From cf3a29feb5887344b6633ead1b4b6d5657a15a4b Mon Sep 17 00:00:00 2001
From: Patrick Simianer <p@simianer.de>
Date: Sun, 15 Jun 2014 03:24:33 +0200
Subject: old stuff: algorithms

---
 algorithms/ibm1_em.py | 93 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 93 insertions(+)
 create mode 100755 algorithms/ibm1_em.py

(limited to 'algorithms/ibm1_em.py')

diff --git a/algorithms/ibm1_em.py b/algorithms/ibm1_em.py
new file mode 100755
index 0000000..09b7d78
--- /dev/null
+++ b/algorithms/ibm1_em.py
@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+"""
+ibm1_em.py
+
+Patrick Simianer <p@simianer.de>
+2010-11-01
+"""
+
+
+def print_probabilities(e_words, f_words, t):
+  s = ""
+  for i in range(len(f_words)):
+    s = s + "\t" + f_words[i]
+
+  s = s + "\n"
+
+  for j in range(len(e_words)):
+    s = s + e_words[j]
+    for i in range(len(f_words)):
+      s = s + "\t%.2f" % t[i][j]
+    s = s + "\n"
+
+  print  s
+
+
+def em(sentence_pairs, iterations):
+  pairs = [(p[0].lower().split(), p[1].lower().split()) for p in sentence_pairs]
+
+  e_vocab = []
+  f_vocab = []
+  for (e, f) in pairs:
+    e_vocab.extend([w for w in e if w not in e_vocab])
+    f_vocab.extend([w for w in f if w not in f_vocab])
+
+  # uniform init
+  e_len = len(e_vocab)
+  f_len = len(f_vocab)
+
+  t = []
+  for i in range(f_len):
+    t.append([])
+    for ew in e_vocab:
+      t[i].append(1./e_len)
+
+  print "Initial:"
+  print_probabilities(e_vocab, f_vocab, t)
+
+  while iterations > 0:
+    # initialize
+    count = []
+    total = []
+    for j in range(f_len):
+      total.append(0.)
+      count.append([])
+      for k in range(e_len):
+        count[j].append(0.)
+
+    s_total = []
+    for (e, f) in pairs:
+      # normalize
+      for ew in e:
+        s_total.append(0.)
+        ei = e_vocab.index(ew)
+        for fw in f:
+          fi = f_vocab.index(fw)
+          s_total[ei] = s_total[ei] + t[fi][ei]
+      # count
+      for ew in e:
+        ei = e_vocab.index(ew)
+        for fw in f:
+          fi = f_vocab.index(fw)
+          count[fi][ei] += t[fi][ei]/s_total[ei]
+          total[fi]   += t[fi][ei]/s_total[ei]
+
+    # estimate
+    for i in range(f_len):
+      for j in range(e_len):
+        t[i][j] = count[i][j]/total[i]
+
+    print "Step"
+    print_probabilities(e_vocab, f_vocab, t)
+    print iterations
+    iterations = iterations-1
+
+def main():
+     sp = [("The house", "Das Haus"), ("The book", "Das Buch"), ("A book", "Ein Buch")]
+     em(sp, 5)
+
+if __name__ == '__main__':
+  main()
+
-- 
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