diff --git a/.travis.yml b/.travis.yml
index e962e095..852d8c84 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -1,6 +1,6 @@
 language: dart
 dart:
-- "2.1.0"
+- "2.2.0"
 dart_task:
 - test: --platform vm
 - dartanalyzer: true
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 93d9101b..e4ba5b4d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,10 @@
 # Changelog
 
+## 6.0.0
+- prefix `ML` removed from entities' names
+- Float32x4Vector: equality operator override added
+- Matrix: `uniqueRows` method added
+
 ## 5.5.1
 - MLMatrix: fixed bug in `setColumn` method when rows cache was not cleared 
 
diff --git a/README.md b/README.md
index e4ba415d..f0421089 100644
--- a/README.md
+++ b/README.md
@@ -56,12 +56,13 @@ computation architecture. Actually, the main purpose of the library - connect su
 the pure math. So, the library contains a high performance SIMD vector class, based on 
 [Float32x4](https://api.dartlang.org/stable/2.1.0/dart-typed_data/Float32x4-class.html) - 
 [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart). 
-Most of operations in the vector are performed in four "threads". This kind of concurrency is reached by special 
+Most of operations in the vector class are performed in four "threads". This kind of concurrency is reached by special 
 128-bit processor registers, which are used directly by program code.  For better understanding of the topic please read 
 the [article](https://www.dartlang.org/articles/dart-vm/simd). It is also possible to implement [Float64x2](https://api.dartlang.org/stable/2.1.0/dart-typed_data/Float64x2-class.html)-based
-version of the vector using existing codebase, but so far there is no need to do so.
-    The class [Float32x4Vector] is hidden from the library's user. You can create an [Float32x4Vector] instance
-via [MLVector] factory (see examples below).
+version of vector using existing codebase, but so far there is no need to do so.
+    The class [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart) 
+is hidden from the library's users. You can create a [Float32x4Vector](https://github.com/gyrdym/linalg/blob/master/lib/src/vector/float32x4/float32x4_vector.dart) 
+instance via [Vector](https://github.com/gyrdym/ml_linalg/blob/master/lib/vector.dart) factory (see examples below).
 
 #### Vector operations examples
 At the present moment most common vector operations are implemented:
@@ -70,8 +71,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1 + vector2;
   print(result.toList()); // [3.0, 5.0, 7.0, 9.0, 11.0]
 ````
@@ -80,8 +81,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([4.0, 5.0, 6.0, 7.0, 8.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 2.0, 3.0, 2.0]);
+  final vector1 = Vector.from([4.0, 5.0, 6.0, 7.0, 8.0]);
+  final vector2 = Vector.from([2.0, 3.0, 2.0, 3.0, 2.0]);
   final result = vector1 - vector2;
   print(result.toList()); // [2.0, 2.0, 4.0, 4.0, 6.0]
 ````
@@ -90,8 +91,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1 * vector2;
   print(result.toList()); // [2.0, 6.0, 12.0, 20.0, 30.0]
 ````
@@ -100,8 +101,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([6.0, 12.0, 24.0, 48.0, 96.0]);
-  final vector2 = MLVector.from([3.0, 4.0, 6.0, 8.0, 12.0]);
+  final vector1 = Vector.from([6.0, 12.0, 24.0, 48.0, 96.0]);
+  final vector2 = Vector.from([3.0, 4.0, 6.0, 8.0, 12.0]);
   final result = vector1 / vector2;
   print(result.toList()); // [2.0, 3.0, 4.0, 6.0, 8.0]
 ````
@@ -110,7 +111,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.norm();
   print(result); // sqrt(2^2 + 3^2 + 4^2 + 5^2 + 6^2) = sqrt(90) ~~ 9.48
 ````
@@ -119,7 +120,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.norm(Norm.manhattan);
   print(result); // 2 + 3 + 4 + 5 + 6 = 20.0
 ````
@@ -128,7 +129,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.mean();
   print(result); // (2 + 3 + 4 + 5 + 6) / 5 = 4.0
 ````
@@ -137,7 +138,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.sum();
   print(result); // 2 + 3 + 4 + 5 + 6 = 20.0 (equivalent to Manhattan norm)
 ````
@@ -146,8 +147,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.dot(vector2);
   print(result); // 1.0 * 2.0 + 2.0 * 3.0 + 3.0 * 4.0 + 4.0 * 5.0 + 5.0 * 6.0 = 70.0
 ````
@@ -156,7 +157,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 + scalar;
   print(result.toList()); // [6.0, 7.0, 8.0, 9.0, 10.0]
@@ -166,7 +167,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 - scalar;
   print(result.toList()); // [-4.0, -3.0, -2.0, -1.0, 0.0]
@@ -176,7 +177,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final scalar = 5.0;
   final result = vector1 * scalar;
   print(result.toList()); // [5.0, 10.0, 15.0, 20.0, 25.0]
@@ -186,7 +187,7 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([25.0, 50.0, 75.0, 100.0, 125.0]);
+  final vector1 = Vector.from([25.0, 50.0, 75.0, 100.0, 125.0]);
   final scalar = 5.0;
   final result = vector1.scalarDiv(scalar);
   print(result.toList()); // [5.0, 10.0, 15.0, 20.0, 25.0]
@@ -196,8 +197,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.distanceTo(vector2);
   print(result); // ~~2.23
 ````
@@ -206,8 +207,8 @@ At the present moment most common vector operations are implemented:
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector1 = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
-  final vector2 = MLVector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
+  final vector1 = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector2 = Vector.from([2.0, 3.0, 4.0, 5.0, 6.0]);
   final result = vector1.distanceTo(vector2, Norm.manhattan);
   print(result); // 5.0
 ````
@@ -219,7 +220,7 @@ Performs mapping from one vector to another in efficient way (using simd computa
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final vector = MLVector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+  final vector = Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
   final result = vector.fastMap<Float32x4>((Float32x4 element, int offsetStart, int offsetEnd) {
     // offsetStart - start index for the current vectorized element, e.g. if `element` is second in the inner collection,
     // the offsetStart will be 4 (because Float32x4 contains 4 elements)
@@ -240,12 +241,12 @@ Also, a class for matrix is available. It is based on Float32x4 and Float32x4Vec
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix1 = MLMatrix.from([
+final matrix1 = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
 ]);
-final matrix2 = MLMatrix.from([
+final matrix2 = Matrix.from([
   [10.0, 20.0, 30.0, 40.0],
   [-5.0, 16.0, 2.0, 18.0],
   [2.0, -1.0, -2.0, -7.0],
@@ -262,7 +263,7 @@ print(matrix1 + matrix2);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
@@ -279,12 +280,12 @@ print(matrix + 7);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
   ]);
-  final vector = MLVector.from([2.0, 3.0, 4.0, 5.0]);
+  final vector = Vector.from([2.0, 3.0, 4.0, 5.0]);
   final result = matrix * vector;
   print(result); 
   // a vector-column [
@@ -298,12 +299,12 @@ print(matrix + 7);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix1 = MLMatrix.from([
+  final matrix1 = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
   ]);
-  final matrix2 = MLMatrix.from([
+  final matrix2 = Matrix.from([
     [1.0, 2.0],
     [5.0, 6.0],
     [9.0, .0],
@@ -322,7 +323,7 @@ print(matrix + 7);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
@@ -339,12 +340,12 @@ print(matrix * 3);
 ````Dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix1 = MLMatrix.from([
+final matrix1 = Matrix.from([
   [1.0, 2.0, 3.0, 4.0],
   [5.0, 6.0, 7.0, 8.0],
   [9.0, .0, -2.0, -3.0],
 ]);
-final matrix2 = MLMatrix.from([
+final matrix2 = Matrix.from([
   [10.0, 20.0, 30.0, 40.0],
   [-5.0, 16.0, 2.0, 18.0],
   [2.0, -1.0, -2.0, -7.0],
@@ -361,7 +362,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
   
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
     [9.0, .0, -2.0, -3.0],
@@ -380,7 +381,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
@@ -392,7 +393,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -405,11 +406,11 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
-  final modifier = MLVector.filled(4, 2.0);
+  final modifier = Vector.filled(4, 2.0);
   final newMatrix = matrix.rowsMap((row) => row + modifier);
   print(newMatrix); 
   // [
@@ -422,11 +423,11 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [1.0, 2.0, 3.0, 4.0],
     [5.0, 6.0, 7.0, 8.0],
   ]); 
-  final modifier = MLVector.filled(2, 2.0);
+  final modifier = Vector.filled(2, 2.0);
   final newMatrix = matrix.columnsMap((column) => column + modifier);
   print(newMatrix); 
   // [
@@ -439,7 +440,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -457,7 +458,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 17.0, 18.0],
     [21.0, 22.0, 23.0, 24.0],
@@ -472,7 +473,7 @@ print(matrix1 - matrix2);
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 0.0, 18.0],
     [21.0, 22.0, -23.0, 24.0],
@@ -490,7 +491,7 @@ Performs mapping from one matrix to another in efficient way (using simd computa
 ````Dart
   import 'package:ml_linalg/linalg.dart';
 
-  final matrix = MLMatrix.from([
+  final matrix = Matrix.from([
     [11.0, 12.0, 13.0, 14.0],
     [15.0, 16.0, 0.0, 18.0],
     [21.0, 22.0, -23.0, 24.0],
@@ -536,7 +537,7 @@ print(result);
 ````dart
 import 'package:ml_linalg/linalg.dart';
 
-final matrix = MLMatrix.from([
+final matrix = Matrix.from([
   [11.0, 12.0, 13.0, 14.0],
   [15.0, 16.0, 0.0, 18.0],
   [21.0, 22.0, -23.0, 24.0],
diff --git a/lib/matrix.dart b/lib/matrix.dart
index fdbb650d..f3bdb779 100644
--- a/lib/matrix.dart
+++ b/lib/matrix.dart
@@ -6,9 +6,9 @@ import 'package:ml_linalg/src/matrix/float32x4/float32x4_matrix.dart';
 import 'package:ml_linalg/vector.dart';
 
 /// An algebraic matrix
-abstract class MLMatrix {
+abstract class Matrix {
   /// Creates a matrix from a two dimensional list
-  factory MLMatrix.from(List<List<double>> source, {Type dtype = Float32x4}) {
+  factory Matrix.from(List<List<double>> source, {Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
         return Float32x4Matrix.from(source);
@@ -18,7 +18,7 @@ abstract class MLMatrix {
   }
 
   /// Creates a matrix with predefined row vectors
-  factory MLMatrix.rows(List<MLVector> source, {Type dtype = Float32x4}) {
+  factory Matrix.rows(List<Vector> source, {Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
         return Float32x4Matrix.rows(source);
@@ -28,7 +28,7 @@ abstract class MLMatrix {
   }
 
   /// Creates a matrix with predefined column vectors
-  factory MLMatrix.columns(List<MLVector> source, {Type dtype = Float32x4}) {
+  factory Matrix.columns(List<Vector> source, {Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
         return Float32x4Matrix.columns(source);
@@ -37,6 +37,8 @@ abstract class MLMatrix {
     }
   }
 
+  Type get dtype;
+
   /// Returns a number of matrix row
   int get rowsNum;
 
@@ -47,61 +49,61 @@ abstract class MLMatrix {
   Iterable<double> operator [](int index);
 
   /// Performs sum of the matrix and a matrix/ a vector/ a scalar/ whatever
-  MLMatrix operator +(Object value);
+  Matrix operator +(Object value);
 
   /// Performs subtraction of the matrix and a matrix/ a vector/ a scalar/
   /// whatever
-  MLMatrix operator -(Object value);
+  Matrix operator -(Object value);
 
   /// Performs multiplication of the matrix and a matrix/ a vector/ a scalar/
   /// whatever
-  MLMatrix operator *(Object value);
+  Matrix operator *(Object value);
 
   /// Performs division of the matrix by a matrix/ a vector/ a scalar/
   /// whatever
-  MLMatrix operator /(Object value);
+  Matrix operator /(Object value);
 
   /// Performs transposition of the matrix
-  MLMatrix transpose();
+  Matrix transpose();
 
   /// Cuts out a part of the matrix bounded by [rows] and [columns] range
-  MLMatrix submatrix({Range rows, Range columns});
+  Matrix submatrix({Range rows, Range columns});
 
   /// Creates a new matrix, consisted of different segments of the matrix
-  MLMatrix pick({Iterable<Range> rowRanges, Iterable<Range> columnRanges});
+  Matrix pick({Iterable<Range> rowRanges, Iterable<Range> columnRanges});
 
   /// Returns a column of the matrix, resided on [index]
-  MLVector getColumn(int index, {bool tryCache = true, bool mutable = false});
+  Vector getColumn(int index, {bool tryCache = true, bool mutable = false});
 
   /// Returns a row of the matrix, resided on [index]
-  MLVector getRow(int index, {bool tryCache = true, bool mutable = false});
+  Vector getRow(int index, {bool tryCache = true, bool mutable = false});
 
   /// Reduces all the matrix columns to only column, using [combiner] function
-  MLVector reduceColumns(MLVector combiner(MLVector combine, MLVector vector),
-      {MLVector initValue});
+  Vector reduceColumns(Vector combiner(Vector combine, Vector vector),
+      {Vector initValue});
 
   /// Reduces all the matrix rows to only row, using [combiner] function
-  MLVector reduceRows(MLVector combiner(MLVector combine, MLVector vector),
-      {MLVector initValue});
+  Vector reduceRows(Vector combiner(Vector combine, Vector vector),
+      {Vector initValue});
 
   /// Performs column-wise mapping of this matrix to a new one via passed
   /// [mapper] function
-  MLMatrix mapColumns(MLVector mapper(MLVector column));
+  Matrix mapColumns(Vector mapper(Vector column));
 
   /// Performs row-wise mapping of this matrix to a new one via passed
   /// [mapper] function
-  MLMatrix mapRows(MLVector mapper(MLVector row));
+  Matrix mapRows(Vector mapper(Vector row));
 
   /// Creates a new matrix, efficiently iterating through all the matrix
   /// elements (several floating point elements in a time) and applying the
   /// [mapper] function
-  MLMatrix fastMap<E>(E mapper(E columnElement));
+  Matrix fastMap<E>(E mapper(E columnElement));
 
   /// Tries to convert the matrix to a vector.
   ///
   /// It fails, if the matrix's both numbers of columns and rows are greater
   /// than `1`
-  MLVector toVector({bool mutable = false});
+  Vector toVector({bool mutable = false});
 
   /// Returns max value of the matrix
   double max();
@@ -118,4 +120,7 @@ abstract class MLMatrix {
   /// [columnValues] - values, that are going to be placed one by one in the
   /// target column
   void setColumn(int columnNum, Iterable<double> columnValues);
+
+  /// Extracts non-repeated matrix rows and pack them into matrix
+  Matrix uniqueRows();
 }
diff --git a/lib/src/matrix/float32x4/float32_data_helper_mixin.dart b/lib/src/matrix/float32x4/float32_data_helper_mixin.dart
index 65f75a32..613ccdd6 100644
--- a/lib/src/matrix/float32x4/float32_data_helper_mixin.dart
+++ b/lib/src/matrix/float32x4/float32_data_helper_mixin.dart
@@ -1,8 +1,8 @@
 import 'dart:typed_data';
 
-import 'package:ml_linalg/src/matrix/ml_matrix_data_store.dart';
+import 'package:ml_linalg/src/matrix/matrix_data_store.dart';
 
-abstract class Float32DataHelperMixin implements MLMatrixDataStore {
+abstract class Float32DataHelperMixin implements MatrixDataStore {
 
   @override
   void updateByteData(int elementNum, double value) {
diff --git a/lib/src/matrix/float32x4/float32x4_matrix.dart b/lib/src/matrix/float32x4/float32x4_matrix.dart
index c7c1764d..065325f9 100644
--- a/lib/src/matrix/float32x4/float32x4_matrix.dart
+++ b/lib/src/matrix/float32x4/float32x4_matrix.dart
@@ -6,46 +6,31 @@ import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/src/matrix/float32x4/float32_data_helper_mixin.dart';
 import 'package:ml_linalg/src/matrix/float32x4/float32_matrix_iterator.dart';
 import 'package:ml_linalg/src/matrix/float32x4/float32x4_matrix_factory_mixin.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_data_store.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_fast_iterable_mixin.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_mixin.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_validator_mixin.dart';
+import 'package:ml_linalg/src/matrix/matrix_data_store.dart';
+import 'package:ml_linalg/src/matrix/matrix_fast_iterable_mixin.dart';
+import 'package:ml_linalg/src/matrix/matrix_mixin.dart';
+import 'package:ml_linalg/src/matrix/matrix_validator_mixin.dart';
 import 'package:ml_linalg/src/vector/float32x4/float32x4_vector_factory_mixin.dart';
 import 'package:ml_linalg/vector.dart';
 
 class Float32x4Matrix extends Object
     with
         IterableMixin<Iterable<double>>,
-        MLMatrixValidatorMixin<Float32x4>,
+        MatrixValidatorMixin<Float32x4>,
         Float32x4MatrixFactoryMixin,
         Float32x4VectorFactoryMixin,
         Float32DataHelperMixin,
-        MLMatrixFastIterableMixin,
-        MLMatrixMixin<Float32x4, Float32x4List>
-    implements MLMatrixDataStore, MLMatrix {
-
-  @override
-  final int rowsNum;
-
-  @override
-  final int columnsNum;
-
-  @override
-  final ByteData data;
-
-  @override
-  final List<MLVector> columnsCache;
-
-  @override
-  final List<MLVector> rowsCache;
+        MatrixFastIterableMixin,
+        MatrixMixin<Float32x4, Float32x4List>
+    implements MatrixDataStore, Matrix {
 
   Float32x4Matrix.from(Iterable<Iterable<double>> source)
       : rowsNum = source.length,
         columnsNum = source.first.length,
         data = ByteData(
             source.length * source.first.length * Float32List.bytesPerElement),
-        rowsCache = List<MLVector>(source.length),
-        columnsCache = List<MLVector>(source.first.length) {
+        rowsCache = List<Vector>(source.length),
+        columnsCache = List<Vector>(source.first.length) {
     final flattened = flatten2dimList(source, (i, j) => i * columnsNum + j);
     data.buffer.asFloat32List().setAll(0, flattened);
   }
@@ -65,12 +50,12 @@ class Float32x4Matrix extends Object
   ///   {a3} {b3} {c3}
   ///   {a4} {b4} {c4}
   /// ```
-  Float32x4Matrix.columns(Iterable<MLVector> source)
+  Float32x4Matrix.columns(Iterable<Vector> source)
       : rowsNum = source.first.length,
         columnsNum = source.length,
         data = ByteData(
             source.length * source.first.length * Float32List.bytesPerElement),
-        rowsCache = List<MLVector>(source.first.length),
+        rowsCache = List<Vector>(source.first.length),
         columnsCache = source.toList(growable: false) {
     // TODO: try to set the source values right in the byte data buffer
     // inside `flatten2dimList` method
@@ -79,13 +64,13 @@ class Float32x4Matrix extends Object
   }
 
   /// vectors from [source] will serve as rows of the matrix
-  Float32x4Matrix.rows(Iterable<MLVector> source)
+  Float32x4Matrix.rows(Iterable<Vector> source)
       : rowsNum = source.length,
         columnsNum = source.first.length,
         data = ByteData(
             source.length * source.first.length * Float32List.bytesPerElement),
         rowsCache = source.toList(growable: false),
-        columnsCache = List<MLVector>(source.first.length) {
+        columnsCache = List<Vector>(source.first.length) {
     // TODO: try to set the source values right in the byte data buffer
     // inside `flatten2dimList` method
     final flattened = flatten2dimList(source, (i, j) => i * columnsNum + j);
@@ -95,14 +80,32 @@ class Float32x4Matrix extends Object
   Float32x4Matrix.flattened(
       Iterable<double> source, this.rowsNum, this.columnsNum)
       : data = ByteData(rowsNum * columnsNum * Float32List.bytesPerElement),
-        rowsCache = List<MLVector>(rowsNum),
-        columnsCache = List<MLVector>(columnsNum) {
+        rowsCache = List<Vector>(rowsNum),
+        columnsCache = List<Vector>(columnsNum) {
     if (source.length != rowsNum * columnsNum) {
       throw Exception('Invalid matrix dimension is provided');
     }
     data.buffer.asFloat32List().setAll(0, source);
   }
 
+  @override
+  final Type dtype = Float32x4;
+
+  @override
+  final int rowsNum;
+
+  @override
+  final int columnsNum;
+
+  @override
+  final ByteData data;
+
+  @override
+  final List<Vector> columnsCache;
+
+  @override
+  final List<Vector> rowsCache;
+
   @override
   Iterator<Iterable<double>> get iterator =>
       Float32MatrixIterator(data, columnsNum);
diff --git a/lib/src/matrix/float32x4/float32x4_matrix_factory_mixin.dart b/lib/src/matrix/float32x4/float32x4_matrix_factory_mixin.dart
index c407fc91..6b917b32 100644
--- a/lib/src/matrix/float32x4/float32x4_matrix_factory_mixin.dart
+++ b/lib/src/matrix/float32x4/float32x4_matrix_factory_mixin.dart
@@ -1,23 +1,23 @@
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/src/matrix/float32x4/float32x4_matrix.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_factory.dart';
+import 'package:ml_linalg/src/matrix/matrix_factory.dart';
 import 'package:ml_linalg/vector.dart';
 
-abstract class Float32x4MatrixFactoryMixin implements MLMatrixFactory {
+abstract class Float32x4MatrixFactoryMixin implements MatrixFactory {
   @override
-  MLMatrix createMatrixFrom(Iterable<Iterable<double>> source) =>
+  Matrix createMatrixFrom(Iterable<Iterable<double>> source) =>
       Float32x4Matrix.from(source);
 
   @override
-  MLMatrix createMatrixFromFlattened(
+  Matrix createMatrixFromFlattened(
           Iterable<double> source, int rowsNum, int columnsNum) =>
       Float32x4Matrix.flattened(source, rowsNum, columnsNum);
 
   @override
-  MLMatrix createMatrixFromRows(Iterable<MLVector> source) =>
+  Matrix createMatrixFromRows(Iterable<Vector> source) =>
       Float32x4Matrix.rows(source);
 
   @override
-  MLMatrix createMatrixFromColumns(Iterable<MLVector> source) =>
+  Matrix createMatrixFromColumns(Iterable<Vector> source) =>
       Float32x4Matrix.columns(source);
 }
diff --git a/lib/src/matrix/ml_matrix_data_store.dart b/lib/src/matrix/matrix_data_store.dart
similarity index 58%
rename from lib/src/matrix/ml_matrix_data_store.dart
rename to lib/src/matrix/matrix_data_store.dart
index a1c1e787..412b14a5 100644
--- a/lib/src/matrix/ml_matrix_data_store.dart
+++ b/lib/src/matrix/matrix_data_store.dart
@@ -2,9 +2,9 @@ import 'dart:typed_data';
 
 import 'package:ml_linalg/vector.dart';
 
-abstract class MLMatrixDataStore {
-  List<MLVector> get columnsCache;
-  List<MLVector> get rowsCache;
+abstract class MatrixDataStore {
+  List<Vector> get columnsCache;
+  List<Vector> get rowsCache;
   ByteData get data;
   void updateByteData(int elementNum, double value);
 }
diff --git a/lib/src/matrix/matrix_factory.dart b/lib/src/matrix/matrix_factory.dart
new file mode 100644
index 00000000..ca261ec3
--- /dev/null
+++ b/lib/src/matrix/matrix_factory.dart
@@ -0,0 +1,10 @@
+import 'package:ml_linalg/matrix.dart';
+import 'package:ml_linalg/vector.dart';
+
+abstract class MatrixFactory {
+  Matrix createMatrixFrom(Iterable<Iterable<double>> source);
+  Matrix createMatrixFromFlattened(
+      Iterable<double> source, int rowsNum, int columnsNum);
+  Matrix createMatrixFromRows(Iterable<Vector> source);
+  Matrix createMatrixFromColumns(Iterable<Vector> source);
+}
diff --git a/lib/src/matrix/ml_matrix_fast_iterable_mixin.dart b/lib/src/matrix/matrix_fast_iterable_mixin.dart
similarity index 62%
rename from lib/src/matrix/ml_matrix_fast_iterable_mixin.dart
rename to lib/src/matrix/matrix_fast_iterable_mixin.dart
index bc25c929..617d4f7b 100644
--- a/lib/src/matrix/ml_matrix_fast_iterable_mixin.dart
+++ b/lib/src/matrix/matrix_fast_iterable_mixin.dart
@@ -1,11 +1,11 @@
 import 'package:ml_linalg/matrix.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_factory.dart';
+import 'package:ml_linalg/src/matrix/matrix_factory.dart';
 import 'package:ml_linalg/vector.dart';
 
-abstract class MLMatrixFastIterableMixin implements MLMatrixFactory, MLMatrix {
+mixin MatrixFastIterableMixin implements MatrixFactory, Matrix {
   @override
-  MLMatrix fastMap<T>(T mapper(T element)) {
-    final source = List<MLVector>.generate(
+  Matrix fastMap<T>(T mapper(T element)) {
+    final source = List<Vector>.generate(
         rowsNum,
         (int i) => (getRow(i)).fastMap(
             (T element, int startOffset, int endOffset) => mapper(element)));
diff --git a/lib/src/matrix/ml_matrix_mixin.dart b/lib/src/matrix/matrix_mixin.dart
similarity index 68%
rename from lib/src/matrix/ml_matrix_mixin.dart
rename to lib/src/matrix/matrix_mixin.dart
index 9cdaee6e..d2711502 100644
--- a/lib/src/matrix/ml_matrix_mixin.dart
+++ b/lib/src/matrix/matrix_mixin.dart
@@ -4,23 +4,23 @@ import 'dart:typed_data';
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/matrix_norm.dart';
 import 'package:ml_linalg/range.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_data_store.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_factory.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_validatior.dart';
-import 'package:ml_linalg/src/vector/ml_vector_factory.dart';
+import 'package:ml_linalg/src/matrix/matrix_data_store.dart';
+import 'package:ml_linalg/src/matrix/matrix_factory.dart';
+import 'package:ml_linalg/src/matrix/matrix_validatior.dart';
+import 'package:ml_linalg/src/vector/vector_factory.dart';
 import 'package:ml_linalg/vector.dart';
 
-abstract class MLMatrixMixin<E, S extends List<E>>
+mixin MatrixMixin<E, S extends List<E>>
     implements
         Iterable<Iterable<double>>,
-        MLMatrixDataStore,
-        MLMatrixFactory,
-        MLVectorFactory<E, S>,
-        MLMatrixValidator,
-        MLMatrix {
+        MatrixDataStore,
+        MatrixFactory,
+        VectorFactory<E, S>,
+        MatrixValidator,
+        Matrix {
   @override
-  MLMatrix operator +(Object value) {
-    if (value is MLMatrix) {
+  Matrix operator +(Object value) {
+    if (value is Matrix) {
       return _matrixAdd(value);
     } else if (value is num) {
       return _matrixScalarAdd(value.toDouble());
@@ -31,8 +31,8 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLMatrix operator -(Object value) {
-    if (value is MLMatrix) {
+  Matrix operator -(Object value) {
+    if (value is Matrix) {
       return _matrixSub(value);
     } else if (value is num) {
       return _matrixScalarSub(value.toDouble());
@@ -49,10 +49,10 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   /// let `N` be a number of columns, so the multiplication is
   /// available only for X by N * N by Y matrices, that causes X by Y matrix
   @override
-  MLMatrix operator *(Object value) {
-    if (value is MLVector) {
+  Matrix operator *(Object value) {
+    if (value is Vector) {
       return _matrixVectorMul(value);
-    } else if (value is MLMatrix) {
+    } else if (value is Matrix) {
       return _matrixMul(value);
     } else if (value is num) {
       return _matrixScalarMul(value.toDouble());
@@ -64,10 +64,10 @@ abstract class MLMatrixMixin<E, S extends List<E>>
 
   /// Performs division of the matrix by vector, matrix or scalar
   @override
-  MLMatrix operator /(Object value) {
-    if (value is MLVector) {
+  Matrix operator /(Object value) {
+    if (value is Vector) {
       return _matrixByVectorDiv(value);
-    } else if (value is MLMatrix) {
+    } else if (value is Matrix) {
       return _matrixByMatrixDiv(value);
     } else if (value is num) {
       return _matrixByScalarDiv(value.toDouble());
@@ -81,13 +81,13 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   List<double> operator [](int index) => _query(index * columnsNum, columnsNum);
 
   @override
-  MLMatrix transpose() {
-    final source = List<MLVector>.generate(rowsNum, getRow);
+  Matrix transpose() {
+    final source = List<Vector>.generate(rowsNum, getRow);
     return createMatrixFromColumns(source);
   }
 
   @override
-  MLVector getRow(int index, {bool tryCache = true, bool mutable = false}) {
+  Vector getRow(int index, {bool tryCache = true, bool mutable = false}) {
     if (tryCache) {
       rowsCache[index] ??= createVectorFrom(this[index], mutable);
       return rowsCache[index];
@@ -97,7 +97,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector getColumn(int index, {bool tryCache = true, bool mutable = false}) {
+  Vector getColumn(int index, {bool tryCache = true, bool mutable = false}) {
     if (columnsCache[index] == null || !tryCache) {
       final result = List<double>(rowsNum);
       for (int i = 0; i < rowsNum; i++) {
@@ -114,7 +114,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLMatrix submatrix({Range rows, Range columns}) {
+  Matrix submatrix({Range rows, Range columns}) {
     rows ??= Range(0, rowsNum);
     columns ??= Range(0, columnsNum);
 
@@ -131,7 +131,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLMatrix pick({Iterable<Range> rowRanges, Iterable<Range> columnRanges}) {
+  Matrix pick({Iterable<Range> rowRanges, Iterable<Range> columnRanges}) {
     rowRanges ??= [Range(0, rowsNum)];
     columnRanges ??= [Range(0, columnsNum)];
     final rows = _collectVectors(rowRanges, getRow, rowsNum);
@@ -142,26 +142,38 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector reduceColumns(
-          MLVector Function(MLVector combine, MLVector vector) combiner,
-          {MLVector initValue}) =>
+  Vector reduceColumns(
+          Vector Function(Vector combine, Vector vector) combiner,
+          {Vector initValue}) =>
       _reduce(combiner, columnsNum, getColumn, initValue: initValue);
 
   @override
-  MLVector reduceRows(
-          MLVector Function(MLVector combine, MLVector vector) combiner,
-          {MLVector initValue}) =>
+  Vector reduceRows(
+          Vector Function(Vector combine, Vector vector) combiner,
+          {Vector initValue}) =>
       _reduce(combiner, rowsNum, getRow, initValue: initValue);
 
   @override
-  MLMatrix mapColumns(MLVector mapper(MLVector columns)) =>
-      MLMatrix.columns(List<MLVector>.generate(columnsNum,
-              (int i) => mapper(getColumn(i))));
+  Matrix mapColumns(Vector mapper(Vector columns)) =>
+      Matrix.columns(List<Vector>.generate(columnsNum,
+              (int i) => mapper(getColumn(i))), dtype: dtype);
 
   @override
-  MLMatrix mapRows(MLVector mapper(MLVector row)) =>
-      MLMatrix.rows(List<MLVector>.generate(rowsNum,
-              (int i) => mapper(getRow(i))));
+  Matrix mapRows(Vector mapper(Vector row)) =>
+      Matrix.rows(List<Vector>.generate(rowsNum,
+              (int i) => mapper(getRow(i))), dtype: dtype);
+
+  @override
+  Matrix uniqueRows() {
+    final checked = <Vector>[];
+    for (int i = 0; i < rowsNum; i++) {
+      final row = getRow(i);
+      if (!checked.contains(row)) {
+        checked.add(row);
+      }
+    }
+    return Matrix.rows(checked, dtype: dtype);
+  }
 
   List<double> flatten2dimList(
       Iterable<Iterable<double>> rows, int Function(int i, int j) accessor) {
@@ -180,7 +192,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector toVector({bool mutable = false}) {
+  Vector toVector({bool mutable = false}) {
     if (columnsNum == 1) {
       return getColumn(0, tryCache: !mutable, mutable: mutable);
     } else if (rowsNum == 1) {
@@ -211,10 +223,10 @@ abstract class MLMatrixMixin<E, S extends List<E>>
   }
 
   @override
-  double max() => _findExtrema((MLVector row) => row.max());
+  double max() => _findExtrema((Vector row) => row.max());
 
   @override
-  double min() => _findExtrema((MLVector row) => row.min());
+  double min() => _findExtrema((Vector row) => row.min());
 
   @override
   double norm([MatrixNorm norm = MatrixNorm.frobenius]) {
@@ -239,8 +251,8 @@ abstract class MLMatrixMixin<E, S extends List<E>>
     }
     // clear rows cache
     rowsCache.fillRange(0, rowsNum, null);
-    columnsCache[columnNum] = columnValues is MLVector
-        ? columnValues : MLVector.from(columnValues);
+    columnsCache[columnNum] = columnValues is Vector
+        ? columnValues : Vector.from(columnValues);
     final values = columnValues.toList(growable: false);
     for (int i = 0, j = 0; i < rowsNum * columnsNum; i++) {
       if (i == 0 && columnNum != 0) {
@@ -252,19 +264,19 @@ abstract class MLMatrixMixin<E, S extends List<E>>
     }
   }
 
-  double _findExtrema(double callback(MLVector vector)) {
+  double _findExtrema(double callback(Vector vector)) {
     int i = 0;
-    return callback(reduceRows((MLVector result, MLVector row) {
+    return callback(reduceRows((Vector result, Vector row) {
       result[i++] = callback(row);
       return result;
-    }, initValue: MLVector.zero(rowsNum, isMutable: true)));
+    }, initValue: Vector.zero(rowsNum, isMutable: true)));
   }
 
-  MLVector _reduce(
-      MLVector Function(MLVector combine, MLVector vector) combiner,
+  Vector _reduce(
+      Vector Function(Vector combine, Vector vector) combiner,
       int length,
-      MLVector Function(int index) getVector,
-      {MLVector initValue}) {
+      Vector Function(int index) getVector,
+      {Vector initValue}) {
     var reduced = initValue ?? getVector(0);
     final startIndex = initValue != null ? 0 : 1;
     for (int i = startIndex; i < length; i++) {
@@ -273,7 +285,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
     return reduced;
   }
 
-  MLMatrix _matrixVectorMul(MLVector vector) {
+  Matrix _matrixVectorMul(Vector vector) {
     if (vector.length != columnsNum) {
       throw Exception(
           'The dimension of the vector ${vector} and the columns number of '
@@ -285,7 +297,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
     return createMatrixFromColumns([vectorColumn]);
   }
 
-  MLMatrix _matrixMul(MLMatrix matrix) {
+  Matrix _matrixMul(Matrix matrix) {
     checkColumnsAndRowsNumber(this, matrix);
     final source = List<double>(rowsNum * matrix.columnsNum);
     for (int i = 0; i < rowsNum; i++) {
@@ -297,7 +309,7 @@ abstract class MLMatrixMixin<E, S extends List<E>>
     return createMatrixFromFlattened(source, rowsNum, matrix.columnsNum);
   }
 
-  MLMatrix _matrixByVectorDiv(MLVector vector) {
+  Matrix _matrixByVectorDiv(Vector vector) {
     if (vector.length == rowsNum) {
       return mapColumns((column) => column / vector);
     }
@@ -308,60 +320,60 @@ abstract class MLMatrixMixin<E, S extends List<E>>
         'vector of length equals ${vector.length}');
   }
 
-  MLMatrix _matrixByMatrixDiv(MLMatrix matrix) {
+  Matrix _matrixByMatrixDiv(Matrix matrix) {
     checkDimensions(this, matrix, errorTitle: 'Cannot perform matrix by matrix '
         'division');
     return _matrix2matrixOperation(
-        matrix, (MLVector first, MLVector second) => first / second);
+        matrix, (Vector first, Vector second) => first / second);
   }
 
-  MLMatrix _matrixAdd(MLMatrix matrix) {
+  Matrix _matrixAdd(Matrix matrix) {
     checkDimensions(this, matrix, errorTitle: 'Cannot perform matrix addition');
     return _matrix2matrixOperation(
-        matrix, (MLVector first, MLVector second) => first + second);
+        matrix, (Vector first, Vector second) => first + second);
   }
 
-  MLMatrix _matrixSub(MLMatrix matrix) {
+  Matrix _matrixSub(Matrix matrix) {
     checkDimensions(this, matrix,
         errorTitle: 'Cannot perform matrix subtraction');
     return _matrix2matrixOperation(
-        matrix, (MLVector first, MLVector second) => first - second);
+        matrix, (Vector first, Vector second) => first - second);
   }
 
-  MLMatrix _matrixScalarAdd(double scalar) => _matrix2scalarOperation(
-      scalar, (double val, MLVector vector) => vector + val);
+  Matrix _matrixScalarAdd(double scalar) => _matrix2scalarOperation(
+      scalar, (double val, Vector vector) => vector + val);
 
-  MLMatrix _matrixScalarSub(double scalar) => _matrix2scalarOperation(
-      scalar, (double val, MLVector vector) => vector - val);
+  Matrix _matrixScalarSub(double scalar) => _matrix2scalarOperation(
+      scalar, (double val, Vector vector) => vector - val);
 
-  MLMatrix _matrixScalarMul(double scalar) => _matrix2scalarOperation(
-      scalar, (double val, MLVector vector) => vector * val);
+  Matrix _matrixScalarMul(double scalar) => _matrix2scalarOperation(
+      scalar, (double val, Vector vector) => vector * val);
 
-  MLMatrix _matrixByScalarDiv(double scalar) => _matrix2scalarOperation(
-    scalar, (double val, MLVector vector) => vector / val);
+  Matrix _matrixByScalarDiv(double scalar) => _matrix2scalarOperation(
+    scalar, (double val, Vector vector) => vector / val);
 
-  MLMatrix _matrix2matrixOperation(
-      MLMatrix matrix, MLVector operation(MLVector first, MLVector second)) {
+  Matrix _matrix2matrixOperation(
+      Matrix matrix, Vector operation(Vector first, Vector second)) {
     final elementGenFn = (int i) => operation(getRow(i), matrix.getRow(i));
-    final source = List<MLVector>.generate(rowsNum, elementGenFn);
+    final source = List<Vector>.generate(rowsNum, elementGenFn);
     return createMatrixFromRows(source);
   }
 
-  MLMatrix _matrix2scalarOperation(
-      double scalar, MLVector operation(double scalar, MLVector vector)) {
+  Matrix _matrix2scalarOperation(
+      double scalar, Vector operation(double scalar, Vector vector)) {
     // TODO: use vectorized type (e.g. Float32x4) instead of `double`
     // TODO: use then `fastMap` to accelerate computations
     final elementGenFn = (int i) => operation(scalar, getRow(i));
-    final source = List<MLVector>.generate(rowsNum, elementGenFn);
+    final source = List<Vector>.generate(rowsNum, elementGenFn);
     return createMatrixFromRows(source);
   }
 
   Float32List _query(int index, int length) =>
       data.buffer.asFloat32List(index * Float32List.bytesPerElement, length);
 
-  List<MLVector> _collectVectors(
-      Iterable<Range> ranges, MLVector getVector(int i), int maxValue) {
-    final vectors = <MLVector>[];
+  List<Vector> _collectVectors(
+      Iterable<Range> ranges, Vector getVector(int i), int maxValue) {
+    final vectors = <Vector>[];
     for (final range in ranges) {
       if (range.end > maxValue) {
         throw RangeError.range(range.end, 0, maxValue);
diff --git a/lib/src/matrix/ml_matrix_validatior.dart b/lib/src/matrix/matrix_validatior.dart
similarity index 51%
rename from lib/src/matrix/ml_matrix_validatior.dart
rename to lib/src/matrix/matrix_validatior.dart
index 539c9389..6dd12249 100644
--- a/lib/src/matrix/ml_matrix_validatior.dart
+++ b/lib/src/matrix/matrix_validatior.dart
@@ -1,8 +1,8 @@
 import 'package:ml_linalg/matrix.dart';
 
-abstract class MLMatrixValidator {
-  void checkDimensions(MLMatrix first, MLMatrix second,
+abstract class MatrixValidator {
+  void checkDimensions(Matrix first, Matrix second,
       {String errorTitle = 'Cannot perform the operation'});
-  void checkColumnsAndRowsNumber(MLMatrix first, MLMatrix second,
+  void checkColumnsAndRowsNumber(Matrix first, Matrix second,
       {String errorTitle = 'Cannot perform the operation'});
 }
diff --git a/lib/src/matrix/ml_matrix_validator_mixin.dart b/lib/src/matrix/matrix_validator_mixin.dart
similarity index 73%
rename from lib/src/matrix/ml_matrix_validator_mixin.dart
rename to lib/src/matrix/matrix_validator_mixin.dart
index 4fb3dd95..e0f3e347 100644
--- a/lib/src/matrix/ml_matrix_validator_mixin.dart
+++ b/lib/src/matrix/matrix_validator_mixin.dart
@@ -1,9 +1,9 @@
 import 'package:ml_linalg/matrix.dart';
-import 'package:ml_linalg/src/matrix/ml_matrix_validatior.dart';
+import 'package:ml_linalg/src/matrix/matrix_validatior.dart';
 
-abstract class MLMatrixValidatorMixin<E> implements MLMatrixValidator {
+mixin MatrixValidatorMixin<E> implements MatrixValidator {
   @override
-  void checkDimensions(MLMatrix first, MLMatrix second,
+  void checkDimensions(Matrix first, Matrix second,
       {String errorTitle = 'Cannot perform the operation'}) {
     if (first.rowsNum != second.rowsNum ||
         first.columnsNum != second.columnsNum) {
@@ -13,7 +13,7 @@ abstract class MLMatrixValidatorMixin<E> implements MLMatrixValidator {
   }
 
   @override
-  void checkColumnsAndRowsNumber(MLMatrix first, MLMatrix second,
+  void checkColumnsAndRowsNumber(Matrix first, Matrix second,
       {String errorTitle = 'Cannot perform the operation'}) {
     if (first.columnsNum != second.rowsNum) {
       throw Exception(
diff --git a/lib/src/matrix/ml_matrix_factory.dart b/lib/src/matrix/ml_matrix_factory.dart
deleted file mode 100644
index 0b4856be..00000000
--- a/lib/src/matrix/ml_matrix_factory.dart
+++ /dev/null
@@ -1,10 +0,0 @@
-import 'package:ml_linalg/matrix.dart';
-import 'package:ml_linalg/vector.dart';
-
-abstract class MLMatrixFactory {
-  MLMatrix createMatrixFrom(Iterable<Iterable<double>> source);
-  MLMatrix createMatrixFromFlattened(
-      Iterable<double> source, int rowsNum, int columnsNum);
-  MLMatrix createMatrixFromRows(Iterable<MLVector> source);
-  MLMatrix createMatrixFromColumns(Iterable<MLVector> source);
-}
diff --git a/lib/src/vector/common/float32_list_factory_mixin.dart b/lib/src/vector/common/float32_list_factory_mixin.dart
index 68292436..6da7337f 100644
--- a/lib/src/vector/common/float32_list_factory_mixin.dart
+++ b/lib/src/vector/common/float32_list_factory_mixin.dart
@@ -1,8 +1,8 @@
 import 'dart:typed_data';
 
-import 'package:ml_linalg/src/vector/ml_typed_list_factory.dart';
+import 'package:ml_linalg/src/vector/typed_list_factory.dart';
 
-class Float32ListFactoryMixin implements MLTypedListFactory {
+mixin Float32ListFactoryMixin implements TypedListFactory {
   @override
   Float32List createTypedList(int length) => Float32List(length);
 
diff --git a/lib/src/vector/float32x4/float32x4_data_store_mixin.dart b/lib/src/vector/float32x4/float32x4_data_store_mixin.dart
index 1e653324..0670c43b 100644
--- a/lib/src/vector/float32x4/float32x4_data_store_mixin.dart
+++ b/lib/src/vector/float32x4/float32x4_data_store_mixin.dart
@@ -1,9 +1,9 @@
 import 'dart:typed_data';
 
-import 'package:ml_linalg/src/vector/ml_vector_data_store.dart';
+import 'package:ml_linalg/src/vector/vector_data_store.dart';
 
-class Float32x4DataStoreMixin
-    implements MLVectorDataStore<Float32x4, Float32x4List> {
+mixin Float32x4DataStoreMixin
+    implements VectorDataStore<Float32x4, Float32x4List> {
   @override
   Float32x4List data;
 
diff --git a/lib/src/vector/float32x4/float32x4_operations_mixin.dart b/lib/src/vector/float32x4/float32x4_operations_mixin.dart
index f9acc59d..b91c5659 100644
--- a/lib/src/vector/float32x4/float32x4_operations_mixin.dart
+++ b/lib/src/vector/float32x4/float32x4_operations_mixin.dart
@@ -1,10 +1,10 @@
 import 'dart:math' as math;
 import 'dart:typed_data';
 
-import 'package:ml_linalg/src/vector/ml_simd_operations_helper.dart';
+import 'package:ml_linalg/src/vector/simd_operations_helper.dart';
 
-class Float32x4OperationsMixin
-    implements MLSimdOperationsHelper<Float32x4, Float32x4List> {
+mixin Float32x4OperationsMixin
+    implements SimdOperationsHelper<Float32x4, Float32x4List> {
   @override
   final bucketSize = 4;
 
@@ -38,6 +38,10 @@ class Float32x4OperationsMixin
   @override
   Float32x4 simdAbs(Float32x4 a) => a.abs();
 
+  @override
+  bool areValuesEqual(Float32x4 a, Float32x4 b) =>
+    a.equal(b).signMask == 15;
+
   @override
   double singleSIMDSum(Float32x4 a) =>
       (a.x.isNaN ? 0.0 : a.x) +
diff --git a/lib/src/vector/float32x4/float32x4_vector.dart b/lib/src/vector/float32x4/float32x4_vector.dart
index 5558726c..ee3ef24b 100644
--- a/lib/src/vector/float32x4/float32x4_vector.dart
+++ b/lib/src/vector/float32x4/float32x4_vector.dart
@@ -7,8 +7,8 @@ import 'package:ml_linalg/src/vector/common/float32_list_factory_mixin.dart';
 import 'package:ml_linalg/src/vector/float32x4/float32x4_data_store_mixin.dart';
 import 'package:ml_linalg/src/vector/float32x4/float32x4_operations_mixin.dart';
 import 'package:ml_linalg/src/vector/float32x4/float32x4_vector_factory_mixin.dart';
-import 'package:ml_linalg/src/vector/ml_simd_vector_fast_iterable_mixin.dart';
-import 'package:ml_linalg/src/vector/ml_simd_vector_operations_mixin.dart';
+import 'package:ml_linalg/src/vector/simd_vector_fast_iterable_mixin.dart';
+import 'package:ml_linalg/src/vector/simd_vector_mixin.dart';
 import 'package:ml_linalg/vector.dart';
 
 /// Vector with SIMD (single instruction, multiple data) architecture support
@@ -22,24 +22,19 @@ import 'package:ml_linalg/vector.dart';
 /// - Each SIMD-typed value is a "cell", that contains several floating point values (2 or 4).
 /// - Sequence of SIMD-values forms a "computation lane", where computations are performed with each floating point element
 /// simultaneously (in parallel)
-class Float32x4Vector extends Object
-    with
+class Float32x4Vector with
         IterableMixin<double>,
         Float32ListFactoryMixin,
         Float32x4OperationsMixin,
         Float32x4VectorFactoryMixin,
         Float32x4DataStoreMixin,
-        MLSimdVectorFastIterableMixin<Float32x4, Float32x4List>,
-        MLSimdVectorOperationsMixin<Float32x4, Float32x4List>
-    implements MLVector {
-  @override
-  final bool isMutable;
-
+        SimdVectorFastIterableMixin<Float32x4, Float32x4List>,
+        SimdVectorMixin<Float32x4, Float32x4List>
+    implements Vector {
   /// Creates a vector from collection
   Float32x4Vector.from(Iterable<double> source, {this.isMutable = false}) {
     length = source.length;
-    final List<double> _source =
-        source is List ? source : source.toList(growable: false);
+    final _source = source.toList(growable: false);
     data = convertCollectionToSIMDList(_source);
   }
 
@@ -71,4 +66,10 @@ class Float32x4Vector extends Object
     final source = List<double>.generate(length, (_) => random.nextDouble());
     data = convertCollectionToSIMDList(source);
   }
+
+  @override
+  final Type dtype = Float32x4;
+
+  @override
+  final bool isMutable;
 }
diff --git a/lib/src/vector/float32x4/float32x4_vector_factory_mixin.dart b/lib/src/vector/float32x4/float32x4_vector_factory_mixin.dart
index d3e7e155..90469e44 100644
--- a/lib/src/vector/float32x4/float32x4_vector_factory_mixin.dart
+++ b/lib/src/vector/float32x4/float32x4_vector_factory_mixin.dart
@@ -1,17 +1,17 @@
 import 'dart:typed_data';
 
 import 'package:ml_linalg/src/vector/float32x4/float32x4_vector.dart';
-import 'package:ml_linalg/src/vector/ml_vector_factory.dart';
+import 'package:ml_linalg/src/vector/vector_factory.dart';
 import 'package:ml_linalg/vector.dart';
 
-abstract class Float32x4VectorFactoryMixin
-    implements MLVectorFactory<Float32x4, Float32x4List> {
+mixin Float32x4VectorFactoryMixin
+    implements VectorFactory<Float32x4, Float32x4List> {
   @override
-  MLVector createVectorFrom(Iterable<double> source, [bool mutable = false]) =>
+  Vector createVectorFrom(Iterable<double> source, [bool mutable = false]) =>
       Float32x4Vector.from(source, isMutable: mutable);
 
   @override
-  MLVector createVectorFromSIMDList(Float32x4List source,
+  Vector createVectorFromSIMDList(Float32x4List source,
           [int length, bool mutable = false]) =>
       Float32x4Vector.fromSIMDList(source,
           origLength: length, isMutable: mutable);
diff --git a/lib/src/vector/ml_vector_factory.dart b/lib/src/vector/ml_vector_factory.dart
deleted file mode 100644
index ec503293..00000000
--- a/lib/src/vector/ml_vector_factory.dart
+++ /dev/null
@@ -1,7 +0,0 @@
-import 'package:ml_linalg/vector.dart';
-
-abstract class MLVectorFactory<E, S extends List<E>> {
-  MLVector createVectorFrom(Iterable<double> source, [bool mutable = false]);
-  MLVector createVectorFromSIMDList(S source,
-      [int length, bool mutable = false]);
-}
diff --git a/lib/src/vector/ml_simd_operations_helper.dart b/lib/src/vector/simd_operations_helper.dart
similarity index 94%
rename from lib/src/vector/ml_simd_operations_helper.dart
rename to lib/src/vector/simd_operations_helper.dart
index fc4b731f..9f586e80 100644
--- a/lib/src/vector/ml_simd_operations_helper.dart
+++ b/lib/src/vector/simd_operations_helper.dart
@@ -1,4 +1,4 @@
-abstract class MLSimdOperationsHelper<E, S extends List<E>> {
+abstract class SimdOperationsHelper<E, S extends List<E>> {
   /// number of lanes (it is 2 or 4 elements currently supported to be processed simultaneously, this characteristic
   /// restricted by computing platform architecture)
   int get bucketSize;
@@ -39,6 +39,8 @@ abstract class MLSimdOperationsHelper<E, S extends List<E>> {
   /// prepares a simd value comprised of maximum values of both [a] and [b]
   E selectMax(E a, E b);
 
+  bool areValuesEqual(E a, E b);
+
   /// returns a maximal element (lane) of [a]
   double getMaxLane(E a);
 
diff --git a/lib/src/vector/ml_simd_vector_fast_iterable_mixin.dart b/lib/src/vector/simd_vector_fast_iterable_mixin.dart
similarity index 56%
rename from lib/src/vector/ml_simd_vector_fast_iterable_mixin.dart
rename to lib/src/vector/simd_vector_fast_iterable_mixin.dart
index c8f04709..615c5eb7 100644
--- a/lib/src/vector/ml_simd_vector_fast_iterable_mixin.dart
+++ b/lib/src/vector/simd_vector_fast_iterable_mixin.dart
@@ -1,18 +1,18 @@
 import 'dart:math' as math;
 
-import 'package:ml_linalg/src/vector/ml_simd_operations_helper.dart';
-import 'package:ml_linalg/src/vector/ml_vector_data_store.dart';
-import 'package:ml_linalg/src/vector/ml_vector_factory.dart';
+import 'package:ml_linalg/src/vector/simd_operations_helper.dart';
+import 'package:ml_linalg/src/vector/vector_data_store.dart';
+import 'package:ml_linalg/src/vector/vector_factory.dart';
 import 'package:ml_linalg/vector.dart';
 
-abstract class MLSimdVectorFastIterableMixin<E, S extends List<E>>
+mixin SimdVectorFastIterableMixin<E, S extends List<E>>
     implements
-        MLVectorFactory<E, S>,
-        MLVectorDataStore<E, S>,
-        MLSimdOperationsHelper<E, S>,
-        MLVector {
+        VectorFactory<E, S>,
+        VectorDataStore<E, S>,
+        SimdOperationsHelper<E, S>,
+        Vector {
   @override
-  MLVector fastMap<T>(
+  Vector fastMap<T>(
       T mapper(T element, int offsetStartIdx, int offsetEndIdx)) {
     final list = createSIMDList(data.length) as List<T>;
     for (int i = 0; i < data.length; i++) {
diff --git a/lib/src/vector/ml_simd_vector_operations_mixin.dart b/lib/src/vector/simd_vector_mixin.dart
similarity index 79%
rename from lib/src/vector/ml_simd_vector_operations_mixin.dart
rename to lib/src/vector/simd_vector_mixin.dart
index bb9691b1..50c678fc 100644
--- a/lib/src/vector/ml_simd_vector_operations_mixin.dart
+++ b/lib/src/vector/simd_vector_mixin.dart
@@ -4,20 +4,21 @@ import 'dart:typed_data';
 
 import 'package:ml_linalg/matrix.dart';
 import 'package:ml_linalg/norm.dart';
-import 'package:ml_linalg/src/vector/ml_simd_operations_helper.dart';
-import 'package:ml_linalg/src/vector/ml_vector_data_store.dart';
-import 'package:ml_linalg/src/vector/ml_vector_factory.dart';
-import 'package:ml_linalg/src/vector/ml_typed_list_factory.dart';
+import 'package:ml_linalg/src/vector/simd_operations_helper.dart';
+import 'package:ml_linalg/src/vector/vector_data_store.dart';
+import 'package:ml_linalg/src/vector/vector_factory.dart';
+import 'package:ml_linalg/src/vector/typed_list_factory.dart';
 import 'package:ml_linalg/vector.dart';
+import 'package:quiver/core.dart';
 
-abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
+mixin SimdVectorMixin<E, S extends List<E>>
     implements
         IterableMixin<double>,
-        MLSimdOperationsHelper<E, S>,
-        MLTypedListFactory,
-        MLVectorDataStore<E, S>,
-        MLVectorFactory<E, S>,
-        MLVector {
+        SimdOperationsHelper<E, S>,
+        TypedListFactory,
+        VectorDataStore<E, S>,
+        VectorFactory<E, S>,
+        Vector {
   S get dataWithoutLastBucket => sublist(data, 0, data.length - 1);
 
   @override
@@ -27,10 +28,35 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   bool get _isLastBucketNotFull => length % bucketSize > 0;
 
   @override
-  MLVector operator +(Object value) {
-    if (value is MLVector) {
+  bool operator ==(Object obj) {
+    if (obj is Vector) {
+      // TODO: consider checking hashcode here to compare two vectors
+      if (length != obj.length) {
+        return false;
+      }
+      for (int i = 0; i < data.length; i++) {
+        if (!areValuesEqual(data[i], (obj as VectorDataStore<E, S>).data[i])) {
+          return false;
+        }
+      }
+      return true;
+    }
+    return false;
+  }
+
+  @override
+  int get hashCode {
+    _hash ??= hashObjects(data);
+    return _hash;
+  }
+
+  int _hash;
+
+  @override
+  Vector operator +(Object value) {
+    if (value is Vector) {
       return _elementWiseVectorOperation(value, simdSum);
-    } else if (value is MLMatrix) {
+    } else if (value is Matrix) {
       final other = value.toVector();
       return _elementWiseVectorOperation(other, simdSum);
     } else if (value is num) {
@@ -41,10 +67,10 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector operator -(Object value) {
-    if (value is MLVector) {
+  Vector operator -(Object value) {
+    if (value is Vector) {
       return _elementWiseVectorOperation(value, simdSub);
-    } else if (value is MLMatrix) {
+    } else if (value is Matrix) {
       final other = value.toVector();
       return _elementWiseVectorOperation(other, simdSub);
     } else if (value is num) {
@@ -55,10 +81,10 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector operator *(Object value) {
-    if (value is MLVector) {
+  Vector operator *(Object value) {
+    if (value is Vector) {
       return _elementWiseVectorOperation(value, simdMul);
-    } else if (value is MLMatrix) {
+    } else if (value is Matrix) {
       return _matrixMul(value);
     } else if (value is num) {
       return _elementWiseFloatScalarOperation(value.toDouble(), simdScale);
@@ -67,8 +93,8 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector operator /(Object value) {
-    if (value is MLVector) {
+  Vector operator /(Object value) {
+    if (value is Vector) {
       return _elementWiseVectorOperation(value, simdDiv);
     } else if (value is num) {
       return _elementWiseFloatScalarOperation(1 / value, simdScale);
@@ -77,22 +103,22 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector toIntegerPower(int power) => _elementWisePow(power);
+  Vector toIntegerPower(int power) => _elementWisePow(power);
 
   /// Returns a vector filled with absolute values of an each component of [this] vector
   @override
-  MLVector abs() =>
+  Vector abs() =>
       _elementWiseSelfOperation((E element, [int i]) => simdAbs(element));
 
   @override
-  double dot(MLVector vector) => (this * vector).sum();
+  double dot(Vector vector) => (this * vector).sum();
 
   /// Returns sum of all vector components
   @override
   double sum() => singleSIMDSum(data.reduce(simdSum));
 
   @override
-  double distanceTo(MLVector vector, [Norm norm = Norm.euclidean]) =>
+  double distanceTo(Vector vector, [Norm norm = Norm.euclidean]) =>
       (this - vector).norm(norm);
 
   @override
@@ -140,7 +166,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector query(Iterable<int> indexes) {
+  Vector query(Iterable<int> indexes) {
     final list = createTypedList(indexes.length);
     int i = 0;
     for (final idx in indexes) {
@@ -150,7 +176,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector unique() {
+  Vector unique() {
     final unique = <double>[];
     for (int i = 0; i < length; i++) {
       final el = this[i];
@@ -179,7 +205,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   @override
-  MLVector subvector(int start, [int end]) {
+  Vector subvector(int start, [int end]) {
     final collection = bufferAsTypedList((data as TypedData).buffer, start,
         (end > length ? length : end) - start);
     return createVectorFrom(collection);
@@ -234,7 +260,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   /// Returns a vector as a result of applying to [this] any element-wise operation with a simd value
-  MLVector _elementWiseFloatScalarOperation(
+  Vector _elementWiseFloatScalarOperation(
       double scalar, E operation(E a, double b)) {
     final list = createSIMDList(data.length);
     for (int i = 0; i < data.length; i++) {
@@ -244,7 +270,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   /// Returns a vector as a result of applying to [this] any element-wise operation with a simd value
-  MLVector _elementWiseSimdScalarOperation(E simdVal, E operation(E a, E b)) {
+  Vector _elementWiseSimdScalarOperation(E simdVal, E operation(E a, E b)) {
     final list = createSIMDList(data.length);
     for (int i = 0; i < data.length; i++) {
       list[i] = operation(data[i], simdVal);
@@ -253,16 +279,16 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   /// Returns a vector as a result of applying to [this] any element-wise operation with a vector (e.g. vector addition)
-  MLVector _elementWiseVectorOperation(MLVector vector, E operation(E a, E b)) {
+  Vector _elementWiseVectorOperation(Vector vector, E operation(E a, E b)) {
     if (vector.length != length) throw _mismatchLengthError();
     final list = createSIMDList(data.length);
     for (int i = 0; i < data.length; i++) {
-      list[i] = operation(data[i], (vector as MLVectorDataStore<E, S>).data[i]);
+      list[i] = operation(data[i], (vector as VectorDataStore<E, S>).data[i]);
     }
     return createVectorFromSIMDList(list, length);
   }
 
-  MLVector _elementWiseSelfOperation(E operation(E element, [int index])) {
+  Vector _elementWiseSelfOperation(E operation(E element, [int index])) {
     final list = createSIMDList(data.length);
     for (int i = 0; i < data.length; i++) {
       list[i] = operation(data[i], i);
@@ -271,7 +297,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
   }
 
   /// Returns a vector as a result of applying to [this] element-wise raising to the integer power
-  MLVector _elementWisePow(int exp) {
+  Vector _elementWisePow(int exp) {
     final list = createSIMDList(data.length);
     for (int i = 0; i < data.length; i++) {
       list[i] = _simdToIntPow(data[i], exp);
@@ -279,7 +305,7 @@ abstract class MLSimdVectorOperationsMixin<E, S extends List<E>>
     return createVectorFromSIMDList(list, length);
   }
 
-  MLVector _matrixMul(MLMatrix matrix) {
+  Vector _matrixMul(Matrix matrix) {
     if (length != matrix.rowsNum) {
       throw Exception(
           'Multiplication by a matrix with diffrent number of rows than the vector length is not allowed:'
diff --git a/lib/src/vector/ml_typed_list_factory.dart b/lib/src/vector/typed_list_factory.dart
similarity index 94%
rename from lib/src/vector/ml_typed_list_factory.dart
rename to lib/src/vector/typed_list_factory.dart
index e23ea6f3..5f2a5575 100644
--- a/lib/src/vector/ml_typed_list_factory.dart
+++ b/lib/src/vector/typed_list_factory.dart
@@ -1,6 +1,6 @@
 import 'dart:typed_data' show ByteBuffer;
 
-abstract class MLTypedListFactory {
+abstract class TypedListFactory {
   /// returns a typed list with
   List<double> createTypedListFromByteBuffer(ByteBuffer data);
 
diff --git a/lib/src/vector/ml_vector_data_store.dart b/lib/src/vector/vector_data_store.dart
similarity index 56%
rename from lib/src/vector/ml_vector_data_store.dart
rename to lib/src/vector/vector_data_store.dart
index 80ca9e89..dd3c41ad 100644
--- a/lib/src/vector/ml_vector_data_store.dart
+++ b/lib/src/vector/vector_data_store.dart
@@ -1,4 +1,4 @@
-abstract class MLVectorDataStore<E, S extends List<E>> {
+abstract class VectorDataStore<E, S extends List<E>> {
   S data;
   int length;
   List<double> toList({bool growable = false});
diff --git a/lib/src/vector/vector_factory.dart b/lib/src/vector/vector_factory.dart
new file mode 100644
index 00000000..485bb934
--- /dev/null
+++ b/lib/src/vector/vector_factory.dart
@@ -0,0 +1,7 @@
+import 'package:ml_linalg/vector.dart';
+
+abstract class VectorFactory<E, S extends List<E>> {
+  Vector createVectorFrom(Iterable<double> source, [bool mutable = false]);
+  Vector createVectorFromSIMDList(S source,
+      [int length, bool mutable = false]);
+}
diff --git a/lib/vector.dart b/lib/vector.dart
index b3cc5005..2b489bfe 100644
--- a/lib/vector.dart
+++ b/lib/vector.dart
@@ -5,13 +5,13 @@ import 'package:ml_linalg/src/vector/float32x4/float32x4_vector.dart';
 import 'norm.dart';
 
 /// An algebraic vector (ordered set of elements).
-abstract class MLVector implements Iterable<double> {
+abstract class Vector implements Iterable<double> {
   /// Creates a vector from a collection [source].
   ///
   /// It converts the collection of [double]-type elements into a collection of
   /// [Float32x4] elements. If [isMutable] is true, one can alter the vector,
   /// for example, via `[]=` operator
-  factory MLVector.from(Iterable<double> source,
+  factory Vector.from(Iterable<double> source,
       {bool isMutable, Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
@@ -25,7 +25,7 @@ abstract class MLVector implements Iterable<double> {
   ///
   /// If [isMutable] is true, one can alter the vector, for example, via `[]=`
   /// operator
-  factory MLVector.filled(int length, double value,
+  factory Vector.filled(int length, double value,
       {bool isMutable, Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
@@ -39,7 +39,7 @@ abstract class MLVector implements Iterable<double> {
   ///
   /// If [isMutable] is true, one can alter the vector, for example, via `[]=`
   /// operator
-  factory MLVector.zero(int length, {bool isMutable, Type dtype = Float32x4}) {
+  factory Vector.zero(int length, {bool isMutable, Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
         return Float32x4Vector.zero(length, isMutable: isMutable);
@@ -53,7 +53,7 @@ abstract class MLVector implements Iterable<double> {
   ///
   /// If [isMutable] is true, one can alter the vector, for example, via `[]=`
   /// operator
-  factory MLVector.randomFilled(int length,
+  factory Vector.randomFilled(int length,
       {int seed, bool isMutable, Type dtype = Float32x4}) {
     switch (dtype) {
       case Float32x4:
@@ -64,6 +64,8 @@ abstract class MLVector implements Iterable<double> {
     }
   }
 
+  Type get dtype;
+
   /// Can someone mutate the vector, e.g. via []= operator, or not
   bool get isMutable;
 
@@ -74,30 +76,30 @@ abstract class MLVector implements Iterable<double> {
   void operator []=(int index, double value);
 
   /// Vector addition (element-wise operation)
-  MLVector operator +(Object value);
+  Vector operator +(Object value);
 
   /// Vector subtraction (element-wise operation)
-  MLVector operator -(Object value);
+  Vector operator -(Object value);
 
   /// Vector multiplication (element-wise operation)
-  MLVector operator *(Object value);
+  Vector operator *(Object value);
 
   /// Element-wise division
-  MLVector operator /(Object value);
+  Vector operator /(Object value);
 
-  /// Creates a new [MLVector] containing elements of this [MLVector] raised to
+  /// Creates a new [Vector] containing elements of this [Vector] raised to
   /// the integer [power]
-  MLVector toIntegerPower(int power);
+  Vector toIntegerPower(int power);
 
   /// Returns a vector with absolute value of each vector element
-  MLVector abs();
+  Vector abs();
 
   /// Returns a dot (inner) product of [this] and [vector]
-  double dot(MLVector vector);
+  double dot(Vector vector);
 
   /// Returns a distance between [this] and [vector] with vector norm type
   /// considering
-  double distanceTo(MLVector vector, [Norm norm = Norm.euclidean]);
+  double distanceTo(Vector vector, [Norm norm = Norm.euclidean]);
 
   /// Returns a mean value of [this] vector
   double mean();
@@ -116,14 +118,14 @@ abstract class MLVector implements Iterable<double> {
 
   /// Returns a vector composed of elements which are located on the passed
   /// indexes
-  MLVector query(Iterable<int> indexes);
+  Vector query(Iterable<int> indexes);
 
   /// Returns a vector composed of unique vector's elements
-  MLVector unique();
+  Vector unique();
 
-  MLVector fastMap<E>(
+  Vector fastMap<E>(
       E mapper(E element, int offsetStartIdx, int offsetEndIdx));
 
   /// Cuts out a part of the vector
-  MLVector subvector(int start, [int end]);
+  Vector subvector(int start, [int end]);
 }
diff --git a/pubspec.lock b/pubspec.lock
index 04a75a5b..71f269d5 100644
--- a/pubspec.lock
+++ b/pubspec.lock
@@ -56,7 +56,7 @@ packages:
       name: build_daemon
       url: "https://pub.dartlang.org"
     source: hosted
-    version: "0.4.0"
+    version: "0.4.1"
   build_resolvers:
     dependency: transitive
     description:
@@ -70,7 +70,7 @@ packages:
       name: build_runner
       url: "https://pub.dartlang.org"
     source: hosted
-    version: "1.2.7"
+    version: "1.2.8"
   build_runner_core:
     dependency: transitive
     description:
@@ -98,7 +98,7 @@ packages:
       name: built_value
       url: "https://pub.dartlang.org"
     source: hosted
-    version: "6.3.0"
+    version: "6.3.1"
   charcode:
     dependency: transitive
     description:
@@ -336,7 +336,7 @@ packages:
       name: package_resolver
       url: "https://pub.dartlang.org"
     source: hosted
-    version: "1.0.7"
+    version: "1.0.10"
   path:
     dependency: transitive
     description:
@@ -350,7 +350,7 @@ packages:
       name: pedantic
       url: "https://pub.dartlang.org"
     source: hosted
-    version: "1.4.0"
+    version: "1.5.0"
   platform:
     dependency: transitive
     description:
@@ -394,7 +394,7 @@ packages:
     source: hosted
     version: "0.1.4"
   quiver:
-    dependency: transitive
+    dependency: "direct main"
     description:
       name: quiver
       url: "https://pub.dartlang.org"
@@ -562,4 +562,4 @@ packages:
     source: hosted
     version: "2.1.15"
 sdks:
-  dart: ">=2.1.0 <3.0.0"
+  dart: ">=2.2.0 <3.0.0"
diff --git a/pubspec.yaml b/pubspec.yaml
index f4a1f75a..16ed6225 100644
--- a/pubspec.yaml
+++ b/pubspec.yaml
@@ -5,7 +5,10 @@ author: Ilia Gyrdymov <ilgyrd@gmail.com>
 homepage: https://github.com/gyrdym/ml_linalg
 
 environment:
-  sdk: '>=2.1.0 <3.0.0'
+  sdk: '>=2.2.0 <3.0.0'
+
+dependencies:
+  quiver: '>=2.0.0 <3.0.0'
 
 dev_dependencies:
   benchmark_harness: ">=1.0.0 <2.0.0"
diff --git a/test/float32x4_matrix_test.dart b/test/float32x4_matrix_test.dart
index 52bbcc2e..bf3760fe 100644
--- a/test/float32x4_matrix_test.dart
+++ b/test/float32x4_matrix_test.dart
@@ -95,10 +95,10 @@ void main() {
       final row1 = matrix.getRow(0);
       final row2 = matrix.getRow(1);
 
-      expect(row1 is MLVector, isTrue);
+      expect(row1 is Vector, isTrue);
       expect(row1, [11.0, 12.0, 13.0, 14.0]);
 
-      expect(row2 is MLVector, isTrue);
+      expect(row2 is Vector, isTrue);
       expect(row2, [15.0, 16.0, 17.0, 18.0]);
     });
 
@@ -144,16 +144,16 @@ void main() {
       final column3 = matrix.getColumn(2);
       final column4 = matrix.getColumn(3);
 
-      expect(column1 is MLVector, isTrue);
+      expect(column1 is Vector, isTrue);
       expect(column1, [11.0, 15.0, 21.0]);
 
-      expect(column2 is MLVector, isTrue);
+      expect(column2 is Vector, isTrue);
       expect(column2, [12.0, 16.0, 22.0]);
 
-      expect(column3 is MLVector, isTrue);
+      expect(column3 is Vector, isTrue);
       expect(column3, [13.0, 17.0, 23.0]);
 
-      expect(column4 is MLVector, isTrue);
+      expect(column4 is Vector, isTrue);
       expect(column4, [14.0, 18.0, 24.0]);
     });
 
@@ -370,7 +370,7 @@ void main() {
         [15.0, 16.0, 17.0, 18.0],
         [21.0, 22.0, 23.0, 24.0],
       ]);
-      final modifier = MLVector.filled(3, 2.0);
+      final modifier = Vector.filled(3, 2.0);
       final actual = matrix.mapColumns((column) => column + modifier);
       final expected = [
         [13.0, 14.0, 15.0, 16.0],
@@ -386,7 +386,7 @@ void main() {
         [15.0, 16.0, 17.0, 18.0],
         [21.0, 22.0, 23.0, 24.0],
       ]);
-      final modifier = MLVector.filled(4, 1.0);
+      final modifier = Vector.filled(4, 1.0);
       final actual = matrix.mapRows((row) => row - modifier);
       final expected = [
         [10.0, 11.0, 12.0, 13.0],
@@ -749,7 +749,7 @@ void main() {
       ]);
       final column1 = matrix.toVector();
       final column2 = matrix.toVector();
-      expect(column1 is MLVector, isTrue);
+      expect(column1 is Vector, isTrue);
       expect(column1, equals([1.0, 5.0, 9.0]));
       expect(identical(column1, column2), isTrue);
     });
@@ -763,7 +763,7 @@ void main() {
       final column1 = matrix.toVector(mutable: true);
       final column2 = matrix.toVector(mutable: true);
 
-      expect(column1 is MLVector, isTrue);
+      expect(column1 is Vector, isTrue);
       expect(column1.isMutable, isTrue);
       expect(column1, equals([1.0, 5.0, 9.0]));
       expect(identical(column1, column2), isFalse);
@@ -776,7 +776,7 @@ void main() {
       ]);
       final row1 = matrix.toVector();
       final row2 = matrix.toVector();
-      expect(row1 is MLVector, isTrue);
+      expect(row1 is Vector, isTrue);
       expect(row1, equals([4.0, 8.0, 12.0, 16.0]));
       expect(identical(row1, row2), isTrue);
     });
@@ -788,7 +788,7 @@ void main() {
       final row1 = matrix.toVector(mutable: true);
       final row2 = matrix.toVector(mutable: true);
 
-      expect(row1 is MLVector, isTrue);
+      expect(row1 is Vector, isTrue);
       expect(row1.isMutable, isTrue);
       expect(row1, equals([4.0, 8.0, 12.0, 16.0]));
       expect(identical(row1, row2), isFalse);
@@ -806,6 +806,46 @@ void main() {
     });
   });
 
+  group('Float32x4Matrix.uniqueRows', () {
+    test('should return the same row vectors as the matrix rows if all the '
+        'rows are unique', () {
+      final matrix = Float32x4Matrix.from([
+        [4.0, 8.0, 12.0, 16.0, 34.0],
+        [20.0, 24.0, 28.0, 32.0, 23.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+        [16.0, 1.0, -18.0, 3.0, 11.0],
+        [112.0, 10.0, 34.0, 2.0, 10.0],
+      ]);
+      final actual = matrix.uniqueRows();
+      final expected = [
+        [4.0, 8.0, 12.0, 16.0, 34.0],
+        [20.0, 24.0, 28.0, 32.0, 23.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+        [16.0, 1.0, -18.0, 3.0, 11.0],
+        [112.0, 10.0, 34.0, 2.0, 10.0],
+      ];
+      expect(actual, equals(expected));
+    });
+
+    test('should return just unique rows', () {
+      final matrix = Float32x4Matrix.from([
+        [4.0, 8.0, 12.0, 16.0, 34.0],
+        [20.0, 24.0, 28.0, 32.0, 23.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+        [20.0, 24.0, 28.0, 32.0, 23.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+      ]);
+      final actual = matrix.uniqueRows();
+      final expected = [
+        [4.0, 8.0, 12.0, 16.0, 34.0],
+        [20.0, 24.0, 28.0, 32.0, 23.0],
+        [36.0, .0, -8.0, -12.0, 12.0],
+      ];
+      expect(actual, equals(expected));
+    });
+  });
+
   group('Float32x4Matrix.setColumn', () {
     test('should set a new column by column index', () {
       final matrix = Float32x4Matrix.from([
diff --git a/test/float32x4_vector_test.dart b/test/float32x4_vector_test.dart
index 8317e023..e168b1cd 100644
--- a/test/float32x4_vector_test.dart
+++ b/test/float32x4_vector_test.dart
@@ -123,9 +123,57 @@ void main() {
       expect(actual.length, equals(5));
     });
 
-    test(
-        'should throw an exception if one tries to sum vectors of different lengths',
-        () {
+    test('should compare with another vector and return `true` if vectors are'
+        'equal', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      final vector2 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      expect(vector1 == vector2, isTrue);
+    });
+
+    test('should compare with another vector and return `true` if vectors are'
+        'have all zero values', () {
+      final vector1 = Float32x4Vector.from([0.0, 0.0, 0.0, 0.0, 0.0]);
+      final vector2 = Float32x4Vector.from([0.0, 0.0, 0.0, 0.0, 0.0]);
+      expect(vector1 == vector2, isTrue);
+    });
+
+    test('should compare with another vector and return `false` if vectors are'
+        'different', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      final vector2 = Float32x4Vector.from([1.0, 2.0, 30.0, 4.0, 5.0]);
+      expect(vector1 == vector2, isFalse);
+    });
+
+    test('should compare with another vector and return `false` if vectors are'
+        'have opposite values', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      final vector2 = Float32x4Vector.from([-1.0, -2.0, -3.0, -4.0, -5.0]);
+      expect(vector1 == vector2, isFalse);
+    });
+
+    test('should compare with another vector and return `false` if one vector'
+        'have all zero values', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      final vector2 = Float32x4Vector.from([0.0, 0.0, 0.0, 0.0, 0.0]);
+      expect(vector1 == vector2, isFalse);
+    });
+
+    test('should compare with another vector and return `false` if vectors '
+        'have different lengths', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+      final vector2 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
+      expect(vector1 == vector2, isFalse);
+    });
+
+    test('should compare with another object and return `false` if another '
+        'object is not a vector', () {
+      final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
+      final vector2 = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
+      expect(vector1 == vector2, isFalse);
+    });
+
+    test('should throw an exception if one tries to sum vectors of different '
+        'lengths', () {
       final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
       final vector2 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
       expect(() => vector1 + vector2, throwsRangeError);
@@ -165,9 +213,8 @@ void main() {
       expect(result.length, equals(5));
     });
 
-    test(
-        'should throw an exception if one tries to subtract a vector of different length',
-        () {
+    test('should throw an exception if one tries to subtract a vector of '
+        'different length', () {
       final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
       final vector2 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
       expect(() => vector1 - vector2, throwsRangeError);
@@ -216,9 +263,8 @@ void main() {
       expect(actual.length, equals(5));
     });
 
-    test(
-        'should throw an error if one tries to multiple by a vector of different length',
-        () {
+    test('should throw an error if one tries to multiple by a vector of '
+        'different length', () {
       final vector1 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0]);
       final vector2 = Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]);
       expect(() => vector1 * vector2, throwsRangeError);
@@ -471,16 +517,14 @@ void main() {
       expect(() => vector[100], throwsRangeError);
     });
 
-    test(
-        'should not allow to assign a value via indexed access if it is immutable ([]= operator)',
-        () {
+    test('should not allow to assign a value via indexed access if it is '
+        'immutable ([]= operator)', () {
       final vector = Float32x4Vector.from([1.0, 2.0]); // immutable by default
       expect(() => vector[0] = 3.0, throwsUnsupportedError);
     });
 
-    test(
-        'should perform value assignment via indexed access if it is mutable ([]= operator)',
-        () {
+    test('should perform value assignment via indexed access if it is mutable '
+        '([]= operator)', () {
       final vector =
           Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0], isMutable: true);
       vector[0] = 6.0;
@@ -491,9 +535,8 @@ void main() {
       expect(vector, equals([6.0, 7.0, 8.0, 9.0, 10.0]));
     });
 
-    test(
-        'should throw range error if one tries to set a value on non-existing index',
-        () {
+    test('should throw range error if one tries to set a value on non-existing '
+        'index', () {
       final vector =
           Float32x4Vector.from([1.0, 2.0, 3.0, 4.0, 5.0], isMutable: true);
       expect(() => vector[20] = 2.0, throwsRangeError);