Updated README

Author: Guled

Example/Tests/RidgeRegressionTest.swift

@@ -15,7 +15,7 @@ class RidgeRegressionTests: XCTestCase {
 
     func testFitGradientDescent() {
         // Obtain data from csv file
-        let path = Bundle(for: PolynomialRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
+        let path = Bundle(for: RidgeRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
         let csvUrl = NSURL(fileURLWithPath: path!)
         let file = try! String(contentsOf: csvUrl as URL, encoding: String.Encoding.utf8)
         let data = CSVReader(with: file)
@@ -46,7 +46,7 @@ class RidgeRegressionTests: XCTestCase {
 
     func testRSS() {
         // Obtain data from csv file
-        let path = Bundle(for: PolynomialRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
+        let path = Bundle(for: RidgeRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
         let csvUrl = NSURL(fileURLWithPath: path!)
         let file = try! String(contentsOf: csvUrl as URL, encoding: String.Encoding.utf8)
         let data = CSVReader(with: file)
@@ -80,7 +80,7 @@ class RidgeRegressionTests: XCTestCase {
     func testOneTimePrediction() {
 
         // Obtain data from csv file
-        let path = Bundle(for: PolynomialRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
+        let path = Bundle(for: RidgeRegressionTests.self).path(forResource: "kc_house_data", ofType: "csv")
         let csvUrl = NSURL(fileURLWithPath: path!)
         let file = try! String(contentsOf: csvUrl as URL, encoding: String.Encoding.utf8)
         let data = CSVReader(with: file)

README.md

@@ -20,36 +20,66 @@ it, simply add the following line to your Podfile:
 pod 'MachineLearningKit', '0.1.3'
 ```
 
+
+----------------------------------------------
+
+## Contributing 
+The mission of this project is to give developers the ability to create add Machine Learning algorithms to their projects with ease and to enable the creation of advanced projects using the Swift programing language. With this being said, I encourage all developers interested in making Machine Learning accessible to the developers who work with the iOS apps and TVOS apps to contribute to this project. 
+
+To contribute an algorithm **not** currently available within the framework, please create an issue and state what algorithm you have implemented. Make sure that there are unit tests involved where applicable. Also, provide a brief overview of how to use your algorithm. You are also welcome to impelment algorithms within the **Roadmap** section (below). 
+
+To contribute to an already existant algorithm within the framework, please create an issue and state any changes or additions you have made.
+
+----------------------------------------------
+
 ## Wiki 
 
 - [x] [Simple Linear Regression (1 Feature)](https://github.com/Somnibyte/MLKit/wiki/Simple-Linear-Regression-Tutorial)
 - [x] [Polynomial Regression](https://github.com/Somnibyte/MLKit/wiki/Polynomial-Regression-Tutorial)
 - [x] [Lasso Regression] (https://github.com/Somnibyte/MLKit/wiki/Lasso-Regression-Tutorial)
+- [x] [Ridge Regression] (https://github.com/Somnibyte/MLKit/wiki/Ridge-Regression-Tutorial)
 
+----------------------------------------------
 
-### TODO:
+### Roadmap:
 
 - [ ] KMeans++ Implementation 
 - [ ] KMeans Clustering Documentation
 - [ ] Neural Network Documentation 
-- [ ] Ridge Regression Documentation 
-
+- [ ] Enable Neural Network class to allow for multiple hidden layers (currently 1 is only allowed)
+- [ ] Logistic Regression
+- [ ] Genetic Algorithms
+- [ ] Self Organizing Maps
+- [ ] Decision Trees 
+
+### Future Releases:
+- [ ] Convolutional Neural Network 
+- [ ] Recurrent Neural Network 
+- [ ] Artificial Neural Network using Metal
+- [ ] Game Playing AI (MiniMax, Alpha-Beta Pruning)
 
+----------------------------------------------
 ## Features (So Far)
 
 - [x] Matrix and Vector Operations (uses [Upsurge framework](https://github.com/aleph7/Upsurge))
 - [x] Simple Linear Regression (Allows for 1 feature set)
 - [x] Polynomial Regression (Allows for multiple features)
 - [x] Ridge Regression
+- [x] Neural Network (/w BackPropagation, Single Layer Perceptron, Multi-Layer Perceptron (1 Hidden Layer limit), and Single Layer Adaline
+- [x] K-Means Clustering 
 - [x] Allows for splitting your data into training, validation, and test sets.
 - [x] K-Fold Cross Validation & Ability to test various L2 penalties for Ridge Regression
 - [x] Single Layer Perceptron, Multi-Layer Perceptron, & Adaline ANN Architectures 
 
+----------------------------------------------
+
 ## Frameworks that MLKit uses
 
 - 🙌 [Upsurge](https://github.com/aleph7/Upsurge) (Matrix and Vector Operations)
 - 🙌 [CSVReader](https://github.com/peterentwistle/SwiftCSVReader) (CSV Reading) (Used in Unit Testing)
 
+----------------------------------------------
+
 ## Development Schedule
 
 ### Week of Feb 20th
@@ -68,6 +98,7 @@ pod 'MachineLearningKit', '0.1.3'
 |---|---|---|---|---|
 |Neural Network Documentation & BackPropagation Algorithm|BackPropagation Algorithm & K-Means++|TBD|TBD|
 
+----------------------------------------------
 
 # License
 MIT License