namespace WhiteRabbit { using System; using System.Collections.Generic; using System.Collections.Immutable; using System.Linq; using System.Numerics; internal sealed class VectorsProcessor { private const byte MaxComponentValue = 8; private const int LeastCommonMultiple = 840; // Ensure that permutations are precomputed prior to main run, so that processing times will be correct static VectorsProcessor() { PrecomputedPermutationsGenerator.HamiltonianPermutations(0); } public VectorsProcessor(Vector target, int maxVectorsCount, IEnumerable> dictionary) { if (Enumerable.Range(0, Vector.Count).Any(i => target[i] > MaxComponentValue)) { throw new ArgumentException($"Every value should be at most {MaxComponentValue} (at most {MaxComponentValue} same characters allowed in the source string)", nameof(target)); } this.Target = target; this.MaxVectorsCount = maxVectorsCount; this.Dictionary = ImmutableArray.Create(FilterVectors(dictionary, target).ToArray()); } private Vector Target { get; } private int MaxVectorsCount { get; } private ImmutableArray Dictionary { get; } // Produces all sequences of vectors with the target sum public ParallelQuery[]> GenerateSequences() { return GenerateUnorderedSequences(this.Target, GetVectorNorm(this.Target, this.Target), this.MaxVectorsCount, this.Dictionary, 0) .AsParallel() .Select(Enumerable.ToArray) .SelectMany(GeneratePermutations); } // We want words with more letters (and among these, words with more "rare" letters) to appear first, to reduce the searching time somewhat. // Applying such a sort, we reduce the total number of triplets to check for anagrams from ~62M to ~29M. // Total number of quadruplets is reduced from 1468M to mere 311M. // And total number of quintuplets becomes reasonable 1412M. // Also, it produces the intended results faster (as these are more likely to contain longer words - e.g. "poultry outwits ants" is more likely than "p o u l t r y o u t w i t s a n t s"). // This method basically gives us the 1-norm of the vector in the space rescaled so that the target is [1, 1, ..., 1]. private static int GetVectorNorm(Vector vector, Vector target) { var norm = 0; for (var i = 0; target[i] != 0; i++) { norm += (LeastCommonMultiple * vector[i]) / target[i]; } return norm; } private static VectorInfo[] FilterVectors(IEnumerable> vectors, Vector target) { return vectors .Where(vector => Vector.GreaterThanOrEqualAll(target, vector)) .Select(vector => new VectorInfo(vector, GetVectorNorm(vector, target))) .OrderByDescending(vectorInfo => vectorInfo.Norm) .ToArray(); } // This method takes most of the time, so everything related to it must be optimized. // In every sequence, next vector always goes after the previous one from dictionary. // E.g. if dictionary is [x, y, z], then only [x, y] sequence could be generated, and [y, x] will never be generated. // That way, the complexity of search goes down by a factor of MaxVectorsCount! (as if [x, y] does not add up to a required target, there is no point in checking [y, x]) private static IEnumerable>> GenerateUnorderedSequences(Vector remainder, int remainderNorm, int allowedRemainingWords, ImmutableArray dictionary, int currentDictionaryPosition) { if (allowedRemainingWords > 1) { var newAllowedRemainingWords = allowedRemainingWords - 1; // E.g. if remainder norm is 7, 8 or 9, and allowedRemainingWords is 3, // we need the largest remaining word to have a norm of at least 3 var requiredRemainderPerWord = (remainderNorm + allowedRemainingWords - 1) / allowedRemainingWords; for (var i = FindFirstWithNormLessOrEqual(remainderNorm, dictionary, currentDictionaryPosition); i < dictionary.Length; i++) { var currentVectorInfo = dictionary[i]; if (currentVectorInfo.Vector == remainder) { yield return ImmutableStack.Create(currentVectorInfo.Vector); } else if (currentVectorInfo.Norm < requiredRemainderPerWord) { break; } else if (Vector.LessThanOrEqualAll(currentVectorInfo.Vector, remainder)) { var newRemainder = remainder - currentVectorInfo.Vector; var newRemainderNorm = remainderNorm - currentVectorInfo.Norm; foreach (var result in GenerateUnorderedSequences(newRemainder, newRemainderNorm, newAllowedRemainingWords, dictionary, i)) { yield return result.Push(currentVectorInfo.Vector); } } } } else { for (var i = FindFirstWithNormLessOrEqual(remainderNorm, dictionary, currentDictionaryPosition); i < dictionary.Length; i++) { var currentVectorInfo = dictionary[i]; if (currentVectorInfo.Vector == remainder) { yield return ImmutableStack.Create(currentVectorInfo.Vector); } else if (currentVectorInfo.Norm < remainderNorm) { break; } } } } // BCL BinarySearch would find any vector with required norm, not the first one; or would find nothing if there is no such vector private static int FindFirstWithNormLessOrEqual(int expectedNorm, ImmutableArray dictionary, int offset) { var start = offset; var end = dictionary.Length - 1; if (dictionary[start].Norm <= expectedNorm) { return start; } if (dictionary[end].Norm > expectedNorm) { return dictionary.Length; } // Norm for start is always greater than expected norm, or start is the required position; norm for end is always less than or equal to expected norm // The loop always ends, because the difference always decreases; if start + 1 = end, then middle will be equal to start, and either end := middle = start or start := middle + 1 = end. while (start < end) { var middle = (start + end) / 2; var newNorm = dictionary[middle].Norm; if (dictionary[middle].Norm <= expectedNorm) { end = middle; } else { start = middle + 1; } } return start; } private static IEnumerable GeneratePermutations(T[] original) { var length = original.Length; foreach (var permutation in PrecomputedPermutationsGenerator.HamiltonianPermutations(length)) { var result = new T[length]; for (var i = 0; i < length; i++) { result[i] = original[permutation[i]]; } yield return result; } } private struct VectorInfo { public VectorInfo(Vector vector, int norm) { this.Vector = vector; this.Norm = norm; } public Vector Vector { get; } public int Norm { get; } } } }