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River

online-ml/river

Home
Getting started
Getting started
- In a nutshell
- Installation
User guide
User guide
API reference
API reference
- Overview
- anomaly
  anomaly
- base
  base
- cluster
  cluster
  - CluStream
  - DBSTREAM
  - DenStream
  - KMeans
  - STREAMKMeans
- compat
  compat
- compose
  compose
- datasets
  datasets
  - AirlinePassengers
  - Bananas
  - Bikes
  - ChickWeights
  - CreditCard
  - Elec2
  - HTTP
  - Higgs
  - ImageSegments
  - Insects
  - Keystroke
  - MaliciousURL
  - MovieLens100K
  - Music
  - Phishing
  - Restaurants
  - SMSSpam
  - SMTP
  - SolarFlare
  - TREC07
  - Taxis
  - TrumpApproval
- drift
  drift
  - ADWIN
  - DDM
  - EDDM
  - HDDM_A
  - HDDM_W
  - KSWIN
  - PageHinkley
- dummy
  dummy
- ensemble
  ensemble
- evaluate
  evaluate
- facto
  facto
- feature_extraction
  feature_extraction
  - Agg
  - BagOfWords
  - Lagger
  - PolynomialExtender
  - RBFSampler
  - TFIDF
  - TargetAgg
  - TargetLagger
- feature_selection
  feature_selection
- imblearn
  imblearn
- linear_model
  linear_model
- metrics
  metrics
  - Accuracy
  - AdjustedMutualInfo
  - AdjustedRand
  - BalancedAccuracy
  - BinaryMetric
  - ClassificationMetric
  - ClassificationReport
  - CohenKappa
  - Completeness
  - ConfusionMatrix
  - CrossEntropy
  - ExactMatch
  - ExampleF1
  - ExampleFBeta
  - ExamplePrecision
  - ExampleRecall
  - F1
  - FBeta
  - FowlkesMallows
  - GeometricMean
  - Hamming
  - HammingLoss
  - Homogeneity
  - Jaccard
  - KappaM
  - KappaT
  - LogLoss
  - MAE
  - MCC
  - MSE
  - MacroF1
  - MacroFBeta
  - MacroPrecision
  - MacroRecall
  - MatthewsCorrCoef
  - Metric
  - Metrics
  - MicroF1
  - MicroFBeta
  - MicroPrecision
  - MicroRecall
  - MultiClassMetric
  - MultiFBeta
  - MultiLabelConfusionMatrix
  - MultiOutputClassificationMetric
  - MultiOutputRegressionMetric
  - MutualInfo
  - NormalizedMutualInfo
  - PairConfusionMatrix
  - Precision
  - PrevalenceThreshold
  - Purity
  - Q0
  - Q2
  - R2
  - RMSE
  - RMSLE
  - ROCAUC
  - Rand
  - Recall
  - RegressionMetric
  - RegressionMultiOutput
  - Rolling
  - SMAPE
  - SorensenDice
  - TimeRolling
  - VBeta
  - VariationInfo
  - WeightedF1
  - WeightedFBeta
  - WeightedPrecision
  - WeightedRecall
  - WrapperMetric
  - cluster
    cluster
    
    BIC
    
    BallHall
    
    CalinskiHarabasz
    
    Cohesion
    
    DaviesBouldin
    
    GD43
    
    GD53
    
    Hartigan
    
    IIndex
    
    InternalMetric
    
    MSSTD
    
    PS
    
    R2
    
    RMSSTD
    
    SD
    
    SSB
    
    SSW
    
    Separation
    
    Silhouette
    
    WB
    
    XieBeni
    
    Xu
- model_selection
  model_selection
- multiclass
  multiclass
- multioutput
  multioutput
- naive_bayes
  naive_bayes
- neighbors
  neighbors
- neural_net
  neural_net
  - MLPRegressor
  - activations
    activations
    
    Identity
    
    ReLU
    
    Sigmoid
- optim
  optim
  - AMSGrad
  - AdaBound
  - AdaDelta
  - AdaGrad
  - AdaMax
  - Adam
  - Averager
  - FTRLProximal
  - Momentum
  - Nadam
  - NesterovMomentum
  - Optimizer
  - RMSProp
  - SGD
  - initializers
    initializers
    
    Constant
    
    Normal
    
    Zeros
  - losses
    losses
    
    Absolute
    
    BinaryFocalLoss
    
    BinaryLoss
    
    Cauchy
    
    CrossEntropy
    
    EpsilonInsensitiveHinge
    
    Hinge
    
    Huber
    
    Log
    
    MultiClassLoss
    
    Poisson
    
    Quantile
    
    RegressionLoss
    
    Squared
  - schedulers
    schedulers
    
    Constant
    
    InverseScaling
    
    Optimal
    
    Scheduler
- preprocessing
  preprocessing
- proba
  proba
  - Gaussian
  - Multinomial
- reco
  reco
- rules
  rules
  - AMRules
- stats
  stats
  - AbsMax
  - AutoCorr
  - BayesianMean
  - Bivariate
  - Count
  - Cov
  - EWMean
  - EWVar
  - Entropy
  - IQR
  - Kurtosis
  - Link
  - MAD
  - Max
  - Mean
  - Min
  - Mode
  - NUnique
  - PeakToPeak
  - PearsonCorr
  - Quantile
  - RollingAbsMax
  - RollingCov
  - RollingIQR
  - RollingMax
  - RollingMean
  - RollingMin
  - RollingMode
  - RollingPeakToPeak
  - RollingPearsonCorr
  - RollingQuantile
  - RollingSEM
  - RollingSum
  - RollingVar
  - SEM
  - Shift
  - Skew
  - Sum
  - Univariate
  - Var
- stream
  stream
  - Cache
  - iter_arff
  - iter_array
  - iter_csv
  - iter_libsvm
  - iter_pandas
  - iter_sklearn_dataset
  - iter_sql
  - iter_vaex
  - shuffle
  - simulate_qa
- synth
  synth
  - Agrawal
  - AnomalySine
  - ConceptDriftStream
  - Friedman
  - FriedmanDrift
  - Hyperplane
  - LED
  - LEDDrift
  - Logical
  - Mixed
  - Mv
  - Planes2D
  - RandomRBF
  - RandomRBFDrift
  - RandomTree
  - SEA
  - STAGGER
  - Sine
  - Waveform
- time_series
  time_series
- tree
  tree
- utils
  utils
  - Histogram
  - SDFT
  - Skyline
  - SortedWindow
  - VectorDict
  - Window
  - check_estimator
  - dict2numpy
  - expand_param_grid
  - log_method_calls
  - numpy2dict
  - poisson
  - pure_inference_mode
  - warm_up_mode
  - math
    math
    
    argmax
    
    chain_dot
    
    clamp
    
    dot
    
    dotvecmat
    
    matmul2d
    
    minkowski_distance
    
    norm
    
    outer
    
    prod
    
    sherman_morrison
    
    sigmoid
    
    sign
    
    softmax
  - pretty
    pretty
    
    humanize_bytes humanize_bytes
    Table of contents
    
    Parameters
    
    print_table
Examples
Examples
FAQ
FAQ
- Frequently Asked Questions
Releases
Releases

Table of contents

Parameters

humanize_bytes¶

Returns a human-friendly byte size.

Parameters¶

n_bytes (int)

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