algorithm</summary>
+ /// <param name="k">the number of neighbors to analyze as an
<code>int</code></param>
+ public KNearestNeighborClassifier(int k)
+ {
+ this._k = k;
+ }
+
+ /// <summary>Create a {@link Classifier} using kNN
algorithm</summary>
+ /// <param name="k">the number of neighbors to analyze as an
<code>int</code></param>
+ /// <param name="minDocsFreq">the minimum number of docs frequency
for MLT to be set with {@link MoreLikeThis#setMinDocFreq(int)}</param>
+ /// <param name="minTermFreq">the minimum number of term frequency
for MLT to be set with {@link MoreLikeThis#setMinTermFreq(int)}</param>
+ public KNearestNeighborClassifier(int k, int minDocsFreq, int
minTermFreq)
+ {
+ this._k = k;
+ this._minDocsFreq = minDocsFreq;
+ this._minTermFreq = minTermFreq;
+ }
+
+ public ClassificationResult<BytesRef> AssignClass(String text)
+ {
+ if (_mlt == null)
+ {
+ throw new IOException("You must first call
Classifier#train");
+ }
+
+ BooleanQuery mltQuery = new BooleanQuery();
+ foreach (String textFieldName in _textFieldNames)
+ {
+ mltQuery.Add(new BooleanClause(_mlt.Like(new
StringReader(text), textFieldName), BooleanClause.Occur.SHOULD));
+ }
+ Query classFieldQuery = new WildcardQuery(new
Term(_classFieldName, "*"));
+ mltQuery.Add(new BooleanClause(classFieldQuery,
BooleanClause.Occur.MUST));
+ if (_query != null) {
+ mltQuery.Add(_query, BooleanClause.Occur.MUST);
+ }
+ TopDocs topDocs = _indexSearcher.Search(mltQuery, _k);
+ return SelectClassFromNeighbors(topDocs);
+ }
+
+ private ClassificationResult<BytesRef>
SelectClassFromNeighbors(TopDocs topDocs)
+ {
+ // TODO : improve the nearest neighbor selection
+ Dictionary<BytesRef, int> classCounts = new
Dictionary<BytesRef, int>();
+
+ foreach (ScoreDoc scoreDoc in topDocs.ScoreDocs)
+ {
+ BytesRef cl = new
BytesRef(_indexSearcher.Doc(scoreDoc.Doc).GetField(_classFieldName).StringValue);
+ int count = classCounts[cl];
+ if (classCounts.ContainsKey(cl))
+ {
+ classCounts[cl] = count + 1;
+ }
+ else
+ {
+ classCounts.Add(cl, 1);
+ }
+ }
+ double max = 0;
+ BytesRef assignedClass = new BytesRef();
+ foreach (KeyValuePair<BytesRef, int> entry in classCounts)
+ {
+ int count = entry.Value;
+ if (count > max)
+ {
+ max = count;
+ assignedClass = (BytesRef)entry.Key.Clone();
+ }
+ }
+ double score = max / (double) _k;
+ return new ClassificationResult<BytesRef>(assignedClass,
score);
+ }
+
+ public void Train(AtomicReader atomicReader, String textFieldName,
String classFieldName, Analyzer analyzer)
+ {
+ Train(atomicReader, textFieldName, classFieldName, analyzer,
null);
+ }
+
+
+ public void Train(AtomicReader atomicReader, String textFieldName,
String classFieldName, Analyzer analyzer, Query query)
+ {
+ Train(atomicReader, new String[]{textFieldName},
classFieldName, analyzer, query);
+ }
+
+ public void Train(AtomicReader atomicReader, String[]
textFieldNames, String classFieldName, Analyzer analyzer, Query query)
+ {
+ this._textFieldNames = textFieldNames;
--- End diff --
could probably get rid of the references to `this.` since the private
members are prefixed with an underscore
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