cdec cleanup, remove bayesian stuff, parsing stuff

1 files changed, 0 insertions, 161 deletions

diff --git a/gi/pf/nuisance_test.cc b/gi/pf/nuisance_test.cc
deleted file mode 100644
index fc0af9cb..00000000
--- a/gi/pf/nuisance_test.cc
+++ /dev/null
@@ -1,161 +0,0 @@
-#include "ccrp.h"
-
-#include <vector>
-#include <iostream>
-
-#include "tdict.h"
-#include "transliterations.h"
-
-using namespace std;
-
-MT19937 rng;
-
-ostream& operator<<(ostream&os, const vector<int>& v) {
-  os << '[' << v[0];
-  if (v.size() == 2) os << ' ' << v[1];
-  return os << ']';
-}
-
-struct Base {
-  Base() : llh(), v(2), v1(1), v2(1), crp(0.25, 0.5) {}
-  inline double p0(const vector<int>& x) const {
-    double p = 0.75;
-    if (x.size() == 2) p = 0.25;
-    p *= 1.0 / 3.0;
-    if (x.size() == 2) p *= 1.0 / 3.0;
-    return p;
-  }
-  double est_deriv_prob(int a, int b, int seg) const {
-    assert(a > 0 && a < 4);  // a \in {1,2,3}
-    assert(b > 0 && b < 4);  // b \in {1,2,3}
-    assert(seg == 0 || seg == 1);   // seg \in {0,1}
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      return crp.prob(v, p0(v));
-    } else {
-      v1[0] = a;
-      v2[0] = b;
-      return crp.prob(v1, p0(v1)) * crp.prob(v2, p0(v2));
-    }
-  }
-  double est_marginal_prob(int a, int b) const {
-    return est_deriv_prob(a,b,0) + est_deriv_prob(a,b,1);
-  }
-  int increment(int a, int b, double* pw = NULL) {
-    double p1 = est_deriv_prob(a, b, 0);
-    double p2 = est_deriv_prob(a, b, 1);
-    //p1 = 0.5; p2 = 0.5;
-    int seg = rng.SelectSample(p1,p2);
-    double tmp = 0;
-    if (!pw) pw = &tmp;
-    double& w = *pw;
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      w = crp.prob(v, p0(v)) / p1;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      w = crp.prob(v1, p0(v1)) / p2;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      w *= crp.prob(v2, p0(v2));
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-    return seg;
-  }
-  void increment(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.increment(v, p0(v), &rng)) {
-        llh += log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.increment(v1, p0(v1), &rng)) {
-        llh += log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.increment(v2, p0(v2), &rng)) {
-        llh += log(p0(v2));
-      }
-    }
-  }
-  void decrement(int a, int b, int seg) {
-    if (seg == 0) {
-      v[0] = a;
-      v[1] = b;
-      if (crp.decrement(v, &rng)) {
-        llh -= log(p0(v));
-      }
-    } else {
-      v1[0] = a;
-      if (crp.decrement(v1, &rng)) {
-        llh -= log(p0(v1));
-      }
-      v2[0] = b;
-      if (crp.decrement(v2, &rng)) {
-        llh -= log(p0(v2));
-      }
-    }
-  }
-  double log_likelihood() const {
-    return llh + crp.log_crp_prob();
-  }
-  double llh;
-  mutable vector<int> v, v1, v2;
-  CCRP<vector<int> > crp;
-};
-
-int main(int argc, char** argv) {
-  double tl = 0;
-  const int ITERS = 1000;
-  const int PARTICLES = 20;
-  const int DATAPOINTS = 50;
-  WordID x = TD::Convert("souvenons");
-  WordID y = TD::Convert("remember");
-  vector<WordID> src; TD::ConvertSentence("s o u v e n o n s", &src);
-  vector<WordID> trg; TD::ConvertSentence("r e m e m b e r", &trg);
-//  Transliterations xx;
-//  xx.Initialize(x, src, y, trg);
-//  return 1;
-
- for (int j = 0; j < ITERS; ++j) {
-  Base b;
-  vector<int> segs(DATAPOINTS);
-  SampleSet<double> ss;
-  vector<int> sss;
-  for (int i = 0; i < DATAPOINTS; i++) {
-    ss.clear();
-    sss.clear();
-    int x = ((i / 10) % 3) + 1;
-    int y = (i % 3) + 1;
-    //double ep = b.est_marginal_prob(x,y);
-    //cerr << "est p(" << x << "," << y << ") = " << ep << endl;
-    for (int n = 0; n < PARTICLES; ++n) {
-      double w;
-      int seg = b.increment(x,y,&w);
-      //cerr << seg << " w=" << w << endl;
-      ss.add(w);
-      sss.push_back(seg);
-      b.decrement(x,y,seg);
-    }
-    int seg = sss[rng.SelectSample(ss)];
-    b.increment(x, y, seg);
-    //cerr << "Selected: " << seg << endl;
-    //return 1;
-    segs[i] = seg;
-  }
-  tl += b.log_likelihood();
- }
-  cerr << "LLH=" << tl / ITERS << endl;
-}
-