refactoring
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@ -28,38 +28,45 @@ namespace vcg {
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namespace math {
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/**
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* RandomGenerator includes two Random Number Generation algortihms.
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*
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* The first one is derived from a STL extension of sgi:
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* it is based on the Subtractive Ring method.
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* Note: this code assumes that int is 32 bits.
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*
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* The second one is an improved Marsenne-Twister algorithm (MT19937)
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* Coded by Takuji Nishimura and Makoto Matsumoto (see copyright note below)
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* and successively modified to be a C++ class by Daniel Dunbar.
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*
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* References for Subtractive Ring:
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*
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* D. E. Knuth, The Art of Computer Programming. Volume 2: Seminumerical Algorithms, 2nd Edition. Addison-Wesley, 1981.
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* (section 3.6 of Knuth for an implementation of the subtractive method in FORTRAN)
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* (section 3.2.2 of Knuth analyzes this class of algorithms)
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*
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* References for improved Marsenne-Twister:
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*
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* http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
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* Common interface for random generation (with uniform distribution).
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*
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* Two RNGs are available: Subtractive Ring and an improved Marsenne-Twister.
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*/
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class RandomGenerator
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{
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// definitions (used by the improved Marsenne-Twister algorithm)
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private:
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// construction
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public:
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static const long N = 624;
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static const long M = 397;
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static const unsigned long MATRIX_A = 0x9908b0dfUL; // constant vector a
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static const unsigned long UPPER_MASK = 0x80000000UL; // most significant w-r bits
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static const unsigned long LOWER_MASK = 0x7fffffffUL; // least significant r bits
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RandomGenerator(){}
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// public methods
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public:
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/// (Re-)initialize with a given seed.
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virtual void initialize(unsigned int seed)=0;
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/// Return a random number in the given range (note that not all the RNG can handle a given limit).
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virtual unsigned int generate(unsigned int limit)=0;
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/// Return a random number in the [0,1) real interval.
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virtual double generate01()=0;
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};
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/**
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* Uniform RNG derived from a STL extension of sgi.
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*
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* It is based on the Subtractive Ring method.
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* This implementation assumes that int is 32 bits.
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*
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* References
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*
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* D. E. Knuth, The Art of Computer Programming. Volume 2: Seminumerical Algorithms, 2nd Edition. Addison-Wesley, 1981.
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* (section 3.6 of Knuth for an implementation of the subtractive method in FORTRAN)
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* (section 3.2.2 of Knuth analyzes this class of algorithms)
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*/
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class SubtractiveRingRNG : public RandomGenerator
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{
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// private data member
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private:
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@ -69,32 +76,31 @@ private:
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size_t _M_index1;
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size_t _M_index2;
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// Improved Marsenne-Twister RNG status variables
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unsigned long mt[N]; // the array for the state vector
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int mti;
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// construction
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public:
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// ctor
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RandomGenerator(){}
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SubtractiveRingRNG()
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{
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initialize(161803398u);
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}
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// public methods
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public:
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/// Initialize Subtractive Ring RNG with a given seed.
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void initializeSubtractiveRing(unsigned int __seed = 161803398u)
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/// (Re-)initialize with a given seed.
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void initialize(unsigned int seed)
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{
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unsigned int __k = 1;
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_M_table[54] = __seed;
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_M_table[54] = seed;
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size_t __i;
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for (__i = 0; __i < 54; __i++)
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{
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size_t __ii = (21 * (__i + 1) % 55) - 1;
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_M_table[__ii] = __k;
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__k = __seed - __k;
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__seed = _M_table[__ii];
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__k = seed - __k;
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seed = _M_table[__ii];
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}
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for (int __loop = 0; __loop < 4; __loop++)
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{
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@ -105,27 +111,79 @@ public:
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_M_index2 = 31;
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}
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/// Return a random number in the given range (__limit) using the Subtractive Ring method.
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unsigned int generateWithSubtractiveRing(unsigned int __limit)
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/// Return a random number in the given range (limit) using the Subtractive Ring method.
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unsigned int generate(unsigned int limit)
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{
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_M_index1 = (_M_index1 + 1) % 55;
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_M_index2 = (_M_index2 + 1) % 55;
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_M_table[_M_index1] = _M_table[_M_index1] - _M_table[_M_index2];
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return _M_table[_M_index1] % __limit;
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return _M_table[_M_index1] % limit;
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}
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/// Initialize Improved Marsenne Twister RNG with the given seed.
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void initializeImprovedMarsenneTwister(unsigned long seed = 5489UL)
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/// Return a random number in the [0,1) real interval using the Subtractive Ring method.
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double generate01()
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{
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mt[0]= seed & 0xffffffffUL;
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unsigned int lmt = 0xffffffffu;
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unsigned int number = generate(lmt);
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return static_cast<double>(number) / static_cast<double>(lmt);
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}
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};
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/**
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* The second one is an improved Marsenne-Twister algorithm (MT19937)
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* Coded by Takuji Nishimura and Makoto Matsumoto (see copyright note below)
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* and successively modified to be a C++ class by Daniel Dunbar.
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*
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*
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* References for improved Marsenne-Twister:
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*
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* http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
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*
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*/
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class MarsenneTwisterRNG : public RandomGenerator
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{
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// definitions
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private:
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static const int N = 624;
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static const int M = 397;
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static const unsigned int MATRIX_A = 0x9908b0dfu; // constant vector a
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static const unsigned int UPPER_MASK = 0x80000000u; // most significant w-r bits
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static const unsigned int LOWER_MASK = 0x7fffffffu; // least significant r bits
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// private data member
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private:
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// Improved Marsenne-Twister RNG status variables
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unsigned int mt[N]; // the array for the state vector
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int mti;
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// construction
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public:
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// ctor
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MarsenneTwisterRNG()
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{
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initialize(5489u);
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}
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// public methods
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public:
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/// (Re-)initialize with the given seed.
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void initialize(unsigned int seed)
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{
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mt[0]= seed & 0xffffffffu;
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for (mti=1; mti<N; mti++)
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{
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mt[mti] = (1812433253UL * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti);
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mt[mti] = (1812433253u * (mt[mti-1] ^ (mt[mti-1] >> 30)) + mti);
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/* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
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/* In the previous versions, MSBs of the seed affect */
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/* only MSBs of the array mt[]. */
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/* 2002/01/09 modified by Makoto Matsumoto */
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mt[mti] &= 0xffffffffUL;
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mt[mti] &= 0xffffffffu;
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/* for >32 bit machines */
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}
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}
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@ -136,16 +194,16 @@ public:
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* init_key is the array for initializing keys
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* key_length is its length
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*/
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void initializeImprovedMarsenneTwister(unsigned long init_key[], int key_length)
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void initializeByArray(unsigned int init_key[], int key_length)
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{
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int i, j, k;
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initializeImprovedMarsenneTwister(19650218UL);
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initialize(19650218u);
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i=1; j=0;
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k = (N>key_length ? N : key_length);
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for (; k; k--)
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{
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mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525UL)) + init_key[j] + j; /* non linear */
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mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
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mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1664525u)) + init_key[j] + j; /* non linear */
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mt[i] &= 0xffffffffu; /* for WORDSIZE > 32 machines */
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i++; j++;
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if (i>=N)
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@ -159,8 +217,8 @@ public:
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for (k=N-1; k; k--)
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{
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mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941UL)) - i; /* non linear */
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mt[i] &= 0xffffffffUL; /* for WORDSIZE > 32 machines */
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mt[i] = (mt[i] ^ ((mt[i-1] ^ (mt[i-1] >> 30)) * 1566083941u)) - i; /* non linear */
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mt[i] &= 0xffffffffu; /* for WORDSIZE > 32 machines */
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i++;
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if (i>=N)
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{
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@ -169,14 +227,18 @@ public:
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}
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}
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mt[0] = 0x80000000UL; /* MSB is 1; assuring non-zero initial array */
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mt[0] = 0x80000000u; /* MSB is 1; assuring non-zero initial array */
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}
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/// Return a random number in the [0,0xffffffff] interval using the improved Marsenne Twister algorithm.
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unsigned long generateWithImprovedMarsenneTwister()
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/**
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* Return a random number in the [0,0xffffffff] interval using the improved Marsenne Twister algorithm.
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*
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* NOTE: Limit is not considered, the interval is fixed.
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*/
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unsigned int generate(unsigned int limit)
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{
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unsigned long y;
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static unsigned long mag01[2]={0x0UL, MATRIX_A};
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unsigned int y;
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static unsigned int mag01[2]={0x0u, MATRIX_A};
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/* mag01[x] = x * MATRIX_A for x=0,1 */
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if (mti >= N) // generate N words at one time
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@ -186,17 +248,17 @@ public:
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for (kk=0;kk<N-M;kk++)
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{
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y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
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mt[kk] = mt[kk+M] ^ (y >> 1) ^ mag01[y & 0x1UL];
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mt[kk] = mt[kk+M] ^ (y >> 1) ^ mag01[y & 0x1u];
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}
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for (;kk<N-1;kk++)
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{
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y = (mt[kk]&UPPER_MASK)|(mt[kk+1]&LOWER_MASK);
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mt[kk] = mt[kk+(M-N)] ^ (y >> 1) ^ mag01[y & 0x1UL];
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mt[kk] = mt[kk+(M-N)] ^ (y >> 1) ^ mag01[y & 0x1u];
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}
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y = (mt[N-1]&UPPER_MASK)|(mt[0]&LOWER_MASK);
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mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1UL];
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mt[N-1] = mt[M-1] ^ (y >> 1) ^ mag01[y & 0x1u];
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mti = 0;
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}
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@ -205,29 +267,29 @@ public:
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/* Tempering */
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y ^= (y >> 11);
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y ^= (y << 7) & 0x9d2c5680UL;
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y ^= (y << 15) & 0xefc60000UL;
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y ^= (y << 7) & 0x9d2c5680u;
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y ^= (y << 15) & 0xefc60000u;
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y ^= (y >> 18);
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return y;
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}
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/// Generates a random number in the [0,1] real interval using the improved Marsenne-Twister.
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double generateDoubleLRwithImprovedMT()
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/// Returns a random number in the [0,1] real interval using the improved Marsenne-Twister.
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double generate01closed()
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{
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return generateWithImprovedMarsenneTwister()*(1.0/4294967295.0);
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return generate(0)*(1.0/4294967295.0);
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}
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/// Generates a random number in the [0,1) real interval using the improved Marsenne-Twister.
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double generateDoubleLwithImprovedMT()
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/// Returns a random number in the [0,1) real interval using the improved Marsenne-Twister.
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double generate01()
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{
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return generateWithImprovedMarsenneTwister()*(1.0/4294967296.0);
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return generate(0)*(1.0/4294967296.0);
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}
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/// Generates a random number in the (0,1) real interval using the improved Marsenne-Twister.
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double generateDoubleWithImprovedMT()
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double generate01open()
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{
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return (((double)generateWithImprovedMarsenneTwister()) + 0.5)*(1.0/4294967296.0);
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return (((double)generate(0)) + 0.5)*(1.0/4294967296.0);
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}
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};
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