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108 lines
4.1 KiB
108 lines
4.1 KiB
namespace WhiteRabbit
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{
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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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internal sealed class StringsProcessor
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{
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private const byte SPACE = 32;
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// Ensure that permutations are precomputed prior to main run, so that processing times will be correct
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static StringsProcessor()
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{
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PrecomputedPermutationsGenerator.HamiltonianPermutations(0);
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}
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public StringsProcessor(byte[] sourceString, int maxWordsCount, IEnumerable<byte[]> words)
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{
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var filteredSource = sourceString.Where(ch => ch != SPACE).ToArray();
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this.NumberOfCharacters = filteredSource.Length;
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this.VectorsConverter = new VectorsConverter(filteredSource);
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// Dictionary of vectors to array of words represented by this vector
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var vectorsToWords = words
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.Where(word => word != null && word.Length > 0)
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.Select(word => new { word = word.Concat(new byte[] { SPACE }).ToArray(), vector = this.VectorsConverter.GetVector(word) })
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.Where(tuple => tuple.vector != null)
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.Select(tuple => new { tuple.word, vector = tuple.vector.Value })
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.GroupBy(tuple => tuple.vector)
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.Select(group => new { vector = group.Key, words = group.Select(tuple => tuple.word).Distinct(new ByteArrayEqualityComparer()).ToArray() })
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.ToList();
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this.WordsDictionary = vectorsToWords.Select(tuple => tuple.words).ToArray();
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this.VectorsProcessor = new VectorsProcessor(
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this.VectorsConverter.GetVector(filteredSource).Value,
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maxWordsCount,
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vectorsToWords.Select(tuple => tuple.vector).ToArray());
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}
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private VectorsConverter VectorsConverter { get; }
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/// <summary>
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/// WordsDictionary[vectorIndex] = [word1, word2, ...]
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/// </summary>
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private byte[][][] WordsDictionary { get; }
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private VectorsProcessor VectorsProcessor { get; }
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private int NumberOfCharacters { get; }
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#if SINGLE_THREADED
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public IEnumerable<PhraseSet> GeneratePhrases()
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#else
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public ParallelQuery<PhraseSet> GeneratePhrases()
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#endif
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{
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// task of finding anagrams could be reduced to the task of finding sequences of dictionary vectors with the target sum
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var sums = this.VectorsProcessor.GenerateSequences();
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// converting sequences of vectors to the sequences of words...
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return sums
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.Select(this.ConvertVectorsToWords)
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.SelectMany(Flattener.Flatten)
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.SelectMany(this.ConvertWordsToPhrases);
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}
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public long GetPhrasesCount()
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{
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return this.VectorsProcessor.GenerateSequences()
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.Select(this.ConvertVectorsToWordsNumber)
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.Sum(tuple => tuple.Item2 * PrecomputedPermutationsGenerator.GetPermutationsNumber(tuple.Item1));
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}
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private byte[][][] ConvertVectorsToWords(int[] vectors)
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{
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var length = vectors.Length;
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var words = new byte[length][][];
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for (var i = 0; i < length; i++)
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{
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words[i] = this.WordsDictionary[vectors[i]];
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}
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return words;
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}
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private Tuple<int, long> ConvertVectorsToWordsNumber(int[] vectors)
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{
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long result = 1;
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for (var i = 0; i < vectors.Length; i++)
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{
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result *= this.WordsDictionary[vectors[i]].Length;
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}
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return Tuple.Create(vectors.Length, result);
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}
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private IEnumerable<PhraseSet> ConvertWordsToPhrases(byte[][] words)
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{
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var permutations = PrecomputedPermutationsGenerator.HamiltonianPermutations(words.Length);
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var permutationsLength = permutations.Length;
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for (var i = 0; i < permutationsLength; i += Constants.PhrasesPerSet)
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{
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yield return new PhraseSet(words, permutations, i, this.NumberOfCharacters);
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}
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}
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}
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}
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