From 4a129e055387baf922f30f1502c6b6efad7dd8eb Mon Sep 17 00:00:00 2001
From: Chris Dyer <cdyer@cs.cmu.edu>
Date: Thu, 15 Mar 2012 22:47:04 -0400
Subject: bayes bayes bayes

---
 gi/pf/poisson_uniform_word_model.h | 50 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 gi/pf/poisson_uniform_word_model.h

(limited to 'gi/pf/poisson_uniform_word_model.h')

diff --git a/gi/pf/poisson_uniform_word_model.h b/gi/pf/poisson_uniform_word_model.h
new file mode 100644
index 00000000..76204a0e
--- /dev/null
+++ b/gi/pf/poisson_uniform_word_model.h
@@ -0,0 +1,50 @@
+#ifndef _POISSON_UNIFORM_WORD_MODEL_H_
+#define _POISSON_UNIFORM_WORD_MODEL_H_
+
+#include <cmath>
+#include <vector>
+#include "prob.h"
+#include "m.h"
+
+// len ~ Poisson(lambda)
+//   for (1..len)
+//     e_i ~ Uniform({Vocabulary})
+struct PoissonUniformWordModel {
+  explicit PoissonUniformWordModel(const unsigned vocab_size,
+                                   const unsigned alphabet_size,
+                                   const double mean_len = 5) :
+    lh(prob_t::One()),
+    v0(-std::log(vocab_size)),
+    u0(-std::log(alphabet_size)),
+    mean_length(mean_len) {}
+
+  void ResampleHyperparameters(MT19937*) {}
+
+  inline prob_t operator()(const std::vector<WordID>& s) const {
+    prob_t p;
+    p.logeq(Md::log_poisson(s.size(), mean_length) + s.size() * u0);
+    //p.logeq(v0);
+    return p;
+  }
+
+  inline void Increment(const std::vector<WordID>& w, MT19937*) {
+    lh *= (*this)(w);
+  }
+
+  inline void Decrement(const std::vector<WordID>& w, MT19937 *) {
+    lh /= (*this)(w);
+  }
+
+  inline prob_t Likelihood() const { return lh; }
+
+  void Summary() const {}
+
+ private:
+
+  prob_t lh;  // keeps track of the draws from the base distribution
+  const double v0;  // uniform log prob of generating a word
+  const double u0;  // uniform log prob of generating a letter
+  const double mean_length;  // mean length of a word in the base distribution
+};
+
+#endif
-- 
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