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TrustPilotChallenge/dotnet/WhiteRabbit/StringsProcessor.cs

99 lines
3.8 KiB

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