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  • 2.5.2

    • Fix bug in One-class SVM inferencing

  • 2.5.1

    • Fix bug in SVM (SVC and SVR) inferencing

  • 2.5.0

    • Added Vantage Point Spatial tree
    • Blob Generator can now simulate() a Dataset object
    • Added Wrapper interface
    • Plus Plus added check for min number of sample seeds
    • LOF prevent div by 0 local reachability density

  • 2.4.1

    • Sentence Tokenizer fix Arabic and Farsi language support
    • Optimize online variance updating

  • 2.4.0

    • Add GELU activation function
    • Add numParams() method to Network
    • Neural Network Learners now report number of trainable parameters
    • Regex Filter added pattern to match unicode emojis
    • Custom escape character for CSV Extractor

  • 2.3.4

    • Add string literal type-hints

  • 2.3.3

    • Optimize Adam and AdaMax Optimizers

  • 2.3.2

    • Update PHP Stemmer to version 3

  • 2.3.1

    • Fix PSR-3 log version compatibility issue
    • Check for correct version of RBX format

  • 2.3.0

    • Added BM25 Transformer
    • Add dropFeature() method to the dataset object API
    • Add neural network architecture visualization via GraphViz

  • 2.2.2

    • Fix Grid Search best model selection

  • 2.2.1

    • Fix Extra Tree divide by zero when split finding

  • 2.2.0

    • Added Image Rotator transformer
    • Added One Vs Rest ensemble classifier
    • Add variance and range to the Dataset describe() report
    • Added Gower distance kernel
    • Added types() method to Dataset
    • Concatenator now accepts an iterator of iterators

  • 2.1.1

    • Do not consider unset properties when determining revision

  • 2.1.0

    • Added Probabilistic Metric interface
    • Added Probabilistic and Top K Accuracy
    • Added Brier Score Probabilistic Metric
    • Export Decision Tree-based models in Graphviz "dot" format
    • Added Graphviz helper class
    • Graph subsystem memory and storage optimizations

  • 2.0.2

    • Fix Decision Tree max height terminating condition

  • 2.0.1

    • Compensate for PHP 8.1 backward compatibility issues

  • 2.0.0

    • Gradient Boost now uses gradient-based subsampling
    • Allow Token Hashing Vectorizer custom hash functions
    • Gradient Boost base estimator no longer configurable
    • Move dummy estimators to the Extras package
    • Increase default MLP window from 3 to 5
    • Decrease default Gradient Boost window from 10 to 5
    • Rename alpha regularization parameter to L2 penalty
    • Added RBX serializer class property type change detection
    • Rename boosting estimators param to epochs
    • Neural net-based learners can now train for 0 epochs
    • Rename Labeled stratify() to stratifyByLabel()
    • Added Sparse Cosine distance kernel
    • Cosine distance now optimized for dense and sparse vectors
    • Word Count Vectorizer now uses min count and max ratio DFs
    • Numeric String Converter now handles NAN and INFs
    • Numeric String Converter is now Reversible
    • Removed Numeric String Converter NAN_PLACEHOLDER constant
    • Added MurmurHash3 and FNV1a 32-bit hashing functions to Token Hashing Vectorizer
    • Changed Token Hashing Vectorizer max dimensions to 2,147,483,647
    • Increase SQL Table Extractor batch size from 100 to 256
    • Ranks Features interface no longer extends Stringable
    • Verbose Learners now log change in loss
    • Numerical instability logged as warning instead of info
    • Added header() method to CSV and SQL Table Extractors
    • Argmax() now throws exception when undefined
    • MLP Learners recover from numerical instability with snapshot
    • Rename Gzip serializer to Gzip Native
    • Change RBX serializer constructor argument from base to level
    • Rename Writeable extractor interface to Exporter

  • 1.3.4

    • Fix Decision Tree max height terminating condition

  • 1.3.3

    • Forego unnecessary logistic computation in Logit Boost

  • 1.3.2

    • Optimize Binary output layer

  • 1.3.1

    • Update to Ok Bloomer 1.0 stable

  • 1.3.0

    • Switch back to original fork of Tensor
    • Added maxBins hyper-parameter to CART-based learners
    • Added stream Deduplicator extractor
    • Added the SiLU activation function
    • Added Swish activation layer

  • 1.2.4

    • Refactor neural network parameter updates
    • Allow set null logger

  • 1.2.3

    • Fix Multiclass layer cross entropy gradient optimization

  • 1.2.2

    • Allow empty dataset objects in stack()

  • 1.2.1

    • Refactor stratified methods on Labeled dataset
    • Narrower typehints

  • 1.2.0

    • Added Logit Boost classifier
    • Interval Discretizer variable or equi-depth binning
    • Text Normalizers now lower or upper case

  • 1.1.3

    • Min Max Normalizer compensate for 0 variance features

  • 1.1.2

    • Improved random floating point number precision
    • Deduplicate Preset seeder centroids
    • Fix Gradient Boost learning rate upper bound
    • Fix Loda histogram edge alignment

  • 1.1.1

    • Fix Gradient Boost subsampling and importance scores

  • 1.1.0

    • Update to Scienide Tensor 3.0
    • Added Nesterov's lookahead to Momentum Optimizer
    • Added Reversible transformer interface
    • MaxAbs, Z Score, and Robust scalers are now Reversible
    • Min Max Normalizer now implements Reversible
    • TF-IDF Transformer is now Reversible
    • Added Preset cluster seeder
    • Added Concatenator extractor

  • 1.0.3

    • Do not remove groups property from symbol table

  • 1.0.2

    • Fix KNN and Hot Deck imputer reset donor samples

  • 1.0.1

    • Fix AdaMax optimizer when tensor extension loaded
    • Prevent certain specification false negatives
    • Add extension minimum version specification

  • 1.0.0

    • No changes

  • 1.0.0-rc1

    • Added Token Hashing Vectorizer transformer
    • Added Word Stemmer tokenizer from Extras
    • Remove HTML Stripper and Whitespace Remover transformers
    • Rename steps() method to losses()
    • Steps() now returns iterable progress table w/ header
    • Remove rules() method on CART
    • Removed results() and best() methods from Grid Search
    • Change string representation of NAN to match PHP
    • Added extra whitespace pattern to Regex Filter

  • 1.0.0-beta2

    • Interval Discretizer now uses variable width histograms
    • Added TF-IDF sublinear TF scaling and document length normalization
    • Dataset filterByColumn() is now filter()
    • Added Lambda Function transformer from Extras
    • Rename Dataset column methods to feature
    • Added Dataset general sort() using callback
    • Confusion Matrix classes no longer selectable
    • Remove Recursive Feature Eliminator transformer
    • Metric range() now returns a Tuple object

  • 1.0.0-beta1

    • Added variance smoothing to Gaussian NB, Mixture, and MLE
    • Added MAD smoothing to Robust Z Score
    • Added Writable extractor interface
    • NDJSON and CSV extractors are now Writable
    • Added SQL Table dataset extractor
    • Changed Word Count Vectorizer DF constraints to proportions
    • Change order of Naive Bayes hyper-parameters
    • Persisters use RBX serializer by default
    • Removed previously deprecated portions of the API
    • Removed Embedder interface and namespace
    • Change Robust Z Score alpha parameter name to beta
    • Hold Out validator does not randomize by default
    • Move Redis DB persister to extras package
    • Remove Loda estimate bins static method
    • Change Grid Search base estimator param name to class
    • Remove Dataset cast to string preview
    • Add Error Analysis error standard deviation and drop midrange
    • Naive Bayes Laplace smoothing no longer effects priors
    • Nearest Neighbors distance weighting off by default
    • Promoted the Other namespace
    • Moved Flysystem persister to the Extras package
    • Change order of Loda hyper-parameters
    • Persistent Model now accepts an optional serializer
    • Persisters no longer interact directly with Persistables
    • Remove Wrapper interface
    • RBX serializer now accepts base Gzip parameter
    • Gzip serializer no longer accepts base serializer
    • Changed Gzip default compression level from 1 to 6
    • Changed RBX default compression level from 9 to 6
    • Do not persist training progress information
    • Change underscores in Report property names to spaces
    • Add saveTo() method to Encoding object
    • Add Dataset exportTo() method
    • Pipeline and Committee Machine are no longer Verbose
    • Remove K Best feature selector (special case of RFE)
    • Changed Error Analysis metrics
    • Remove threat score from Multiclass Breakdown
    • Rename Labels Are Missing exception
    • Feature importances are no longer normalized
    • Optimized CART binary categorical node splitting
    • Interval Discretizer outputs numeric string categories
    • Renamed Random Hot Deck Imputer
    • Changed order of decision tree hyper-parameters

  • 0.4.1

    • Optimized CART node splitting for low variance continuous features
    • Fixed RBX serializer string representation
    • Prevent overwrites when instantiating Unlabeled from iterator

  • 0.4.0

    • Added Truncated SVD transformer
    • Added Rubix Object File (RBX) format serializer
    • Added class revision() method to the Persistable interface
    • Added custom class revision mismatch exception
    • Add Boolean Converter transformer
    • Deprecated Igbinary serializer and move to Extras package
    • Deprecate explainedVar() and noiseVar() methods on PCA and LDA
    • Added missing extension specification and exception

  • 0.3.2

    • Fix t-SNE momentum gain bus error when using Tensor extension
    • Optimize t-SNE matrix instantiation
    • Refactor single sample inference methods
    • Update the docs site

  • 0.3.1

    • Fix CART feature importances purity increase overflow

  • 0.3.0

    • Added K Best feature selector
    • Added Flysystem 2.0 Persister
    • Stateful and Elastic Transformers are now Persistable
    • Added Gzip serializer for Persistable objects
    • Added Sentence tokenizer
    • Library now throws Rubix\ML namespaced exceptions
    • Added Scoring interface for estimators that score samples
    • Deprecated the Ranking interface
    • Add generic Trainable interface
    • Decision Trees are now iterable
    • Added K-Skip-N-Gram tokenizer and deprecated Skip Gram
    • Single sample inference methods are now marked internal
    • Deprecated Variance Threshold Filter

  • 0.2.4

    • Categorized and annotated internal API
    • Fix context of preprocess() and combinations() methods
    • Added version constants

  • 0.2.3

    • Now compatible with PHP 8 GD Image types
    • Dataset cast sample to array upon validation

  • 0.2.2

    • Optimized CART quantile-based node splitting
    • Fixed CART and Extra Tree min purity increase post pruning
    • Fix ITree infinite loop splitting same samples

  • 0.2.1

    • Optimized Stop Word Filter
    • Allow list of empty regex patterns in Regex Filter
    • Handle missing class definitions in Native and Igbinary
    • Fixed infinite loop in Ball Tree & KD Tree grow method

  • 0.2.0

    • Add Recursive Feature Eliminator feature selector
    • Can now disable holdout validation in MLP learners
    • TF-IDF Transformer additive Laplace smoothing now variable
    • Added instability detection to gradient-based learners
    • Gradient Boost validation set holdout can now be 0
    • Specifications now extend base class
    • Rename Dataset validate argument to verify
    • Ball Tree Cluster nodes are now called Cliques
    • ITree cells are now called Depth nodes
    • Added Dataset join() method and deprecated augment()
    • Added score() method to Ranking API and deprecated rank()
    • Renamed Radius Neighbors anomalyClass to outlierClass
    • HTML Stripper can now allow user-specified tags
    • Sparse Random Projector now has variable sparsity
    • Deprecated Dense Random Projector transformer

  • 0.1.6

    • Fix KNN Imputer spatial tree dependency injection

  • 0.1.5

    • Compensate for zero vectors in Cosine kernel
    • Fixed KMC2 random threshold calculation
    • Fix Naive Bayes divide by zero when smoothing is 0

  • 0.1.4

    • Optimized Cosine distance for sparse vectors

  • 0.1.3

    • Optimized Cosine distance kernel
    • Optimized (NaN) Safe Euclidean distance kernel
    • Fixed markedness calculation in Multiclass Breakdown
    • Prevent infinite loop during spatial tree path finding

  • 0.1.2

    • Fixed Grid Search best hyper-parameters method
    • Fixed K Means average loss calculation
    • Fixed bootstrap estimators tiny bootstrap sets

  • 0.1.1

    • Fixed Image Resizer placeholder image
    • Fixed Filesystem no write permissions on instantiation
    • Nicer Stringable object string representations
    • Do not terminate empty Spatial tree leaf nodes
    • Additional Filesystem persister checks
    • Nicer Dataset object validation error messages

  • 0.1.0

    • CV Report Generators now return Report objects
    • Dataset describe methods now return Report objects
    • Allow hyphens and apostrophes in Word Tokenizer
    • Dataset conversion methods now return an Encoding object
    • Encodings are now writeable to disk
    • Allow classes to be selected for Confusion Matrix
    • Fixed divide by zero in Multiclass Breakdown report
    • Changed Random Projector minDimensions default max distortion
    • Fixed Naive Bayes user-defined class prior probabilities
    • Internal CV Learners now check for sufficient hold out data
    • Fixed randomize empty dataset object
    • Removed setPersister method from Persistent Model
    • Added Dataset Has Dimensionality Specification
    • Changed name of Tree max depth parameter to max height
    • Fixed F Beta division by zero
    • Dataset toCSV and toNDJSON accept optional header
    • Nicer Verbose Learner logger output
    • Screen Logger uses empty channel name by default

  • 0.1.0-rc5

    • Improved logging for Verbose Learners
    • Added max document frequency to Word Count Vectorizer
    • Whitespace Trimmer is now a separate Transformer
    • Text Normalizers no longer remove extra whitespace
    • Added extra characters pattern to Regex Filter class constants
    • Moved Lambda Function transformer to Extras package
    • GaussianNB new class labels during partial train
    • Decision Tree print ruleset now accepts a header
    • Fixed Variance Threshold Filter drop categorical by default
    • Removed AdaBoost return learned sample weights

  • 0.1.0-rc4

    • Added Multibyte Text Normalizer transformer
    • V Measure now has adjustable beta parameter
    • Persistent Model is no longer Verbose
    • Stop Word Filter now handles unicode characters

  • 0.1.0-rc3

    • Embedders now adopt the Transformer API
    • Added RanksFeatures interface
    • Logistic Regression and Adaline now implement RanksFeatures
    • Ridge now implements the RanksFeatures interface
    • Added L2 regularization to Dense hidden layers
    • Neural Network L2 regularization now optional
    • Added MLP numerical instability checks
    • Optimized Ball Tree nearest neighbors search
    • Pipeline is now more verbose
    • Renamed Dataset partition method to partitionByColumn
    • Decreased default neural net learner batch size to 128
    • Increased default K Means batch size to 128
    • Renamed Dataset types method to featureTypes
    • Efficient serialization of Word Count Vectorizer
    • Decoupled Persistable interface from Learner
    • Moved Gower Distance kernel to Extras package
    • Moved SiLU activation function to Extras package
    • Removed array_first and array_last from global functions
    • Abstracted deferred Backend computations into Tasks
    • Removed unused BST interface

  • 0.1.0-rc2

    • Persistent Model now implements Verbose interface
    • Tuned CART continuous feature quantile-based split finding
    • N-gram and SkipGram use configurable base word tokenizer
    • Moved Alpha Dropout hidden layer to Extras package
    • Added Dataset merge and augment methods
    • Removed Dataset prepend and append methods
    • Lambda Function transformer now takes any callable
    • Text Normalizer trim extra whitespace not optional
    • Mean Shift minimum seeds now set at 20
    • Standardized K Means inertial loss over batch count
    • Added set persister method to Persistent Model
    • Removed range() from neural network Cost Function interface
    • Increased default neural net learner batch size to 200

  • 0.1.0-rc1

    • Random Forest now handles imbalanced datasets
    • Added early stopping window to AdaBoost
    • Gaussian MLE now has automatic and adaptive threshold
    • Loda now has automatic and adaptive threshold
    • Variance Threshold Filter now selects top k features
    • Added params method to Estimator and Embedder interface
    • t-SNE now compatible with categorical distance kernels
    • Grid Search implements the Wrapper interface
    • Grid Search memorizes all results from last search
    • Dataset fromIterator method accepts any iterable
    • Column Picker throws exception if column not found
    • Better hyper-parameter stringification
    • Improved Dataset exception messages
    • RMSE now default validation Metric for Regressors
    • Added balanced accuracy and threat score to Multi-class report
    • Pipeline and Persistent Model now implement Ranking
    • Changed percentile to quantile in Stats helper
    • Renamed Residual Analysis report to Error Analysis
    • Changed namespace of specification objects

  • 0.0.19-beta

    • Added SiLU self-stabilizing neural network activation function
    • Dense hidden layers now have optional bias parameter
    • KNN-based imputers accelerated by spatial tree
    • Changed the default anomaly class for Radius Neighbors
    • Removed additional methods from guessing Strategies
    • Numeric String Converter now uses fixed NaN placeholder
    • Missing Data Imputer now passes through other data types
    • Changed order of Missing Data Imputer params
    • Renamed high-level resource type to image type
    • Added comb (n choose k) to global functions
    • Image Vectorizer now has grayscale option
    • Clusterers and Anomaly Detectors return integer predictions
    • Ball Tree now compatible with categorical distance kernels
    • Parallel Learners using Amp Backend are now persistable
    • Changed order of Radius Neighbors hyper-parameters

  • 0.0.18-beta

    • Now requires PHP 7.2 and above
    • Added phpbench performance benchmarks
    • Added JSON, NDJSON, CSV, and Column Picker Extractors
    • Changed the way fromIterator method works on Dataset object
    • Added Hyperplane dataset generator
    • Changed the way noise is applied to Circle, Half Moon, etc.
    • Changed name of Multilayer Perceptron classifier
    • Deferred computations are now callable
    • Removed range() from the activation function interface
    • Added label type validation for supervised learners
    • Added toArray, toJson, toCsv, toNdjson methods to Dataset API
    • Can now preview a Dataset object in console by echoing it
    • Changed Labeled dataset objects iteration and array access
    • Removed zip and unzip methods on Labeled dataset
    • Added describe by label method to Labeled dataset
    • Changed the way fromIterator works on Dataset
    • Added Regex Filter transformer
    • Changed name of Igbinary serializer
    • Changed dataset and label description

  • 0.0.17-beta

    • Added Tensor extension compatibility
    • Migrated to new Tensor library namespace
    • Anomaly detector predictions now categorical
    • Clusterers now predict categorical cluster labels
    • Added extracting data section to docs
    • Added code metrics
    • Added training and inference sections to the docs
    • Decision tree rules method now outputs a string
    • Added drop row and column methods to dataset interface
    • Dataset row() method is now sample()

  • 0.0.16-beta

    • Radius Neighbors allows user-definable anomaly class
    • Added KNN Imputer
    • Added Random Hot Deck Imputer
    • Missing Data Imputer now handles NaNs by default
    • Added NaN safe Euclidean distance kernel
    • Added Gower distance kernel
    • Added Hamming distance kernel
    • Dataset now requires homogeneous feature columns
    • KNN now compatible with categorical features
    • Added transform column method to dataset object
    • Added describe method to dataset object
    • Added describe labels method to Labeled dataset
    • Added deduplicate method to dataset object
    • Added unzip static factory for Labeled datasets from data table
    • Changed the order of t-SNE hyper-parameters
    • Added global transpose array helper function
    • Renamed label key to classes in Multiclass Breakdown report
    • Changed order of Gradient Boost and AdaBoost hyper-parameters
    • Changed order of Loda hyper-parameters
    • Added asString method to the Data Type helper class
    • Added check for NaN labels in Labeled dataset
    • Changed namespace of Data Type helper
    • Numeric String Converter now handles NaN strings
    • Added predict probabilities of a single sample method
    • Added rank single sample trait

  • 0.0.15-beta

    • Added Gaussian MLE anomaly detector
    • Added early stopping window to Gradient Descent-based Learners
    • Changed early stopping behavior of MLP-based estimators
    • Added predict single sample method to Learner interface
    • Changed method signature of random subset without replacement
    • Changed K Means default max iterations
    • Robust Z-Score now uses weighted combination of scores
    • Cross validators now stratify dataset automatically
    • Changed default k in K Fold validator
    • Changed order of Loda hyperparameters
    • Changed hyperparameter order of KNN-based learners
    • Added method to return categories from One Hot Encoder
    • Removed Lottery and Blurry Percentile guessing strategy
    • Added Percentile guessing strategy
    • Added shrinkage parameter to Wild Guess strategy
    • Added additional methods to random Strategies
    • Renamed Popularity Contest strategy to Prior
    • Datasets now inherit from abstract parent Dataset class
    • Removed Dataset interface
    • Neural net parameter update in Layer instead of Optimizer
    • Changed order of distance-based clusterer hyperparameters
    • Improved cluster radius estimation in Mean Shift
    • Naive Bayes now adaptive to new class labels
    • Changed order of neural network learner hyperparameters
    • Added safety switch to AdaBoost if weak learner worse than random
    • Added min change early stopping to AdaBoost
    • Added Patreon funding support

  • 0.0.14-beta

    • Added feature importances to Gradient Boost
    • Added progress monitoring to Gradient Boost w/ early stop
    • Added Spatial and Decision tree interface
    • Mean Shift compatible with Spatial trees
    • K-d Neighbors base spatial tree configurable
    • Radius Neighbors now uses base spatial tree
    • Local Outlier Factor interchangeable base search tree
    • DBSCAN now uses any Spatial tree for range searches
    • CART uses downsampling on continuous features
    • LOF and Isolation Forest contamination off by default
    • Embed method now returns an array instead of dataset
    • Fixed issue with Dataset partitioning
    • Renamed Coordinate node to Hypercube
    • KNN default k is now 5 instead of 3
    • CART can now print a text representation of the decision rules
    • Removed Local Outlier Factor brute force version
    • Changed namespace of trees to Graph/Trees
    • CART impurity tolerances are now hardcoded
    • Changed order of CART hyperparameters
    • Added Extra Tree base implementation
    • Extra Tree splits are now unbiased
    • Extra Tree Classifier now minimizes entropy
    • Reduced the memory footprint of Binary Nodes
    • Gradient Boost shrinkage bounded between 0 and 1
    • Added random subset without replacement to dataset API
    • Changed order of Gradient Boost hyperparameters
    • Changed order of MLP hyperparameters
    • Ranking interface is now a general interface
    • Changed default t-SNE minimum gradient

  • 0.0.13-beta

    • Added documentation site
    • Added Regression and Classification Loss interfaces
    • Robust Z-Score is now a Ranking anomaly detector
    • Loda now defaults to auto detect bin count
    • Removed tolerance param from Gradient Boost and AdaBoost
    • Screen logger timestamp format now configurable
    • Dropped Persistable contract between SVM-based learners
    • Random Forest feature importances now serial
    • Removed Robust Z-Score tolerance parameter
    • Added slice method to Dataset API
    • Loda now performs density estimation on the fly
    • Transform labels now returns self for method chaining

  • 0.0.12-beta

    • Added AdaMax neural network Optimizer
    • Added Parallel interface for multiprocessing
    • Added Backend processing interface
    • Added Amp parallel and Serial processing Backends
    • Random Forest uses parallel processing
    • Added CPU helper and core auto detection
    • Committee Machine is now a meta estimator
    • Committee Machine now Parallel and Verbose
    • Bootstrap Aggregator uses multiple processes
    • Grid Search now trains in parallel
    • K Fold, Leave P Out, and Monte Carlo validators now Parallel
    • Added momentum to Batch Norm moving averages
    • Custom Batch Norm and PReLU parameter initialization
    • Added custom bias initialization to Dense layer
    • Output layers now accept custom initializers
    • Added Constant neural network parameter initializer
    • Removed Exponential neural network Cost Function
    • Filesystem save history is now either on or off
    • Removed save history from Redis DB Persister
    • Removed Model Orchestra meta-estimator
    • Grid Search automatically retrains base estimator
    • Added neural net Parameter namespace and interface
    • Changed order of Loda hyperparameters
    • Replaced F1 Score with F Beta metric
    • Removed ISRU and Gaussian activation functions
    • Fixed SELU derivative computation
    • Changed adaptive optimizer default decay parameters
    • Changed default learning rate of Stochastic Optimizer
    • Added SMAPE (Symmetric MAPE) regression metric
    • Added MAPE to Residual Analysis report
    • Fixed MSLE computation in Residual Analysis report
    • Renamed RMSError Metric to RMSE
    • Embedders no longer implement Estimator interface
    • Added error statistics to Residual Analysis report

  • 0.0.11-beta

    • K Means now uses mini batch GD instead of SGD
    • K Means in now an Online learner
    • Added Adjusted Rand Index clustering metric
    • Added Seeder Interface
    • Added Random, K-MC2, and Plus Plus seeders
    • Accelerated Mean Shift with Ball Tree
    • Added radius estimation to Mean Shift
    • K Means and Mean Shift now implement Probabilistic
    • Gaussian Mixture now supports seeders
    • Changed order of K Means hyperparameters
    • Moved Ranking interface to anomaly detector namespace
    • N-gram Tokenizer now outputs ranges of word tokens
    • Changed default Fuzzy C Means hyper-parameters
    • Added spatial partitioning to Dataset API
    • Added Image Resizer transformer
    • Image Vectorizer no longer resizes images
    • Fixed adaptive optimizer bug upon binary unserialization
    • Removed Quartile Standardizer
    • Optimized Image Vectorizer using bitwise operations
    • Pipeline is now more verbose

  • 0.0.10-beta

    • Added Loda online anomaly detector
    • Added Radius Neighbors classifier and regressor
    • Added fast k-d LOF anomaly detector
    • Added base Ball Tree implementation
    • Added Ranking interface
    • Changed Manifold namespace to Embedders
    • Isolation Forest and LOF are now Ranking
    • K Means is now Verbose
    • Accelerated DBSCAN with Ball Tree
    • Added upper bound to contamination hyperparameter
    • Changed hyper-parameter order of Isolation Forest
    • Optimized Interval Discretizer transformer
    • K Means is no longer Online
    • Removed Sign function
    • Added Binary Tree interface
    • Added bin count heuristic to Loda
    • Changed order of k-d neighbors hyperparameters
    • Removed Hamming distance kernel

  • 0.0.9-beta

    • Added transform labels method to Labeled Dataset
    • Added Data Type helper
    • Pipeline and Persistent Model are now Probabilistic
    • Added stack method to dataset API
    • Changed merge method on dataset to append and prepend
    • Implemented specifications
    • Added data type compatibility for estimators
    • Added compatibility method to validation metrics
    • Added estimator compatibility to reports
    • Added trained method to learner API
    • Added fitted method to Stateful transformer API
    • Changed ordinal of integer encoded data types
    • Added Adaptive optimizer interface
    • Changed Transformer transform API
    • Removed prompt method from Persistent Model
    • Removed JsonSerializable from Dataset Interface

  • 0.0.8-alpha

    • Added Model Orchestra meta estimator
    • Added Stop Word Filter transformer
    • Added document frequency smoothing to TF-IDF Transformer
    • Added Uniform neural net weight initializer
    • Improved Gaussian Mixture numerical stability
    • Fixed missing probabilities in Classification Tree
    • Removed MetaEstimator interface
    • Added model Wrapper interface
    • AdaBoost is now probabilistic
    • Added Constant guessing strategy
    • Added N-Gram word tokenizer
    • Added Skip-Gram word tokenizer
    • Changed FCM and K Means default max epochs
    • Added zip method to Labeled dataset
    • Removed stop word filter from Word Count Vectorizer
    • Changed order of t-SNE hyper-parameters
    • Grid search now has automatic default Metric
    • Base k-D Tree now uses highest variance splits
    • Renamed Raw Pixel Encoder to Image Vectorizer

  • 0.0.7-alpha

    • Added Support Vector Machine classifier and regressor
    • Added One Class SVM anomaly detector
    • Added Verbose interface for logging
    • Added Linear Discriminant Analysis (LDA) transformer
    • Manifold learners are now considered Estimators
    • Transformers can now transform labels
    • Added Cyclic neural net Optimizer
    • Added k-d neighbors search with pruning
    • Added post pruning to CART estimators
    • Estimators with explicit loss functions are now Verbose
    • Grid Search: Added option to retrain best model on full dataset
    • Filesystem Persister now keeps backups of latest models
    • Added loading backup models to Persister API
    • Added PSR-3 compatible screen logger
    • Grid Search is now Verbose
    • t-SNE embedder is now Verbose
    • Added Serializer interface
    • Added Native and Binary serializers
    • Fixed Naive Bayes reset category counts during partial train
    • Pipeline and Persistent Model are now Verbose
    • Classification and Regression trees now Verbose
    • Random Forest can now return feature importances
    • Gradient Boost now accepts base and booster estimators
    • Blurry Median strategy is now Blurry Percentile
    • Added Mean strategy
    • Removed dataset save and load methods
    • Subsumed Extractor api into Transformer
    • Removed Concentration metric
    • Changed Metric and Report API
    • Added Text Normalizer transformer
    • Added weighted predictions to KNN estimators
    • Added HTML Stripper transformer

  • 0.0.6-alpha

    • Added Gradient Boost regressor
    • Added t-SNE embedder
    • AdaBoost now uses SAMME multiclass algorithm
    • Added Redis persister
    • Added Max Absolute Scaler
    • Added Principal Component Analysis transformer
    • Pipeline is now Online and has elastic option
    • Added Elastic interface for transformers
    • Z Scale Standardizer is now Elastic
    • Min Max Normalizer is now Elastic
    • TF-IDF Transformer is now Elastic
    • Added Huber Loss cost function
    • Added Swiss Roll generator
    • Moved Generators to the Datasets directory
    • Added Persister interface for Persistable objects
    • Added overwrite protection to Persistent Model meta estimator
    • Multiclass Breakdown report now breaks down user-defined classes
    • Renamed restore method to load on Datasets and Persisters
    • Random Forest now accepts a base estimator instance
    • CARTs now use max features heuristic by default
    • Added build/quick factory methods to Datasets
    • Added Interval Discretizer transformer
    • GaussianNB and Naive Bayes now accept class prior probabilities
    • Removed Image Patch Descriptor
    • Added Learner interface for trainable estimators
    • Added smart cluster initialization to K Means and Fuzzy C Means
    • Circle and Half Moon generators now generate Labeled datasets
    • Gaussian Mixture now uses K Means initialization
    • Removed Isolation Tree anomaly detector

  • 0.0.5-alpha

    • Added Gaussian Mixture clusterer
    • Added Batch Norm hidden layer
    • Added PReLU hidden layer
    • Added Relative Entropy cost function to nn
    • Added random weighted subset to datasets
    • Committee Machine classifier only and added expert influence
    • Added type method to Estimator API
    • Removed classifier, detector, clusterer, regressor interfaces
    • Added epsilon smoothing to Gaussian Naive Bayes
    • Added option to fit priors in Naive Bayes classifiers
    • Added Jaccard distance kernel
    • Fixed Hamming distance calculation
    • Added Alpha Dropout layer
    • Fixed divide by 0 in Cross Entropy cost function
    • Added scaling parameter to Exponential cost function
    • Added Image Patch Descriptor extractor
    • Added Texture Histogram descriptor
    • Added Average Color descriptor
    • Removed parameters from Dropout and Alpha Dropout layers
    • Added option to remove biases in Dense and Placeholder1D layers
    • Optimized Dataset objects
    • Optimized matrix and vector operations
    • Added grid params to Param helper
    • Added Gaussian RBF activation function
    • Renamed Quadratic cost function to Least Squares
    • Added option to stratify dataset in Hold Out and K Fold
    • Added Monte Carlo cross validator
    • Implemented noise as layer instead of activation function
    • Removed Identity activation function
    • Added Xavier 1 and 2 initializers
    • Added He initializer
    • Added Le Cun initializer
    • Added Normal (Gaussian) initializer

  • 0.0.4-alpha

    • Added Dropout hidden layer
    • Added K-d Neighbors classifier and regressor
    • Added Extra Tree Regressor
    • Added Adaline regressor
    • Added sorting by column to Dataset
    • Added sort by label to Labeled Dataset
    • Added appending and prepending to Dataset
    • Added Dataset Generators
    • Added Noisy ReLU activation function
    • Fixed bug in dataset stratified fold
    • Added stop word filter to Word Count Vectorizer
    • Added centering and scaling options for standardizers
    • Added min dimensionality estimation on random projectors
    • Added Gaussian Random Projector
    • Removed Ellipsoidal distance kernel
    • Added Thresholded ReLU activation function
    • Changed API of Raw Pixel Encoder

  • 0.0.3-alpha

    • Added Extra Tree classifier
    • Random Forest now supports Extra Trees
    • New Decision Tree implementation
    • Added Canberra distance kernel
    • Committee Machine is now a Meta Estimator Ensemble
    • Added Bootstrap Aggregator Meta Estimator Ensemble
    • Added Gaussian Naive Bayes
    • Naive Bayes classifiers are now Online learners
    • Added tolerance to Robust Z-Score detector
    • Added Concentration clustering metric (Calinski Harabasz)

  • 0.0.2-alpha

    • Added Anomaly Detection
    • New Neural Net implementation
    • Added static analysis
    • Added Travis CI configuration

  • 0.0.1-alpha