1 files changed, 101 insertions, 0 deletions
diff --git a/nlp_tools/dict_utils.py b/nlp_tools/dict_utils.py
new file mode 100644
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+++ b/nlp_tools/dict_utils.py
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+"""
+Utilities for doing math on sparse vectors indexed by arbitrary objects.
+(These will usually be feature vectors.)
+"""
+
+import math_utils as mu
+import math
+
+def d_elt_op_keep(op, zero, args):
+  """
+  Applies op to arguments elementwise, keeping entries that don't occur in
+  every argument (i.e. behaves like a sum).
+  """
+  ret = {}
+  for d in args:
+    for key in d:
+      if key not in ret:
+        ret[key] = d[key]
+      else:
+        ret[key] = op([ret[key], d[key]])
+  for key in ret.keys():
+    if ret[key] == zero:
+      del ret[key]
+  return ret
+
+def d_elt_op_drop(op, args):
+  """
+  Applies op to arguments elementwise, discarding entries that don't occur in
+  every argument (i.e. behaves like a product).
+  """
+  # avoid querying lots of nonexistent keys
+  smallest = min(args, key=len)
+  sindex = args.index(smallest)
+  ret = dict(smallest)
+  for i in range(len(args)):
+    if i == sindex:
+      continue
+    d = args[i]
+    for key in ret.keys():
+      if key in d:
+        ret[key] = op([ret[key], d[key]])
+      else:
+        del ret[key]
+  return ret
+
+def d_sum(args):
+  """
+  Computes a sum of vectors.
+  """
+  return d_elt_op_keep(sum, 0, args)
+
+def d_logspace_sum(args):
+  """
+  Computes a sum of vectors whose elements are represented in logspace.
+  """
+  return d_elt_op_keep(mu.logspace_sum, -float('inf'), args)
+
+def d_elt_prod(args):
+  """
+  Computes an elementwise product of vectors.
+  """
+  return d_elt_op_drop(lambda l: reduce(lambda a,b: a*b, l), args)
+
+def d_dot_prod(d1, d2):
+  """
+  Takes the dot product of the two arguments.
+  """
+  # avoid querying lots of nonexistent keys
+  if len(d2) < len(d1):
+    d1, d2 = d2, d1
+  dot_prod = 0
+  for key in d1:
+    if key in d2:
+      dot_prod += d1[key] * d2[key]
+  return dot_prod
+
+def d_logspace_scalar_prod(c, d):
+  """
+  Multiplies every element of d by c, where c and d are both represented in
+  logspace.
+  """
+  ret = {}
+  for key in d:
+    ret[key] = c + d[key]
+  return ret
+
+def d_op(op, d):
+  """
+  Applies op to every element of the dictionary.
+  """
+  ret = {}
+  for key in d:
+    ret[key] = op(d[key])
+  return ret
+
+# convenience methods
+def d_log(d):
+  return d_op(math.log, d)
+
+def d_exp(d):
+  return d_op(math.exp, d)