Add documentation.

src/lib/classify.mli

@@ -1,23 +1,53 @@
 
+(** The classifiers below assign a discrete probability distribution over the
+    list of class 'a in their training set. *)
 type 'a probabilities = ('a * float) list
 
+(** [most_likely probabilities] returns the most likely class from the
+    discrete probability distribution. *)
 val most_likely : 'a probabilities -> 'a
 
+(** A discrete Naive Bayes classifier of class ['cls] by observing
+    features ['ftr]. *)
 type ('cls, 'ftr) naive_bayes
 
+(** When estimating a probability distribution by counting observed instances
+    in the feature space we may want to smooth the values, particularly if our
+    training data is sparse.
+
+    [http://en.wikipedia.org/wiki/Additive_smoothing]
+  *)
 type smoothing =
-  { factor              : float
-  ; feature_space_size  : int array
+  { factor              : float     (** Multiplicative factor *)
+  ; feature_space_size  : int array (** Size of the space of each feature.
+                                        Must be at least [feature_size] long.*)
   }
 
+(** [estimate smoothing feature_size to_feature_array training_data] trains a
+    discrete Naive Bayes classifier based on the [training_data].
+    [to_feature_array] maps a feature to an integer array of length
+    [feature_size]. Optionally, additive [smoothing] is applied to the final
+    estimates if provided.
+*)
 val estimate : ?smoothing:smoothing -> feature_size:int ->
               ('ftr -> int array) -> ('cls * 'ftr) list ->
               ('cls, 'ftr) naive_bayes
 
+(** [eval bernoulli classifier feature] classifies [feature]
+    according to [classifier]. if [bernoulli] is specified we treat the
+    underlying distribution as Bernoulli (as opposed to Multinomial) and
+    estimate the likelihood with (1-p_i) for features [i] that are missing
+    from [feature].
+*)
 val eval : ?bernoulli:bool -> ('cls, 'ftr) naive_bayes -> 'ftr -> 'cls probabilities
 
+(** A continuous Gaussian Naive Bayes classifier of class ['cls]. The
+    feature space is assumed to be a float array. *)
 type 'cls gauss_bayes
 
+(** [gauss_estimate training_data] trains a Gaussian Naive Bayes classifier from
+    [training_data], where all of the data are of the same length; feature size. *)
 val gauss_estimate : ('cls * float array) list -> 'cls gauss_bayes
 
+(** [gauss_eval classifier feature] classify the [feature] using the [classifier]. *)
 val gauss_eval : 'cls gauss_bayes -> float array -> 'cls probabilities