add function for calculating R^2 score of the prediction

Author: kortirso

lib/learn_kit/math.ex

@@ -65,7 +65,7 @@ defmodule LearnKit.Math do
 
   def variance(list, list_mean) when is_list(list) do
     list
-    |> Enum.map(fn x -> pow(list_mean - x, 2) end)
+    |> Enum.map(fn x -> :math.pow(list_mean - x, 2) end)
     |> mean
   end
 
@@ -145,21 +145,6 @@ defmodule LearnKit.Math do
     x / y
   end
 
-  @doc """
-  Pow number
-
-  ## Examples
-
-      iex> LearnKit.Math.pow(10, 2)
-      100
-
-  """
-  @spec pow(number, number) :: number
-
-  def pow(x, y) do
-    :math.pow(x, y)
-  end
-
   @doc """
   Calculate the covariance of two lists
 
@@ -201,10 +186,10 @@ defmodule LearnKit.Math do
             |> Enum.map(fn {xi, yi} -> (xi - mean_x) * (yi - mean_y) end)
             |> Enum.sum
     denom_x = x
-              |> Enum.map(fn xi -> pow(xi - mean_x, 2) end)
+              |> Enum.map(fn xi -> :math.pow(xi - mean_x, 2) end)
               |> Enum.sum
     denom_y = y
-              |> Enum.map(fn yi -> pow(yi - mean_y, 2) end)
+              |> Enum.map(fn yi -> :math.pow(yi - mean_y, 2) end)
               |> Enum.sum
 
     divider / :math.sqrt(denom_x * denom_y)

lib/learn_kit/regression/linear.ex

@@ -89,8 +89,26 @@ defmodule LearnKit.Regression.Linear do
   @spec predict(%LearnKit.Regression.Linear{coefficients: coefficients}, list) :: list
 
   def predict(%Linear{coefficients: coefficients}, samples) do
-    IO.inspect coefficients
     samples
     |> Enum.map(fn sample -> predict_sample(sample, coefficients) end)
   end
+
+  @doc """
+  Returns the coefficient of determination R^2 of the prediction
+
+  ## Parameters
+
+    - predictor: %LearnKit.Regression.Linear{}
+
+  ## Examples
+
+      iex> predictor |> LearnKit.Regression.Linear.score
+      0.9876543209876543
+
+  """
+  @spec score(%LearnKit.Regression.Linear{factors: factors, results: results, coefficients: coefficients}) :: number
+
+  def score(%Linear{factors: factors, results: results, coefficients: coefficients}) do
+    calculate_score(coefficients, factors, results)
+  end
 end

lib/learn_kit/regression/linear/predict.ex

@@ -2,11 +2,37 @@ defmodule LearnKit.Regression.Linear.Predict do
   @moduledoc """
   Module for fit functions
   """
+
+  alias LearnKit.Math
+
   defmacro __using__(_opts) do
     quote do
       defp predict_sample(sample, [alpha, beta]) do
         sample * beta + alpha
       end
+
+      defp calculate_score([], _, _), do: raise("There was no fit for model")
+
+      defp calculate_score(coefficients, factors, results) do
+        1.0 - sum_of_squared_errors(coefficients, factors, results) / total_sum_of_squares(results)
+      end
+
+      defp total_sum_of_squares(list) do
+        mean_list = Math.mean(list)
+        list
+        |> Enum.map(fn x -> :math.pow(x - mean_list, 2) end)
+        |> Enum.sum
+      end
+
+      defp sum_of_squared_errors(coefficients, factors, results) do
+        Enum.zip(factors, results)
+        |> Enum.map(fn {xi, yi} -> :math.pow(prediction_error(coefficients, xi, yi), 2) end)
+        |> Enum.sum
+      end
+
+      defp prediction_error(coefficients, x, y) do
+        y - predict_sample(x, coefficients)
+      end
     end
   end
 end