som-batch-train

Usage: (som-batch-train data & {:keys [alpha beta cycles], :or {alpha 0.5, beta 3, cycles 10}})

Performs BL-SOM (batch-learning self organizing map) learning on
the given data, returning a hashmap containing resulting BL-SOM
values.


Arguments:
  data -- data matrix

Options:
  :cycles -- number of cycles of learning
  :alpha -- initial value of alpha learning parameter
  :beta -- initial value of beta learning parameter


Returns: A hashmap containing the following fields:

  :fit -- array of fitness values for each cycle of SOM learning
  :weights -- hashmap of weight vectors, keyed by lattice indices
  :sets -- hashmap mapping data elements to lattice nodes
           (key lattice index) (value list of row indices from data)
  :dims -- dimensions of SOM lattice
  :data-means -- column means of input data matrix


Examples:

  (use '(incanter core som stats charts datasets))
  (def data (to-matrix (sel (get-dataset :iris)
                         :cols ["Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"])))

  (def som (som-batch-train data :cycles 10 :alpha 0.5 :beta 3))

  ;; plot the fitness for each cycle of training
  (view (xy-plot (range (count (:fit som))) (:fit som)))
  ;; view indices of data items in each cell
  (:sets som)
  ;; view the species in each cell
  (doseq [rws (vals (:sets som))]
    (println (sel (get-dataset :iris) :cols "Species" :rows rws) \newline))

  ;; plot the means of the data vectors in each cell/cluster
  (def cell-means (map #(map mean (trans (sel data :rows ((:sets som) %)))) (keys (:sets som))))
  (def x (range (ncol data)))
  (doto (xy-plot x (first cell-means))
        view
        (add-lines x (nth cell-means 1))
        (add-lines x (nth cell-means 2)))


References:

  http://en.wikipedia.org/wiki/Self-organizing_map