feat: resolve null safety issues and achieve dart 3 compatibility

CHANGELOG.md

@@ -1,3 +1,6 @@
+# 0.1.0
+- **Breaking change** svd returns a dart3 record (E, U, D) instead of a map {'E': E, 'U': U, 'D': D}
+- Fix null safety issues.
 # 0.0.29+1
 
 - Fix percentile index error in `Percentile.value` getter ([zeshuaro](https://github.com/zeshuaro)).

example/extended_math_example.dart

@@ -4,7 +4,7 @@ void main() {
   final v1 = Vector(<double>[1, 2, 3]);
   final v2 = Vector(<double>[4, 5, 6]);
   // Multiply vectors
-  final double res = v1.dot(v2);
+  final num res = v1.dot(v2);
   // Add vectors
   final Vector res1 = v1 + v2;
 
@@ -14,12 +14,12 @@ void main() {
   print(v5.angleBetween(v6));
 
   final v3 = SquareMatrix(<List<double>>[
-    <double>[9, 3, 5], 
-    <double>[-6, -9, 7], 
+    <double>[9, 3, 5],
+    <double>[-6, -9, 7],
     <double>[-1, -8, 1]
   ]);
   // Gets determinant of matrix
-  final double det = v3.determinant();
+  final num det = v3.determinant();
 
   // Computes eigenvalues and eigenvectors of square matrix
   final Map<num, Vector> result = v3.eigen();

lib/src/applied_mathematics/numerical_analysis/newtons_method.dart

@@ -85,7 +85,7 @@ class NewtonsMethod {
 
   /// Counting Lowest limit for negative roots
   num lowerLimit() {
-    final helpArray = List<num>(_equationCoef.length);
+    final helpArray = List<num>.filled(_equationCoef.length, 0);
     for (var i = 0; i < _equationCoef.length; i++) {
       if (_equationPower[i] % 2 == 0) {
         helpArray[i] = _equationCoef[i];
@@ -113,8 +113,8 @@ class NewtonsMethod {
 
     var point1 = lowLim;
     var point2 = lowLim + delta;
-    var value1 = 0.0;
-    var value2 = 0.0;
+    num value1 = 0.0;
+    num value2 = 0.0;
 
     while (point2 < upLim) {
       value1 = _calcValue(point1);

lib/src/applied_mathematics/probability_theory/numbers_generator.dart

@@ -1,7 +1,5 @@
 import 'dart:math';
 
-import 'package:meta/meta.dart';
-
 /// Generator of random numbers
 ///
 /// It isn't extend `Random` of `dart:math`, but provide functionality
@@ -25,7 +23,7 @@ class NumbersGenerator {
   ///
   /// Generates `integer` number in range [from] - [to] inclusively.
   /// [from] should be less than [to].
-  Iterable<int> intIterableSync({@required int to, int from = 0}) sync* {
+  Iterable<int> intIterableSync({required int to, int from = 0}) sync* {
     while (true) {
       yield nextInt(to, from: from);
     }

lib/src/applied_mathematics/statistic/central_tendency.dart

@@ -21,15 +21,15 @@ class CentralTendency {
   ///
   /// If [weights] isn't `null` then the weighted arithmetic mean are computed.
   /// If provided, [weights] must have the same shape as the number's set.
-  num arithmetic({TensorBase weights}) {
+  num arithmetic({TensorBase? weights}) {
     if (_set.any((e) => e <= 0)) {
       throw MeanException('All numbers in set must be greatest than zero!');
     }
 
     var set = _set.copy();
     final w = weights ?? TensorBase.generate(set.shape, (_) => 1);
 
-    if (!mapsEqual(set.shape, w.shape)) {
+    if (!listsEqual(set.shape, w.shape)) {
       throw ArgumentError.value(weights, 'weights',
           'Items count of weights don\'t match set of numbers!');
     }
@@ -43,15 +43,15 @@ class CentralTendency {
   ///
   /// If [weights] isn't `null` then the weighted geometric mean are computed.
   /// If provided, [weights] must have the same shape as the number's set.
-  num geometric({TensorBase weights}) {
+  num geometric({TensorBase? weights}) {
     if (_set.any((e) => e <= 0)) {
       throw MeanException('All numbers in set must be greatest than zero!');
     }
 
     final set = _set.copy();
     final w = weights ?? TensorBase.generate(set.shape, (_) => 1);
 
-    if (!mapsEqual(set.shape, w.shape)) {
+    if (!listsEqual(set.shape, w.shape)) {
       throw ArgumentError.value(weights, 'weights',
           'Items count of weights don\'t match set of numbers!');
     }
@@ -71,15 +71,15 @@ class CentralTendency {
   ///
   /// If [weights] isn't `null` then the weighted harmonic mean are computed.
   /// If provided, [weights] must have the same shape as the number's set.
-  num harmonic({TensorBase weights}) {
+  num harmonic({TensorBase? weights}) {
     if (_set.any((e) => e <= 0)) {
       throw MeanException('All numbers in set must be greatest than zero!');
     }
 
     final set = _set.copy();
     final w = weights ?? TensorBase.generate(set.shape, (_) => 1);
 
-    if (!mapsEqual(set.shape, w.shape)) {
+    if (!listsEqual(set.shape, w.shape)) {
       throw ArgumentError.value(weights, 'weights',
           'Items count of weights don\'t match set of numbers!');
     }

lib/src/applied_mathematics/statistic/dispersion.dart

@@ -10,7 +10,7 @@ class Dispersion {
   ///
   /// By default means that all values have an equal propabilities if
   /// it isn't so, you can provide your own.
-  Dispersion(this._values, {TensorBase probabilities})
+  Dispersion(this._values, {TensorBase? probabilities})
       : _probabilities = probabilities ??
             TensorBase.generate(_values.shape, (_) => 1 / _values.itemsCount);
 
@@ -28,7 +28,8 @@ class Dispersion {
 
   /// Computes expected value for all possible [values] of a random number
   /// for finite case with its probabilities
-  double expectedValue() => (values * probabilities).reduce((f, s) => f + s);
+  double expectedValue() =>
+      (values * probabilities).reduce((f, s) => f + s).toDouble();
 
   /// Computes `population` or `sample` [type]s of standard deviation of
   /// [values]
@@ -47,7 +48,7 @@ class Dispersion {
       default:
         final summ = (values.map((v) => pow(v, 2)) * probabilities)
             .reduce((f, s) => f + s);
-        return summ - pow(mu, 2);
+        return (summ - pow(mu, 2)).toDouble();
     }
   }
 

lib/src/complex_analysis/complex.dart

@@ -35,7 +35,7 @@ class Complex with CopyableMixin<Complex> {
   ///
   /// [other] can be either number or complex number.
   Complex operator +(Object other) {
-    Complex c;
+    var c = Complex();
     if (other is num) {
       c = Complex(re: re + other, im: im);
     } else if (other is Complex) {
@@ -50,7 +50,7 @@ class Complex with CopyableMixin<Complex> {
   ///
   /// [other] can be either number or complex number.
   Complex operator -(Object other) {
-    Complex c;
+    var c = Complex();
     if (other is Complex) {
       c = this + -other;
     } else if (other is num) {
@@ -68,7 +68,7 @@ class Complex with CopyableMixin<Complex> {
   ///
   /// [other] can be either number or complex number.
   Complex operator *(Object other) {
-    Complex c;
+    var c = Complex();
     if (other is num) {
       final newRe = re * other;
       final newIm = im * other;
@@ -89,7 +89,7 @@ class Complex with CopyableMixin<Complex> {
   ///
   /// [other] can be either number or complex number.
   Complex operator /(Object other) {
-    Complex c;
+    var c = Complex();
     if (other is num) {
       final down = m.pow(other, 2);
       final newRe = (re * other) / down;
@@ -137,7 +137,7 @@ class Complex with CopyableMixin<Complex> {
     }
 
     // final rootModule = m.pow(module, 1 / root);
-    final rootModule = Double(module).rootOf(root).toDouble();
+    final rootModule = Double(module.toDouble()).rootOf(root).toDouble();
 
     for (var i = 0; i < root; i++) {
       final newRe = m.cos((argument + 2 * argument * i) / root) * rootModule;

lib/src/discrete_mathematics/general_algebraic_systems/number/base/number.dart

@@ -23,8 +23,7 @@ class Number extends TensorBase {
   num get data => _value;
 
   @override
-  Map<String, int> get shape =>
-      <String, int>{}; // Empty because numbers haven't shape
+  List<int> get shape => []; // Empty because numbers haven't shape
 
   /// Gets nth root of this number
   ///
@@ -62,7 +61,7 @@ class Number extends TensorBase {
   /// [other] can be either `num` or [Number].
   @override
   Number operator +(Object other) {
-    Number n;
+    var n = Number(0);
     if (other is num) {
       n = Number(_value + other);
     } else if (other is Number) {
@@ -79,7 +78,7 @@ class Number extends TensorBase {
   /// [other] can be either `num` or [Number].
   @override
   Number operator -(Object other) {
-    Number n;
+    var n = Number(0);
     if (other is num) {
       n = Number(_value - other);
     } else if (other is Number) {
@@ -93,7 +92,7 @@ class Number extends TensorBase {
   /// [other] can be either `num` or [Number].
   @override
   Number operator *(Object other) {
-    Number n;
+    var n = Number(0);
     if (other is num) {
       n = Number(_value * other);
     } else if (other is Number) {
@@ -107,7 +106,7 @@ class Number extends TensorBase {
   /// [other] can be either `num` or [Number].
   @override
   Double operator /(Object other) {
-    Double n;
+    var n = Double(0);
     if (other is num) {
       if (other == 0) {
         throw DivisionByZeroException();
@@ -124,7 +123,7 @@ class Number extends TensorBase {
 
   @override
   bool operator ==(Object other) {
-    bool result;
+    var result = false;
     if (other is num) {
       result = _value == other;
     } else if (other is Number) {

lib/src/discrete_mathematics/linear_algebra/tensor/base/tensor_base.dart

@@ -24,25 +24,25 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// - [2, 3] -> `Matrix` with 2 rows and 3 columns
   /// - [4, 2, 5] -> `Tensor3` with 4 width, 2 length and 5 depth
   factory TensorBase.generate(
-      Map<String, int> shape, num Function(num number) generator) {
+      List<int> shape, num Function(num number) generator) {
     switch (shape.length) {
       case 1:
-        return Vector(List<num>.generate(shape['width'], generator));
+        return Vector(List<num>.generate(shape[0], generator));
       case 2:
-        final row = List<num>.generate(shape['width'], generator);
-        return Matrix(List<List<num>>.generate(shape['length'], (_) => row));
+        final row = List<num>.generate(shape[1], generator);
+        return Matrix(List<List<num>>.generate(shape[0], (_) => row));
       case 3:
-        final depth = List<num>.generate(shape['depth'], generator);
-        final width = List<List<num>>.generate(shape['width'], (_) => depth);
+        final depth = List<num>.generate(shape[2], generator);
+        final width = List<List<num>>.generate(shape[1], (_) => depth);
         return Tensor3(
-            List<List<List<num>>>.generate(shape['length'], (_) => width));
+            List<List<List<num>>>.generate(shape[0], (_) => width));
       case 4:
-        final depth2 = List<num>.generate(shape['depth2'], generator);
-        final depth = List<List<num>>.generate(shape['depth'], (_) => depth2);
+        final depth2 = List<num>.generate(shape[3], generator);
+        final depth = List<List<num>>.generate(shape[2], (_) => depth2);
         final width =
-            List<List<List<num>>>.generate(shape['width'], (_) => depth);
+            List<List<List<num>>>.generate(shape[1], (_) => depth);
         return Tensor4(List<List<List<List<num>>>>.generate(
-            shape['length'], (_) => width));
+            shape[0], (_) => width));
       default:
         return Number(generator(1));
     }
@@ -59,9 +59,10 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
 
   /// Gets shape of this tensor
   ///
-  /// [shape] may contain numbers that denote count of `width`(columns),
-  /// `length`(rows), `depth` and `depth2` in this order.
-  Map<String, int> get shape;
+  /// [shape] may contain numbers that denote count of entries in each
+  /// dimension of the tensor, with shape[0] being the count for the first
+  /// dimension shape[1] in the second and so on.
+  List<int> get shape;
 
   /// Reduces data to number with provided [f] reduce function
   num reduce(num Function(num prev, num next) f);
@@ -79,7 +80,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// otherwise throws [TensorException]
   Number toScalar() {
     if (dimension == 0) {
-      return Number(data);
+      return Number(data as num);
     } else {
       throw TensorException('Tensor cannot be converted to Number, because '
           'dimension of this tensor isn\'t equal to 0!');
@@ -90,7 +91,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// otherwise throws [TensorException]
   Vector toVector() {
     if (dimension == 1) {
-      return Vector(data);
+      return Vector(data as List<num>);
     } else {
       throw TensorException('Tensor cannot be converted to Vector, because '
           'dimension of this tensor isn\'t equal to 1!');
@@ -101,7 +102,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// otherwise throws [TensorException]
   Matrix toMatrix() {
     if (dimension == 2) {
-      return Matrix(data);
+      return Matrix(data as List<List<num>>);
     } else {
       throw TensorException('Tensor cannot be converted to Matrix, because '
           'dimension of this tensor isn\'t equal to 2!');
@@ -112,7 +113,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// otherwise throws [TensorException]
   Tensor3 toTensor3() {
     if (dimension == 3) {
-      return Tensor3(data);
+      return Tensor3(data as List<List<List<num>>>);
     } else {
       throw TensorException('Tensor cannot be converted to Tensor3, because '
           'dimension of this tensor isn\'t equal to 3!');
@@ -123,7 +124,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// otherwise throws [TensorException]
   Tensor4 toTensor4() {
     if (dimension == 4) {
-      return Tensor4(data);
+      return Tensor4(data as List<List<List<List<num>>>>);
     } else {
       throw TensorException('Tensor cannot be converted to Tensor4, because '
           'dimension of this tensor isn\'t equal to 4!');
@@ -137,7 +138,7 @@ abstract class TensorBase with CopyableMixin<TensorBase> {
   /// of known data points [a] (alpha) may be in range from 0 to 1
   /// inclusively. Otherwise throws [TensorException].
   TensorBase lerp(TensorBase other, double a) {
-    if (dimension != other.dimension && !mapsEqual(shape, other.shape)) {
+    if (dimension != other.dimension && !listsEqual(shape, other.shape)) {
       throw ArgumentError('Tensors aren\'t equals!');
     }