Elixir Formatter

Author: davidrichey

lib/learn_kit/regression/linear.ex

@@ -42,7 +42,8 @@ defmodule LearnKit.Regression.Linear do
   """
   @spec new(factors, results) :: %Linear{factors: factors, results: results, coefficients: []}
 
-  def new(factors, results) when is_list(factors) and is_list(results), do: %Linear{factors: factors, results: results}
+  def new(factors, results) when is_list(factors) and is_list(results),
+    do: %Linear{factors: factors, results: results}
 
   @doc """
   Fit train data
@@ -73,13 +74,18 @@ defmodule LearnKit.Regression.Linear do
       }
 
   """
-  @spec fit(%Linear{factors: factors, results: results}) :: %Linear{factors: factors, results: results, coefficients: coefficients}
+  @spec fit(%Linear{factors: factors, results: results}) :: %Linear{
+          factors: factors,
+          results: results,
+          coefficients: coefficients
+        }
 
   def fit(%Linear{factors: factors, results: results}, options \\ []) when is_list(options) do
     coefficients =
       Keyword.merge([method: ""], options)
       |> define_method_for_fit()
       |> do_fit(factors, results)
+
     %Linear{factors: factors, results: results, coefficients: coefficients}
   end
 
@@ -126,7 +132,8 @@ defmodule LearnKit.Regression.Linear do
       {:ok, 0.9876543209876543}
 
   """
-  @spec score(%Linear{factors: factors, results: results, coefficients: coefficients}) :: {:ok, number}
+  @spec score(%Linear{factors: factors, results: results, coefficients: coefficients}) ::
+          {:ok, number}
 
   def score(%Linear{factors: factors, results: results, coefficients: coefficients}) do
     {

lib/learn_kit/regression/linear/calculations.ex

@@ -8,11 +8,21 @@ defmodule LearnKit.Regression.Linear.Calculations do
   defmacro __using__(_opts) do
     quote do
       defp do_fit(method, factors, results) when method == "gradient descent" do
-        gradient_descent_iteration([:rand.uniform, :rand.uniform], 0.0001, nil, 1000000, Enum.zip(factors, results), 0)
+        gradient_descent_iteration(
+          [:rand.uniform(), :rand.uniform()],
+          0.0001,
+          nil,
+          1_000_000,
+          Enum.zip(factors, results),
+          0
+        )
       end
 
       defp do_fit(_, factors, results) do
-        beta = Math.correlation(factors, results) * Math.standard_deviation(results) / Math.standard_deviation(factors)
+        beta =
+          Math.correlation(factors, results) * Math.standard_deviation(results) /
+            Math.standard_deviation(factors)
+
         alpha = Math.mean(results) - beta * Math.mean(factors)
         [alpha, beta]
       end
@@ -24,7 +34,8 @@ defmodule LearnKit.Regression.Linear.Calculations do
       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)
+        1.0 -
+          sum_of_squared_errors(coefficients, factors, results) / total_sum_of_squares(results)
       end
 
       defp total_sum_of_squares(list) do
@@ -34,7 +45,9 @@ defmodule LearnKit.Regression.Linear.Calculations do
 
       defp sum_of_squared_errors(coefficients, factors, results) do
         Enum.zip(factors, results)
-        |> Enum.reduce(0, fn {xi, yi}, acc -> acc + squared_prediction_error(coefficients, xi, yi) end)
+        |> Enum.reduce(0, fn {xi, yi}, acc ->
+          acc + squared_prediction_error(coefficients, xi, yi)
+        end)
       end
 
       defp squared_prediction_error(coefficients, x, y) do
@@ -45,6 +58,7 @@ defmodule LearnKit.Regression.Linear.Calculations do
 
       defp squared_error_gradient(coefficients, x, y) do
         error_variable = prediction_error(coefficients, x, y)
+
         [
           -2 * error_variable,
           -2 * error_variable * x
@@ -55,9 +69,18 @@ defmodule LearnKit.Regression.Linear.Calculations do
         y - predict_sample(x, coefficients)
       end
 
-      defp gradient_descent_iteration(_, _, min_theta, _, _, iterations_with_no_improvement) when iterations_with_no_improvement >= 100, do: min_theta
-
-      defp gradient_descent_iteration(theta, alpha, min_theta, min_value, data, iterations_with_no_improvement) do
+      defp gradient_descent_iteration(_, _, min_theta, _, _, iterations_with_no_improvement)
+           when iterations_with_no_improvement >= 100,
+           do: min_theta
+
+      defp gradient_descent_iteration(
+             theta,
+             alpha,
+             min_theta,
+             min_value,
+             data,
+             iterations_with_no_improvement
+           ) do
         [
           min_theta,
           min_value,
@@ -72,11 +95,23 @@ defmodule LearnKit.Regression.Linear.Calculations do
             gradient_i = squared_error_gradient(acc, xi, yi)
             acc |> Math.vector_subtraction(alpha |> Math.scalar_multiply(gradient_i))
           end)
-        gradient_descent_iteration(theta, alpha, min_theta, min_value, data, iterations_with_no_improvement)
+
+        gradient_descent_iteration(
+          theta,
+          alpha,
+          min_theta,
+          min_value,
+          data,
+          iterations_with_no_improvement
+        )
       end
 
       defp check_value(data, min_value, theta, min_theta, iterations_with_no_improvement, alpha) do
-        value = Enum.reduce(data, 0, fn {xi, yi}, acc -> acc + squared_prediction_error(theta, xi, yi) end)
+        value =
+          Enum.reduce(data, 0, fn {xi, yi}, acc ->
+            acc + squared_prediction_error(theta, xi, yi)
+          end)
+
         cond do
           value < min_value ->
             [theta, value, 0, 0.0001]