site_name: 'Rubix ML'

theme:
  name: material
  logo: images/app-icon-medium.png
  favicon: images/app-icon-small.png
  icon:
    repo: fontawesome/brands/github
  features:
    - navigation.tabs

nav:
  - Home: https://rubixml.com
  - Getting Started:
    - Welcome: index.md
    - What is Machine Learning?: what-is-machine-learning.md
    - Installation: installation.md
    - Basic Introduction: basic-introduction.md
  - User Guide:
    - Representing Your Data: representing-your-data.md
    - Extracting Data: extracting-data.md
    - Preprocessing: preprocessing.md
    - Exploring Data: exploring-data.md
    - Choosing an Estimator: choosing-an-estimator.md
    - Training: training.md
    - Inference: inference.md
    - Cross-validation: cross-validation.md
    - Hyper-parameter Tuning: hyper-parameter-tuning.md
    - Model Ensembles: model-ensembles.md
    - Model Persistence: model-persistence.md
  - API Reference:
    - Fundamental Interfaces:
      - Estimator: estimator.md
      - Learner: learner.md
      - Online: online.md
      - Parallel: parallel.md
      - Persistable: persistable.md
      - Probabilistic: probabilistic.md
      - Ranks Features: ranks-features.md
      - Scoring: scoring.md
      - Verbose: verbose.md
    - Extractors:
      - API Reference: extractors/api.md
      - Column Picker: extractors/column-picker.md
      - Concatenator: extractors/concatenator.md
      - CSV: extractors/csv.md
      - NDJSON: extractors/ndjson.md
      - SQL Table: extractors/sql-table.md
    - Dataset Objects:
      - API Reference: datasets/api.md
      - Generators:
        - API Reference: datasets/generators/api.md
        - Agglomerate: datasets/generators/agglomerate.md
        - Blob: datasets/generators/blob.md
        - Circle: datasets/generators/circle.md
        - Half Moon: datasets/generators/half-moon.md
        - Hyperplane: datasets/generators/hyperplane.md
        - Swiss Roll: datasets/generators/swiss-roll.md
      - Labeled: datasets/labeled.md
      - Unlabeled: datasets/unlabeled.md
    - Classifiers:
      - AdaBoost: classifiers/adaboost.md
      - Classification Tree: classifiers/classification-tree.md
      - Dummy Classifier: classifiers/dummy-classifier.md
      - Extra Tree Classifier: classifiers/extra-tree-classifier.md
      - Gaussian Naive Bayes: classifiers/gaussian-naive-bayes.md
      - K-d Neighbors: classifiers/kd-neighbors.md
      - K Nearest Neighbors: classifiers/k-nearest-neighbors.md
      - Logistic Regression: classifiers/logistic-regression.md
      - Multilayer Perceptron: classifiers/multilayer-perceptron.md
      - Naive Bayes: classifiers/naive-bayes.md
      - Radius Neighbors: classifiers/radius-neighbors.md
      - Random Forest: classifiers/random-forest.md
      - Softmax Classifier: classifiers/softmax-classifier.md
      - SVC: classifiers/svc.md
    - Regressors:
      - Adaline: regressors/adaline.md
      - Dummy Regressor: regressors/dummy-regressor.md
      - Extra Tree Regressor: regressors/extra-tree-regressor.md
      - Gradient Boost: regressors/gradient-boost.md
      - K-d Neighbors Regressor: regressors/kd-neighbors-regressor.md
      - KNN Regressor: regressors/knn-regressor.md
      - MLP Regressor: regressors/mlp-regressor.md
      - Radius Neighbors Regressor: regressors/radius-neighbors-regressor.md
      - Regression Tree: regressors/regression-tree.md
      - Ridge: regressors/ridge.md
      - SVR: regressors/svr.md
    - Clusterers:
      - Seeders:
        - K-MC2: clusterers/seeders/k-mc2.md
        - Plus Plus: clusterers/seeders/plus-plus.md
        - Preset: clusterers/seeders/preset.md
        - Random: clusterers/seeders/random.md
      - DBSCAN: clusterers/dbscan.md
      - Fuzzy C Means: clusterers/fuzzy-c-means.md
      - Gaussian Mixture: clusterers/gaussian-mixture.md
      - K Means: clusterers/k-means.md
      - Mean Shift: clusterers/mean-shift.md
    - Anomaly Detectors:
      - Gaussian MLE: anomaly-detectors/gaussian-mle.md
      - Isolation Forest: anomaly-detectors/isolation-forest.md
      - Loda: anomaly-detectors/loda.md
      - Local Outlier Factor: anomaly-detectors/local-outlier-factor.md
      - One Class SVM: anomaly-detectors/one-class-svm.md
      - Robust Z-Score: anomaly-detectors/robust-z-score.md
    - Meta Estimators:
      - Bootstrap Aggregator: bootstrap-aggregator.md
      - Committee Machine: committee-machine.md
      - Grid Search: grid-search.md
      - Persistent Model: persistent-model.md
      - Pipeline: pipeline.md
    - Transformers:
      - API Reference: transformers/api.md
      - Standardization and Normalization:
        - L1 Normalizer: transformers/l1-normalizer.md
        - L2 Normalizer: transformers/l2-normalizer.md
        - Max Absolute Scaler: transformers/max-absolute-scaler.md
        - Min Max Normalizer: transformers/min-max-normalizer.md
        - Robust Standardizer: transformers/robust-standardizer.md
        - Z Scale Standardizer: transformers/z-scale-standardizer.md
      - Dimensionality Reduction:
        - Gaussian Random Projector: transformers/gaussian-random-projector.md
        - Linear Discriminant Analysis: transformers/linear-discriminant-analysis.md
        - Principal Component Analysis: transformers/principal-component-analysis.md
        - Sparse Random Projector: transformers/sparse-random-projector.md
        - Truncated SVD: transformers/truncated-svd.md
        - t-SNE: transformers/t-sne.md
      - Feature Conversion:
        - Interval Discretizer: transformers/interval-discretizer.md
        - One Hot Encoder: transformers/one-hot-encoder.md
        - Numeric String Converter: transformers/numeric-string-converter.md
        - Boolean Converter: transformers/boolean-converter.md
      - Feature Expansion:
        - Polynomial Expander: transformers/polynomial-expander.md
      - Imputation:
        - Hot Deck Imputer: transformers/hot-deck-imputer.md
        - KNN Imputer: transformers/knn-imputer.md
        - Missing Data Imputer: transformers/missing-data-imputer.md
      - Natural Language:
        - Regex Filter: transformers/regex-filter.md
        - Text Normalizer: transformers/text-normalizer.md
        - Multibyte Text Normalizer: transformers/multibyte-text-normalizer.md
        - Stop Word Filter: transformers/stop-word-filter.md
        - TF-IDF Transformer: transformers/tf-idf-transformer.md
        - Token Hashing Vectorizer: transformers/token-hashing-vectorizer.md
        - Word Count Vectorizer: transformers/word-count-vectorizer.md
      - Images:
        - Image Resizer: transformers/image-resizer.md
        - Image Vectorizer: transformers/image-vectorizer.md
      - Other:
        - Lambda Function: transformers/lambda-function.md
    - Neural Network:
      - Hidden Layers:
        - Activation: neural-network/hidden-layers/activation.md
        - Batch Norm: neural-network/hidden-layers/batch-norm.md
        - Dense: neural-network/hidden-layers/dense.md
        - Dropout: neural-network/hidden-layers/dropout.md
        - Noise: neural-network/hidden-layers/noise.md
        - PReLU: neural-network/hidden-layers/prelu.md
      - Activation Functions:
        - ELU: neural-network/activation-functions/elu.md
        - Hyperbolic Tangent: neural-network/activation-functions/hyperbolic-tangent.md
        - Leaky ReLU: neural-network/activation-functions/leaky-relu.md
        - ReLU: neural-network/activation-functions/relu.md
        - SELU: neural-network/activation-functions/selu.md
        - Sigmoid: neural-network/activation-functions/sigmoid.md
        - Softmax: neural-network/activation-functions/softmax.md
        - Soft Plus: neural-network/activation-functions/soft-plus.md
        - Soft Sign: neural-network/activation-functions/softsign.md
        - Thresholded ReLU: neural-network/activation-functions/thresholded-relu.md
      - Cost Functions:
        - Cross Entropy: neural-network/cost-functions/cross-entropy.md
        - Huber Loss: neural-network/cost-functions/huber-loss.md
        - Least Squares: neural-network/cost-functions/least-squares.md
        - Relative Entropy: neural-network/cost-functions/relative-entropy.md
      - Initializers:
        - Constant: neural-network/initializers/constant.md
        - He: neural-network/initializers/he.md
        - LeCun: neural-network/initializers/lecun.md
        - Normal: neural-network/initializers/normal.md
        - Uniform: neural-network/initializers/uniform.md
        - Xavier 1: neural-network/initializers/xavier-1.md
        - Xavier 2: neural-network/initializers/xavier-2.md
      - Optimizers:
        - AdaGrad: neural-network/optimizers/adagrad.md
        - Adam: neural-network/optimizers/adam.md
        - AdaMax: neural-network/optimizers/adamax.md
        - Cyclical: neural-network/optimizers/cyclical.md
        - Momentum: neural-network/optimizers/momentum.md
        - RMS Prop: neural-network/optimizers/rms-prop.md
        - Step Decay: neural-network/optimizers/step-decay.md
        - Stochastic: neural-network/optimizers/stochastic.md
    - Graph:
      - Trees:
        - Ball Tree: graph/trees/ball-tree.md
        - K-d Tree: graph/trees/k-d-tree.md
    - Kernels:
      - Distance:
        - Canberra: kernels/distance/canberra.md
        - Cosine: kernels/distance/cosine.md
        - Diagonal: kernels/distance/diagonal.md
        - Euclidean: kernels/distance/euclidean.md
        - Hamming: kernels/distance/hamming.md
        - Jaccard: kernels/distance/jaccard.md
        - Manhattan: kernels/distance/manhattan.md
        - Minkowski: kernels/distance/minkowski.md
        - Safe Euclidean: kernels/distance/safe-euclidean.md
      - SVM:
        - Linear: kernels/svm/linear.md
        - Polynomial: kernels/svm/polynomial.md
        - RBF: kernels/svm/rbf.md
        - Sigmoidal: kernels/svm/sigmoidal.md
    - Cross Validation:
      - Metrics:
        - API Reference: cross-validation/metrics/api.md
        - Accuracy: cross-validation/metrics/accuracy.md
        - F Beta: cross-validation/metrics/f-beta.md
        - Informedness: cross-validation/metrics/informedness.md
        - MCC: cross-validation/metrics/mcc.md
        - Mean Absolute Error: cross-validation/metrics/mean-absolute-error.md
        - Mean Squared Error: cross-validation/metrics/mean-squared-error.md
        - Median Absolute Error: cross-validation/metrics/median-absolute-error.md
        - RMSE: cross-validation/metrics/rmse.md
        - R Squared: cross-validation/metrics/r-squared.md
        - SMAPE: cross-validation/metrics/smape.md
        - Completeness: cross-validation/metrics/completeness.md
        - Homogeneity: cross-validation/metrics/homogeneity.md
        - Rand Index: cross-validation/metrics/rand-index.md
        - V Measure: cross-validation/metrics/v-measure.md
      - Reports:
        - API Reference: cross-validation/reports/api.md
        - Aggregate Report: cross-validation/reports/aggregate-report.md
        - Confusion Matrix: cross-validation/reports/confusion-matrix.md
        - Contingency Table: cross-validation/reports/contingency-table.md
        - Error Analysis: cross-validation/reports/error-analysis.md
        - Multiclass Breakdown: cross-validation/reports/multiclass-breakdown.md
      - Validators:
        - API Reference: cross-validation/api.md
        - Hold Out: cross-validation/hold-out.md
        - K Fold: cross-validation/k-fold.md
        - Leave P Out: cross-validation/leave-p-out.md
        - Monte Carlo: cross-validation/monte-carlo.md
    - Tokenizers:
      - K-Skip-N-Gram: tokenizers/k-skip-n-gram.md
      - N-Gram: tokenizers/n-gram.md
      - Sentence: tokenizers/sentence.md
      - Whitespace: tokenizers/whitespace.md
      - Word: tokenizers/word.md
      - Word Stemmer: tokenizers/word-stemmer.md
    - Persisters:
      - API Reference: persisters/api.md
      - Filesystem: persisters/filesystem.md
    - Serializers:
      - API Reference: serializers/api.md
      - Gzip: serializers/gzip.md
      - Native: serializers/native.md
      - RBX: serializers/rbx.md
    - Loggers:
      - Screen: loggers/screen.md
    - Backends:
      - Amp: backends/amp.md
      - Serial: backends/serial.md
    - Helpers:
      - Params: helpers/params.md
    - Strategies:
      - Constant: strategies/constant.md
      - K Most Frequent: strategies/k-most-frequent.md
      - Mean: strategies/mean.md
      - Percentile: strategies/percentile.md
      - Prior: strategies/prior.md
      - Wild Guess: strategies/wild-guess.md
  - FAQ: faq.md

extra:
  version:
    provider: mike
  social:
    - icon: fontawesome/brands/github
      link: https://github.com/RubixML
    - icon: fontawesome/brands/telegram
      link: https://t.me/RubixML
    - icon: fontawesome/brands/twitter
      link: https://twitter.com/RubixML
    - icon: fontawesome/brands/linkedin
      link: https://www.linkedin.com/groups/8952251/

use_directory_urls: false

plugins:
  - search
  - git-revision-date

markdown_extensions:
  - attr_list
  - abbr
  - admonition
  - pymdownx.highlight:
      extend_pygments_lang:
        - name: php
          lang: php
          options:
            startinline: true
  - pymdownx.superfences
  - pymdownx.arithmatex:
      generic: true
  - toc:
      permalink: "#"
  - footnotes

google_analytics:
  - UA-136137674-1
  - auto

extra_javascript:
  - https://polyfill.io/v3/polyfill.min.js?features=es6
  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js
  - js/custom.js

extra_css:
  - css/custom.css

repo_url: https://github.com/RubixML/ML
site_url: https://rubixml.com
site_description: 'A high-level machine learning and deep learning library for the PHP language.'

copyright: '&copy; 2021 Rubix ML'
