KnnClassifier: Proofreading the documentation (#222)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 16.10.5
+- KnnClassifier:
+    - Proofreading the documentation
+
 ## 16.10.4
 - DecisionTreeClassifier:
     - Proofreading the documentation

lib/src/classifier/knn_classifier/knn_classifier.dart

@@ -10,18 +10,17 @@ import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/distance.dart';
 import 'package:ml_linalg/dtype.dart';
 
-/// A class that performs classification basing on `k nearest neighbours` (KNN)
+/// A class that performs classification based on `k nearest neighbours` (KNN)
 /// algorithm
 ///
-/// K nearest neighbours algorithm is an algorithm that is targeted to search for
-/// the most similar labelled observations (number of these observations is equal
-/// to `k`) to the given unlabelled one.
+/// K nearest neighbours algorithm is an algorithm that is targeted to search
+/// for the most similar k observations to the given one.
 ///
-/// It is possible to use majority class among the `k` found observations as a
-/// prediction for the given unlabelled observation, but it may lead to the
-/// imprecise result. Thus the weighted version of KNN algorithm is used in the
-/// classifier. To get weight of a particular observation one may use a kernel
-/// function.
+/// It is possible to use the majority class among the `k` found observations
+/// as a prediction for the given unlabelled observation, but it may lead to an
+/// imprecise result. To overcome this, the weighted version of KNN algorithm
+/// is used in the classifier. To get the weight of a particular observation
+/// one may use a kernel function.
 abstract class KnnClassifier
     implements
         Assessable,
@@ -35,17 +34,15 @@ abstract class KnnClassifier
   /// [targetName] A string that serves as a name of the column containing
   /// outcomes.
   ///
-  /// [k] a number of nearest neighbours to be found among [trainData]
+  /// [k] A number of nearest neighbours to be found among [trainData]
   ///
-  /// [kernel] a type of a kernel function that is used to predict an
-  /// outcome for a new observation
+  /// [kernel] A kernel function that will be used to predict an outcome for a
+  /// new observation
   ///
-  /// [distance] a distance type that is used to measure a distance between two
-  /// observation vectors
+  /// [distance] A way to measure a distance between two observation vectors
   ///
-  /// [dtype] A data type for all the numeric values, used by the algorithm. Can
-  /// affect performance or accuracy of the computations. Default value is
-  /// [DType.float32]
+  /// [dtype] A data type for all the numeric values, used by the algorithm.
+  /// Default value is [DType.float32]
   factory KnnClassifier(
     DataFrame trainData,
     String targetName,
@@ -95,8 +92,7 @@ abstract class KnnClassifier
   /// final json = await file.readAsString();
   /// final restoredClassifier = KnnClassifier.fromJson(json);
   ///
-  /// // here you can use previously fitted restored classifier to make
-  /// // some prediction, e.g. via `KnnClassifier.predict(...)`;
+  /// // here you can use previously fitted restored classifier
   /// ````
   factory KnnClassifier.fromJson(String json) =>
       initKnnClassifierModule().get<KnnClassifierFactory>().fromJson(json);
@@ -106,13 +102,12 @@ abstract class KnnClassifier
   /// The value is read-only, it's a hyperparameter of the model
   int get k;
 
-  /// A kernel type
+  /// A kernel function type
   ///
   /// The value is read-only, it's a hyperparameter of the model
   KernelType get kernelType;
 
-  /// A distance type that is used to measure a distance between two
-  /// observations
+  /// A distance type that is used to measure how far are observations from each other
   ///
   /// The value is read-only, it's a hyperparameter of the model
   Distance get distanceType;

lib/src/knn_kernel/kernel_type.dart

@@ -1,4 +1,4 @@
-/// A type of kernel function.
+/// A type of a kernel function.
 ///
 /// Defines a way the weights of the nearest observations are being calculated
 /// for K nearest neighbours algorithm (KNN).
@@ -11,31 +11,31 @@
 /// returns a weight denoting, how much the evaluating observation will
 /// contribute in prediction for the target one.
 enum KernelType {
-  /// A kernel, that calculates weights, using the following formula:
+  /// A kernel that calculates weights using the following formula:
   ///
   ///
-  /// ![K(x)=\left\{\begin{matrix}1/2, & |x|\leqslant \lambda \\ 0, & |x|> \lambda \end{matrix}\right](https://latex.codecogs.com/png.latex?K%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D1/2%2C%20%26%20%7Cx%7C%5Cleqslant%20%5Clambda%20%5C%5C%200%2C%20%26%20%7Cx%7C%3E%20%5Clambda%20%5Cend%7Bmatrix%7D%5Cright)
+  /// ![K(x,\lambda)=\left\{\begin{matrix}1/2,&|x|\leqslant\lambda\\0,&|x|>\lambda\end{matrix}\right.](https://latex.codecogs.com/svg.image?K(x,%5Clambda)=%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D1/2,&%7Cx%7C%5Cleqslant%5Clambda%5C%5C0,&%7Cx%7C%3E%5Clambda%5Cend%7Bmatrix%7D%5Cright.)
   ///
   ///
   uniform,
 
-  /// A kernel, that calculates weights, using the following formula:
+  /// A kernel that calculates weights using the following formula:
   ///
   ///
-  /// ![K(x)=\left\{\begin{matrix}3/4(1 - x^{2}), & |x|\leqslant \lambda \\ 0, & |x|> \lambda \end{matrix}\right](https://latex.codecogs.com/png.latex?K%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D3/4%281%20-%20x%5E%7B2%7D%29%2C%20%26%20%7Cx%7C%5Cleqslant%20%5Clambda%20%5C%5C%200%2C%20%26%20%7Cx%7C%3E%20%5Clambda%20%5Cend%7Bmatrix%7D%5Cright)
+  /// ![K(x,\lambda)=\left\{\begin{matrix}3/4(1-x^{2}),&|x|\leqslant\lambda\\0,&|x|>\lambda\end{matrix}\right.](https://latex.codecogs.com/svg.image?K(x,%5Clambda)=%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D3/4(1-x%5E%7B2%7D),&%7Cx%7C%5Cleqslant%5Clambda%5C%5C0,&%7Cx%7C%3E%5Clambda%5Cend%7Bmatrix%7D%5Cright.)
   ///
   ///
   epanechnikov,
 
-  /// A kernel, that calculates weights, using the following formula:
+  /// A kernel that calculates weights using the following formula:
   ///
   ///
-  /// ![K(x)=\left\{\begin{matrix} 0.25\pi cos(0.5\pi x) & |x|\leqslant \lambda \\ 0, & |x|>\lambda \end{matrix}\right](https://latex.codecogs.com/png.latex?K%28x%29%3D%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%200.25%5Cpi%20cos%280.5%5Cpi%20x%29%20%26%20%7Cx%7C%5Cleqslant%20%5Clambda%20%5C%5C%200%2C%20%26%20%7Cx%7C%3E%5Clambda%20%5Cend%7Bmatrix%7D%5Cright)
+  /// ![K(x,\lambda)=\left\{\begin{matrix} 0.25\pi cos(0.5\pi x), & |x|\leqslant \lambda \\ 0, & |x|>\lambda \end{matrix}\right.](https://latex.codecogs.com/svg.image?K(x,%5Clambda)=%5Cleft%5C%7B%5Cbegin%7Bmatrix%7D%200.25%5Cpi%20cos(0.5%5Cpi%20x),%20&%20%7Cx%7C%5Cleqslant%20%5Clambda%20%5C%5C%200,%20&%20%7Cx%7C%3E%5Clambda%20%5Cend%7Bmatrix%7D%5Cright.)
   ///
   ///
   cosine,
 
-  /// A kernel, that calculates weights, using the following formula:
+  /// A kernel that calculates weights using the following formula:
   ///
   ///
   /// ![K(x)=\frac{1 }{\sqrt{2\pi }} e^{-\frac{1}{2}x^{2}}](https://latex.codecogs.com/png.latex?K%28x%29%3D%5Cfrac%7B1%20%7D%7B%5Csqrt%7B2%5Cpi%20%7D%7D%20e%5E%7B-%5Cfrac%7B1%7D%7B2%7Dx%5E%7B2%7D%7D)

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 16.10.4
+version: 16.10.5
 homepage: https://github.com/gyrdym/ml_algo
 
 environment: