namespace WhiteRabbit { using System; using System.Collections.Generic; using System.Linq; internal sealed class StringsProcessor { 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 words) { var filteredSource = sourceString.Where(ch => ch != SPACE).ToArray(); this.NumberOfCharacters = filteredSource.Length; 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(); this.VectorsProcessor = new VectorsProcessor( this.VectorsConverter.GetVector(filteredSource).Value, maxWordsCount, vectorsToWords.Select(tuple => tuple.vector).ToArray()); } private VectorsConverter VectorsConverter { get; } /// /// WordsDictionary[vectorIndex] = [word1, word2, ...] /// private byte[][][] WordsDictionary { get; } private VectorsProcessor VectorsProcessor { get; } private int NumberOfCharacters { get; } #if SINGLE_THREADED public IEnumerable GeneratePhrases() #else public ParallelQuery GeneratePhrases() #endif { // 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(); // 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.ConvertVectorsToWordsNumber) .Sum(tuple => tuple.Item2 * PrecomputedPermutationsGenerator.GetPermutationsNumber(tuple.Item1)); } 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 Tuple ConvertVectorsToWordsNumber(int[] vectors) { long result = 1; for (var i = 0; i < vectors.Length; i++) { result *= this.WordsDictionary[vectors[i]].Length; } return Tuple.Create(vectors.Length, result); } private IEnumerable 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); } } } }