add score function for calc accuracy

Author: kortirso

lib/learn_kit/naive_bayes/gaussian.ex

@@ -9,6 +9,7 @@ defmodule LearnKit.NaiveBayes.Gaussian do
 
   use Gaussian.Fit
   use Gaussian.Classify
+  use Gaussian.Score
 
   @type label :: atom
   @type feature :: [integer]
@@ -145,4 +146,26 @@ defmodule LearnKit.NaiveBayes.Gaussian do
     |> Enum.sort_by(&(elem(&1, 1)))
     |> Enum.at(-1)
   end
+
+  @doc """
+  Returns the mean accuracy on the given test data and labels
+
+  ## Parameters
+
+    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+
+  ## Examples
+
+      iex> classificator = LearnKit.NaiveBayes.Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
+      iex> classificator = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+      iex> classificator |> LearnKit.NaiveBayes.Gaussian.score
+      0.857143
+
+  """
+  @spec score(%LearnKit.NaiveBayes.Gaussian{data_set: data_set, fit_data: fit_data}) :: number
+
+  def score(%Gaussian{data_set: data_set, fit_data: fit_data}) do
+    fit_data
+    |> calc_score(data_set)
+  end
 end

lib/learn_kit/naive_bayes/gaussian/score.ex

@@ -0,0 +1,36 @@
+defmodule LearnKit.NaiveBayes.Gaussian.Score do
+  @moduledoc """
+  Module for calculating accuracy of prediction
+  """
+
+  alias LearnKit.NaiveBayes.Gaussian
+  alias LearnKit.Math
+
+  defmacro __using__(_opts) do
+    quote do
+      defp calc_score(fit_data, data_set) do
+        data_set
+        |> Enum.map(fn {label, features} ->
+          features
+          |> check_features(fit_data, label)
+        end)
+        |> List.flatten
+        |> Math.mean
+        |> Float.ceil(6)
+      end
+
+      defp check_features(features, fit_data, label) do
+        features
+        |> Enum.map(fn feature ->
+          feature
+          |> check_feature(fit_data, label)
+        end)
+      end
+
+      defp check_feature(feature, fit_data, label) do
+        {predicted_label, _} = Gaussian.predict(%Gaussian{fit_data: fit_data}, feature)
+        if predicted_label == label, do: 1, else: 0
+      end
+    end
+  end
+end