modify tests

Author: kortirso

lib/learn_kit/math.ex

@@ -136,7 +136,7 @@ defmodule LearnKit.Math do
   ## Examples
 
       iex> LearnKit.Math.division(10, 2)
-      5
+      5.0
 
   """
   @spec division(number, number) :: number
@@ -154,7 +154,7 @@ defmodule LearnKit.Math do
       5.5
 
   """
-  @spec covariance(list,list) :: number
+  @spec covariance(list, list) :: number
 
   def covariance(x, y) when length(x) == length(y) do
     mean_x = mean(x)

lib/learn_kit/regression/linear.ex

@@ -83,14 +83,14 @@ defmodule LearnKit.Regression.Linear do
   ## Examples
 
       iex> predictor |> LearnKit.Regression.Linear.predict([4, 8, 13])
-      [14.5, 30.5, 50.5]
+      {:ok, [14.5, 30.5, 50.5]}
 
   """
-  @spec predict(%LearnKit.Regression.Linear{coefficients: coefficients}, list) :: list
+  @spec predict(%LearnKit.Regression.Linear{coefficients: coefficients}, list) :: {:ok, list}
 
   def predict(%Linear{coefficients: coefficients}, samples) do
-    samples
-    |> Enum.map(fn sample -> predict_sample(sample, coefficients) end)
+    result = samples |> Enum.map(fn sample -> predict_sample(sample, coefficients) end)
+    {:ok, result}
   end
 
   @doc """
@@ -103,12 +103,12 @@ defmodule LearnKit.Regression.Linear do
   ## Examples
 
       iex> predictor |> LearnKit.Regression.Linear.score
-      0.9876543209876543
+      {:ok, 0.9876543209876543}
 
   """
-  @spec score(%LearnKit.Regression.Linear{factors: factors, results: results, coefficients: coefficients}) :: number
+  @spec score(%LearnKit.Regression.Linear{factors: factors, results: results, coefficients: coefficients}) :: {:ok, number}
 
   def score(%Linear{factors: factors, results: results, coefficients: coefficients}) do
-    calculate_score(coefficients, factors, results)
+    {:ok, calculate_score(coefficients, factors, results)}
   end
 end

test/learn_kit/knn_test.exs

@@ -1,7 +1,7 @@
 defmodule LearnKit.KnnTest do
   use ExUnit.Case
 
-  alias LearnKit.{Knn}
+  alias LearnKit.Knn
 
   test "create new knn classificator with empty data set" do
     assert %Knn{data_set: data_set} = Knn.new
@@ -19,15 +19,15 @@ defmodule LearnKit.KnnTest do
   end
 
   test "classify new feature" do
-    classificator = LearnKit.Knn.new
-                    |> LearnKit.Knn.add_train_data({:a1, [-1, -1]})
-                    |> LearnKit.Knn.add_train_data({:a1, [-2, -1]})
-                    |> LearnKit.Knn.add_train_data({:a1, [-3, -2]})
-                    |> LearnKit.Knn.add_train_data({:a2, [1, 1]})
-                    |> LearnKit.Knn.add_train_data({:a2, [2, 1]})
-                    |> LearnKit.Knn.add_train_data({:a2, [3, 2]})
-                    |> LearnKit.Knn.add_train_data({:a2, [-2, -2]})
+    classificator = Knn.new
+                    |> Knn.add_train_data({:a1, [-1, -1]})
+                    |> Knn.add_train_data({:a1, [-2, -1]})
+                    |> Knn.add_train_data({:a1, [-3, -2]})
+                    |> Knn.add_train_data({:a2, [1, 1]})
+                    |> Knn.add_train_data({:a2, [2, 1]})
+                    |> Knn.add_train_data({:a2, [3, 2]})
+                    |> Knn.add_train_data({:a2, [-2, -2]})
 
-    assert {:ok, :a1} = LearnKit.Knn.classify(classificator, [feature: [-1, -2], k: 3, weight: "distance"])
+    assert {:ok, :a1} = Knn.classify(classificator, [feature: [-1, -2], k: 3, weight: "distance"])
   end
 end

test/learn_kit/math_test.exs

@@ -1,7 +1,7 @@
 defmodule LearnKit.MathTest do
   use ExUnit.Case
 
-  alias LearnKit.{Math}
+  alias LearnKit.Math
 
   test "calculate mean" do
     assert 2.0 = Math.mean([1, 2, 3])
@@ -22,4 +22,16 @@ defmodule LearnKit.MathTest do
   test "calculate standard deviation from variance" do
     assert 1.118033988749895 = Math.standard_deviation_from_variance(1.25)
   end
+
+  test "calculate division" do
+    assert 5.0 = Math.division(10, 2)
+  end
+
+  test "calculate covariance" do
+    assert 5.5 = Math.covariance([1, 2, 3], [14, 17, 25])
+  end
+
+  test "calculate correlation" do
+    assert 0.9672471299049061 = Math.correlation([1, 2, 3], [14, 17, 25])
+  end
 end

test/learn_kit/naive_bayes/gaussian_test.exs

@@ -19,8 +19,8 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   end
 
   test "fit data set" do
-    classificator = LearnKit.NaiveBayes.Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
-    %LearnKit.NaiveBayes.Gaussian{fit_data: fit_data} = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+    classificator = Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
+    %Gaussian{fit_data: fit_data} = classificator |> Gaussian.fit
 
     assert fit_data == [
                         label1: [
@@ -35,26 +35,26 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   end
 
   test "return probability estimates for the feature" do
-    classificator = LearnKit.NaiveBayes.Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
-    classificator = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+    classificator = Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
+    classificator = classificator |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> LearnKit.NaiveBayes.Gaussian.predict_proba([1, 2])
+    assert {:ok, result} = classificator |> Gaussian.predict_proba([1, 2])
     assert result == [label1: 0.0, label2: 0.017199571]
   end
 
   test "return exact prediction for the feature" do
-    classificator = LearnKit.NaiveBayes.Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
-    classificator = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+    classificator = Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
+    classificator = classificator |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> LearnKit.NaiveBayes.Gaussian.predict([1, 2])
+    assert {:ok, result} = classificator |> Gaussian.predict([1, 2])
     assert result == {:label2, 0.017199571}
   end
 
   test "returns the mean accuracy on the given test data and labels" do
-    classificator = LearnKit.NaiveBayes.Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
-    classificator = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+    classificator = Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])
+    classificator = classificator |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> LearnKit.NaiveBayes.Gaussian.score
+    assert {:ok, result} = classificator |> Gaussian.score
     assert result == 0.857143
   end
 end

test/learn_kit/regression/linear_test.exs

@@ -0,0 +1,45 @@
+defmodule LearnKit.Regression.LinearTest do
+  use ExUnit.Case
+
+  alias LearnKit.Regression.Linear
+
+  setup_all do
+    {:ok, predictor: Linear.new([1, 2, 3, 4], [3, 6, 10, 15])}
+  end
+
+  test "create new linear predictor with empty data set" do
+    assert %Linear{factors: factors, results: results, coefficients: coefficients} = Linear.new
+
+    assert factors == []
+    assert results == []
+    assert coefficients == []
+  end
+
+  test "create new linear predictor with data", state do
+    assert %Linear{factors: factors, results: results, coefficients: coefficients} = state[:predictor]
+
+    assert factors == [1, 2, 3, 4]
+    assert results == [3, 6, 10, 15]
+    assert coefficients == []
+  end
+
+  test "fit data set", state do
+    %Linear{coefficients: coefficients} = state[:predictor] |> Linear.fit
+
+    assert coefficients == [-1.5, 4.0]
+  end
+
+  test "return prediction using the linear model", state do
+    predictor = state[:predictor] |> Linear.fit
+
+    assert {:ok, result} = predictor |> Linear.predict([4, 8, 13])
+    assert result == [14.5, 30.5, 50.5]
+  end
+
+  test "returns coefficient of determination R^2 of the prediction", state do
+    predictor = state[:predictor] |> Linear.fit
+
+    assert {:ok, result} = predictor |> Linear.score
+    assert result == 0.9876543209876543
+  end
+end