merge with upstream

1 files changed, 58 insertions, 0 deletions

diff --git a/gi/pf/pyp_word_model.h b/gi/pf/pyp_word_model.h
new file mode 100644
index 00000000..ff366865
--- /dev/null
+++ b/gi/pf/pyp_word_model.h
@@ -0,0 +1,58 @@
+#ifndef _PYP_WORD_MODEL_H_
+#define _PYP_WORD_MODEL_H_
+
+#include <iostream>
+#include <cmath>
+#include <vector>
+#include "prob.h"
+#include "ccrp.h"
+#include "m.h"
+#include "tdict.h"
+#include "os_phrase.h"
+
+// PYP(d,s,poisson-uniform) represented as a CRP
+struct PYPWordModel {
+  explicit PYPWordModel(const unsigned vocab_e_size, const double mean_len = 5) :
+      base(prob_t::One()), r(1,1,1,1,0.66,50.0), u0(-std::log(vocab_e_size)), mean_length(mean_len) {}
+
+  void ResampleHyperparameters(MT19937* rng);
+
+  inline prob_t operator()(const std::vector<WordID>& s) const {
+    return r.prob(s, p0(s));
+  }
+
+  inline void Increment(const std::vector<WordID>& s, MT19937* rng) {
+    if (r.increment(s, p0(s), rng))
+      base *= p0(s);
+  }
+
+  inline void Decrement(const std::vector<WordID>& s, MT19937 *rng) {
+    if (r.decrement(s, rng))
+      base /= p0(s);
+  }
+
+  inline prob_t Likelihood() const {
+    prob_t p; p.logeq(r.log_crp_prob());
+    p *= base;
+    return p;
+  }
+
+  void Summary() const;
+
+ private:
+  inline double logp0(const std::vector<WordID>& s) const {
+    return Md::log_poisson(s.size(), mean_length) + s.size() * u0;
+  }
+
+  inline prob_t p0(const std::vector<WordID>& s) const {
+    prob_t p; p.logeq(logp0(s));
+    return p;
+  }
+
+  prob_t base;  // keeps track of the draws from the base distribution
+  CCRP<std::vector<WordID> > r;
+  const double u0;  // uniform log prob of generating a letter
+  const double mean_length;  // mean length of a word in the base distribution
+};
+
+#endif