### Install Rumale Gem

Source: https://yoshoku.github.io/rumale/doc/index

Instructions for installing the Rumale gem. This involves adding the gem to your application's Gemfile and running bundle install, or installing it directly using the gem install command.

```ruby
gem 'rumale'

```

```bash
$ bundle

```

```bash
$ gem install rumale

```

--------------------------------

### RVFLClassifier Initialization and Usage Example (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/NeuralNetwork/RVFLClassifier

Demonstrates how to initialize and use the RVFLClassifier for a machine learning task. It requires the 'numo/linalg' and 'rumale/neural_network/rvfl_classifier' libraries. The example shows fitting the model to training data and predicting on testing data.

```ruby
require 'numo/linalg'
require 'rumale/neural_network/rvfl_classifier'

estimator = Rumale::NeuralNetwork::RVFLClassifier.new(hidden_units: 128, reg_param: 100.0)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### NMF Initialization and Transformation Example (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/NMF

Demonstrates how to initialize an NMF decomposer and use it to transform sample data. It requires the 'rumale/decomposition/nmf' library.

```ruby
require 'rumale/decomposition/nmf'

decomposer = Rumale::Decomposition::NMF.new(n_components: 2)
representaion = decomposer.fit_transform(samples)
```

--------------------------------

### GradientBoostingClassifier - Ruby Example

Source: https://yoshoku.github.io/rumale/doc/Rumale/Ensemble/GradientBoostingClassifier

Example of how to initialize and use the GradientBoostingClassifier from the Rumale library. This involves fitting the model to training data and predicting on testing data. Requires the 'rumale/ensemble/gradient_boosting_classifier' library.

```ruby
require 'rumale/ensemble/gradient_boosting_classifier'

estimator = \
  Rumale::Ensemble::GradientBoostingClassifier.new(
    n_estimators: 100, learning_rate: 0.3, reg_lambda: 0.001, random_seed: 1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)
```

--------------------------------

### VRTreeClassifier Initialization and Usage Example

Source: https://yoshoku.github.io/rumale/doc/Rumale/Tree/VRTreeClassifier

This example demonstrates how to initialize and use the VRTreeClassifier for classification tasks. It shows setting hyperparameters like criterion, max_depth, and random_seed, fitting the model to training data, and making predictions on testing data. It requires the 'rumale/tree/vr_tree_classifier' library.

```ruby
require 'rumale/tree/vr_tree_classifier'

estimator =
  Rumale::Tree::VRTreeClassifier.new(
    criterion: 'gini',
    max_depth: 3,
    max_leaf_nodes: 10,
    min_samples_leaf: 5,
    random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### VRTreeClassifier Initialization and Usage

Source: https://yoshoku.github.io/rumale/doc/Rumale/Tree/VRTreeClassifier

This section details how to initialize and use the VRTreeClassifier, including its parameters and a code example.

```APIDOC
## Rumale::Tree::VRTreeClassifier

### Description
VRTreeClassifier is a class that implements Variable-Random (VR) tree for classification.

**Reference**
  * Liu, F. T., Ting, K. M., Yu, Y., and Zhou, Z. H., “Spectrum of Variable-Random Trees,” Journal of Artificial Intelligence Research, vol. 32, pp. 355–384, 2008.

#### Examples:
```ruby
require 'rumale/tree/vr_tree_classifier'

estimator = 
  Rumale::Tree::VRTreeClassifier.new(
    criterion: 'gini', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

### Instance Attribute Summary

*   **classes** (Numo::Int32, readonly)
    Return the class labels.
*   **feature_importances** (Numo::DFloat, readonly)
    Return the importance for each feature.
*   **leaf_labels** (Numo::Int32, readonly)
    Return the labels assigned each leaf.
*   **rng** (Random, readonly)
    Return the random generator for random selection of feature index.
*   **tree** (Node, readonly)
    Return the learned tree.

### Attributes inherited from Base::Estimator

*   #params

### Instance Method Summary

*   **initialize**(criterion: 'gini', alpha: 0.5, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil) (VRTreeClassifier)
    Create a new classifier with variable-random tree algorithm.

### Methods inherited from DecisionTreeClassifier

*   #fit
*   #predict
*   #predict_proba

### Methods included from Base::Classifier

*   #fit
*   #predict
*   #score

### Methods inherited from BaseDecisionTree

*   #apply

## Constructor Details

### initialize(criterion: 'gini', alpha: 0.5, max_depth: nil, max_leaf_nodes: nil, min_samples_leaf: 1, max_features: nil, random_seed: nil)

Create a new classifier with variable-random tree algorithm.

**Parameters**

*   **criterion** (String) - The function to measure the quality of a split. Supported criteria are "gini" and "entropy".
*   **alpha** (Float) - The parameter that controls the degree of randomness in splitting. Default is 0.5.
*   **max_depth** (Integer, nil) - The maximum depth of the tree. If nil, then nodes are expanded until all leaves are pure or contain less than min_samples_split samples.
*   **max_leaf_nodes** (Integer, nil) - Grow a tree with max_leaf_nodes in best-first fashion. Best-first grows a node by node based as the reduction of impurity. If nil, then number of leaf nodes is not restricted.
*   **min_samples_leaf** (Integer) - The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may prevent overfitting the model.
*   **max_features** (Integer, Float, String, nil) - The number of features to consider when looking for the best split. If integer, then consider max_features features at each split. If float, then max_features is a percentage and rounds up the children of n_samples.
*   **random_seed** (Integer, nil) - If an integer value is given, then that value is used as the seed for the random number generator. Otherwise, the random number generator is initialized randomly. 

**Returns**

*   (VRTreeClassifier) - The initialized VRTreeClassifier object.
```

--------------------------------

### ExtraTreesRegressor Example in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Ensemble/ExtraTreesRegressor

This code snippet demonstrates how to initialize and use the ExtraTreesRegressor class from the Rumale library for regression tasks. It shows how to set hyperparameters, fit the model to training data, and make predictions on testing data. Ensure the 'rumale-ensemble' gem is installed and accessible.

```ruby
@require 'rumale/ensemble/extra_trees_regressor'

estimator = \
  Rumale::Ensemble::ExtraTreesRegressor.new(
    n_estimators: 10, criterion: 'mse', max_depth: 3, max_leaf_nodes: 10, min_samples_leaf: 5, random_seed: 1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)

```

--------------------------------

### HDBSCAN Clustering with Rumale

Source: https://yoshoku.github.io/rumale/doc/Rumale/Clustering/HDBSCAN

Example of initializing and using the HDBSCAN analyzer to fit and predict cluster labels for given samples.

```ruby
require 'rumale/clustering/hdbscan'

analyzer = Rumale::Clustering::HDBSCAN.new(min_samples: 5)
cluster_labels = analyzer.fit_predict(samples)
```

--------------------------------

### StackingClassifier Example in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Ensemble/StackingClassifier

This example demonstrates how to initialize and use the StackingClassifier in Ruby. It involves setting up base estimators, a meta-estimator, fitting the model to training data, and predicting on testing data. Dependencies include various Rumale estimators like LogisticRegression, MLPClassifier, and RandomForestClassifier.

```ruby
require 'rumale/ensemble/stacking_classifier'

estimators = {
  lgr: Rumale::LinearModel::LogisticRegression.new(reg_param: 1e-2),
  mlp: Rumale::NeuralNetwork::MLPClassifier.new(hidden_units: [256], random_seed: 1),
  rnd: Rumale::Ensemble::RandomForestClassifier.new(random_seed: 1)
}
meta_estimator = Rumale::LinearModel::LogisticRegression.new
classifier = Rumale::Ensemble::StackedClassifier.new(
  estimators: estimators, meta_estimator: meta_estimator, random_seed: 1
)
classifier.fit(training_samples, training_labels)
results = classifier.predict(testing_samples)
```

--------------------------------

### Install Numo::Linalg Alternative Gem (Bash)

Source: https://yoshoku.github.io/rumale/doc/index

This command installs the `numo-linalg-alt` gem, which provides an alternative implementation for linear algebra operations using Numo::NArray. This can potentially accelerate machine learning algorithms in Rumale that frequently perform matrix and vector products by leveraging OpenBLAS libraries.

```bash
$ gem install numo-linalg-alt
```

--------------------------------

### MeanShift Clustering with Rumale

Source: https://yoshoku.github.io/rumale/doc/Rumale/Clustering/MeanShift

This example demonstrates how to initialize and use the MeanShift clustering algorithm from the Rumale library. It takes samples as input and predicts cluster labels.

```ruby
require 'rumale/clustering/mean_shift'

analyzer = Rumale::Clustering::MeanShift.new(bandwidth: 1.5)
cluster_labels = analyzer.fit_predict(samples)
```

--------------------------------

### FactorAnalysis Instance Methods

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/FactorAnalysis

Details of the instance methods available for the FactorAnalysis class, including fit, fit_transform, initialize, and transform.

```APIDOC
## Instance Method Summary

* #**fit**(x) ⇒ FactorAnalysis
  Fit the model with given training data.
* #**fit_transform**(x) ⇒ Numo::DFloat
  Fit the model with training data, and then transform them with the learned model.
* #**initialize**(n_components: 2, max_iter: 100, tol: 1e-8) ⇒ FactorAnalysis (constructor)
  Create a new transformer with factor analysis.
* #**transform**(x) ⇒ Numo::DFloat
  Transform the given data with the learned model.
```

--------------------------------

### Install Parallel Gem (Bash)

Source: https://yoshoku.github.io/rumale/doc/index

This command installs the `parallel` gem, which is used by Rumale to enable parallel processing for several of its estimators. Installing this gem allows Rumale to distribute computations across multiple processors, potentially speeding up training times.

```bash
$ gem install parallel
```

--------------------------------

### FScore Example Usage (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/EvaluationMeasure/FScore

Example of how to instantiate and use the FScore class to calculate scores. It requires the 'rumale/evaluation_measure/f_score' library.

```Ruby
require 'rumale/evaluation_measure/f_score'

evaluator = Rumale::EvaluationMeasure::FScore.new
puts evaluator.score(ground_truth, predicted)
```

--------------------------------

### KernelCalculator: Initialize and Use in a Pipeline

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/KernelCalculator

This example demonstrates how to initialize Rumale::Preprocessing::KernelCalculator with specific kernel parameters (rbf, gamma) and integrate it into a Rumale::Pipeline for a complete machine learning workflow. It shows fitting the pipeline with training data and making predictions on test data.

```ruby
require 'rumale/preprocessing/kernel_calculator'
require 'rumale/kernel_machine/kernel_ridge'
require 'rumale/pipeline/pipeline'

transformer = Rumale::Preprocessing::KernelCalculator.new(kernel: 'rbf', gamma: 0.5)
regressor = Rumale::KernelMachine::KernelRidge.new
pipeline = Rumale::Pipeline::Pipeline.new(
  steps: { trs: transformer, est: regressor }
)
pipeline.fit(x_train, y_train)
results = pipeline.predict(x_test)
```

--------------------------------

### KNeighborsClassifier Example (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/NearestNeighbors/KNeighborsClassifier

Demonstrates how to initialize, train, and predict using the KNeighborsClassifier. It requires the 'rumale/nearest_neighbors/k_neighbors_classifier' library and uses training samples and labels to fit the model, then predicts labels for testing samples.

```ruby
require 'rumale/nearest_neighbors/k_neighbors_classifier'

estimator = 
  Rumale::NearestNeighbors::KNeighborsClassifier.new(n_neighbors: 5)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### Calculate Silhouette Score Example

Source: https://yoshoku.github.io/rumale/doc/Rumale/EvaluationMeasure/SilhouetteScore

An example demonstrating how to use the SilhouetteScore class to calculate the silhouette coefficient. It requires the 'rumale/evaluation_measure/silhouette_score' library and takes input data 'x' and cluster predictions 'predicted' to compute the score.

```ruby
require 'rumale/evaluation_measure/silhouette_score'

evaluator = Rumale::EvaluationMeasure::SilhouetteScore.new
puts evaluator.score(x, predicted)
```

--------------------------------

### NegationNB Classifier Initialization and Usage (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/NaiveBayes/NegationNB

Demonstrates how to initialize and use the Rumale::NaiveBayes::NegationNB classifier. This involves creating an instance, fitting it with training data, and then predicting labels for testing data. The smoothing_param can be adjusted during initialization.

```ruby
require 'rumale/naive_bayes/negation_nb'

estimator = Rumale::NaiveBayes::NegationNB.new(smoothing_param: 1.0)
estimator.fit(training_samples, training_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### SVC: Initialize, Fit, and Predict in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/LinearModel/SVC

Demonstrates the basic usage of Rumale::LinearModel::SVC. It includes initializing the estimator, fitting it with training data, and predicting labels for testing samples. Requires the 'rumale/linear_model/svc' library.

```ruby
require 'rumale/linear_model/svc'

estimator = 
  Rumale::LinearModel::SVC.new(reg_param: 1.0)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### GaussianMixture Clustering with Rumale

Source: https://yoshoku.github.io/rumale/doc/Rumale/Clustering/GaussianMixture

Demonstrates how to initialize and use the GaussianMixture class for cluster analysis. It shows fitting the model and predicting cluster labels for samples. This example assumes the 'samples' data is already prepared.

```ruby
require 'rumale/clustering/gaussian_mixture'

analyzer = Rumale::Clustering::GaussianMixture.new(n_clusters: 10, max_iter: 50)
cluster_labels = analyzer.fit_predict(samples)
```

--------------------------------

### FeatureUnion Usage Example (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/Pipeline/FeatureUnion

Demonstrates how to use FeatureUnion to combine different transformers, such as RBF kernel approximation and PCA. It shows the process of initializing FeatureUnion, fitting it with training data, and then predicting or transforming test data. The example highlights how the number of output features is determined by the combined transformers.

```ruby
require 'rumale/kernel_approximation/rbf'
require 'rumale/decomposition/pca'
require 'rumale/pipeline/feature_union'

fu = Rumale::Pipeline::FeatureUnion.new(
  transformers: {
    'rbf': Rumale::KernelApproximation::RBF.new(gamma: 1.0, n_components: 96, random_seed: 1),
    'pca': Rumale::Decomposition::PCA.new(n_components: 32)
  }
)
fu.fit(training_samples, traininig_labels)
results = fu.predict(testing_samples)

# > p results.shape[1]
# > 128
```

--------------------------------

### Bernoulli Naive Bayes Classifier Example (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/NaiveBayes/BernoulliNB

Demonstrates how to initialize and use the Bernoulli Naive Bayes classifier from the Rumale library. It shows the process of creating an estimator, fitting it with training data, and making predictions on testing data. Dependencies include the 'rumale/naive_bayes/bernoulli_nb' library.

```ruby
require 'rumale/naive_bayes/bernoulli_nb'

estimator = Rumale::NaiveBayes::BernoulliNB.new(smoothing_param: 1.0, bin_threshold: 0.0)
estimator.fit(training_samples, training_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### TF-IDF Transformation Example using HashVectorizer and TfidfTransformer in Rumale

Source: https://yoshoku.github.io/rumale/doc/Rumale/FeatureExtraction/TfidfTransformer

This example demonstrates how to use Rumale's HashVectorizer to convert sparse text data into a term frequency matrix, followed by TfidfTransformer to convert this matrix into a tf-idf representation. It shows the input and output of both steps.

```ruby
require 'rumale/feature_extraction/hash_vectorizer'
require 'rumale/feature_extraction/tfidf_transformer'

encoder = Rumale::FeatureExtraction::HashVectorizer.new
x = encoder.fit_transform([
  { foo: 1, bar: 2 },
  { foo: 3, baz: 1 }
])

# > pp x
# Numo::DFloat#shape=[2,3]
# [[2, 0, 1],
#  [0, 1, 3]]

transformer = Rumale::FeatureExtraction::TfidfTransformer.new
x_tfidf = transformer.fit_transform(x)

# > pp x_tfidf
# Numo::DFloat#shape=[2,3]
# [[0.959056, 0, 0.283217],
#  [0, 0.491506, 0.870874]]
```

--------------------------------

### KernelRidge Class API

Source: https://yoshoku.github.io/rumale/doc/Rumale/KernelMachine/KernelRidge

Documentation for the KernelRidge class, including its overview, instance attributes, and methods.

```APIDOC
## Class: Rumale::KernelMachine::KernelRidge

### Description
KernelRidge is a class that implements kernel ridge regression.

#### Examples:
```ruby
require 'numo/linalg/autoloader'
require 'rumale/pairwise_metric'
require 'rumale/kernel_machine/kernel_ridge'

kernel_mat_train = Rumale::PairwiseMetric::rbf_kernel(training_samples)
kridge = Rumale::KernelMachine::KernelRidge.new(reg_param: 1.0)
kridge.fit(kernel_mat_train, traininig_values)

kernel_mat_test = Rumale::PairwiseMetric::rbf_kernel(test_samples, training_samples)
results = kridge.predict(kernel_mat_test)
```

### Instance Attribute Summary

* **weight_vec** (Numo::DFloat) - readonly
  Return the weight vector.

### Instance Method Summary

* **fit**(x, y) (KernelRidge)
  Fit the model with given training data.
* **initialize**(reg_param: 1.0) (KernelRidge) - constructor
  Create a new regressor with kernel ridge regression.
* **predict**(x) (Numo::DFloat)
  Predict values for samples.

### Constructor Details

#### #**initialize**(reg_param: 1.0) ⇒ `KernelRidge`
  Create a new regressor with kernel ridge regression.

Parameters:

* **reg_param** (Float/Numo::DFloat) _(defaults to: 1.0)_ — 
  The regularization parameter.

### Request Example
```json
{
  "reg_param": 1.0
}
```

### Response
#### Success Response (200)
- **weight_vec** (Numo::DFloat) - The weight vector.

#### Response Example
```json
{
  "weight_vec": [1.0, 2.0, 3.0]
}
```
```

--------------------------------

### PCA Instance Methods

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/PCA

Provides documentation for core PCA instance methods: fit, fit_transform, and inverse_transform.

```APIDOC
## fit(x)

### Description
Fit the model with given training data.

### Method
`fit`

### Parameters
#### Path Parameters
- **x** (`Numo::DFloat`) - Required - The training data to be used for fitting the model. (shape: [n_samples, n_features])

### Returns
- `PCA` - The learned transformer itself.

---

## fit_transform(x)

### Description
Fit the model with training data, and then transform them with the learned model.

### Method
`fit_transform`

### Parameters
#### Path Parameters
- **x** (`Numo::DFloat`) - Required - The training data to be used for fitting the model. (shape: [n_samples, n_features])

### Returns
- `Numo::DFloat` - The transformed data. (shape: [n_samples, n_components])

---

## inverse_transform(z)

### Description
Inverse transform the given transformed data with the learned model.

### Method
`inverse_transform`

### Parameters
#### Path Parameters
- **z** (`Numo::DFloat`) - Required - The transformed data to be inverted.

### Returns
- `Numo::DFloat` - The original data.
```

--------------------------------

### Rumale Dataset Handling

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MaxAbsScaler

Documentation for dataset handling utilities in the Rumale library.

```APIDOC
## Namespace Rumale::Dataset

### Description
Utilities for handling and managing datasets within the Rumale library.
```

--------------------------------

### Get Estimator Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/CrossValidation

Retrieves the estimator that is being evaluated. This attribute holds the classifier instance passed during initialization.

```ruby
def estimator
  @estimator
end
```

--------------------------------

### SparsePCA: Initialize, Fit, and Transform Data (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/SparsePCA

This example demonstrates how to use the Rumale::Decomposition::SparsePCA class. It shows initialization with specific parameters, fitting the model to sample data, and transforming the data to obtain its sparse representation. Dependencies include 'numo/linalg' for numerical operations and 'rumale/decomposition/sparse_pca' for the SparsePCA class itself.

```ruby
require 'numo/linalg'
require 'rumale/decomposition/sparse_pca'

decomposer = Rumale::Decomposition::SparsePCA.new(n_components: 2, reg_param: 0.1)
representaion = decomposer.fit_transform(samples)
sparse_components = decomposer.components
```

--------------------------------

### Get Evaluator Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/CrossValidation

Retrieves the evaluator used for calculating scores. This attribute holds the evaluator instance passed during initialization.

```ruby
def evaluator
  @evaluator
end
```

--------------------------------

### Initialize and Use Linear Regression

Source: https://yoshoku.github.io/rumale/doc/Rumale/LinearModel/LinearRegression

Demonstrates how to initialize a LinearRegression estimator, fit it with training data, and make predictions on testing data. It also shows how to specify the 'svd' solver if Numo::Linalg is installed.

```ruby
require 'rumale/linear_model/linear_regression'

estimator = Rumale::LinearModel::LinearRegression.new
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)

# If Numo::Linalg is installed, you can specify 'svd' for the solver option.
require 'numo/linalg/autoloader'
require 'rumale/linear_model/linear_regression'

estimator = Rumale::LinearModel::LinearRegression.new(solver: 'svd')
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)
```

--------------------------------

### Get Max Absolute Vector in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MaxAbsScaler

Retrieves the pre-calculated vector containing the maximum absolute value for each feature. This is a read-only attribute.

```ruby
def max_abs_vec
  @max_abs_vec
end
```

--------------------------------

### SVR: Initialize and Fit Model (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/LinearModel/SVR

This snippet demonstrates how to initialize a Support Vector Regressor (SVR) model with specified parameters and then fit it to training data. It requires the 'rumale/linear_model/svr' library. The input 'training_samples' and 'traininig_target_values' are expected to be compatible data structures for the model.

```ruby
require 'rumale/linear_model/svr'

estimator = Rumale::LinearModel::SVR.new(reg_param: 1.0, epsilon: 0.1)
estimator.fit(training_samples, traininig_target_values)
results = estimator.predict(testing_samples)
```

--------------------------------

### Get VotingClassifier Estimators Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/Ensemble/VotingClassifier

Returns the sub-classifiers that were used for voting in the VotingClassifier. This attribute is read-only.

```ruby
# File 'rumale-ensemble/lib/rumale/ensemble/voting_classifier.rb', line 33

def estimators
  @estimators
end
```

--------------------------------

### Get VotingClassifier Classes Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/Ensemble/VotingClassifier

Returns the class labels learned by the VotingClassifier. This attribute is read-only.

```ruby
# File 'rumale-ensemble/lib/rumale/ensemble/voting_classifier.rb', line 37

def classes
  @classes
end
```

--------------------------------

### MinMaxScaler API

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MinMaxScaler

Documentation for the MinMaxScaler class, including its attributes, methods, and constructor.

```APIDOC
## Class: Rumale::Preprocessing::MinMaxScaler

### Description
Normalize samples by scaling each feature to a given range.

### Attributes
#### Instance Attributes
- **max_vec** (Numo::DFloat) - Readonly: Returns the vector consisting of the maximum value for each feature.
- **min_vec** (Numo::DFloat) - Readonly: Returns the vector consisting of the minimum value for each feature.

### Methods
#### Instance Methods
- **fit**(x)
  - Description: Calculate the minimum and maximum value of each feature for scaling.
  - Returns: MinMaxScaler
- **fit_transform**(x)
  - Description: Calculate the minimum and maximum values, and then normalize samples to feature_range.
  - Returns: Numo::DFloat
- **initialize**(feature_range: [0.0, 1.0])
  - Description: Creates a new normalizer for scaling each feature to a given range.
  - Parameters:
    - **feature_range** (Array<Float>) - Optional - The desired range of samples. Defaults to `[0.0, 1.0]`.
  - Returns: MinMaxScaler
- **transform**(x)
  - Description: Perform scaling the given samples according to feature_range.
  - Returns: Numo::DFloat

### Constructor Details
#### #initialize(feature_range: [0.0, 1.0])
Creates a new normalizer for scaling each feature to a given range.

Parameters:
- **feature_range** (Array<Float>) _(defaults to:`[0.0, 1.0]`)_ — 
The desired range of samples.

### Examples
```ruby
require 'rumale/preprocessing/min_max_scaler'

normalizer = Rumale::Preprocessing::MinMaxScaler.new(feature_range: [0.0, 1.0])
new_training_samples = normalizer.fit_transform(training_samples)
new_testing_samples = normalizer.transform(testing_samples)
```
```

--------------------------------

### Factor Analysis Initialization and Transformation (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/FactorAnalysis

Demonstrates how to initialize a FactorAnalysis object and use it to fit and transform sample data. Requires the 'numo/linalg' and 'rumale/decomposition/factor_analysis' libraries. The fit_transform method takes a dataset (samples) and returns its lower-dimensional representation.

```ruby
require 'numo/linalg/autoloader'
require 'rumale/decomposition/factor_analysis'

decomposer = Rumale::Decomposition::FactorAnalysis.new(n_components: 2)
representaion = decomposer.fit_transform(samples)
```

--------------------------------

### Get Number of Output Features

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/PolynomialFeatures

Returns the calculated number of output polynomial features. This is a read-only attribute.

```ruby
# File 'rumale-preprocessing/lib/rumale/preprocessing/polynomial_features.rb', line 40

def n_output_features
  @n_output_features
end
```

--------------------------------

### Get Return Train Score Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/CrossValidation

Retrieves the boolean flag indicating whether to calculate the score of the training dataset. This is determined at initialization.

```ruby
def return_train_score
  @return_train_score
end
```

--------------------------------

### PCA Initialization and Transformation in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/PCA

Demonstrates how to initialize and use the PCA class for dimensionality reduction. It shows examples with different solver options ('fpt' and 'evd') and how the solver is chosen automatically if Numo::Linalg is loaded. The fit_transform method takes sample data and returns the transformed representation.

```ruby
require 'rumale/decomposition/pca'

decomposer = Rumale::Decomposition::PCA.new(n_components: 2, solver: 'fpt')
representaion = decomposer.fit_transform(samples)
```

```ruby
require 'numo/linalg/autoloader'
require 'rumale/decomposition/pca'

decomposer = Rumale::Decomposition::PCA.new(n_components: 2, solver: 'evd')
representaion = decomposer.fit_transform(samples)
```

```ruby
# If Numo::Linalg is loaded and the solver option is not given,
# the solver option is choosen 'evd' automatically.
decomposer = Rumale::Decomposition::PCA.new(n_components: 2)
representaion = decomposer.fit_transform(samples)
```

--------------------------------

### Rumale Metric Learning Algorithms

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MaxAbsScaler

API documentation for metric learning algorithms in the Rumale library.

```APIDOC
## Namespace Rumale::MetricLearning

### Description
This namespace provides algorithms for learning distance metrics.

### Classes
*   **FisherDiscriminantAnalysis** (inherits from Estimator)
*   **LocalFisherDiscriminantAnalysis** (inherits from Estimator)
```

--------------------------------

### Get FScore Average Type (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/EvaluationMeasure/FScore

Retrieves the average type used for F1-score calculation. This attribute is read-only.

```Ruby
def average
  @average
end
```

--------------------------------

### FastICA Instance Methods

Source: https://yoshoku.github.io/rumale/doc/Rumale/Decomposition/FastICA

Documentation for instance methods of the FastICA class.

```APIDOC
## FastICA Instance Methods

### `fit(x)`

#### Description
Fit the model with given training data.

#### Method
`fit`

#### Parameters
- **x** (Numo::DFloat) - The training data to be used for fitting the model. (shape: [n_samples, n_features])

#### Returns
- **FastICA** - The learned transformer itself.

### `fit_transform(x)`

#### Description
Fit the model with training data, and then transform them with the learned model.

#### Method
`fit_transform`

#### Parameters
- **x** (Numo::DFloat) - The training data to be used for fitting the model. (shape: [n_samples, n_features])

#### Returns
- **Numo::DFloat** - The transformed data (shape: [n_samples, n_components])

### `inverse_transform(z)`

#### Description
Inverse transform the given transformed data with the learned model.

#### Method
`inverse_transform`

#### Parameters
- **z** (Numo::DFloat) - The transformed data to be inverted. (shape: [n_samples, n_components])

#### Returns
- **Numo::DFloat** - The original data (shape: [n_samples, n_features])

### `transform(x)`

#### Description
Transform the given data with the learned model.

#### Method
`transform`

#### Parameters
- **x** (Numo::DFloat) - The data to be transformed. (shape: [n_samples, n_features])

#### Returns
- **Numo::DFloat** - The transformed data (shape: [n_samples, n_components])
```

--------------------------------

### ROCAUC Class API

Source: https://yoshoku.github.io/rumale/doc/Rumale/EvaluationMeasure/ROCAUC

Documentation for the ROCAUC class, including its overview, examples, and instance methods for calculating ROC AUC.

```APIDOC
## Rumale::EvaluationMeasure::ROCAUC

### Description
ROCAUC is a class that calculates the area under the receiver operating characteristic curve from predicted scores.

### Method
N/A (Class Documentation)

### Endpoint
N/A (Class Documentation)

### Parameters
N/A (Class Documentation)

### Request Example
```ruby
require 'rumale/preprocessing'
require 'rumale/linear_model'
require 'rumale/evaluation_measure/roc_auc'

# Encode labels to integer array.
labels = %w[A B B C A A C C C A]
label_encoder = Rumale::Preprocessing::LabelEncoder.new
y = label_encoder.fit_transform(labels)

# Fit classifier.
classifier = Rumale::LinearModel::LogisticRegression.new
classifier.fit(x, y)

# Predict class probabilities.
y_score = classifier.predict_proba(x)

# Encode labels to one-hot vectors.
one_hot_encoder = Rumale::Preprocessing::OneHotEncoder.new
y_onehot = one_hot_encoder.fit_transform(y)

# Calculate ROC AUC.
evaluator = Rumale::EvaluationMeasure::ROCAUC.new
puts evaluator.score(y_onehot, y_score)
```

### Response
N/A (Class Documentation)

---
```

```APIDOC
## POST /roc_auc/auc

### Description
Calculates the area under the ROC curve using the trapezoidal rule.

### Method
POST

### Endpoint
`/roc_auc/auc`

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
- **x** (Array<Array<Float>>) - Required - The ground truth target values.
- **y** (Array<Array<Float>>) - Required - The target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded prediction values.

### Request Example
```json
{
  "x": [[0, 1], [1, 0], [0, 1], [1, 0]],
  "y": [[0.1, 0.9], [0.8, 0.2], [0.3, 0.7], [0.6, 0.4]]
}
```

### Response
#### Success Response (200)
- **auc_score** (Float) - The calculated area under the ROC curve.

#### Response Example
```json
{
  "auc_score": 0.75
}
```

---
```

```APIDOC
## POST /roc_auc/roc_curve

### Description
Calculates the receiver operating characteristic curve.

### Method
POST

### Endpoint
`/roc_auc/roc_curve`

### Parameters
#### Path Parameters
N/A

#### Query Parameters
- **pos_label** (Integer) - Optional - The label considered as the positive label.

#### Request Body
- **y_true** (Array<Integer>) - Required - True binary labels.
- **y_score** (Array<Float>) - Required - Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded prediction values.

### Request Example
```json
{
  "y_true": [0, 1, 1, 0, 1, 0],
  "y_score": [0.1, 0.8, 0.7, 0.2, 0.9, 0.3],
  "pos_label": 1
}
```

### Response
#### Success Response (200)
- **fpr** (Array<Float>) - False positive rates.
- **tpr** (Array<Float>) - True positive rates.
- **thresholds** (Array<Float>) - Thresholds used to compute fpr and tpr.

#### Response Example
```json
{
  "fpr": [0.0, 0.0, 0.5, 0.5, 1.0],
  "tpr": [0.0, 0.6666666666666666, 0.6666666666666666, 1.0, 1.0],
  "thresholds": [1.0, 0.9, 0.8, 0.7, 0.3]
}
```

---
```

```APIDOC
## POST /roc_auc/score

### Description
Calculates the area under the receiver operation characteristic curve (ROC AUC).

### Method
POST

### Endpoint
`/roc_auc/score`

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
- **y_true** (Array<Array<Float>>) - Required - Ground truth (correct) labels.
- **y_score** (Array<Array<Float>>) - Required - Estimated probabilities or confidence values of the positive class.

### Request Example
```json
{
  "y_true": [[0, 1], [1, 0], [0, 1], [1, 0]],
  "y_score": [[0.1, 0.9], [0.8, 0.2], [0.3, 0.7], [0.6, 0.4]]
}
```

### Response
#### Success Response (200)
- **roc_auc_score** (Float) - The calculated ROC AUC score.

#### Response Example
```json
{
  "roc_auc_score": 0.75
}
```

---
```

--------------------------------

### Get Class Labels from DecisionTreeClassifier in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Tree/DecisionTreeClassifier

Retrieves the unique class labels present in the training data. This attribute is read-only and is determined after the model has been fitted.

```ruby
# File 'rumale-tree/lib/rumale/tree/decision_tree_classifier.rb', line 25

def classes
  @classes
end
```

--------------------------------

### Rumale Kernel Machines

Source: https://yoshoku.github.io/rumale/doc/Rumale/Base

Documentation for kernel machine algorithms in Rumale.

```APIDOC
## Rumale Kernel Machines

### Description
This section covers kernel machine algorithms implemented in Rumale, utilizing kernel methods for non-linear modeling.

### Classes

*   **Rumale::KernelMachine::KernelFDA**
    *   Inherits from: EstimatorRumale::KernelMachine
    *   Description: Kernel Fisher Discriminant Analysis.
*   **Rumale::KernelMachine::KernelPCA**
    *   Inherits from: EstimatorRumale::KernelMachine
    *   Description: Kernel Principal Component Analysis.
*   **Rumale::KernelMachine::KernelRidge**
    *   Inherits from: EstimatorRumale::KernelMachine
    *   Description: Kernel Ridge Regression.
*   **Rumale::KernelMachine::KernelRidgeClassifier**
    *   Inherits from: EstimatorRumale::KernelMachine
    *   Description: Kernel Ridge Classification.
*   **Rumale::KernelMachine::KernelSVC**
    *   Inherits from: EstimatorRumale::KernelMachine
    *   Description: Kernel Support Vector Classification.
```

--------------------------------

### Get Active Features from OneHotEncoder

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/OneHotEncoder

Retrieves the indices of feature values that are present in the training dataset. This is a read-only attribute.

```ruby
# File 'rumale-preprocessing/lib/rumale/preprocessing/one_hot_encoder.rb', line 33

def active_features
  @active_features
end
```

--------------------------------

### Initialize KernelFDA

Source: https://yoshoku.github.io/rumale/doc/Rumale/KernelMachine/KernelFDA

Initializes a new KernelFDA transformer. It accepts the number of components and a regularization parameter. Dependencies include the base transformer class from Rumale. The method sets up internal parameters for subsequent operations.

```ruby
# File 'rumale-kernel_machine/lib/rumale/kernel_machine/kernel_fda.rb', line 36

def initialize(n_components: nil, reg_param: 1e-8)
  super()
  @params = {
    n_components: n_components,
    reg_param: reg_param
  }
end
```

--------------------------------

### Get Splitter Attribute

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/CrossValidation

Retrieves the splitter used for dividing the dataset into training and testing sets. This attribute holds the splitter instance passed during initialization.

```ruby
def splitter
  @splitter
end
```

--------------------------------

### Lasso Regression: Fit and Predict (Ruby)

Source: https://yoshoku.github.io/rumale/doc/Rumale/LinearModel/Lasso

Demonstrates how to initialize, fit, and predict using the Lasso regression model. Requires the rumale/linear_model/lasso library.

```ruby
require 'rumale/linear_model/lasso'

estimator = Rumale::LinearModel::Lasso.new(reg_param: 0.1)
estimator.fit(training_samples, traininig_values)
results = estimator.predict(testing_samples)
```

--------------------------------

### RVFLClassifier Documentation

Source: https://yoshoku.github.io/rumale/doc/Rumale/NeuralNetwork/RVFLClassifier

Details on the RVFLClassifier class, including its inheritance, included modules, definition path, overview, references, and examples.

```APIDOC
## Class: Rumale::NeuralNetwork::RVFLClassifier

### Description
RVFLClassifier is a class that implements classifier based on random vector functional link (RVFL) network. The current implementation uses sigmoid function as activation function.

**Reference**
  * Malik, A. K., Gao, R., Ganaie, M. A., Tanveer, M., and Suganthan, P. N., “Random vector functional link network: recent developments, applications, and future directions,” Applied Soft Computing, vol. 143, 2023.
  * Zhang, L., and Suganthan, P. N., “A comprehensive evaluation of random vector functional link networks,” Information Sciences, vol. 367–368, pp. 1094–1105, 2016.

### Method
N/A (This is a class documentation)

### Endpoint
N/A (This is a class documentation)

### Parameters
#### Path Parameters
N/A

#### Query Parameters
N/A

#### Request Body
N/A

### Request Example
```ruby
require 'numo/linalg'
require 'rumale/neural_network/rvfl_classifier'

estimator = Rumale::NeuralNetwork::RVFLClassifier.new(hidden_units: 128, reg_param: 100.0)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

### Response
#### Success Response (200)
N/A

#### Response Example
N/A

## Instance Attribute Summary

* **#classes** ⇒ Numo::Int32 (readonly)
  Return the class labels.

* **#random_bias** ⇒ Numo::DFloat (readonly)
  Return the bias vector in the hidden layer of RVFL network.

* **#random_weight_vec** ⇒ Numo::DFloat (readonly)
  Return the weight vector in the hidden layer of RVFL network.

* **#rng** ⇒ Random (readonly)
  Return the random generator.

* **#weight_vec** ⇒ Numo::DFloat (readonly)
  Return the weight vector.

### Attributes inherited from Base::Estimator
* **#params**
```

--------------------------------

### Rumale Kernel Machine Algorithms

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MaxAbsScaler

API documentation for kernel machine algorithms in the Rumale library.

```APIDOC
## Namespace Rumale::KernelMachine

### Description
This namespace provides algorithms based on kernel methods.

### Classes
*   **KernelFDA** (inherits from Estimator)
*   **KernelPCA** (inherits from Estimator)
*   **KernelRidge** (inherits from Estimator)
*   **KernelRidgeClassifier** (inherits from Estimator)
*   **KernelSVC** (inherits from Estimator)
```

--------------------------------

### Initialize and Use MiniBatchKMeans for Clustering

Source: https://yoshoku.github.io/rumale/doc/Rumale/Clustering/MiniBatchKMeans

Demonstrates how to create an instance of MiniBatchKMeans, fit it to sample data, and predict cluster labels. This involves initializing the analyzer with specific parameters like the number of clusters and maximum iterations.

```ruby
require 'rumale/clustering/mini_batch_k_means'

analyzer = Rumale::Clustering::MiniBatchKMeans.new(n_clusters: 10, max_iter: 50, batch_size: 50, random_seed: 1)
cluster_labels = analyzer.fit_predict(samples)
```

--------------------------------

### Get Best Score in GridSearchCV

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/GridSearchCV

Retrieves the highest score achieved by the estimator when trained with the best identified parameters. This is a read-only attribute.

```ruby
def best_score
  @best_score
end
```

--------------------------------

### MinMaxScaler Initialization and Usage in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Preprocessing/MinMaxScaler

Demonstrates how to initialize and use the MinMaxScaler for feature scaling. It includes fitting the scaler to training data and transforming both training and testing samples.

```ruby
require 'rumale/preprocessing/min_max_scaler'

normalizer = Rumale::Preprocessing::MinMaxScaler.new(feature_range: [0.0, 1.0])
new_training_samples = normalizer.fit_transform(training_samples)
new_testing_samples = normalizer.transform(testing_samples)
```

--------------------------------

### Get Best Index in GridSearchCV

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/GridSearchCV

Returns the index corresponding to the best parameter set identified during the cross-validation process. This is a read-only attribute.

```ruby
def best_index
  @best_index
end
```

--------------------------------

### GroupShuffleSplit Initialization and Usage Example

Source: https://yoshoku.github.io/rumale/doc/Rumale/ModelSelection/GroupShuffleSplit

Demonstrates how to initialize and use the GroupShuffleSplit class for cross-validation. It generates train and test indices based on provided data and group labels.

```ruby
require 'rumale/model_selection/group_shuffle_split'

cv = Rumale::ModelSelection::GroupShuffleSplit.new(n_splits: 2, test_size: 0.2, random_seed: 1)
x = Numo::DFloat.new(8, 2).rand
groups = Numo::Int32[1, 1, 1, 2, 2, 3, 3, 3]
cv.split(x, nil, groups).each do |train_ids, test_ids|
  puts '---'
  pp train_ids
  pp test_ids
end
```

--------------------------------

### SGDClassifier: Fit and Predict with Rumale

Source: https://yoshoku.github.io/rumale/doc/Rumale/LinearModel/SGDClassifier

This example demonstrates how to instantiate and use the SGDClassifier from the Rumale library. It shows the process of initializing the estimator with specific parameters, fitting it to training data, and then using it to predict on testing data. Dependencies include the 'rumale/linear_model/sgd_classifier' library.

```ruby
require 'rumale/linear_model/sgd_classifier'

estimator =
  Rumale::LinearModel::SGDClassifier.new(loss: 'hinge', reg_param: 1.0, max_iter: 1000, batch_size: 50, random_seed: 1)
estimator.fit(training_samples, traininig_labels)
results = estimator.predict(testing_samples)
```

--------------------------------

### Get Random Generator from DecisionTreeClassifier in Ruby

Source: https://yoshoku.github.io/rumale/doc/Rumale/Tree/DecisionTreeClassifier

Returns the random number generator used internally by the classifier. This is useful for controlling the randomness in feature selection during tree building.

```ruby
# File 'rumale-tree/lib/rumale/tree/decision_tree_classifier.rb', line 37

def rng
  @rng
end
```