update readme, release 0.1.5

Author: kortirso

CHANGELOG.md

@@ -4,7 +4,7 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
 
-## Unreleased
+## [0.1.5] - 2018-12-18
 ### Added
 - preprocessing normalization for data set
 - normalize train data for Gaussian NB

README.md

@@ -2,6 +2,10 @@
 
 Elixir package for machine learning
 
+Available preprocessing methods:
+
+- Normalization
+
 Available algorithms for prediction:
 
 - Linear Regression
@@ -19,11 +23,41 @@ by adding `learn_kit` to your list of dependencies in `mix.exs`:
 ```elixir
 def deps do
   [
-    {:learn_kit, "~> 0.1.4"}
+    {:learn_kit, "~> 0.1.5"}
   ]
 end
 ```
 
+### Normalization
+
+Normalize data set with minimax normalization
+
+```elixir
+  alias LearnKit.Preprocessing
+  Preprocessing.normalize([[1, 2], [3, 4], [5, 6]])
+```
+
+Or normalize data set with selected type
+
+```elixir
+  Preprocessing.normalize([[1, 2], [3, 4], [5, 6]], [type: "z_normalization"])
+```
+  options - array of options
+
+Additionally you can prepare coefficients for normalization
+
+```elixir
+  Preprocessing.coefficients([[1, 2], [3, 4], [5, 6]], "minimax")
+```
+    type - method of normalization, one of the [minimax|z_normalization], required
+
+And then normalize 1 feature with predefined coefficients
+
+```elixir
+  Preprocessing.normalize_feature([1, 2], [{1, 5}, {2, 6}], "minimax")
+```
+    type - method of normalization, one of the [minimax|z_normalization], required
+
 ### Linear Regression
 
 Initialize predictor with data:
@@ -64,33 +98,43 @@ Initialize classifier with data set consists from labels and features:
 
 ```elixir
   alias LearnKit.Knn
-  classifier = Knn.new
-                  |> Knn.add_train_data({:a1, [-1, -1]})
-                  |> Knn.add_train_data({:a1, [-2, -1]})
-                  |> Knn.add_train_data({:a2, [1, 1]})
+  classifier =
+    Knn.new
+    |> Knn.add_train_data({:a1, [-1, -1]})
+    |> Knn.add_train_data({:a1, [-2, -1]})
+    |> Knn.add_train_data({:a2, [1, 1]})
 ```
 
 Predict label for new feature:
 
 ```elixir
-  Knn.classify(classifier, [feature: [-1, -2], k: 3, weight: "distance"])
+  Knn.classify(classifier, [feature: [-1, -2], k: 3, weight: "distance", normalization: "minimax"])
 ```
     feature - new feature for prediction, required
     k - number of nearest neighbors, optional, default - 3
     algorithm - algorithm for calculation of distances, one of the [brute], optional, default - "brute"
     weight - method of weighted neighbors, one of the [uniform|distance], optional, default - "uniform"
+    normalization - method of normalization, one of the [none|minimax|z_normalization], optional, default - "none"
 
 ### Gaussian Naive Bayes classification
 
 Initialize classifier with data set consists from labels and features:
 
 ```elixir
   alias LearnKit.NaiveBayes.Gaussian
-  classifier = Gaussian.new
-                  |> Gaussian.add_train_data({:a1, [-1, -1]})
-                  |> Gaussian.add_train_data({:a1, [-2, -1]})
-                  |> Gaussian.add_train_data({:a2, [1, 1]})
+  classifier =
+    Gaussian.new
+    |> Gaussian.add_train_data({:a1, [-1, -1]})
+    |> Gaussian.add_train_data({:a1, [-2, -1]})
+    |> Gaussian.add_train_data({:a2, [1, 1]})
+```
+
+Normalize data set:
+
+```elixir
+  classifier = classifier |> Gaussian.normalize_train_data("minimax")
 ```
+    type - method of normalization, one of the [none|minimax|z_normalization], optional, default - "none"
 
 Fit data set:
 

lib/learn_kit/naive_bayes/gaussian/classify.ex

@@ -2,6 +2,7 @@ defmodule LearnKit.NaiveBayes.Gaussian.Classify do
   @moduledoc """
   Module for prediction functions
   """
+
   defmacro __using__(_opts) do
     quote do
       # classify data

mix.exs

@@ -8,7 +8,7 @@ defmodule LearnKit.MixProject do
   def project do
     [
       app: :learn_kit,
-      version: "0.1.4",
+      version: "0.1.5",
       elixir: "~> 1.7",
       name: "LearnKit",
       description: @description,

test/learn_kit/naive_bayes/gaussian_test.exs

@@ -4,16 +4,16 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   alias LearnKit.NaiveBayes.Gaussian
 
   setup_all do
-    {:ok, classificator: Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])}
+    {:ok, classifier: Gaussian.new([{:label1, [[-1, -1], [-2, -1], [-3, -2]]}, {:label2, [[1, 1], [2, 1], [3, 2], [-2, -2]]}])}
   end
 
-  test "create new knn classificator with empty data set" do
+  test "create new knn classifier with empty data set" do
     assert %Gaussian{data_set: data_set} = Gaussian.new
 
     assert data_set == []
   end
 
-  test "add train data to classificator" do
+  test "add train data to classifier" do
     %Gaussian{data_set: data_set} =
       Gaussian.new
       |> Gaussian.add_train_data({:a1, [1, 2]})
@@ -24,7 +24,7 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   end
 
   test "normalize data set", state do
-    %Gaussian{data_set: data_set} = state[:classificator] |> Gaussian.normalize_train_data("minimax")
+    %Gaussian{data_set: data_set} = state[:classifier] |> Gaussian.normalize_train_data("minimax")
 
     assert data_set ==
       [
@@ -34,7 +34,7 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   end
 
   test "fit data set", state do
-    %Gaussian{fit_data: fit_data} = state[:classificator] |> Gaussian.fit
+    %Gaussian{fit_data: fit_data} = state[:classifier] |> Gaussian.fit
 
     assert fit_data ==
       [
@@ -50,23 +50,23 @@ defmodule LearnKit.NaiveBayes.GaussianTest do
   end
 
   test "return probability estimates for the feature", state do
-    classificator = state[:classificator] |> Gaussian.fit
+    classifier = state[:classifier] |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> Gaussian.predict_proba([1, 2])
+    assert {:ok, result} = classifier |> Gaussian.predict_proba([1, 2])
     assert result == [label1: 0.0, label2: 0.017199571]
   end
 
   test "return exact prediction for the feature", state do
-    classificator = state[:classificator] |> Gaussian.fit
+    classifier = state[:classifier] |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> Gaussian.predict([1, 2])
+    assert {:ok, result} = classifier |> Gaussian.predict([1, 2])
     assert result == {:label2, 0.017199571}
   end
 
   test "returns the mean accuracy on the given test data and labels", state do
-    classificator = state[:classificator] |> Gaussian.fit
+    classifier = state[:classifier] |> Gaussian.fit
 
-    assert {:ok, result} = classificator |> Gaussian.score
+    assert {:ok, result} = classifier |> Gaussian.score
     assert result == 0.857143
   end
 end