/*
 *******************************************************************
 *                                                                 *
 *                        Open Bloom Filter                        *
 *                                                                 *
 * Author: Arash Partow - 2000                                     *
 * URL: http://www.partow.net/programming/hashfunctions/index.html *
 *                                                                 *
 * Copyright notice:                                               *
 * Free use of the Open Bloom Filter Library is permitted under    *
 * the guidelines and in accordance with the most current version  *
 * of the Boost Software License, Version 1.0                      *
 * http://www.opensource.org/licenses/bsl1.0.html                  *
 *                                                                 *
 *******************************************************************
*/


#ifndef INCLUDE_BLOOM_FILTER_HPP
#define INCLUDE_BLOOM_FILTER_HPP

#include <cstddef>
#include <algorithm>
#include <cmath>
#include <limits>
#include <string>
#include <vector>


static const std::size_t bits_per_char = 0x08;    // 8 bits in 1 char(unsigned)
static const unsigned char bit_mask[bits_per_char] = {
                                                       0x01,  //00000001
                                                       0x02,  //00000010
                                                       0x04,  //00000100
                                                       0x08,  //00001000
                                                       0x10,  //00010000
                                                       0x20,  //00100000
                                                       0x40,  //01000000
                                                       0x80   //10000000
                                                     };


class bloom_filter
{
protected:

   typedef unsigned int bloom_type;
   typedef unsigned char cell_type;

public:

   bloom_filter(const std::size_t& predicted_inserted_element_count,
                const double& false_positive_probability,
                const std::size_t& random_seed)
   : bit_table_(0),
     predicted_inserted_element_count_(predicted_inserted_element_count),
     inserted_element_count_(0),
     random_seed_((random_seed) ? random_seed : 0xA5A5A5A5),
     desired_false_positive_probability_(false_positive_probability)
   {
      find_optimal_parameters();
      generate_unique_salt();
      raw_table_size_ = table_size_ / bits_per_char;
      bit_table_ = new cell_type[raw_table_size_];
      std::fill_n(bit_table_,raw_table_size_,0x00);
   }

   bloom_filter(const bloom_filter& filter)
   {
      this->operator=(filter);
   }

   bloom_filter& operator = (const bloom_filter& filter)
   {
      salt_count_ = filter.salt_count_;
      table_size_ = filter.table_size_;
      raw_table_size_ = filter.raw_table_size_;
      predicted_inserted_element_count_ = filter.predicted_inserted_element_count_;
      inserted_element_count_ = filter.inserted_element_count_;
      random_seed_ = filter.random_seed_;
      desired_false_positive_probability_ = filter.desired_false_positive_probability_;
      delete[] bit_table_;
      bit_table_ = new cell_type[raw_table_size_];
      std::copy(filter.bit_table_,filter.bit_table_ + raw_table_size_,bit_table_);
      salt_ = filter.salt_;
      return *this;
   }

   virtual ~bloom_filter()
   {
      delete[] bit_table_;
   }

   inline bool operator!() const
   {
      return (0 == table_size_);
   }

   inline void clear()
   {
      std::fill_n(bit_table_,raw_table_size_,0x00);
      inserted_element_count_ = 0;
   }

   inline void insert(const unsigned char* key_begin, const std::size_t& length)
   {
      std::size_t bit_index = 0;
      std::size_t bit = 0;
      for (std::size_t i = 0; i < salt_.size(); ++i)
      {
         compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
         bit_table_[bit_index / bits_per_char] |= bit_mask[bit];
      }
      ++inserted_element_count_;
   }

   template<typename T>
   inline void insert(const T& t)
   {
      // Note: T must be a C++ POD type.
      insert(reinterpret_cast<const unsigned char*>(&t),sizeof(T));
   }

   inline void insert(const std::string& key)
   {
      insert(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
   }

   inline void insert(const char* data, const std::size_t& length)
   {
      insert(reinterpret_cast<const unsigned char*>(data),length);
   }

   template<typename InputIterator>
   inline void insert(const InputIterator begin, const InputIterator end)
   {
      InputIterator itr = begin;
      while (end != itr)
      {
         insert(*(itr++));
      }
   }

   inline virtual bool contains(const unsigned char* key_begin, const std::size_t length) const
   {
      std::size_t bit_index = 0;
      std::size_t bit = 0;
      for (std::size_t i = 0; i < salt_.size(); ++i)
      {
         compute_indices(hash_ap(key_begin,length,salt_[i]),bit_index,bit);
         if ((bit_table_[bit_index / bits_per_char] & bit_mask[bit]) != bit_mask[bit])
         {
            return false;
         }
      }
      return true;
   }

   template<typename T>
   inline bool contains(const T& t) const
   {
      return contains(reinterpret_cast<const unsigned char*>(&t),static_cast<std::size_t>(sizeof(T)));
   }

   inline bool contains(const std::string& key) const
   {
      return contains(reinterpret_cast<const unsigned char*>(key.c_str()),key.size());
   }

   inline bool contains(const char* data, const std::size_t& length) const
   {
      return contains(reinterpret_cast<const unsigned char*>(data),length);
   }

   template<typename InputIterator>
   inline InputIterator contains_all(const InputIterator begin, const InputIterator end) const
   {
      InputIterator itr = begin;
      while (end != itr)
      {
         if (!contains(*itr))
         {
            return itr;
         }
         ++itr;
      }
      return end;
   }

   template<typename InputIterator>
   inline InputIterator contains_none(const InputIterator begin, const InputIterator end) const
   {
      InputIterator itr = begin;
      while (end != itr)
      {
         if (contains(*itr))
         {
            return itr;
         }
         ++itr;
      }
      return end;
   }

   inline virtual std::size_t size() const
   {
      return table_size_;
   }

   inline std::size_t element_count() const
   {
      return inserted_element_count_;
   }

   inline double effective_fpp() const
   {
      /*
        Note:
        The effective false positive probability is calculated using the
        designated table size and hash function count in conjunction with
        the current number of inserted elements - not the user defined
        predicated/expected number of inserted elements.
      */
      return std::pow(1.0 - std::exp(-1.0 * salt_.size() * inserted_element_count_ / size()), 1.0 * salt_.size());
   }

   inline bloom_filter& operator &= (const bloom_filter& filter)
   {
      /* intersection */
      if (
          (salt_count_  == filter.salt_count_) &&
          (table_size_  == filter.table_size_) &&
          (random_seed_ == filter.random_seed_)
         )
      {
         for (std::size_t i = 0; i < raw_table_size_; ++i)
         {
            bit_table_[i] &= filter.bit_table_[i];
         }
      }
      return *this;
   }

   inline bloom_filter& operator |= (const bloom_filter& filter)
   {
      /* union */
      if (
          (salt_count_  == filter.salt_count_) &&
          (table_size_  == filter.table_size_) &&
          (random_seed_ == filter.random_seed_)
         )
      {
         for (std::size_t i = 0; i < raw_table_size_; ++i)
         {
            bit_table_[i] |= filter.bit_table_[i];
         }
      }
      return *this;
   }

   inline bloom_filter& operator ^= (const bloom_filter& filter)
   {
      /* difference */
      if (
          (salt_count_  == filter.salt_count_) &&
          (table_size_  == filter.table_size_) &&
          (random_seed_ == filter.random_seed_)
         )
      {
         for (std::size_t i = 0; i < raw_table_size_; ++i)
         {
            bit_table_[i] ^= filter.bit_table_[i];
         }
      }
      return *this;
   }

   inline const cell_type* table() const
   {
      return bit_table_;
   }

protected:

   inline virtual void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const
   {
      bit_index = hash % table_size_;
      bit = bit_index % bits_per_char;
   }

   void generate_unique_salt()
   {
      /*
        Note:
        A distinct hash function need not be implementation-wise
        distinct. In the current implementation "seeding" a common
        hash function with different values seems to be adequate.
      */
      const unsigned int predef_salt_count = 128;
      static const bloom_type predef_salt[predef_salt_count] =
                                 {
                                    0xAAAAAAAA, 0x55555555, 0x33333333, 0xCCCCCCCC,
                                    0x66666666, 0x99999999, 0xB5B5B5B5, 0x4B4B4B4B,
                                    0xAA55AA55, 0x55335533, 0x33CC33CC, 0xCC66CC66,
                                    0x66996699, 0x99B599B5, 0xB54BB54B, 0x4BAA4BAA,
                                    0xAA33AA33, 0x55CC55CC, 0x33663366, 0xCC99CC99,
                                    0x66B566B5, 0x994B994B, 0xB5AAB5AA, 0xAAAAAA33,
                                    0x555555CC, 0x33333366, 0xCCCCCC99, 0x666666B5,
                                    0x9999994B, 0xB5B5B5AA, 0xFFFFFFFF, 0xFFFF0000,
                                    0xB823D5EB, 0xC1191CDF, 0xF623AEB3, 0xDB58499F,
                                    0xC8D42E70, 0xB173F616, 0xA91A5967, 0xDA427D63,
                                    0xB1E8A2EA, 0xF6C0D155, 0x4909FEA3, 0xA68CC6A7,
                                    0xC395E782, 0xA26057EB, 0x0CD5DA28, 0x467C5492,
                                    0xF15E6982, 0x61C6FAD3, 0x9615E352, 0x6E9E355A,
                                    0x689B563E, 0x0C9831A8, 0x6753C18B, 0xA622689B,
                                    0x8CA63C47, 0x42CC2884, 0x8E89919B, 0x6EDBD7D3,
                                    0x15B6796C, 0x1D6FDFE4, 0x63FF9092, 0xE7401432,
                                    0xEFFE9412, 0xAEAEDF79, 0x9F245A31, 0x83C136FC,
                                    0xC3DA4A8C, 0xA5112C8C, 0x5271F491, 0x9A948DAB,
                                    0xCEE59A8D, 0xB5F525AB, 0x59D13217, 0x24E7C331,
                                    0x697C2103, 0x84B0A460, 0x86156DA9, 0xAEF2AC68,
                                    0x23243DA5, 0x3F649643, 0x5FA495A8, 0x67710DF8,
                                    0x9A6C499E, 0xDCFB0227, 0x46A43433, 0x1832B07A,
                                    0xC46AFF3C, 0xB9C8FFF0, 0xC9500467, 0x34431BDF,
                                    0xB652432B, 0xE367F12B, 0x427F4C1B, 0x224C006E,
                                    0x2E7E5A89, 0x96F99AA5, 0x0BEB452A, 0x2FD87C39,
                                    0x74B2E1FB, 0x222EFD24, 0xF357F60C, 0x440FCB1E,
                                    0x8BBE030F, 0x6704DC29, 0x1144D12F, 0x948B1355,
                                    0x6D8FD7E9, 0x1C11A014, 0xADD1592F, 0xFB3C712E,
                                    0xFC77642F, 0xF9C4CE8C, 0x31312FB9, 0x08B0DD79,
                                    0x318FA6E7, 0xC040D23D, 0xC0589AA7, 0x0CA5C075,
                                    0xF874B172, 0x0CF914D5, 0x784D3280, 0x4E8CFEBC,
                                    0xC569F575, 0xCDB2A091, 0x2CC016B4, 0x5C5F4421
                                 };

      if (salt_count_ <= predef_salt_count)
      {
         std::copy(predef_salt,
                   predef_salt + salt_count_,
                   std::back_inserter(salt_));
          for (unsigned int i = 0; i < salt_.size(); ++i)
          {
            /*
              Note:
              This is done to integrate the user defined random seed,
              so as to allow for the generation of unique bloom filter
              instances.
            */
            salt_[i] = salt_[i] * salt_[(i + 3) % salt_.size()] + random_seed_;
          }
      }
      else
      {
         std::copy(predef_salt,predef_salt + predef_salt_count,std::back_inserter(salt_));
         srand(static_cast<unsigned int>(random_seed_));
         while (salt_.size() < salt_count_)
         {
            bloom_type current_salt = static_cast<bloom_type>(rand()) * static_cast<bloom_type>(rand());
            if (0 == current_salt) continue;
            if (salt_.end() == std::find(salt_.begin(), salt_.end(), current_salt))
            {
               salt_.push_back(current_salt);
            }
         }
      }
   }

   void find_optimal_parameters()
   {
      /*
        Note:
        The following will attempt to find the number of hash functions
        and minimum amount of storage bits required to construct a bloom
        filter consistent with the user defined false positive probability
        and estimated element insertion count.
      */

      double min_m = std::numeric_limits<double>::infinity();
      double min_k = 0.0;
      double curr_m = 0.0;
      double k = 1.0;
      while (k < 1000.0)
      {
         double numerator   = (- k * predicted_inserted_element_count_);
         double denominator = std::log(1.0 - std::pow(desired_false_positive_probability_, 1.0 / k));
         curr_m = numerator / denominator;

         if (curr_m < min_m)
         {
            min_m = curr_m;
            min_k = k;
         }
         k += 1.0;
      }

      salt_count_ = static_cast<std::size_t>(min_k);
      table_size_ = static_cast<std::size_t>(min_m);
      table_size_ += (((table_size_ % bits_per_char) != 0) ? (bits_per_char - (table_size_ % bits_per_char)) : 0);
   }

   inline bloom_type hash_ap(const unsigned char* begin, std::size_t remaining_length, bloom_type hash) const
   {
      const unsigned char* itr = begin;

      while (remaining_length >= 4)
      {
         hash ^=    (hash <<  7) ^  (*itr++) * (hash >> 3);
         hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
         hash ^=    (hash <<  7) ^  (*itr++) * (hash >> 3);
         hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
         remaining_length -= 4;
      }

      while (remaining_length >= 2)
      {
         hash ^=    (hash <<  7) ^  (*itr++) * (hash >> 3);
         hash ^= (~((hash << 11) + ((*itr++) ^ (hash >> 5))));
         remaining_length -= 2;
      }

      if (remaining_length)
      {
         hash ^= (hash <<  7) ^ (*itr) * (hash >> 3);
      }

      return hash;
   }

   std::vector<bloom_type> salt_;
   unsigned char*          bit_table_;
   std::size_t             salt_count_;
   std::size_t             table_size_;
   std::size_t             raw_table_size_;
   std::size_t             predicted_inserted_element_count_;
   std::size_t             inserted_element_count_;
   std::size_t             random_seed_;
   double                  desired_false_positive_probability_;
};

inline bloom_filter operator & (const bloom_filter& a, const bloom_filter& b)
{
   bloom_filter result = a;
   result &= b;
   return result;
}

inline bloom_filter operator | (const bloom_filter& a, const bloom_filter& b)
{
   bloom_filter result = a;
   result |= b;
   return result;
}

inline bloom_filter operator ^ (const bloom_filter& a, const bloom_filter& b)
{
   bloom_filter result = a;
   result ^= b;
   return result;
}


class compressible_bloom_filter : public bloom_filter
{
public:

   compressible_bloom_filter(const std::size_t& predicted_element_count,
                             const double& false_positive_probability,
                             const std::size_t& random_seed)
   : bloom_filter(predicted_element_count,false_positive_probability,random_seed)
   {
      size_list.push_back(table_size_);
   }

   inline virtual std::size_t size() const
   {
      return size_list.back();
   }

   inline bool compress(const double& percentage)
   {
      if ((0.0 >= percentage) || (percentage >= 100.0))
      {
         return false;
      }

      std::size_t original_table_size = size_list.back();
      std::size_t new_table_size = static_cast<std::size_t>((size_list.back() * (1.0 - (percentage / 100.0))));
      new_table_size -= (((new_table_size % bits_per_char) != 0) ? (new_table_size % bits_per_char) : 0);

      if ((bits_per_char > new_table_size) || (new_table_size >= original_table_size))
      {
         return false;
      }

      desired_false_positive_probability_ = effective_fpp();
      cell_type* tmp = new cell_type[new_table_size / bits_per_char];
      std::copy(bit_table_, bit_table_ + (new_table_size / bits_per_char), tmp);
      cell_type* itr = bit_table_ + (new_table_size / bits_per_char);
      cell_type* end = bit_table_ + (original_table_size / bits_per_char);
      cell_type* itr_tmp = tmp;

      while (end != itr)
      {
         *(itr_tmp++) |= (*itr++);
      }

      delete[] bit_table_;
      bit_table_ = tmp;
      size_list.push_back(new_table_size);

      return true;
   }

private:

   inline virtual void compute_indices(const bloom_type& hash, std::size_t& bit_index, std::size_t& bit) const
   {
      bit_index = hash;
      for (std::size_t i = 0; i < size_list.size(); ++i)
      {
         bit_index %= size_list[i];
      }
      bit = bit_index % bits_per_char;
   }

   std::vector<std::size_t> size_list;
};

#endif


/*
  Note 1:
  If it can be guaranteed that bits_per_char will be of the form 2^n then
  the following optimization can be used:

  hash_table[bit_index >> n] |= bit_mask[bit_index & (bits_per_char - 1)];

  Note 2:
  For performance reasons where possible when allocating memory it should
  be aligned (aligned_alloc) according to the architecture being used.
*/