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148 lines
6.8 KiB
148 lines
6.8 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.Collections.Immutable;
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using System.Diagnostics;
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using System.Linq;
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using System.Numerics;
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internal class VectorsProcessor
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{
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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 VectorsProcessor()
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{
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PrecomputedPermutationsGenerator.HamiltonianPermutations(0);
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}
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public VectorsProcessor(Vector<byte> target, int maxVectorsCount, IEnumerable<Vector<byte>> dictionary, Func<Vector<byte>, string> vectorToString)
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{
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this.Target = target;
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this.MaxVectorsCount = maxVectorsCount;
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this.VectorToString = vectorToString;
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this.Dictionary = ImmutableStack.Create<Vector<byte>>(FilterVectors(dictionary, target).ToArray());
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}
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/// <summary>
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/// Negative sign bit.
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/// (byte)b & (byte)128 equals zero for non-negative (0..127) bytes and equals (byte)128 for negative (128..255) bytes.
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/// Similarly, vector & Negative equals zero if all bytes are non-negative, and does not equal zero if some bytes are negative.
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/// Use <code>(vector & Negative) == Vector<byte>.Zero</code> to determine if all components are non-negative.
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/// </summary>
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private static Vector<byte> Negative { get; } = new Vector<byte>(Enumerable.Repeat((byte)128, 16).ToArray());
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private Vector<byte> Target { get; }
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private int MaxVectorsCount { get; }
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private ImmutableStack<Vector<byte>> Dictionary { get; }
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private Func<Vector<byte>, string> VectorToString { get; }
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private long Iterations { get; set; } = 0;
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// Produces all sequences of vectors with the target sum
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public ParallelQuery<Vector<byte>[]> GenerateSequences()
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{
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var unorderedSequences = this.GenerateUnorderedSequences(this.Target, ImmutableStack.Create<Vector<byte>>(), this.Dictionary)
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.AsParallel();
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var allSequences = unorderedSequences.SelectMany(this.GeneratePermutations);
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return allSequences;
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}
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// We want words with more letters (and among these, words with more "rare" letters) to appear first, to reduce the searching time somewhat.
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// Applying such a sort, we reduce the total number of triplets to check for anagrams from ~62M to ~29M.
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// Total number of quadruplets is reduced from 1468M to mere 311M.
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// And total number of quintuplets becomes reasonable 1412M.
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// 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").
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// This method basically gives us the 1-norm of the vector in the space rescaled so that the target is [1, 1, ..., 1].
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private static int GetVectorWeight(Vector<byte> vector, Vector<byte> target)
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{
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var weight = 0;
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for (var i = 0; target[i] != 0; i++)
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{
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weight += (720 * vector[i]) / target[i]; // 720 = 6!, so that the result will be a whole number (unless Target[i] > 6)
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}
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return weight;
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}
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private static IEnumerable<Vector<byte>> FilterVectors(IEnumerable<Vector<byte>> vectors, Vector<byte> target)
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{
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return vectors
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.Where(vector => ((target - vector) & Negative) == Vector<byte>.Zero)
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.OrderBy(vector => GetVectorWeight(vector, target));
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}
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[Conditional("DEBUG")]
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private void DebugState(ImmutableStack<Vector<byte>> partialSumStack, Vector<byte> currentVector)
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{
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this.Iterations++;
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if (this.Iterations % 1000000 == 0)
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{
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Console.WriteLine($"Iteration #{this.Iterations}: {string.Join(" ", partialSumStack.Push(currentVector).Reverse().Select(vector => this.VectorToString(vector)))}");
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}
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}
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// This method takes most of the time, so everything related to it must be optimized.
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// In every sequence, next vector always goes after the previous one from dictionary.
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// E.g. if dictionary is [x, y, z], then only [x, y] sequence could be generated, and [y, x] will never be generated.
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// 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])
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private IEnumerable<Vector<byte>[]> GenerateUnorderedSequences(Vector<byte> remainder, ImmutableStack<Vector<byte>> partialSumStack, ImmutableStack<Vector<byte>> dictionaryStack)
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{
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var count = partialSumStack.Count() + 1;
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if (count < this.MaxVectorsCount)
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{
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var dictionaryTail = dictionaryStack;
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while (!dictionaryTail.IsEmpty)
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{
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Vector<byte> currentVector;
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var nextDictionaryTail = dictionaryTail.Pop(out currentVector);
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this.DebugState(partialSumStack, currentVector);
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var newRemainder = remainder - currentVector;
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if (newRemainder == Vector<byte>.Zero)
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{
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yield return partialSumStack.Push(currentVector).Reverse().ToArray();
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}
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else if ((newRemainder & Negative) == Vector<byte>.Zero)
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{
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foreach (var result in this.GenerateUnorderedSequences(newRemainder, partialSumStack.Push(currentVector), dictionaryTail))
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{
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yield return result;
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}
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}
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dictionaryTail = nextDictionaryTail;
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}
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}
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else if (count == this.MaxVectorsCount)
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{
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var dictionaryTail = dictionaryStack;
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while (!dictionaryTail.IsEmpty)
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{
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Vector<byte> currentVector;
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dictionaryTail = dictionaryTail.Pop(out currentVector);
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this.DebugState(partialSumStack, currentVector);
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var newRemainder = remainder - currentVector;
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if (newRemainder == Vector<byte>.Zero)
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{
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yield return partialSumStack.Push(currentVector).Reverse().ToArray();
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}
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}
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}
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}
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private IEnumerable<T[]> GeneratePermutations<T>(T[] original)
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{
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foreach (var permutation in PrecomputedPermutationsGenerator.HamiltonianPermutations(original.Length))
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{
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yield return permutation.Select(i => original[i]).ToArray();
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}
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}
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}
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}
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