rename classificator to classifier

Author: kortirso

README.md

@@ -60,11 +60,11 @@ Returns the coefficient of determination R^2 of the prediction:
 
 ### K-Nearest Neighbours classification
 
-Initialize classificator with data set consists from labels and features:
+Initialize classifier with data set consists from labels and features:
 
 ```elixir
   alias LearnKit.Knn
-  classificator = Knn.new
+  classifier = Knn.new
                   |> Knn.add_train_data({:a1, [-1, -1]})
                   |> Knn.add_train_data({:a1, [-2, -1]})
                   |> Knn.add_train_data({:a2, [1, 1]})
@@ -73,7 +73,7 @@ Initialize classificator with data set consists from labels and features:
 Predict label for new feature:
 
 ```elixir
-  Knn.classify(classificator, [feature: [-1, -2], k: 3, weight: "distance"])
+  Knn.classify(classifier, [feature: [-1, -2], k: 3, weight: "distance"])
 ```
     feature - new feature for prediction, required
     k - number of nearest neighbors, optional, default - 3
@@ -82,11 +82,11 @@ Predict label for new feature:
 
 ### Gaussian Naive Bayes classification
 
-Initialize classificator with data set consists from labels and features:
+Initialize classifier with data set consists from labels and features:
 
 ```elixir
   alias LearnKit.NaiveBayes.Gaussian
-  classificator = Gaussian.new
+  classifier = Gaussian.new
                   |> Gaussian.add_train_data({:a1, [-1, -1]})
                   |> Gaussian.add_train_data({:a1, [-2, -1]})
                   |> Gaussian.add_train_data({:a2, [1, 1]})
@@ -95,27 +95,27 @@ Initialize classificator with data set consists from labels and features:
 Fit data set:
 
 ```elixir
-  classificator = classificator |> Gaussian.fit
+  classifier = classifier |> Gaussian.fit
 ```
 
 Return probability estimates for the feature:
 
 ```elixir
-  classificator |> Gaussian.predict_proba([1, 2])
+  classifier |> Gaussian.predict_proba([1, 2])
 ```
     feature - new feature for prediction, required
 
 Return exact prediction for the feature:
 
 ```elixir
-  classificator |> Gaussian.predict([1, 2])
+  classifier |> Gaussian.predict([1, 2])
 ```
     feature - new feature for prediction, required
 
 Returns the mean accuracy on the given test data and labels:
 
 ```elixir
-  classificator |> Gaussian.score
+  classifier |> Gaussian.score
 ```
 
 ## Contributing

lib/learn_kit/knn.ex

@@ -16,11 +16,11 @@ defmodule LearnKit.Knn do
   @type data_set :: [{label, features}]
 
   @doc """
-  Creates classificator with empty data_set
+  Creates classifier with empty data_set
 
   ## Examples
 
-      iex> classificator = LearnKit.Knn.new
+      iex> classifier = LearnKit.Knn.new
       %LearnKit.Knn{data_set: []}
 
   """
@@ -32,15 +32,15 @@ defmodule LearnKit.Knn do
   end
 
   @doc """
-  Creates classificator with data_set
+  Creates classifier 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]]}])
+      iex> classifier = LearnKit.Knn.new([{:a1, [[1, 2], [2, 3]]}, {:b1, [[-1, -2]]}])
       %LearnKit.Knn{data_set: [a1: [[1, 2], [2, 3]], b1: [[-1, -2]]]}
 
   """
@@ -51,16 +51,16 @@ defmodule LearnKit.Knn do
   end
 
   @doc """
-  Add train data to classificator
+  Add train data to classifier
 
   ## Parameters
 
-    - classificator: %LearnKit.Knn{}
+    - classifier: %LearnKit.Knn{}
     - train data: tuple with label and feature
 
   ## Examples
 
-      iex> classificator = classificator |> LearnKit.Knn.add_train_data({:a1, [-1, -1]})
+      iex> classifier = classifier |> LearnKit.Knn.add_train_data({:a1, [-1, -1]})
       %LearnKit.Knn{data_set: [a1: [[-1, -1]]]}
 
   """
@@ -77,7 +77,7 @@ defmodule LearnKit.Knn do
 
   ## Parameters
 
-    - classificator: %LearnKit.Knn{}
+    - classifier: %LearnKit.Knn{}
     - options: keyword list with options
 
   ## Options
@@ -89,7 +89,7 @@ defmodule LearnKit.Knn do
 
   ## Examples
 
-      iex> classificator |> LearnKit.Knn.classify([feature: [-1, -2], k: 3, weight: "distance"])
+      iex> classifier |> LearnKit.Knn.classify([feature: [-1, -2], k: 3, weight: "distance"])
       {:ok, :a1}
 
   """

lib/learn_kit/naive_bayes/gaussian.ex

@@ -23,11 +23,11 @@ defmodule LearnKit.NaiveBayes.Gaussian do
   @type fit_data :: [{label, fit_features}]
 
   @doc """
-  Creates classificator with empty data_set
+  Creates classifier with empty data_set
 
   ## Examples
 
-      iex> classificator = LearnKit.NaiveBayes.Gaussian.new
+      iex> classifier = LearnKit.NaiveBayes.Gaussian.new
       %LearnKit.NaiveBayes.Gaussian{data_set: [], fit_data: []}
 
   """
@@ -39,15 +39,15 @@ defmodule LearnKit.NaiveBayes.Gaussian do
   end
 
   @doc """
-  Creates classificator with data_set
+  Creates classifier with data_set
 
   ## Parameters
 
     - data_set: Keyword list with labels and features in tuples
 
   ## Examples
 
-      iex> classificator = LearnKit.NaiveBayes.Gaussian.new([{:a1, [[1, 2], [2, 3]]}, {:b1, [[-1, -2]]}])
+      iex> classifier = LearnKit.NaiveBayes.Gaussian.new([{:a1, [[1, 2], [2, 3]]}, {:b1, [[-1, -2]]}])
       %LearnKit.NaiveBayes.Gaussian{data_set: [a1: [[1, 2], [2, 3]], b1: [[-1, -2]]], fit_data: []}
 
   """
@@ -58,16 +58,16 @@ defmodule LearnKit.NaiveBayes.Gaussian do
   end
 
   @doc """
-  Add train data to classificator
+  Add train data to classifier
 
   ## Parameters
 
-    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+    - classifier: %LearnKit.NaiveBayes.Gaussian{}
     - train data: tuple with label and feature
 
   ## Examples
 
-      iex> classificator = classificator |> LearnKit.NaiveBayes.Gaussian.add_train_data({:a1, [-1, -1]})
+      iex> classifier = classifier |> LearnKit.NaiveBayes.Gaussian.add_train_data({:a1, [-1, -1]})
       %LearnKit.NaiveBayes.Gaussian{data_set: [a1: [[-1, -1]]], fit_data: []}
 
   """
@@ -84,11 +84,11 @@ defmodule LearnKit.NaiveBayes.Gaussian do
 
   ## Parameters
 
-    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+    - classifier: %LearnKit.NaiveBayes.Gaussian{}
 
   ## Examples
 
-      iex> classificator = classificator |> LearnKit.NaiveBayes.Gaussian.fit
+      iex> classifier = classifier |> LearnKit.NaiveBayes.Gaussian.fit
       %LearnKit.NaiveBayes.Gaussian{
         data_set: [a1: [[-1, -1]]],
         fit_data: [
@@ -111,11 +111,11 @@ defmodule LearnKit.NaiveBayes.Gaussian do
 
   ## Parameters
 
-    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+    - classifier: %LearnKit.NaiveBayes.Gaussian{}
 
   ## Examples
 
-      iex> classificator |> LearnKit.NaiveBayes.Gaussian.predict_proba([1, 2])
+      iex> classifier |> LearnKit.NaiveBayes.Gaussian.predict_proba([1, 2])
       {:ok, [a1: 0.0359, a2: 0.0039]}
 
   """
@@ -131,11 +131,11 @@ defmodule LearnKit.NaiveBayes.Gaussian do
 
   ## Parameters
 
-    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+    - classifier: %LearnKit.NaiveBayes.Gaussian{}
 
   ## Examples
 
-      iex> classificator |> LearnKit.NaiveBayes.Gaussian.predict([1, 2])
+      iex> classifier |> LearnKit.NaiveBayes.Gaussian.predict([1, 2])
       {:ok, {:a1, 0.334545454}}
 
   """
@@ -151,11 +151,11 @@ defmodule LearnKit.NaiveBayes.Gaussian do
 
   ## Parameters
 
-    - classificator: %LearnKit.NaiveBayes.Gaussian{}
+    - classifier: %LearnKit.NaiveBayes.Gaussian{}
 
   ## Examples
 
-      iex> classificator |> LearnKit.NaiveBayes.Gaussian.score
+      iex> classifier |> LearnKit.NaiveBayes.Gaussian.score
       {:ok, 0.857143}
 
   """