major refactor, break bad circular deps

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@509 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 147 insertions, 0 deletions

diff --git a/utils/sampler.h b/utils/sampler.h
new file mode 100644
index 00000000..5fef45d0
--- /dev/null
+++ b/utils/sampler.h
@@ -0,0 +1,147 @@
+#ifndef SAMPLER_H_
+#define SAMPLER_H_
+
+#include <algorithm>
+#include <functional>
+#include <numeric>
+#include <iostream>
+#include <fstream>
+#include <vector>
+#include <ctime>
+
+#include <boost/random/mersenne_twister.hpp>
+#include <boost/random/uniform_real.hpp>
+#include <boost/random/variate_generator.hpp>
+#include <boost/random/normal_distribution.hpp>
+#include <boost/random/poisson_distribution.hpp>
+#include <boost/random/uniform_int.hpp>
+
+#include "prob.h"
+
+struct SampleSet;
+
+template <typename RNG>
+struct RandomNumberGenerator {
+  static uint32_t GetTrulyRandomSeed() {
+    uint32_t seed;
+    std::ifstream r("/dev/urandom");
+    if (r) {
+      r.read((char*)&seed,sizeof(uint32_t));
+    }
+    if (r.fail() || !r) {
+      std::cerr << "Warning: could not read from /dev/urandom. Seeding from clock" << std::endl;
+      seed = std::time(NULL);
+    }
+    std::cerr << "Seeding random number sequence to " << seed << std::endl;
+    return seed;
+  }
+
+  RandomNumberGenerator() : m_dist(0,1), m_generator(), m_random(m_generator,m_dist) {
+    uint32_t seed = GetTrulyRandomSeed();
+    m_generator.seed(seed);
+  }
+  explicit RandomNumberGenerator(uint32_t seed) : m_dist(0,1), m_generator(), m_random(m_generator,m_dist) {
+    if (!seed) seed = GetTrulyRandomSeed();
+    m_generator.seed(seed);
+  }
+
+  size_t SelectSample(const prob_t& a, const prob_t& b, double T = 1.0) {
+    if (T == 1.0) {
+      if (this->next() > (a / (a + b))) return 1; else return 0;
+    } else {
+      assert(!"not implemented");
+    }
+  }
+
+  // T is the annealing temperature, if desired
+  size_t SelectSample(const SampleSet& ss, double T = 1.0);
+
+  // draw a value from U(0,1)
+  double next() {return m_random();}
+
+  // draw a value from N(mean,var)
+  double NextNormal(double mean, double var) {
+    return boost::normal_distribution<double>(mean, var)(m_random);
+  }
+
+  // draw a value from a Poisson distribution
+  // lambda must be greater than 0
+  int NextPoisson(int lambda) {
+    return boost::poisson_distribution<int>(lambda)(m_random);
+  }
+
+  bool AcceptMetropolisHastings(const prob_t& p_cur,
+                                const prob_t& p_prev,
+                                const prob_t& q_cur,
+                                const prob_t& q_prev) {
+    const prob_t a = (p_cur / p_prev) * (q_prev / q_cur);
+    if (log(a) >= 0.0) return true;
+    return (prob_t(this->next()) < a);
+  }
+
+  RNG &gen() { return m_generator; }
+  typedef boost::variate_generator<RNG&, boost::uniform_int<> > IntRNG;
+  IntRNG inclusive(int low,int high_incl) {
+    assert(high_incl>=low);
+    return IntRNG(m_generator,boost::uniform_int<>(low,high_incl));
+  }
+
+ private:
+  boost::uniform_real<> m_dist;
+  RNG m_generator;
+  boost::variate_generator<RNG&, boost::uniform_real<> > m_random;
+};
+
+typedef RandomNumberGenerator<boost::mt19937> MT19937;
+
+class SampleSet {
+ public:
+  const prob_t& operator[](int i) const { return m_scores[i]; }
+  prob_t& operator[](int i) { return m_scores[i]; }
+  bool empty() const { return m_scores.empty(); }
+  void add(const prob_t& s) { m_scores.push_back(s); }
+  void clear() { m_scores.clear(); }
+  size_t size() const { return m_scores.size(); }
+  void resize(int size) { m_scores.resize(size); }
+  std::vector<prob_t> m_scores;
+};
+
+template <typename RNG>
+size_t RandomNumberGenerator<RNG>::SelectSample(const SampleSet& ss, double T) {
+  assert(T > 0.0);
+  assert(ss.m_scores.size() > 0);
+  if (ss.m_scores.size() == 1) return 0;
+  const prob_t annealing_factor(1.0 / T);
+  const bool anneal = (annealing_factor != prob_t::One());
+  prob_t sum = prob_t::Zero();
+  if (anneal) {
+    for (int i = 0; i < ss.m_scores.size(); ++i)
+      sum += ss.m_scores[i].pow(annealing_factor);  // p^(1/T)
+  } else {
+    sum = std::accumulate(ss.m_scores.begin(), ss.m_scores.end(), prob_t::Zero());
+  }
+  //for (size_t i = 0; i < ss.m_scores.size(); ++i) std::cerr << ss.m_scores[i] << ",";
+  //std::cerr << std::endl;
+
+  prob_t random(this->next());    // random number between 0 and 1
+  random *= sum;                  // scale with normalization factor
+  //std::cerr << "Random number " << random << std::endl;
+
+  //now figure out which sample
+  size_t position = 1;
+  sum = ss.m_scores[0];
+  if (anneal) {
+    sum.poweq(annealing_factor);
+    for (; position < ss.m_scores.size() && sum < random; ++position)
+      sum += ss.m_scores[position].pow(annealing_factor);
+  } else {
+    for (; position < ss.m_scores.size() && sum < random; ++position)
+      sum += ss.m_scores[position];
+  }
+  //std::cout << "random: " << random <<  " sample: " << position << std::endl;
+  //std::cerr << "Sample: " << position-1 << std::endl;
+  //exit(1);
+  return position-1;
+}
+
+#endif