README: added links to articles (#246)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 16.17.4
+- Documentation:
+  - Added links to articles to README
+
 ## 16.17.3
 - Log Likelihood Cost function:
   - `dtype` passed

README.md

@@ -23,6 +23,8 @@ The library is a part of the ecosystem:
     - [KDTree-based data retrieval](#kdtree-based-data-retrieval)
 - [Models retraining](#models-retraining)
 - [Notes on gradient-based optimisation algorithms](#a-couple-of-words-about-linear-models-which-use-gradient-optimisation-methods)
+- [Helpful articles on algorithms standing behind the library](#helpful-articles-on-algorithms-standing-behind-the-library)
+- [Contacts](#contacts)
 
 
 
@@ -851,6 +853,13 @@ final createClassifier = (DataFrame samples) =>
     );
 ```
 
+## Helpful articles on algorithms standing behind the library
+
+- [Linear Regression in Dart](https://medium.com/mlearning-ai/a-gentle-introduction-to-linear-regression-the-dart-way-9750214e6fa2?source=friends_link&sk=e199d8f5b0bb71c97525be2ee7f5819b)
+- [Ordinary Least Squares (OLS) problem](https://medium.com/mlearning-ai/linear-regression-ordinary-least-squares-in-a-nutshell-c2e0d7ed260f?source=friends_link&sk=5c8bc0228d29bc67ebe524a91d687619)
+- [Closed-Form solution for OLS in Dart](https://medium.com/mlearning-ai/ordinary-least-squares-closed-form-solution-the-dart-way-d7c0ee0e0d02?source=friends_link&sk=9ba5a9da7fd3160b28c450ff6dc446a4)
+- [Gradient Descent in Dart](https://medium.com/mlearning-ai/gradient-descent-the-dart-way-2d6c39416a8a?source=friends_link&sk=992b52c85a51ecea1c1e9e4afe2a8c1e)
+
 ### Contacts
 If you have questions, feel free to text me on
  - [Twitter](https://twitter.com/ilgyrd) 

example/logistic_regression.dart

@@ -9,7 +9,7 @@ void main() {
     'Outcome',
   );
 
-  print('ACURACY:');
+  print('ACСURACY:');
   print(model.assess(splits.last, MetricType.accuracy));
 
   print('RECALL:');

example/main.dart

@@ -1,23 +1,15 @@
 import 'package:ml_algo/ml_algo.dart';
 import 'package:ml_dataframe/ml_dataframe.dart';
 
-/// A simple usage example using synthetic data. To see more complex examples,
-/// please, visit other directories in this folder
 Future<void> main() async {
-  // Let's create a dataframe with fitting data, let's assume, that the target
-  // column is the fifth column (column with index 4)
-  final dataFrame = DataFrame(<Iterable<num>>[
-    [2, 3, 4, 5, 4.3],
-    [12, 32, 1, 3, 3.5],
-    [27, 3, 0, 59, 2.1],
-  ], headerExists: false);
+  final features = DataFrame([
+    ['feature_1', 'feature_2', 'output'],
+    [2, 2, 12],
+    [3, 3, 18],
+    [4, 4, 24],
+    [5, 5, 30],
+  ]);
+  final model = LinearRegressor.SGD(features, 'output', fitIntercept: false);
 
-  // Let's create a regressor itself and train it
-  final regressor = LinearRegressor(dataFrame, 'col_4',
-      iterationsLimit: 100,
-      initialLearningRate: 0.0005,
-      learningRateType: LearningRateType.constant);
-
-  // Let's see adjusted coefficients
-  print('Regression coefficients: ${regressor.coefficients}');
+  print('Coefficients: ${model.coefficients}');
 }

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 16.17.3
+version: 16.17.4
 homepage: https://github.com/gyrdym/ml_algo
 
 environment: