Ticket #8732: trac_8732.patch

sage/stats/hmm/chmm.pyx

@@ -81 +81 @@
     Gaussian emissions Hidden Markov Model.
 
     INPUT:
-        - A  -- matrix; the N x N transition matrix
-        - B -- list of pairs (mu,sigma) that define the distributions
-        - pi -- initial state probabilities
+
+        - ``A`` -- matrix; the N x N transition matrix
+        - ``B`` -- list of pairs (mu,sigma) that define the distributions
+        - ``pi`` -- initial state probabilities
+        - ``normalize`` --bool (default: True)
 
     EXAMPLES::
 
@@ -953 +955 @@
     INPUT:
     
         - ``A``  -- matrix; the N x N transition matrix
+
         - ``B`` -- list of pairs (mu,sigma) that define the distributions
+
         - ``pi`` -- initial state probabilities
+
         - ``normalize`` --bool (default: True); if given, input is
           normalized to define valid probability distributions,
           e.g., the entries of A are made nonnegative and the rows

sage/stats/hmm/hmm.pyx

@@ -228 +228 @@
     A discrete Hidden Markov model implemented using double precision
     floating point arithmetic.
 
+    INPUT::
+
+       - ``A`` -- a list of lists or a square N x N matrix, whose
+         (i,j) entry gives the probability of transitioning from
+         state i to state j.
+
+       - ``B`` -- a list of N lists or a matrix with N rows, such that
+         B[i,k] gives the probability of emitting symbol k while
+         in state i.
+
+       - ``pi`` -- the probabilities of starting in each initial
+         state, i.e,. pi[i] is the probability of starting in
+         state i.
+
+       - ``emission_symbols`` -- None or list (default: None); if
+         None, the emission_symbols are the ints [0..N-1], where N
+         is the number of states.  Otherwise, they are the entries
+         of the list emissions_symbols, which must all be hashable.
+
+       - ``normalize`` --bool (default: True); if given, input is
+         normalized to define valid probability distributions,
+         e.g., the entries of A are made nonnegative and the rows
+         sum to 1, and the probabilities in pi are normalized.
+
     EXAMPLES::
 
         sage: m = hmm.DiscreteHiddenMarkovModel([[0.4,0.6],[0.1,0.9]], [[0.1,0.9],[0.5,0.5]], [.5,.5]); m
@@ -257 +281 @@
         sage: m.sample(10)
         [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]
         sage: m.graph().plot()
+
+    A 3-state model that happens to always outputs 'b'::
+
+        sage: m = hmm.DiscreteHiddenMarkovModel([[1/3]*3]*3, [[0,1,0]]*3, [1/3]*3, ['a','b','c'])
+        sage: m.sample(10)
+        ['b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b', 'b']    
     """
     cdef TimeSeries B
     cdef int n_out
@@ -269 +299 @@
         probabilities pi, and given emission symbols (which default
         to the first few nonnegative integers).
 
-        INPUT::
-
-           - ``A`` -- a list of lists or a square N x N matrix, whose
-             (i,j) entry gives the probability of transitioning from
-             state i to state j.
-
-           - ``B`` -- a list of N lists or a matrix with N rows, such that
-             B[i,k] gives the probability of emitting symbol k while
-             in state i.
-
-           - ``pi`` -- the probabilities of starting in each initial
-             state, i.e,. pi[i] is the probability of starting in
-             state i.
-
-           - ``emission_symbols`` -- None or list (default: None); if
-             None, the emission_symbols are the ints [0..N-1], where N
-             is the number of states.  Otherwise, they are the entries
-             of the list emissions_symbols, which must all be hashable.
-
-           - ``normalize`` --bool (default: True); if given, input is
-             normalized to define valid probability distributions,
-             e.g., the entries of A are made nonnegative and the rows
-             sum to 1, and the probabilities in pi are normalized.
-
         EXAMPLES::
 
             sage: hmm.DiscreteHiddenMarkovModel([.5,0,-1,.5], [[1],[1]],[.5,.5]).transition_matrix()
@@ -773 +779 @@
         # that this should get factored out into a call to something
         # defined in distributions.pyx.
         for j in range(self.n_out):
-            a = self.B._values[q*self.N+j]
+            a = self.B._values[q*self.n_out + j]
             if r < a + accum:
                 return j
             else: