modify docs

Author: kortirso

.DS_Store

@@ -15,7 +15,7 @@ def deps do
 end
 ```
 
-### K-Nearest Neighbors
+### K-Nearest Neighbours
 
 Initialize classificator with data set consists from labels and features:
 

README.md

@@ -9,24 +9,63 @@ defmodule LearnKit.Knn do
 
   use Knn.Classify
 
+  @type label :: atom
+  @type feature :: [integer]
+  @type point :: {label, feature}
+  @type features :: [feature]
+  @type data_set :: [{label, features}]
+
   @doc """
   Creates classificator with empty data_set
+
+  ## Examples
+
+      iex> classificator = LearnKit.Knn.new
+      %LearnKit.Knn{data_set: []}
+
   """
+  @spec new() :: %LearnKit.Knn{data_set: []}
+
   def new do
     []
     |> Knn.new
   end
 
   @doc """
   Creates classificator with data_set
+
+  ## Parameters
+
+    - data_set: Keyword list with labels and features in tuples
+
+  ## Examples
+
+      iex> classificator = LearnKit.Knn.new([{:a1, [[1, 2], [2, 3]]}, {:b1, [[-1, -2]]}])
+      %LearnKit.Knn{data_set: [a1: [[1, 2], [2, 3]], b1: [[-1, -2]]]}
+
   """
+  @spec new(data_set) :: %LearnKit.Knn{data_set: data_set}
+
   def new(data_set) do
     %Knn{data_set: data_set}
   end
 
   @doc """
   Add train data to classificator
+
+  ## Parameters
+
+    - classificator: %LearnKit.Knn{}
+    - train data: tuple with label and feature
+
+  ## Examples
+
+      iex> classificator |> LearnKit.Knn.add_train_data({:a1, [-1, -1]})
+      %LearnKit.Knn{data_set: [a1: [[-1, -1]]]}
+
   """
+  @spec add_train_data(%LearnKit.Knn{data_set: data_set}, point) :: %LearnKit.Knn{data_set: data_set}
+
   def add_train_data(%Knn{data_set: data_set}, {key, value}) do
     features = if Keyword.has_key?(data_set, key), do: Keyword.get(data_set, key), else: []
     data_set = Keyword.put(data_set, key, [value | features])
@@ -35,12 +74,27 @@ defmodule LearnKit.Knn do
 
   @doc """
   Classify label of the new feature
-  Available options:
-  feature - feature for classification, required, example: [1, 2, 3]
-  k - number of nearest neighbours, default is 3, optional
-  algorithm - brute, optional
-  weight - uniform/distance, default is uniform, optional
+
+  ## Parameters
+
+    - classificator: %LearnKit.Knn{}
+    - options: keyword list with options
+
+  ## Options
+
+    - feature: feature for classification, required, example: [1, 2, 3]
+    - k: number of nearest neighbours, default is 3, optional
+    - algorithm: brute, optional
+    - weight: uniform/distance, default is uniform, optional
+
+  ## Examples
+
+      iex> classificator |> LearnKit.Knn.classify([feature: [-1, -2], k: 3, weight: "distance"])
+      {:ok, "Feature has label a1 with weight 1.5"}
+
   """
+  @spec classify(%LearnKit.Knn{data_set: data_set}, [tuple]) :: {atom, String.t}
+
   def classify(%Knn{data_set: data_set}, options \\ []) do
     try do
       unless Keyword.has_key?(options, :feature) do

lib/learn_kit/knn.ex

@@ -1,6 +1,6 @@
 defmodule LearnKit.Knn.Classify do
   @moduledoc """
-  Module for knn prediction functions
+  Module for knn classify functions
   """
   defmacro __using__(_opts) do
     quote do
@@ -113,7 +113,7 @@ defmodule LearnKit.Knn.Classify do
         end
       end
 
-      defp accumulate_weight_of_labels(features, acc) when features == [] do
+      defp accumulate_weight_of_labels([], acc) do
         acc
       end