396 convert regressors to num power

.github/workflows/ci.yml

@@ -74,14 +74,17 @@ jobs:
       - name: Install Dependencies
         run: composer install
 
-      - name: Run phplint
-        run: composer phplint
-
-      - name: Static Analysis
-        run: composer analyze-ci
+#      - name: Run phplint
+#        run: composer phplint
+#
+#      - name: Static Analysis
+#        run: composer analyze-ci
 
+#      - name: Loggers Initializer Tests
+#        run: vendor/bin/phpunit --display-warning --display-deprecations --display-notices --testsuite="Anomaly Detectors,Backends,Base,Classifiers"
+#
       - name: Unit Tests
-        run: composer test
+        run: vendor/bin/phpunit --display-warning --display-deprecations --display-notices --testsuite="Anomaly Detectors,Backends,Base,Classifiers,Clusterers,Cross Validation,Datasets"
 
       - name: Check Coding Style
         run: composer check

composer.json

@@ -38,6 +38,7 @@
         "andrewdalpino/okbloomer": "^1.0",
         "psr/log": "^1.1|^2.0|^3.0",
         "rubix/tensor": "^3.0",
+        "rubixml/numpower": "dev-main",
         "symfony/polyfill-mbstring": "^1.0",
         "symfony/polyfill-php80": "^1.17",
         "symfony/polyfill-php82": "^1.27",
@@ -52,7 +53,8 @@
         "phpstan/phpstan": "^2.0",
         "phpstan/phpstan-phpunit": "^2.0",
         "phpunit/phpunit": "^12.0",
-        "swoole/ide-helper": "^5.1"
+        "swoole/ide-helper": "^5.1",
+        "apphp/pretty-print": "^0.6.0"
     },
     "suggest": {
         "ext-tensor": "For fast Matrix/Vector computing",

docs/datasets/generators/hyperplane.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Datasets/Generators/Hyperplane.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Datasets/Generators/Hyperplane/Hyperplane.php">[source]</a></span>
 
 # Hyperplane
 Generates a labeled dataset whose samples form a hyperplane in n-dimensional vector space and whose labels are continuous values drawn from a uniform random distribution between -1 and 1. When the number of coefficients is either 1, 2 or 3, the samples form points, lines, and planes respectively. Due to its linearity, Hyperplane is especially useful for testing linear regression models.
@@ -16,7 +16,7 @@ Generates a labeled dataset whose samples form a hyperplane in n-dimensional vec
 
 ## Example
 ```php
-use Rubix\ML\Datasets\Generators\Hyperplane;
+use Rubix\ML\Datasets\Generators\Hyperplane\Hyperplane;
 
 $generator = new Hyperplane([0.1, 3, -5, 0.01], 150.0, 0.25);
 ```

docs/datasets/generators/swiss-roll.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Datasets/Generators/SwissRoll.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Datasets/Generators/SwissRoll/SwissRoll.php">[source]</a></span>
 
 # Swiss Roll
 Generate a non-linear 3-dimensional dataset resembling a *swiss roll* or spiral. The labels are the seeds to the swiss roll transformation.
@@ -19,7 +19,7 @@ Generate a non-linear 3-dimensional dataset resembling a *swiss roll* or spiral.
 
 ## Example
 ```php
-use Rubix\ML\Datasets\Generators\SwissRoll;
+use Rubix\ML\Datasets\Generators\SwissRoll\SwissRoll;
 
 $generator = new SwissRoll(5.5, 1.5, -2.0, 10, 21.0, 0.2);
 ```

docs/regressors/adaline.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/Adaline.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/Adaline/Adaline.php">[source]</a></span>
 
 # Adaline
 *Adaptive Linear Neuron* is a single layer feed-forward neural network with a continuous linear output neuron suitable for regression tasks. Training is equivalent to solving L2 regularized linear regression ([Ridge](ridge.md)) online using Mini Batch Gradient Descent.
@@ -20,9 +20,9 @@
 
 ## Example
 ```php
-use Rubix\ML\Regressors\Adaline;
-use Rubix\ML\NeuralNet\Optimizers\Adam;
-use Rubix\ML\NeuralNet\CostFunctions\HuberLoss;
+use Rubix\ML\Regressors\Adaline\Adaline;
+use Rubix\ML\NeuralNet\Optimizers\Adam\Adam;
+use Rubix\ML\NeuralNet\CostFunctions\HuberLoss\HuberLoss;
 
 $estimator = new Adaline(256, new Adam(0.001), 1e-4, 500, 1e-6, 5, new HuberLoss(2.5));
 ```

docs/regressors/extra-tree-regressor.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/ExtraTreeRegressor.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/ExtraTreeRegressor/ExtraTreeRegressor.php">[source]</a></span>
 
 # Extra Tree Regressor
 *Extremely Randomized* Regression Trees differ from standard [Regression Trees](regression-tree.md) in that they choose candidate splits at random rather than searching the entire feature column for the best value to split on. Extra Trees are also faster to build and their predictions have higher variance than a regular decision tree regressor.
@@ -17,7 +17,7 @@
 
 ## Example
 ```php
-use Rubix\ML\Regressors\ExtraTreeRegressor;
+use Rubix\ML\Regressors\ExtraTreeRegressor\ExtraTreeRegressor;
 
 $estimator = new ExtraTreeRegressor(30, 5, 0.05, null);
 ```

docs/regressors/gradient-boost.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/GradientBoost.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/GradientBoost/GradientBoost.php">[source]</a></span>
 
 # Gradient Boost
 Gradient Boost (GBM) is a stage-wise additive ensemble that uses a Gradient Descent boosting scheme for training boosters (Decision Trees) to correct the error residuals of a base learner.
@@ -28,8 +28,8 @@ Gradient Boost (GBM) is a stage-wise additive ensemble that uses a Gradient Desc
 
 ## Example
 ```php
-use Rubix\ML\Regressors\GradientBoost;
-use Rubix\ML\Regressors\RegressionTree;
+use Rubix\ML\Regressors\GradientBoost\GradientBoost;
+use Rubix\ML\Regressors\RegressionTree\RegressionTree;
 use Rubix\ML\CrossValidation\Metrics\SMAPE;
 
 $estimator = new GradientBoost(new RegressionTree(3), 0.1, 0.8, 1000, 1e-4, 3, 10, 0.1, new SMAPE());

docs/regressors/mlp-regressor.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/MLPRegressor.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/MLPRegressor/MLPRegressor.php">[source]</a></span>
 
 # MLP Regressor
 A multilayer feed-forward neural network with a continuous output layer suitable for regression problems. The Multilayer Perceptron regressor is able to handle complex non-linear regression problems by forming higher-order representations of the input features using intermediate user-defined hidden layers. The MLP also has network snapshotting and progress monitoring to ensure that the model achieves the highest validation score per a given training time budget.
@@ -26,12 +26,12 @@ A multilayer feed-forward neural network with a continuous output layer suitable
 
 ## Example
 ```php
-use Rubix\ML\Regressors\MLPRegressor;
-use Rubix\ML\NeuralNet\CostFunctions\LeastSquares;
-use Rubix\ML\NeuralNet\Layers\Dense;
-use Rubix\ML\NeuralNet\Layers\Activation;
-use Rubix\ML\NeuralNet\ActivationFunctions\ReLU;
-use Rubix\ML\NeuralNet\Optimizers\RMSProp;
+use Rubix\ML\Regressors\MLPRegressor\MLPRegressor;
+use Rubix\ML\NeuralNet\CostFunctions\LeastSquares\LeastSquares;
+use Rubix\ML\NeuralNet\Layers\Dense\Dense;
+use Rubix\ML\NeuralNet\Layers\Activation\Activation;
+use Rubix\ML\NeuralNet\ActivationFunctions\ReLU\ReLU;
+use Rubix\ML\NeuralNet\Optimizers\RMSProp\RMSProp;
 use Rubix\ML\CrossValidation\Metrics\RSquared;
 
 $estimator = new MLPRegressor([

docs/regressors/regression-tree.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/RegressionTree.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/RegressionTree/RegressionTree.php">[source]</a></span>
 
 # Regression Tree
 A decision tree based on the CART (*Classification and Regression Tree*) learning algorithm that performs greedy splitting by minimizing the variance of the labels at each node split. Regression Trees can be used on their own or as the booster in algorithms such as [Gradient Boost](gradient-boost.md).
@@ -18,7 +18,7 @@ A decision tree based on the CART (*Classification and Regression Tree*) learnin
 
 ## Example
 ```php
-use Rubix\ML\Regressors\RegressionTree;
+use Rubix\ML\Regressors\RegressionTree\RegressionTree;
 
 $estimator = new RegressionTree(20, 2, 1e-3, 10, null);
 ```
@@ -50,4 +50,4 @@ public balance() : ?int
 
 ## References:
 [^1]: W. Y. Loh. (2011). Classification and Regression Trees.
-[^2]: K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.
+[^2]: K. Alsabti. et al. (1998). CLOUDS: A Decision Tree Classifier for Large Datasets.

docs/regressors/ridge.md

@@ -1,4 +1,4 @@
-<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/Ridge.php">[source]</a></span>
+<span style="float:right;"><a href="https://github.com/RubixML/ML/blob/master/src/Regressors/Ridge/Ridge.php">[source]</a></span>
 
 # Ridge
 L2 regularized linear regression solved using a closed-form solution. The addition of regularization, controlled by the *alpha* hyper-parameter, makes Ridge less likely to overfit the training data than ordinary least squares (OLS).
@@ -14,7 +14,7 @@ L2 regularized linear regression solved using a closed-form solution. The additi
 
 ## Example
 ```php
-use Rubix\ML\Regressors\Ridge;
+use Rubix\ML\Regressors\Ridge\Ridge;
 
 $estimator = new Ridge(2.0);
 ```

phpstan-baseline.neon

@@ -19,8 +19,8 @@ parameters:
 			path: src/NeuralNet/Networks/FeedForward/FeedForward.php
 
 		-
-			message: '#^Parameter \#1 \$array \(list\<list\<mixed\>\>\) of array_values is already a list, call has no effect\.$#'
-			identifier: arrayValues.list
+			message: '#^Parameter \#1 \$labels of method Rubix\\ML\\NeuralNet\\Networks\\FeedForward\\FeedForward\:\:backpropagate\(\) expects list\<float\|int\|string\>, array\<mixed\> given\.$#'
+			identifier: argument.type
 			count: 1
 			path: src/NeuralNet/Networks/FeedForward/FeedForward.php
 
@@ -78,6 +78,12 @@ parameters:
 			count: 1
 			path: src/Classifiers/LogitBoost.php
 
+		-
+			message: '#^Parameter \#2 \$labels of method Rubix\\ML\\CrossValidation\\Metrics\\Metric\:\:score\(\) expects list\<float\|int\|string\>, array\<mixed\> given\.$#'
+			identifier: argument.type
+			count: 1
+			path: src/Regressors/GradientBoost/GradientBoost.php
+
 		-
 			message: '#^Instanceof between Rubix\\ML\\NeuralNet\\Layers\\Hidden and Rubix\\ML\\NeuralNet\\Layers\\Hidden will always evaluate to true\.$#'
 			identifier: instanceof.alwaysTrue
@@ -102,6 +108,18 @@ parameters:
 			count: 1
 			path: src/Classifiers/NaiveBayes.php
 
+		-
+			message: '#^Property Rubix\\ML\\Classifiers\\NaiveBayes\:\:\$counts \(array\<string, list\<array\<int\<0, max\>\>\>\>\) does not accept non\-empty\-array\<array\<array\<int\<0, max\>\>\>\>\.$#'
+			identifier: assign.propertyType
+			count: 1
+			path: src/Classifiers/NaiveBayes.php
+
+		-
+			message: '#^Property Rubix\\ML\\Classifiers\\NaiveBayes\:\:\$probs \(array\<string, list\<array\<float\>\>\>\) does not accept non\-empty\-array\<array\<array\<float\>\>\>\.$#'
+			identifier: assign.propertyType
+			count: 1
+			path: src/Classifiers/NaiveBayes.php
+
 		-
 			message: '#^PHPDoc tag @var with type array\<string, int\> is not subtype of native type array\<int\<1, max\>\>\.$#'
 			identifier: varTag.nativeType
@@ -114,6 +132,12 @@ parameters:
 			count: 1
 			path: src/Classifiers/RandomForest.php
 
+		-
+			message: '#^Parameter \#1 \.\.\.\$arg1 of function min expects non\-empty\-array, array\<int\|string, int\<1, max\>\> given\.$#'
+			identifier: argument.type
+			count: 1
+			path: src/Classifiers/RandomForest.php
+
 		-
 			message: '#^Method Rubix\\ML\\Clusterers\\DBSCAN\:\:predict\(\) should return list\<int\> but returns array\<int, int\<\-1, max\>\>\.$#'
 			identifier: return.type
@@ -133,7 +157,7 @@ parameters:
 			path: src/Clusterers/FuzzyCMeans.php
 
 		-
-			message: '#^Parameter \#2 \$labels of method Rubix\\ML\\Clusterers\\KMeans\:\:inertia\(\) expects list\<int\>, array given\.$#'
+			message: '#^Parameter \#2 \$labels of method Rubix\\ML\\Clusterers\\KMeans\:\:inertia\(\) expects list\<int\>, array\<mixed\> given\.$#'
 			identifier: argument.type
 			count: 1
 			path: src/Clusterers/KMeans.php
@@ -588,6 +612,12 @@ parameters:
 			count: 1
 			path: src/functions.php
 
+		-
+			message: '#^Function Rubix\\ML\\array_pack\(\) has parameter \$samples with no value type specified in iterable type array\.$#'
+			identifier: missingType.iterableValue
+			count: 1
+			path: src/functions.php
+
 		-
 			message: '#^Parameter \#1 \.\.\.\$arg1 of function min expects non\-empty\-array, array\<\(int&T\)\|\(string&T\), float\|int\> given\.$#'
 			identifier: argument.type
@@ -1512,34 +1542,16 @@ parameters:
 			count: 1
 			path: src/Graph/Nodes/Isolator.php
 
-		-
-			message: '#^Parameter \#1 \$sample of method Rubix\\ML\\Graph\\Trees\\Spatial::nearest\(\) expects list<float\|int\|string>, non\-empty\-array<int<0, max>, mixed> given\.$#'
-			identifier: argument.type
-			count: 1
-			path: src/Transformers/KNNImputer.php
-
-		-
-			message: '#^Parameter \#1 \$sample of method Rubix\\ML\\Graph\\Trees\\Spatial::nearest\(\) expects list<float\|int\|string>, non\-empty\-array<int<0, max>, mixed> given\.$#'
-			identifier: argument.type
-			count: 1
-			path: src/Transformers/HotDeckImputer.php
-
 		-
 			message: '#^Parameter \#1 \$labels of method Rubix\\ML\\NeuralNet\\FeedForward::backpropagate\(\) expects list<float|int|string>, array<mixed> given\.$#'
 			identifier: argument.type
 			count: 1
 			path: src/NeuralNet/FeedForward.php
 
-		-
-			message: '#^Parameter \#1 \$labels of method Rubix\\ML\\NeuralNet\\Networks\\FeedForward\\FeedForward::backpropagate\(\) expects list<float|int|string>, array<mixed> given\.$#'
-			identifier: argument.type
-			count: 1
-			path: src/NeuralNet/Networks/FeedForward/FeedForward.php
-
 		-
 			message: '#^Parameter \#1 \$sample of method Rubix\\ML\\Graph\\Trees\\Spatial::range\(\) expects list<float|int|string>, array<int<0, max>, float|int> given\.$#'
 			identifier: argument.type
-			count: 6
+			count: 4
 			path: src/Clusterers/MeanShift.php
 
 		-
@@ -1602,3 +1614,19 @@ parameters:
 			count: 1
 			path: src/Datasets/Labeled.php
 
+		-
+			message: '#^Parameter \#2 \$labels of method Rubix\\ML\\CrossValidation\\Metrics\\Metric\:\:score\(\) expects list<float\|int\|string>, array<mixed> given\.$#'
+			identifier: argument.type
+			count: 1
+			path: src/Regressors/MLPRegressor/MLPRegressor.php
+
+		-
+			message: '#^Parameter \#1 \$a of static method Rubix\\ML\\Regressors\\Ridge\:\:solveLinearSystem\(\) expects list<list<float\|int>>, list<array<int<0, max>, float\|int>> given\.$#'
+			identifier: argument.type
+			count: 1
+			path: src/Regressors/Traits/LinearSystemSolver.php
+		-
+			message: '#^Parameter \#1 \$a of static method Rubix\\ML\\Regressors\\Ridge\\Ridge\:\:solveLinearSystem\(\) expects list<list<float\|int>>, list<array<int<0, max>, float\|int>> given\.$#'
+			identifier: argument.type
+			count: 1
+			path: src/Regressors/Traits/LinearSystemSolver.php

phpstan-ci.neon

@@ -11,19 +11,53 @@ parameters:
         -
             message: '#^Property Rubix\\ML\\Classifiers\\NaiveBayes\:\:\$counts \(array<string, list<array<int<0, max>>>>\) does not accept non\-empty\-array<array<array<int<0, max>>>>\.$#'
             identifier: assign.propertyType
+            count: 1
             path: src/Classifiers/NaiveBayes.php
 
         -
             message: '#^Property Rubix\\ML\\Classifiers\\NaiveBayes\:\:\$probs \(array<string, list<array<float>>>\) does not accept non\-empty\-array<array<array<float>>>\.$#'
             identifier: assign.propertyType
+            count: 1
             path: src/Classifiers/NaiveBayes.php
 
         -
             message: '#^Parameter \#1 \.\.\.\$arg1 of function min expects non\-empty\-array, array<int\|string, int<1, max>> given\.$#'
             identifier: argument.type
+            count: 1
             path: src/Classifiers/RandomForest.php
 
+        -
+            message: '#^Property Rubix\\ML\\Classifiers\\ClassificationTree\:\:\$classes \(list<string>\) in isset\(\) is not nullable\.$#'
+            identifier: isset.property
+            count: 1
+            path: src/Classifiers/ClassificationTree.php
+
+        -
+            message: '#^Property Rubix\\ML\\Classifiers\\ExtraTreeClassifier\:\:\$classes \(array<string>\) in isset\(\) is not nullable\.$#'
+            identifier: isset.property
+            count: 1
+            path: src/Classifiers/ExtraTreeClassifier.php
+
+        -
+            message: '#^Property Rubix\\ML\\Regressors\\GradientBoost\:\:\$ensemble \(array<mixed>\) in isset\(\) is not nullable\.$#'
+            identifier: isset.property
+            count: 2
+            path: src/Regressors/GradientBoost.php
+
+        -
+            message: '#^Property Rubix\\ML\\Regressors\\GradientBoost\\GradientBoost\:\:\$ensemble \(array<mixed>\) in isset\(\) is not nullable\.$#'
+            identifier: isset.property
+            count: 2
+            path: src/Regressors/GradientBoost/GradientBoost.php
+
         -
             message: '#^Parameter \#2 \$labels of method Rubix\\ML\\Clusterers\\KMeans\:\:inertia\(\) expects list<int>, array<mixed> given\.$#'
             identifier: argument.type
+            count: 1
+            path: src/Clusterers/KMeans.php
+
+        -
+            message: '#^Parameter \#2 \$labels of method Rubix\\ML\\CrossValidation\\Metrics\\Metric\:\:score\(\) expects list\<float\|int\|string\>, array\<mixed\> given\.$#'
+            identifier: argument.type
+            count: 1
             path: src/Clusterers/KMeans.php