Default export object.
default
Properties
Tensor (Tensor) : Tensor class
Matrix (Matrix) : Matrix class
Graph (Graph) : Graph class
Static Members
models
Properties
LBABOD (LBABOD) : Lower-bound for the Angle-based Outlier Detection
ABOD (ABOD) : Angle-based Outlier Detection
ADALINE (ADALINE) : Adaptive Linear Neuron model
ADAMENN (ADAMENN) : Adaptive Metric Nearest Neighbor
ALMA (ALMA) : Approximate Large Margin algorithm
AODE (AODE) : Averaged One-Dependence Estimators
AR (AR) : Autoregressive model
ARMA (ARMA) : Autoregressive moving average model
AROW (AROW) : Adaptive regularization of Weight Vectors
ART (ART) : Adaptive resonance theory
BIRCH (BIRCH) : Balanced iterative reducing and clustering using hierarchies
BOGD (BOGD) : Bounded Online Gradient Descent
BoxCox (BoxCox) : Box-Cox transformation
BPA (BPA) : Budgeted online Passive-Aggressive
MulticlassBSGD (MulticlassBSGD) : Multiclass Budgeted Stochastic Gradient Descent
BSGD (BSGD) : Budgeted Stochastic Gradient Descent
C2P (C2P) : Clustering based on Closest Pairs
Canny (Canny) : Canny edge detection
CAST (CAST) : Clustering Affinity Search Technique
CLARA (CLARA) : Clustering LARge Applications
CLARANS (CLARANS) : Clustering Large Applications based on RANdomized Search
CLIQUE (CLIQUE) : CLustering In QUEst
CLUES (CLUES) : CLUstEring based on local Shrinking
COF (COF) : Connectivity-based Outlier Factor
COLL (COLL) : Conscience on-line learning
CRF (CRF) : Conditional random fields
CumSum (CumSum) : Cumulative sum change point detection
CURE (CURE) : Clustering Using REpresentatives
DBCLASD (DBCLASD) : Distribution Based Clustering of LArge Spatial Databases
DBSCAN (DBSCAN) : Density-based spatial clustering of applications with noise
DENCLUE (DENCLUE) : DENsity CLUstering
DIANA (DIANA) : DIvisive ANAlysis Clustering
DiSH (DiSH) : Detecting Subspace cluster Hierarchies
DOC (DOC) : Density-based Optimal projective Clustering
FastDOC (FastDOC) : Fast Density-based Optimal projective Clustering
DQNAgent (DQNAgent) : Deep Q-Network agent
DrakeKMeans (DrakeKMeans) : Drake's accelerated k-Means algorithm
DTSCAN (DTSCAN) : Delaunay triangulation-based spatial clustering of application with noise
DPAgent (DPAgent) : Dynamic programming agent
ElkanKMeans (ElkanKMeans) : Elkan's accelerated k-Means algorithm
ELMClassifier (ELMClassifier) : Extreme learning machine classifier
ELMRegressor (ELMRegressor) : Extreme learning machine regressor
ENaN (ENaN) : Extended Natural Neighbor
ENN (ENN) : Extended Nearest Neighbor
FINDIT (FINDIT) : a Fast and INtelligent subspace clustering algorithm using DImension voting
FuzzyKNN (FuzzyKNN) : Fuzzy k-nearest neighbor
GAN (GAN) : Generative adversarial networks
GasserMuller (GasserMuller) : Gasser–Müller kernel estimator
GBDT (GBDT) : Gradient boosting decision tree
GBDTClassifier (GBDTClassifier) : Gradient boosting decision tree classifier
GeneralizedESD (GeneralizedESD) : Generalized extreme studentized deviate
GMM (GMM) : Gaussian mixture model
GMR (GMR) : Gaussian mixture regression
GPLVM (GPLVM) : Gaussian Process Latent Variable Model
GSOM (GSOM) : Growing Self-Organizing Map
GTM (GTM) : Generative topographic mapping
HamelryKMeans (HamelryKMeans) : Hamelry's accelerated k-Means algorithm
HDBSCAN (HDBSCAN) : Hierarchical Density-based spatial clustering of applications with noise
HLLE (HLLE) : Hessian Locally Linear Embedding
HMM (HMM) : Hidden Markov model
Hotelling (Hotelling) : Hotelling T-square Method
ICA (ICA) : Independent component analysis
CELLIP (CELLIP) : Classical ellipsoid method
IELLIP (IELLIP) : Improved ellipsoid method
IKNN (IKNN) : Locally Informative K-Nearest Neighbor
IncrementalPCA (IncrementalPCA) : Incremental principal component analysis
INFLO (INFLO) : Influenced Outlierness
ISODATA (ISODATA) : Iterative Self-Organizing Data Analysis Technique
KDEOS (KDEOS) : Kernel Density Estimation Outlier Score
KernelizedPegasos (KernelizedPegasos) : Kernelized Primal Estimated sub-GrAdientSOlver for SVM
KLIEP (KLIEP) : Kullback-Leibler importance estimation procedure
KMeans (KMeans) : k-means model
KModes (KModes) : k-modes model
KNN (KNN) : k-nearest neighbor
KNNAnomaly (KNNAnomaly) : k-nearest neighbor anomaly detection
Laplacian (Laplacian) : Laplacian edge detection
Lasso (Lasso) : Least absolute shrinkage and selection operator
LBG (LBG) : Linde-Buzo-Gray algorithm
LDF (LDF) : Local Density Factor
LDOF (LDOF) : Local Distance-based Outlier Factor
LLE (LLE) : Locally Linear Embedding
LMNN (LMNN) : Large Margin Nearest Neighbor
LOCI (LOCI) : Local Correlation Integral
LOESS (LOESS) : Locally estimated scatterplot smoothing
LOF (LOF) : Local Outlier Factor
LoG (LoG) : Laplacian of gaussian filter
LoOP (LoOP) : Local Outlier Probability
LOWESS (LOWESS) : Locally weighted scatter plot smooth
LSA (LSA) : Latent Semantic Analysis
LSDD (LSDD) : Least-squares density difference
LSDDCPD (LSDDCPD) : LSDD for change point detection
LSIF (LSIF) : least-squares importance fitting
LTSA (LTSA) : Local Tangent Space Alignment
LVQClassifier (LVQClassifier) : Learning Vector Quantization classifier
LVQCluster (LVQCluster) : Learning Vector Quantization clustering
MAD (MAD) : Median Absolute Deviation
MADALINE (MADALINE) : Many Adaptive Linear Neuron model
MCD (MCD) : Minimum Covariance Determinant
MDS (MDS) : Multi-dimensional Scaling
MIRA (MIRA) : Margin Infused Relaxed Algorithm
MLLE (MLLE) : Modified Locally Linear Embedding
MLPClassifier (MLPClassifier) : Multi layer perceptron classifier
MLPRegressor (MLPRegressor) : Multi layer perceptron regressor
MOD (MOD) : Method of Optimal Direction
MONA (MONA) : MONothetic Analysis Clustering
MCAgent (MCAgent) : Monte Carlo agent
MT (MT) : Mahalanobis Taguchi method
MutualKNN (MutualKNN) : Mutual k-nearest-neighbor model
NAROW (NAROW) : Narrow Adaptive Regularization Of Weights
NICE (NICE) : Flow-based generative model non-linear independent component estimation
NLMeans (NLMeans) : Non-local means filter
NMF (NMF) : Non-negative matrix factorization
NNBCA (NNBCA) : Natural Neighborhood Based Classification Algorithm
NOF (NOF) : Natural Outlier Factor
OAPBPM (OAPBPM) : Online Aggregate Prank-Bayes Point Machine
OCSVM (OCSVM) : One-class support vector machine
ODIN (ODIN) : Outlier Detection using Indegree Number
OPTICS (OPTICS) : Ordering points to identify the clustering structure
ORCLUS (ORCLUS) : arbitrarily ORiented projected CLUSter generation
PAM (PAM) : Partitioning Around Medoids
PA (PA) : Passive Aggressive
PAUM (PAUM) : Perceptron Algorithm with Uneven Margins
AnomalyPCA (AnomalyPCA) : Principal component analysis for anomaly detection
DualPCA (DualPCA) : Dual Principal component analysis
KernelPCA (KernelPCA) : Kernel Principal component analysis
PCA (PCA) : Principal component analysis
PCR (PCR) : Principal component regression
Pegasos (Pegasos) : Primal Estimated sub-GrAdientSOlver for SVM
PLS (PLS) : Partial least squares regression
PLSA (PLSA) : Probabilistic latent semantic analysis
PGAgent (PGAgent) : Policy gradient agent
PRank (PRank) : Perceptron ranking
Prewitt (Prewitt) : Prewitt edge detection
ProbabilisticPCA (ProbabilisticPCA) : Probabilistic Principal component analysis
PROCLUS (PROCLUS) : PROjected CLUStering algorithm
PTile (PTile) : P-tile thresholding
QAgent (QAgent) : Q-learning agent
RANSAC (RANSAC) : Random sample consensus
GBRBM (GBRBM) : Gaussian-Bernouili Restricted Boltzmann machine
RBM (RBM) : Restricted Boltzmann machine
RBP (RBP) : Randomized Budget Perceptron
RDF (RDF) : Relative Density Factor
RDOS (RDOS) : Relative Density-based Outlier Score
Ridge (Ridge) : Ridge regressioin
RKOF (RKOF) : Robust Kernel-based Outlier Factor
RNN (RNN) : Recurrent neuralnetwork
ROCK (ROCK) : RObust Clustering using linKs
AggressiveROMMA (AggressiveROMMA) : Aggressive Relaxed Online Maximum Margin Algorithm
ROMMA (ROMMA) : Relaxed Online Maximum Margin Algorithm
RVM (RVM) : Relevance vector machine
S3VM (S3VM) : Semi-Supervised Support Vector Machine
Sammon (Sammon) : Sammon mapping
SDAR (SDAR) : Sequentially Discounting Autoregressive model
ILK (ILK) : Implicit online Learning with Kernels
SILK (SILK) : Sparse Implicit online Learning with Kernels
Slerp (Slerp) : Spherical linear interpolation
SMARegression (SMARegression) : Standardizes Major Axis regression
Snakes (Snakes) : Snakes (active contour model)
Sobel (Sobel) : Sobel edge detection
SOM (SOM) : Self-Organizing Map
SquaredLossMICPD (SquaredLossMICPD) : Squared-loss Mutual information change point detection
SST (SST) : Singular-spectrum transformation
STING (STING) : STatistical INformation Grid-based method
SVC (SVC) : Support vector clustering
SVM (SVM) : Support vector machine
SVR (SVR) : Support vector regression
SNE (SNE) : Stochastic Neighbor Embedding
tSNE (tSNE) : T-distributed Stochastic Neighbor Embedding
RuLSIF (RuLSIF) : Relative unconstrained Least-Squares Importance Fitting
uLSIF (uLSIF) : unconstrained Least-Squares Importance Fitting
UMAP (UMAP) : Uniform Manifold Approximation and Projection
VAE (VAE) : Variational Autoencoder
VAR (VAR) : Vector Autoregressive model
VBGMM (VBGMM) : Variational Gaussian Mixture Model
WeightedKNN (WeightedKNN) : Weighted K-Nearest Neighbor
XGBoost (XGBoost) : eXtreme Gradient Boosting regression
YeoJohnson (YeoJohnson) : Yeo-Johnson power transformation
rl
Properties
RLEnvironmentBase (RLEnvironmentBase) : Base class for reinforcement learning environment
RLIntRange (RLIntRange) : Integer number range state/actioin
RLRealRange (RLRealRange) : Real number range state/actioin
evaluate
Properties
cohensKappa (cohensKappa) : Returns Cohen's kappa coefficient.
fScore (fScore) : Returns F-score with macro average.
precision (precision) : Returns precision with macro average.
recall (recall) : Returns recall with macro average.
purity (purity) : Returns Purity.
mad (mad) : Returns MAD (Median Absolute Deviation).
mae (mae) : Returns MAE (Mean Absolute Error).
mape (mape) : Returns MAPE (Mean Absolute Percentage Error).
mse (mse) : Returns MSE (Mean Squared Error).
msle (msle) : Returns MSLE (Mean Squared Logarithmic Error).
r2 (r2) : Returns R2 (coefficient of determination).
rmse (rmse) : Returns RMSE (Root Mean Squared Error).
rmsle (rmsle) : Returns RMSLE (Root Mean Squared Logarithmic Error).
rmspe (rmspe) : Returns RMSPE (Root Mean Squared Percentage Error).
Exception for matrix class
new MatrixException(message:
string, value: any)
Extends Error
Parameters
message (string) Error message
Matrix class
Parameters
cols (number) Number of columns
Static Members
Returns a matrix filled with 0.
Parameters
cols (number) Number of columns
Returns
Matrix: Matrix filled with 0Returns a matrix filled with 1.
Parameters
cols (number) Number of columns
Returns
Matrix: Matrix filled with 1▸ eye(rows, cols, init = 1) Returns a identity matrix.
Parameters
cols (number) Number of columns
init (number? = 1) Diagonal values
Returns
Matrix: Identity matrix▸ random(rows, cols, min = 0, max = 1) Returns a matrix initialized uniform random values.
Parameters
cols (number) Number of columns
min (number? = 0) Minimum value of the Matrix (include)
max (number? = 1) Maximum value of the Matrix (exclude)
Returns
Matrix: Matrix initialized uniform random values▸ randint(rows, cols, min = 0, max = 1) Returns a matrix initialized uniform random integer values.
Parameters
cols (number) Number of columns
min (number? = 0) Minimum value of the Matrix (include)
max (number? = 1) Maximum value of the Matrix (include)
Returns
Matrix: Matrix initialized uniform random values▸ randn(rows, cols, myu = 0, sigma = 1) Returns a matrix initialized normal random values.
Parameters
cols (number) Number of columns
Returns
Matrix: Matrix initialized normal random valuesReturns a diagonal matrix.
Parameters
Returns
Matrix: Diagonal matrixReturns a matrix from some value.
Parameters
Returns
Matrix: Matrix from some valueReturns a matrix that replace all the elements.
Parameters
Returns
Matrix: Mapped matrix▸ resize(mat, rows, cols, init = 0) Return resized matrix.
Parameters
init (number? = 0) Value of the extended region
Returns
Matrix: Resized matrix▸ repeat(mat, n, axis = 0) Returns a matrix that repeat the elements n times along the axis.
Parameters
axis (number? = 0) Axis to be repeated
Returns
Matrix: Repeated matrixReturns a matrix concatenated this and m.
Parameters
axis (number? = 0) Axis to be concatenated
Returns
Matrix: Concatenated matrixReturns a matrix that add two values.
Parameters
Returns
Matrix: Added matrixReturns a matrix that subtract two values.
Parameters
Returns
Matrix: Subtracted matrixReturns a matrix that multiplies by two values element-wise.
Parameters
Returns
Matrix: Multiplied matrixReturns a matrix that divides by two values element-wise.
Parameters
Returns
Matrix: Divided matrixReturns a matrix that takes a remainder divided by two values element-wise.
Parameters
Returns
Matrix: Remainder matrixReturns a matrix that takes logical AND two values.
Parameters
Returns
Matrix: Logical AND matrixReturns a matrix that takes logical OR two values.
Parameters
Returns
Matrix: Logical OR matrixReturns a matrix that takes bitwise AND two values.
Parameters
Returns
Matrix: Bitwise AND matrixReturns a matrix that takes bitwise OR two values.
Parameters
Returns
Matrix: Bitwise OR matrixReturns a matrix that takes bitwise XOR two values.
Parameters
Returns
Matrix: Bitwise XOR matrixInstance Members
Dimension of the matrix.
dimension
Type: number
Number of all elements in the matrix.
length
Type: number
Number of rows of the matrix.
rows
Type: number
Number of columns of the matrix.
cols
Type: number
Transpose matrix.
t
Type: Matrix
Iterate over the elements.
iterator()
Returns a nested array represented this matrix.
Returns
Array<Array<number>>: Nested arrayReturns the only element.
Returns
number: The only elementReturns a string represented this matrix.
Returns
string: String represented this matrixReturns a copy of this matrix.
Parameters
dst (Matrix?) Destination matrix
Returns
Matrix: Copied matrixReturns this matrix is equals to the others.
Parameters
tol (number? = 0) Tolerance to be recognized as the same
Returns
boolean: true if equalReturns a value at the position.
Parameters
Returns
number: Value at the positionSet a value at the position.
Parameters
r ((number | [number, number])) Row index or index values. If this value is an array, the next argument should be the value to be set
Returns a row matrix at r.
Parameters
Returns
Matrix: Row selected matrixReturns a col matrix at c.
Parameters
Returns
Matrix: Column selected matrix▸ slice(from, to, axis = 0) Returns sliced matrix.
Parameters
axis (number? = 0) Axis to be sliced
Returns
Matrix: Sliced matrix▸ block(rows_from?, cols_from?, rows_to?, cols_to?) Returns the sub-matrix corresponding to position.
Parameters
rows_from (number?) Start row index
cols_from (number?) Start column index
rows_to (number?) End row index(exclusive)
cols_to (number?) End column index(exclusive)
Returns
Matrix: Sub matrixRemove specified indexes.
Parameters
axis (number? = 0) Axis to be removed
▸ removeIf(cond, axis = 0) Remove specified indexes.
Parameters
cond (function (Matrix): boolean) Remove condition function. Remove if it returns
true axis (number? = 0) Axis to be removed
▸ sample(n, axis = 0, duplicate = false) Returns a matrix that sampled along the axis.
Parameters
axis (number? = 0) Axis to be sampled
duplicate (number? = false) Allow duplicate index or not
Returns
[Matrix, Array<number>]: Sampled matrix and its original indexesFill in all the elements with the value.
Parameters
Iterate over all the elements and replace the value.
Parameters
Iterate over all the elements.
Parameters
Returns transpose matrix.
Returns
Matrix: Transposed matrixReturns adjoint matrix.
Returns
Matrix: Adjoint matrixFlip values along the axis.
Parameters
axis (number? = 0) Axis to be flipped
Swap the index a and b along the axis.
Parameters
axis (number? = 0) Axis to be swapped
Sort values along the axis.
Parameters
axis (number? = 0) Axis to be sorted
Returns
Array<number>: Original index.Shuffle values along the axis.
Parameters
axis (number? = 0) Axis to be shuffled
Returns
Array<number>: Original index.▸ unique(axis = 0, tol = 0) Make it unique in the specified axis.
Parameters
axis (number? = 0) Axis to be uniqued
tol (number? = 0) Tolerance to be recognized as the same
Returns
Array<number>: Selected indexes▸ resize(rows, cols, init = 0) Resize this matrix.
Parameters
init (number? = 0) Value of the extended region
Repeat the elements n times along the axis this.
Parameters
axis (number? = 0) Axis to be repeated
Concatenate this and m.
Parameters
axis (number? = 0) Axis to be concatenated
▸ reduce(cb, init?, axis = -1, keepdims = null) Returns a matrix reduced along the axis with the callback function.
Parameters
init (any?) Initial value
axis ((number | Array<number>)? = -1) Axis to be reduced. If negative, reduce along all elements.
keepdims (boolean? = null) Keep dimensions or not. If null, negative axis retuns number and other axis returns Matrix.
Returns
(Matrix | number): Reduced matrix or valueDetermines whether all the members of a matrix satisfy the specified test.
Parameters
axis (number? = -1) Axis to be reduced
Returns
(boolean | Matrix): Reduced value or matrixDetermines whether the specified callback function returns true for any element of a matrix.
Parameters
axis (number? = -1) Axis to be reduced
Returns
(boolean | Matrix): Reduced value or matrixReturns maximum values along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns the maximum value of the all element.
Returns
(Matrix | number): Maximum valuesReturns minimum values along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns the minimum value of the all element.
Returns
(Matrix | number): Minimum valuesReturns quantile values along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns the quantile value of the all element.
Returns
(Matrix | number): Quantile valuesReturns maximum indexes along the axis.
Parameters
axis (number) Axis to be reduced
Returns
Matrix: Argmax valuesReturns minimum indexes along the axis.
Parameters
axis (number) Axis to be reduced
Returns
Matrix: Argmin valuesReturns summation values along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns a summation value of the all element.
Returns
(Matrix | number): Summation valuesReturns means along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns a mean value of the all element.
Returns
(Matrix | number): Mean valuesReturns producted values along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns a producted value of the all element.
Returns
(Matrix | number): Producted values▸ variance(axis = -1, ddof = 0) Returns variances along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns a variance of the all element.
ddof (number? = 0) Delta Degrees of Freedom
Returns
(Matrix | number): Variance values▸ std(axis = -1, ddof = 0) Returns standard deviations along the axis.
Parameters
axis (number? = -1) Axis to be reduced. If negative, returns a standard deviation of the all element.
ddof (number? = 0) Delta Degrees of Freedom
Returns
(Matrix | number): Standard deviation valuesReturns if this is square matrix or not.
Returns
boolean: true if this is square matrixReturns if this is diagonal matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is diagonal matrixReturns if this is identity matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0 or 1
Returns
boolean: true if this is identity matrixReturns if this is zero matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is zero matrixReturns if this is triangular matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is triangular matrix▸ isLowerTriangular(tol = 0) Returns if this is lower triangular matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is lower triangular matrix▸ isUpperTriangular(tol = 0) Returns if this is upper triangular matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is upper triangular matrixReturns if this is symmetric matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as the same
Returns
boolean: true if this is symmetric matrixReturns if this is hermitian matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as the same
Returns
boolean: true if this is hermitian matrixReturns if this is alternating matrix or not.
Parameters
tol (number? = 0) Tolerance within which sign-reversed values are recognized as the same
Returns
boolean: true if this is alternating matrix▸ isSkewHermitian(tol = 0) Returns if this is skew-hermitian matrix or not.
Parameters
tol (number? = 0) Tolerance within which sign-reversed values are recognized as the same
Returns
boolean: true if this is skew-hermitian matrixReturns if this is regular matrix or not.
Parameters
tol (number? = 0) Tolerance to recognize the determinant as 0
Returns
boolean: true if this is regular matrixReturns if this is normal matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as the same
Returns
boolean: true if this is normal matrixReturns if this is orthogonal matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0 or 1
Returns
boolean: true if this is orthogonal matrixReturns if this is unitary matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0 or 1
Returns
boolean: true if this is unitary matrixReturns if this is nilpotent matrix or not.
Parameters
tol (number? = 0) Tolerance to be recognized as 0
Returns
boolean: true if this is nilpotent matrixReturns diagonal elements.
Returns
Array<number>: Diagonal valuesReturns a trace.
Returns
number: Trace valueReturns a p-norm.
Parameters
Returns
number: Entry-wise normReturns induced norm.
Parameters
Returns
number: Induced normReturns spectral norm.
Returns
number: Spectral normReturns a entry-wise norm
Parameters
Returns
number: Entry-wise normReturns frobenius norm.
Returns
number: Frobenius normReturns max norm.
Returns
number: Max normReturns schatten norm.
Parameters
Returns
number: Schatten normReturns nuclear norm.
Returns
number: Nuclear normReturns a rank of this matrix.
Parameters
tol (number? = 0) Tolerance to be recognized as the same
Returns
number: Rank of this matrixReturns a determinant.
Returns
number: Determinant valueReturns a spectral radius.
Returns
number: Spectral radiusMultiply all elements by -1 in-place.
negative()
Set all elements to their logical NOT values.
not()
Set all elements to their bitwise NOT values.
bitnot()
Set all elements to their absolute values.
abs()
Set all elements to their rounded values.
round()
Set all elements to their floored values.
floor()
Set all elements to their ceil values.
ceil()
Set all elements to their left shift values.
Parameters
Set all elements to their right shift values.
Parameters
Set all elements to their unsigned right shift values.
Parameters
▸ broadcastOperate(o, fn) Apply function for all elements with broadcasting.
Parameters
Apply function to the position.
Parameters
Returns
number: Old valueAdd a value or matrix.
Parameters
Add a value to the position.
Parameters
r (number) Index of the row to add the value to
c (number) Index of the column to add the value to
Returns
number: Old valueSubtract a value or matrix.
Parameters
Subtract this matrix from a value or matrix.
Parameters
Subtract a value from the value at the position.
Parameters
r (number) Index of the row to subtract the value to
c (number) Index of the column to subtract the value to
Returns
number: Old valueSubtract the value at the position from a value.
Parameters
r (number) Index of the row whose value is to be subtracted
c (number) Index of the column whose value is to be subtracted
v (number) Value to be subtracted
Returns
number: Old valueMultiplies by a value element-wise.
Parameters
Multiplies a value to the position.
Parameters
r (number) Index of the row to multiply the value by
c (number) Index of the column to multiply the value by
Returns
number: Old valueDivides by a value element-wise.
Parameters
Divides a value by this matrix element-wise.
Parameters
Divides the value at the position by a value.
Parameters
r (number) Index of the row to divide the value by
c (number) Index of the column to divide the value by
Returns
number: Old valueDivides a value by the value at the position.
Parameters
r (number) Index of the row whose value is to be divided
c (number) Index of the column whose value is to be divided
v (number) Value to be divided
Returns
number: Old valueTake a remainder divided by a value element-wise.
Parameters
Take a remainder divided a value by this matrix element-wise.
Parameters
Take a remainder divided the value at the position by a value.
Parameters
r (number) Index of the row to divide the value by
c (number) Index of the column to divide the value by
Returns
number: Old valueTake a remainder divided a value by the value at the position.
Parameters
r (number) Index of the row whose value is to be divided
c (number) Index of the column whose value is to be divided
v (number) Value to be divided
Returns
number: Old valueTake a logical AND with a value or matrix.
Parameters
Take logical AND with a value to the position.
Parameters
r (number) Index of the row to take a logical AND with
c (number) Index of the column to take a logical AND with
v (number) Value to take a logical AND
Returns
number: Old valueTake a logical OR with a value or matrix.
Parameters
Take logical OR with a value to the position.
Parameters
r (number) Index of the row to take a logical OR with
c (number) Index of the column to take a logical OR with
v (number) Value to take a logical OR
Returns
number: Old valueTake a bitwise AND with a value or matrix.
Parameters
Take bitwise AND with a value to the position.
Parameters
r (number) Index of the row to take a bitwise AND with
c (number) Index of the column to take a bitwise AND with
v (number) Value to take a bitwise AND
Returns
number: Old valueTake a bitwise OR with a value or matrix.
Parameters
Take bitwise OR with a value to the position.
Parameters
r (number) Index of the row to take a bitwise OR with
c (number) Index of the column to take a bitwise OR with
v (number) Value to take a bitwise OR
Returns
number: Old valueTake a bitwise XOR with a value or matrix.
Parameters
Take bitwise XOR with a value to the position.
Parameters
r (number) Index of the row to take a bitwise XOR with
c (number) Index of the column to take a bitwise XOR with
v (number) Value to take a bitwise XOR
Returns
number: Old valueReturns a matrix product value.
Parameters
Returns
Matrix: Producted matrixReturns a matrix product of the transposed matrix of this and input.
Parameters
Returns
Matrix: Producted matrixReturns kronecker producted value.
Parameters
Returns
Matrix: Kronecker producted matrix▸ convolute(kernel, normalize = true) Convoluted by a kernel.
Parameters
normalize (boolean? = true) Normalize kernel or not
Returns a inverse matrix.
Returns
Matrix: Inversed matrixReturns a inverse matrix for lower triangular matrix.
Returns
Matrix: Inversed matrixReturns a inverse matrix for upper triangular matrix.
Returns
Matrix: Inversed matrixReturns a inverse matrix by row reduction.
Returns
Matrix: Inversed matrixReturns a inverse matrix by LU decompositioin.
Returns
Matrix: Inversed matrixReturns a pseudo inverse matrix.
Returns
Matrix: pseudo inverse matrixReturns a pseudo inverse matrix.
Returns
Matrix: pseudo inverse matrixReturns a Moore–Penrose inverse matrix by QR decomposition.
Returns
Matrix: Moore–Penrose inverse matrixReturns a Moore–Penrose inverse matrix by SVD decomposition.
Returns
Matrix: Moore–Penrose inverse matrix▸ pseudoInvBenIsraelCohen() Returns a Moore–Penrose inverse matrix by Ben-Israel and Cohen iterative method.
pseudoInvBenIsraelCohen():
MatrixReturns
Matrix: Moore–Penrose inverse matrixReturns a square root of this matrix.
Returns
Matrix: Squared matrixReturns a power of this matrix.
Parameters
p (number) Power exponent value
Returns
Matrix: Powered matrixReturns a exponential matrix
Returns
Matrix: Exponential matrixReturns a logarithm matrix
Returns
Matrix: Logarithm matrixReturns a covariance matrix.
Parameters
ddof (number? = 0) Delta Degrees of Freedom
Returns
Matrix: Covariance matrixReturns a gram matrix.
Returns
Matrix: Gram matrixReturns a solved value A of a equation XA=B.
Parameters
b (Matrix) Dependent variable values
Returns
Matrix: Solved matrix▸ solveLowerTriangular(b) Returns a solved value for lower triangular matrix.
Parameters
b (Matrix) Dependent variable values
Returns
Matrix: Solved matrix▸ solveUpperTriangular(b) Returns a solved value for upper triangular matrix.
Parameters
b (Matrix) Dependent variable values
Returns
Matrix: Solved matrix▸ solveJacobi(b, maxIteration = 1.0e3) Returns a solved value with Jacobi method.
Parameters
b (Matrix) Dependent variable values
maxIteration (number? = 1.0e3) Maximum iteration
Returns
Matrix: Solved matrix▸ solveGaussSeidel(b, maxIteration = 1.0e3) Returns a solved value with Gauss-Seidel method.
Parameters
b (Matrix) Dependent variable values
maxIteration (number? = 1.0e3) Maximum iteration
Returns
Matrix: Solved matrix▸ solveSOR(b, w, maxIteration = 1.0e3) Returns a solved value with Successive Over-Relaxation method.
Parameters
b (Matrix) Dependent variable values
maxIteration (number? = 1.0e3) Maximum iteration
Returns
Matrix: Solved matrixReturns a bidiagonal matrix.
Returns
Matrix: Bidiagonal matrix▸ bidiagHouseholder(return_uv = false) Returns a bidiagonal matrix by Householder method.
Parameters
return_uv (boolean? = false) Returns orthogonal matrixes
Returns
(Matrix | [Matrix, Matrix, Matrix]): Bidiagonal matrix, or Bidiagonal matrix and orthogonal matrixesReturns a tridiagonal matrix.
Returns
Matrix: Tridiagonal matrix▸ tridiagHouseholder(return_u = false) Returns a tridiagonal matrix.
Parameters
return_u (boolean? = false) Returns orthogonal matrix
Returns
(Matrix | [Matrix, Matrix]): Tridiagonal matrix, or Tridiagonal matrix and orthogonal matrixReturns a tridiagonal matrix.
Parameters
k (number? = 0) Number of iterations
Returns
Matrix: Tridiagonal matrixReturns a hessenberg matrix.
Returns
Matrix: Hessenberg matrix▸ hessenbergArnoldi(k = 0) Returns a hessenberg matrix.
Parameters
k (number? = 0) Number of iterations
Returns
Matrix: Hessenberg matrix▸ balancingSinkhornKnopp() Returns a LU decomposition.
Returns
[Matrix, Matrix]: Lower triangular matrix and upper triangular matrixReturns a QR decomposition.
Returns
[Matrix, Matrix]: Orthogonal matrix and upper triangular matrixReturns a QR decomposition by Gram-Schmidt method.
Returns
[Matrix, Matrix]: Orthogonal matrix and upper triangular matrixReturns a QR decomposition by Householder method.
Returns
[Matrix, Matrix]: Orthogonal matrix and upper triangular matrixReturns singular values.
Returns
Array<number>: Singular valuesReturns a singular value decomposition by eigen decomposition.
Returns
[Matrix, Array<number>, Matrix]: Unitary matrix and singular valuesReturns a singular value decomposition by Golub-Kahan method.
Returns
[Matrix, Array<number>, Matrix]: Unitary matrix and singular valuesReturns a cholesky decomposition.
Returns
Matrix: Cholesky decomposition matrixReturns a cholesky decomposition by Gaussian algorithm.
Returns
Matrix: Cholesky decomposition matrixReturns a cholesky decomposition by Banachiewicz algorithm.
choleskyBanachiewicz():
MatrixReturns
Matrix: Cholesky decomposition matrixReturns a cholesky decomposition by Crout algorithm.
Returns
Matrix: Cholesky decomposition matrixReturns a modified cholosky decomposition.
Returns
[Matrix, Array<number>]: Cholesky decomposition matrix and diagonal matrixReturns schur decomposition.
Returns
[Matrix, Matrix]: Schur decomposition matrix▸ schurQR(shift = 'single') Returns schur decomposition by QR decomposition.
Parameters
shift (("no" | "single")? = 'single') Shifting type
Returns
[Matrix, Matrix]: Schur decomposition matrixReturns rank factorization.
Returns
[Matrix, Matrix]: Rank factorization matrixReturns eigenvalues and eigenvectors.
Returns
[Array<number>, Matrix]: Eigenvalues and eigenvectors. Eigenvectors correspond to each column of the matrix.Returns eigenvectors.
Returns
Matrix: Eigenvectors. Eigenvectors correspond to each column of the matrix.Returns eigenvalues by Bi-section.
Returns
Array<number>: Eigenvalues▸ eigenValuesLR(maxIteration = 1.0e5) Returns eigenvalues by LU decomposition.
Parameters
maxIteration (number? = 1.0e5) Maximum iteration
Returns
Array<number>: Eigenvalues▸ eigenValuesQR(maxIteration = 1.0e6) Returns eigenvalues by QR decomposition.
Parameters
maxIteration (number? = 1.0e6) Maximum iteration
Returns
Array<number>: Eigenvalues▸ eigenJacobi(maxIteration = 1.0e6) Returns eigenvalues and eigenvectors.
Parameters
maxIteration (number? = 1.0e6) Maximum iteration
Returns
[Array<number>, Matrix]: Eigenvalues and eigenvectors. Eigenvectors correspond to each column of the matrix.▸ eigenPowerIteration(maxIteration = 1.0e4) Returns the maximum eigenvalue and its eigenvector.
Parameters
maxIteration (number? = 1.0e4) Maximum iteration
Returns
[number, Matrix]: Maximum eigenvalue and its eigenvector▸ eigenInverseIteration(ev = 0.0, maxIteration = 1.0e4) Returns the nearest eigenvalue and its eigenvector to the specified value.
Parameters
ev (number? = 0.0) Target value
maxIteration (number? = 1.0e4) Maximum iteration
Returns
[number, Matrix]: Eigenvalue and eigenvectorTensor class
Parameters
Static Members
Returns a tensor filled with 0.
Parameters
Returns
Tensor: Tensor filled with 0Returns a tensor filled with 1.
Parameters
Returns
Tensor: Tensor filled with 1▸ random(size, min = 0, max = 1) Returns a tensor initialized uniform random values.
Parameters
min (number? = 0) Minimum value of the Tensor
max (number? = 1) Maximum value of the Tensor
Returns
Tensor: Tensor initialized uniform random values▸ randn(size, myu = 0, sigma = 1) Returns a tensor initialized normal random values.
Parameters
myu (number? = 0) Mean value of the Tensor
sigma (number? = 1) Variance value of the Tensor
Returns
Tensor: Tensor initialized normal random valuesReturns a tensor from some value.
Parameters
Returns
Tensor: Tensor from some valueInstance Members
Dimension of the tensor.
dimension
Type: number
Number of all elements in the tensor.
length
Type: number
Iterate over the elements.
iterator()
Returns a nested array represented this tensor.
Returns
Array<number>: Nested arrayReturns a string represented this tensor.
Returns
string: String represented this tensorReturns a Matrix if the dimension of this tensor is 2.
Returns
Matrix: MatrixThrows
Returns the only element.
Returns
number: The only elementReturns a copy of this tensor.
Returns
Tensor: Copied tensorReturns this tensor is equals to the others.
Parameters
Returns
boolean: true if equalReturns value at the index position.
Parameters
Returns
number: The valueReturns tensor at the index position.
Parameters
Returns
Tensor: Sub tensorSet the value at the specific position.
Parameters
Returns the sub-tensor corresponding to position i in the first dimension of this.
Parameters
Returns
Tensor: Selected tensor▸ slice(from, to, axis = 0) Returns a tensor sliced by first dimension.
Parameters
Returns
Tensor: Sliced tensorFill in all the elements with the value.
Parameters
Iterate over all the elements and replace the value.
Parameters
Iterate over all the elements.
Parameters
Returns a tensor transposed along the axis.
Parameters
axises (...number) Selected axises
Returns
Tensor: Transposed tensorFlip values along the axis.
Parameters
axis (number? = 0) Axis to be flipped
Shuffle along the axis.
Parameters
▸ resize(sizes, init = 0) Resize this tensor.
Parameters
init (number? = 0) Value of the extended region
Reshape this as the sizes.
Parameters
sizes (...number) New sizes for each dimension
Repeat the elements n times along the axis this.
Parameters
axis (number? = 0) Axis to be repeated
Concatenate this and t.
Parameters
axis (number? = 0) Axis to be concatenated
▸ reduce(cb, init?, axis = -1, keepdims = false) Returns a tensor reduced along the axis with the callback function.
Parameters
init (any?) Initial value
axis ((number | Array<number>)? = -1) Axis to be reduced. If negative, reduce along all elements.
keepdims (boolean? = false) Keep dimensions or not.
Returns
(Tensor | number): Reduced tensor or value▸ broadcastOperate(o, fn) Apply function for all elements with broadcasting.
Parameters
Apply function to the position.
Parameters
Returns
number: Old valueReturns a tensor product value.
Parameters
Returns
Tensor: Producted tensorException for graph class
new GraphException(message:
string, value: any)
Extends Error
Parameters
message (string) Error message
Edge of graph
Parameters
from (number) Index of the starting node of the edge
to (number) Index of the end node of the edge
value (unknown? = null) Value of the edge
direct (boolean? = false) true if the edge is direct
Graph class
new Graph(nodes: (
number |
Array<unknown>)?, edges: any)
Parameters
nodes ((number | Array<unknown>)? = 0) Number of nodes or values of nodes
Static Members
Returns graph from adjacency matrix.
fromAdjacency(mat: any):
GraphParameters
Returns
Graph: Graph from adjacency matrixReturns complete graph.
Parameters
Returns
Graph: Complete graph▸ completeBipartite(n, m) Returns complete bipartite graph.
Parameters
n (number) Size of the first group
m (number) Size of the second group
Returns
Graph: Complete bipartite graph▸ cycle(n, direct = false) Returns cycle graph.
Parameters
direct (boolean? = false) Direct graph or not
Returns
Graph: Cycle graphReturns wheel graph.
Parameters
Returns
Graph: Wheel graphReturns windmill graph.
Parameters
k (number) Size of the sub complete graph
n (number) Number of the sub complete graph
Returns
Graph: Windmill graphReturns named graph
fromName(name: (
"balaban 10 cage" |
"bidiakis cube" |
"biggs smith" |
"brinkmann" |
"bull" |
"butterfly" |
"chvatal" |
"clebsch" |
"coxeter" |
"desargues" |
"diamond" |
"durer" |
"errera" |
"folkman" |
"foster" |
"franklin" |
"frucht" |
"goldner-harary" |
"golomb" |
"gray" |
"grotzsch" |
"harries" |
"heawood" |
"herschel" |
"hoffman" |
"holt" |
"kittell" |
"markstrom" |
"mcgee" |
"meredith" |
"mobius kantor" |
"moser spindle" |
"nauru" |
"pappus" |
"petersen" |
"poussin" |
"robertson" |
"shrikhande" |
"sousselier" |
"sylvester" |
"tutte" |
"tutte coxeter" |
"wagner" |
"wells")):
GraphParameters
name (("balaban 10 cage" | "bidiakis cube" | "biggs smith" | "brinkmann" | "bull" | "butterfly" | "chvatal" | "clebsch" | "coxeter" | "desargues" | "diamond" | "durer" | "errera" | "folkman" | "foster" | "franklin" | "frucht" | "goldner-harary" | "golomb" | "gray" | "grotzsch" | "harries" | "heawood" | "herschel" | "hoffman" | "holt" | "kittell" | "markstrom" | "mcgee" | "meredith" | "mobius kantor" | "moser spindle" | "nauru" | "pappus" | "petersen" | "poussin" | "robertson" | "shrikhande" | "sousselier" | "sylvester" | "tutte" | "tutte coxeter" | "wagner" | "wells")) Name of the graph
Returns
Graph: Named graphInstance Members
Number of nodes
order
Type: number
Number of edges
size
Type: number
Nodes
nodes
Type: Array<unknown>
Returns a string of DOT format.
Returns
string: String of DOT formatReturns a string represented this graph.
Returns
string: String represented this graphReturns a copy of this graph.
Returns
Graph: Copied grpah▸ degree(k, undirect = true, direct = true) Return degree of the node.
Parameters
k (number) Index of target node
undirect ((boolean | "in" | "out")? = true) Count undirected edges. If
in or
out is specified, only direct edges are counted and
direct parameter is ignored.
direct ((boolean | "in" | "out")? = true) Count directed edges
Returns
number: Degree of the node▸ adjacencies(k, undirect = true, direct = true) Return indexes of adjacency nodes.
Parameters
k (number) Index of target node
undirect ((boolean | "in" | "out")? = true) Check undirected edges. If
in or
out is specified, only direct edges are checked and
direct parameter is ignored.
direct ((boolean | "in" | "out")? = true) Check directed edges
Returns
Array<number>: Indexes of adjacency nodesReturns indexes of each components.
components()
▸ biconnectedComponents() Returns indexes of each biconnected components.
biconnectedComponents()
Returns diameter of this graph.
Returns
number: DiameterReturns eccentricity at k of this graph.
Parameters
k (number) Index of target node
Returns
number: EccentricityReturns radius of this graph.
Returns
number: RadiusReturns indexes of center of this graph.
Returns
Array<number>: Indexes of centerReturns girth of this graph.
Returns
number: GirthReturns index of cliques.
Parameters
Returns chromatic number of this graph.
Returns
number: Chromatic number▸ chromaticNumberWelchPowell() Returns chromatic number of this graph with Welch-Powell algorithm.
chromaticNumberWelchPowell():
numberReturns
number: Chromatic numberReturns chromatic index of this graph.
Returns
number: Chromatic indexReturns indexes of articulation (cut) nodes.
Returns
Array<number>: Indexes of articulation nodes▸ articulationsEachNodes() Returns indexes of articulation (cut) nodes with checking each node.
Returns
Array<number>: Indexes of articulation nodesReturns indexes of articulation (cut) nodes with checking lowlinks.
Returns
Array<number>: Indexes of articulation nodesReturns edges of bridge.
Returns
Array<Edge>: Bridge edgesReturns edges of bridge with checking lowlinks.
Returns
Array<Edge>: Bridge edgesAdd the node.
addNode(value: unknown?)
Parameters
value (unknown?) Value of the node
Returns the node value.
Parameters
Returns
(unknown | Array<unknown>): Node valueRemove the node.
Parameters
Remove all nodes.
clearNodes()
▸ addEdge(from, to, value = null, direct = false) Add the edge.
Parameters
from (number) Index of the starting node of the edge
to (number) Index of the end node of the edge
value (unknown? = null) Value of the edge
direct (boolean? = false) true if the edge is direct
▸ getEdges(from, to, undirect = true, direct = true) Returns the edges.
Parameters
from (number) Index of the starting node of the edge
to (number) Index of the end node of the edge
undirect ((boolean | "forward" | "backward")? = true) Get undirected edges or not. If
forward or
backward is specified, only direct edges are get and
direct parameter is ignored.
direct ((boolean | "forward" | "backward")? = true) Get directed edges or not
Returns
Array<Edge>: Edges between from and to▸ removeEdges(from, to, direct = null) Remove the edges.
Parameters
from (number) Index of the starting node of the edge
to (number) Index of the end node of the edge
direct ((boolean | null)? = null) null to remove direct and undirect edges,
true to remove only direct edges,
false to remove only undirect edges.
Remove all edges.
clearEdges()
Returns adjacency matrix
adjacencyMatrix()
▸ adjacencyList(direct = 'both') Returns adjacency list
adjacencyList(direct: ("both" | "in" | "out")?)
Parameters
direct (("both" | "in" | "out")? = 'both') Indegree or outdegree
▸ degreeMatrix(direct = 'both') Returns degree matrix.
degreeMatrix(direct: ("both" | "in" | "out")?)
Parameters
direct (("both" | "in" | "out")? = 'both') Indegree or outdegree
Returns laplacian matrix.
laplacianMatrix()
Returns if this is null graph or not.
Returns
boolean: true if this is null graphReturns if this is edgeless graph or not.
Returns
boolean: true if this is edgeless graphReturns if this is undirected graph or not.
Returns
boolean: true if this is undirected graphReturns if this is directed graph or not.
Returns
boolean: true if this is directed graphReturns if this is mixed graph or not.
Returns
boolean: true if this is mixed graphReturns if this is oriented graph or not.
Returns
boolean: true if this is oriented graphReturns if this is weighted graph or not.
Returns
boolean: true if this is weighted graphReturns if this is simple graph or not.
Returns
boolean: true if this is simple graphReturns if this is connected graph or not.
Returns
boolean: true if this is connected graphReturns if this is biconnected graph or not.
Returns
boolean: true if this is biconnected graphReturns if this is tree or not.
Returns
boolean: true if this is treeReturns if this is forest or not.
Returns
boolean: true if this is forestReturns if this is bipartite graph or not.
Returns
boolean: true if this is bipartite graphReturns if this is complete graph or not.
Returns
boolean: true if this is complete graphReturns if this is regular graph or not.
Parameters
n (number? = null) Degree of vertices
Returns
boolean: true if this is regular graphReturns if this is plainer graph or not.
Returns
boolean: true if this is plainer graphReturns if this is plainer graph or not with add-vertex algorithm.
Hopcroft, J. and Tarjan, R. "Efficient Planarity Testing", J. ACM, Vol. 21, No. 4, pp. 549-568 (1974) 西関 隆夫. "32. グラフの平面性判定法", 情報処理, Vol. 24, No. 4, pp. 521-528 (1983) K. S. Booth, "Testing for the Consecutive Ones Property, Interval Graphs, and Graph Planarity Using PQ-Tree Algorithms", Journal of computer and system sciences, 13, pp. 335-379 (1976)
Returns
boolean: true if this is plainer graphReturns if this is symmetric graph or not.
Returns
boolean: true if this is symmetric graphReturns if this is directed acyclic graph or not.
Returns
boolean: true if this is directed acyclic graphReturns if this is separable graph or not.
Returns
boolean: true if this is separable graphReturns if this is Eulerian graph or not.
Returns
boolean: true if this is Eulerian graphReturns if this is semi-Eulerian graph or not.
Returns
boolean: true if this is semi-Eulerian graphReturns if this is Hamiltonian graph or not.
Returns
boolean: true if this is Hamiltonian graphReturns if this is semi-Hamiltonian graph or not.
Returns
boolean: true if this is semi-Hamiltonian graphReturns if this has cycle or not.
Returns
boolean: true if this has cycleReturns if this has cycle or not with depth-first search.
Returns
boolean: true if this has cycleReturns if this has cycle or not with checking each node.
Returns
boolean: true if this has cycleReturns graph of directed edges converted to undirected.
Returns
Graph: Undirected graphReturns a graph with multiple edges and loops removed.
Returns
Graph: Simple graphReturns (sub) graph isomorphism maps from 'g' to this (sub) graph.
Parameters
Returns (sub) graph isomorphism maps from 'g' to this (sub) graph with Ullmann algorithm.
isomorphismUllmann(g:
Graph)
Parameters
Returns (sub) graph isomorphism maps from 'g' to this (sub) graph with VF2 algorithm.
Parameters
Returns induced sub graph.
Parameters
Returns
Graph: Induced sub graphReturns complement graph.
Returns
Graph: Complement graphReturns line graph.
Returns
Graph: Line graphContract this graph.
Parameters
Subdivision this graph.
Parameters
Cleave the node.
Parameters
Take the disjoint union of this graph and other graph.
Parameters
Substitute other graph at the node.
Parameters
Take the cartesian product of this graph and other graph.
Parameters
Returns
Graph: Cartesian producted graphTake the tensor product of this graph and other graph.
Parameters
Returns
Graph: Tensor producted graphTake the strong product of this graph and other graph.
Parameters
Returns
Graph: Strong producted graph▸ lexicographicProduct(g) Take the lexicographic product of this graph and other graph.
Parameters
Returns
Graph: Lexicographic producted graphReturns shortest path.
Parameters
from (number?) Index of start nodes
▸ shortestPathBreadthFirstSearch(from) Returns shortest path with breadth first search algorithm.
Parameters
from (number) Index of start node
Returns
Array<{length: number, prev: number, path: Array<number>}>: Shortest length and path for all nodes▸ shortestPathDijkstra(from) Returns shortest path with Dijkstra's algorithm.
Parameters
from (number) Index of start node
Returns
Array<{length: number, prev: number, path: Array<number>}>: Shortest length and path for all nodes▸ shortestPathBellmanFord(from) Returns shortest path with Bellman–Ford algorithm.
Parameters
from (number) Index of start node
Returns
Array<{length: number, prev: number, path: Array<number>}>: Shortest length and path for all nodes▸ shortestPathFloydWarshall() Returns shortest path with Floyd–Warshall algorithm.
shortestPathFloydWarshall()
Returns minimum spanning tree.
minimumSpanningTree():
GraphReturns
Graph: Minimum spanning tree▸ minimumSpanningTreePrim() Returns minimum spanning tree with Prim's algorithm.
minimumSpanningTreePrim():
GraphReturns
Graph: Minimum spanning tree▸ minimumSpanningTreeKruskal() Returns minimum spanning tree with Kruskal's algorithm.
minimumSpanningTreeKruskal():
GraphReturns
Graph: Minimum spanning tree▸ minimumSpanningTreeBoruvka() Returns minimum spanning tree with Borůvka's algorithm.
minimumSpanningTreeBoruvka():
GraphReturns
Graph: Minimum spanning treeReturns Hamiltonian path
hamiltonianPath(from:
number?)
Parameters
from (number?) Index of start node
▸ hamiltonianPathDynamicProgramming(from?) Returns Hamiltonian path with dynamic programming
hamiltonianPathDynamicProgramming(from:
number?)
Parameters
from (number?) Index of start node
Returns Hamiltonian cycle
hamiltonianCycle()
Returns cut size.
Parameters
Returns
number: Cut sizeReturns minimum cut.
Parameters
minv (number? = 1) Minimum number for subset
▸ mincutBruteForce(minv = 1) Returns minimum cut.
mincutBruteForce(minv:
number?)
Parameters
minv (number? = 1) Minimum number for subset
▸ mincutStoerWagner(minv = 1, startnode = 0) Returns minimum cut.
Parameters
minv (number? = 1) Minimum number for subset
startnode (number? = 0) Start node index
▸ mincutKargers(minv = 1, trials = null) Returns minimum cut.
Parameters
minv (number? = 1) Minimum number for subset
trials (number? = null) Trial count
Returns bisection cut.
bisectionSpectral()
Complex number
Parameters
real (number? = 0) Real number
imag (number? = 0) Imaginary number
Instance Members
Imaginary value.
imaginary
Type: number
Returns absolute value.
Returns
number: Absolute numberReturns conjugate value.
Returns
Complex: Conjugate numberReturns added value.
Parameters
Returns
Complex: Added complex numberReturns subtracted value.
Parameters
Returns
Complex: Subtracted complex numberReturns multiplicated value.
Parameters
Returns
Complex: Multiplicated complex numberReturns divided value.
Parameters
Returns
Complex: Divided complex numberReturns sqare root values.
Returns
[Complex, Complex]: Sqare root complex numbersReturns value of complex exponential function.
Returns
Complex: Exponential valueReturns value of complex log function.
Returns
Complex: Principal log valueA2C agent
Parameters
resolution (number) Resolution of actions
procs (number) Number of processes
layers (Array<LayerObject>) Network layers
optimizer (string) Optimizer of the network
Instance Members
Returns a action.
Parameters
state (Array<any>) Current states
Returns
Array<any>: Action▸ update(done, learning_rate, batch) Update model.
Parameters
learning_rate (number) Learning rate
Returns
number: Loss valueAngle-based Outlier Detection
Parameters
k (number? = Infinity) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesLower-bound for the Angle-based Outlier Detection
Parameters
k (number? = 10) Number of neighborhoods
l (number? = 5) Number of outliers
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesAdaptive Linear Neuron model
Parameters
Instance Members
Fit this model once.
Parameters
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesAdaptive Metric Nearest Neighbor
Parameters
k0 (number? = null) The number of neighbors of the test point
k1 (number? = 3) The number of neighbors in N1 for estimation
k2 (number? = null) The size of the neighborhood N2 for each of the k0 neighbors for estimation
l (number? = null) The number of points within the delta intervals
k (number? = 3) The number of neighbors in the final nearest neighbor rule
c (number? = 0.5) The positive factor for the exponential weighting scheme
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesAdaptive thresholding
new AdaptiveThresholding(method: (
"mean" |
"gaussian" |
"median" |
"midgray"), k:
number, c:
number)
Parameters
method (("mean" | "gaussian" | "median" | "midgray") = 'mean') Method name
k (number = 3) Size of local range
c (number = 2) Value subtracted from threshold
Instance Members
Returns thresholded values.
Parameters
Returns
Array<Array<(0 | 1)>>: Predicted valuesAffinity propagation model
new AffinityPropagation()
Instance Members
Number of clusters
size
Type: number
Learning epoch
epoch
Type: number
Initialize this model.
Parameters
Fit this model once.
fit()
Returns categories corresponding the data.
Returns
Array<number>: Predicted valuesAgglomerativeClusterNode
Type: object
Properties
index (number?) : Data index of leaf node
distance (number?) : Distance between children nodes
distances (Array<number>?) : Distances of leaf data and others
size (number) : Number of leaf nodes
Agglomerative clustering
Parameters
Instance Members
Fit model parameters.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesReturns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeComplete linkage agglomerative clustering
new CompleteLinkageAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeSingle linkage agglomerative clustering
new SingleLinkageAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeGroup average agglomerative clustering
new GroupAverageAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeWard's agglomerative clustering
new WardsAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeCentroid agglomerative clustering
new CentroidAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeWeighted average agglomerative clustering
new WeightedAverageAgglomerativeClustering()
Extends AgglomerativeClustering
Instance Members
Returns a distance between two nodes.
Parameters
Returns
number: Distance▸ update(ca, cb, ck, ka, kb, ab) Returns new distance.
Parameters
ca (number) Number of datas in a merging node A
cb (number) Number of datas in a merging node B
ck (number) Number of datas in a current node
ka (number) Distance between node A and current node
kb (number) Distance between node B and current node
ab (number) Distance between node A and node B
Returns
number: New distance between current node and merged nodeAkima interpolation
new AkimaInterpolation(modified:
boolean)
Parameters
modified (boolean = false) Use modified method or not
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesApproximate Large Margin algorithm
Parameters
p (number = 2) Power parameter for norm
alpha (number = 1) Degree of approximation to the optimal margin hyperplane
b (number = 1) Tuning parameter
c (number = 1) Tuning parameter
Related
A New Approximate Maximal Margin Classification Algorithm. (2001)Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesAveraged One-Dependence Estimators
Parameters
discrete (number? = 20) Discretized number
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted datas.
Parameters
Returns
Array<(any | null)>: Predicted valuesAutoregressive model
new AR(p:
number, method: (
"lsm" |
"yuleWalker" |
"levinson" |
"householder")?)
Parameters
method (("lsm" | "yuleWalker" | "levinson" | "householder")? = 'lsm') Method name
Instance Members
Returns predicted future values.
Parameters
Returns
Array<number>: Predicted valuesAutoregressive moving average model
Parameters
Instance Members
Returns predicted future values.
Parameters
Returns
Array<number>: Predicted valuesAdaptive regularization of Weight Vectors
Parameters
r (number = 0.1) Learning rate
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesAdaptive resonance theory
new ART(t:
number?, method:
"l2"?)
Parameters
method ("l2"? = 'l2') Method name
Instance Members
Number of clusters
size
Type: number
Fit model and returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesReturns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesApriori algorithm
Parameters
minsup (number) Minimum support
Instance Members
Returns predicted sets.
Parameters
Returns
Array<SetKeyMap>: Predicted valuesAssociation analysis
new AssociationAnalysis(support:
number)
Parameters
support (number) Minimum support
Instance Members
Returns appearing keys.
Parameters
Returns
Iterator<Array<string>>: Appearing keysReturns support value.
Parameters
Returns
number: Support valueReturns confidence value.
confidence(a: any, b: any):
numberParameters
Returns
number: Confidence valueReturns lift value.
Parameters
Returns
number: Lift valueAutoencoder
Parameters
input_size (number) Input size
reduce_size (number) Reduced dimension
enc_layers (Array<LayerObject>) Layers of encoder
dec_layers (Array<LayerObject>) Layers of decoder
optimizer (string) Optimizer of the network
Instance Members
▸ fit(train_x, iteration, rate, batch, rho) Fit model.
Parameters
iteration (number) Iteration count
rho (number) Sparsity parameter
Returns
number: Loss valueAutomatic thresholding
new AutomaticThresholding()
Instance Members
Returns thresholded values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesAverage shifted histogram
Parameters
step (number) Number of bins to average
Instance Members
Returns predicted values.
Parameters
Returns
Array<any>: An array nested by the number of dimensions of the dataReturns predicted counted values.
Parameters
Returns
Array<number>: Predicted valuesBalanced histogram thresholding
new BalancedHistogramThresholding(minCount:
number?)
Parameters
minCount (number? = 500) Minimum data count
Instance Members
Returns thresholded values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesBallseptron
Parameters
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesBanditron
Parameters
Instance Members
Fit model parameters.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesBayesian linear regression
new BayesianLinearRegression(lambda:
number?, sigma:
number?)
Parameters
lambda (number? = 0.1) Tuning parameter
sigma (number? = 0.2) Initial sigma of normal distribution
Instance Members
Fit model once.
Parameters
Bayesian Network
new BayesianNetwork(alpha:
number)
Parameters
alpha (number) Equivalent sample size
Instance Members
Returns probability values.
Parameters
Returns
Array<number>: Predicted valuesBernsen thresholding
Parameters
n (number? = 3) Size of local range
ct (number? = 15) Minimum value of contrast
Instance Members
Returns thresholded values.
Parameters
Returns
Array<Array<(0 | 1)>>: Predicted valuesBessel filter
Parameters
Instance Members
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesBilinear interpolation
new BilinearInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<(number | null)>: Predicted valuesBalanced iterative reducing and clustering using hierarchies
Parameters
b (number? = 10) Maximum number of entries for each non-leaf nodes
l (number? = Infinity) Maximum number of entries for each leaf nodes
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesBisecting k-Means algorithm
new BisectingKMeans()
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesBounded Online Gradient Descent
new BOGD(b:
number?, eta:
number?, lambda:
number?, gamma:
number?, sampling: (
"uniform" |
"nonuniform")?, kernel: any, loss: (
"zero_one" |
"hinge")?)
Parameters
b (number? = 10) Maximum budget size
lambda (number? = 0.1) Regularization parameter
gamma (number? = 0.1) Maximum coefficient
sampling (("uniform" | "nonuniform")? = 'nonuniform') Sampling approach
kernel (any = 'gaussian')
loss (("zero_one" | "hinge")? = 'hinge') Loss type name
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesBox-Cox transformation
Parameters
lambda (number? = null) Lambda
Instance Members
Budgeted online Passive-Aggressive
new BPA(c:
number?, b:
number?, version: (
"simple" |
"projecting" |
"nn")?, kernel: any)
Parameters
c (number? = 1) Regularization parameter
version (("simple" | "projecting" | "nn")? = 'simple') Version
kernel (any = 'gaussian')
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesBrahmagupta interpolation
new BrahmaguptaInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesBRIDGE
Parameters
k (number) K-means clustering size
e_core (number) e for core distance
e_den (number) e for density base clustering
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesBudgeted Stochastic Gradient Descent
new BSGD(b:
number?, eta:
number?, lambda:
number?, maintenance: (
"removal" |
"projection" |
"merging")?, kernel: any)
Parameters
eta (number? = 1) Learning rate
lambda (number? = 1) Regularization parameter
maintenance (("removal" | "projection" | "merging")? = 'removal') Maintenance type
kernel (any = 'gaussian')
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMulticlass Budgeted Stochastic Gradient Descent
new MulticlassBSGD(b:
number?, eta:
number?, lambda:
number?, maintenance: (
"removal" |
"projection" |
"merging")?, kernel: any)
Parameters
eta (number? = 1) Learning rate
lambda (number? = 1) Regularization parameter
maintenance (("removal" | "projection" | "merging")? = 'removal') Maintenance type
kernel (any = 'gaussian')
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesBudget Perceptron
Parameters
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesButterworth filter
Parameters
Instance Members
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesClustering based on Closest Pairs
Parameters
r (number) Number of representative points
m (number) Number of required sub-clusters
Instance Members
Returns the specified number of clusters.
Parameters
number (number) Number of clusters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesCanny edge detection
Parameters
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Clustering Affinity Search Technique
Parameters
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Returns
Array<number>: Predicted valuesCategorical naive bayes
new CategoricalNaiveBayes(alpha:
number?)
Parameters
alpha (number? = 1.0) Smoothing parameter
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted probabilities.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesCatmull-Rom splines interpolation
new CatmullRomSplines()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesCentripetal Catmull-Rom splines interpolation
new CentripetalCatmullRomSplines(alpha:
number)
Parameters
alpha (number = 0.5) Number for knot parameterization
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesCHAMELEON
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Returns the specified number of clusters.
Parameters
number (number) Number of clusters
Returns
Array<any>: Cluster nodesReturns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesChange finder
Deprecated: Does not work properly
Parameters
r (number? = 0.5) Forgetting factor
smooth (number? = 10) Smoothing window size
Instance Members
Returns predicted scores.
Returns
Array<number>: Predicted valuesChebyshev filter
Parameters
type ((1 | 2)? = 1) Type number
ripple (number? = 1) Ripple factor
Instance Members
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesClustering LARge Applications
Parameters
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesClustering Large Applications based on RANdomized Search
Parameters
Instance Members
Initialize model.
Parameters
▸ fit(numlocal, maxneighbor) Fit model once.
Parameters
numlocal (number) Iteration count for local
maxneighbor (number) Iteration count for neighborhoods
Returns predicted categories.
Returns
Array<number>: Predicted valuesCLustering In QUEst
Parameters
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted categoriesCLUstEring based on local Shrinking
Parameters
alpha (number? = 0.05) Speed factor
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Returns
Array<number>: Predicted valuesCo-training
Parameters
Instance Members
Initialize model.
Parameters
y (Array<(any | null)>) Target values
Returns predicted categories.
predict():
Array<(any | null)>
Returns
Array<(any | null)>: Predicted valuesConnectivity-based Outlier Factor
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesConscience on-line learning
Parameters
eta (number? = 1) Initial learning rate
kernel (any = 'gaussian')
Instance Members
Initialize model.
Parameters
Fit model once.
Returns
number: Convergence criterionReturns predicted categories.
Returns
Array<number>: Predicted valuesComplement Naive Bayes
new ComplementNaiveBayes(distribution: "gaussian"?)
Parameters
distribution ("gaussian"? = 'gaussian') Distribution name
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesConfidence weighted
new ConfidenceWeighted(eta:
number)
Parameters
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSoft confidence weighted
new SoftConfidenceWeighted(eta:
number, cost:
number, v: (
1 |
2))
Extends ConfidenceWeighted
Parameters
cost (number) Tradeoff value between passiveness and aggressiveness
v ((1 | 2)) Version number
Cosine interpolation
new CosineInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesConditional random fields
new CRF()
Instance Members
Returns probability P(y|x).
Parameters
x (Array<any>) Sample data
y (Array<any>) Target values
Returns
number: Predicted valuesReturns predicted labels.
Parameters
Returns
Array<Array<any>>: Predicted valuesCubic-convolution interpolation
new CubicConvolutionInterpolation(a:
number)
Parameters
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesCubic Hermite spline
Parameters
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesCubic interpolation
new CubicInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesCumulative moving average
new CumulativeMovingAverage()
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesCumulative sum change point detection
new CumSum()
Instance Members
Initialize model.
Parameters
Returns predicted values.
Returns
Array<boolean>: Predicted valuesCURENode
Type: object
Properties
index (number?) : Data index of leaf node
distance (number?) : Distance between children nodes
size (number) : Number of leaf nodes
Clustering Using REpresentatives
Parameters
c (number) Number of representative points
Instance Members
Returns the specified number of clusters.
Parameters
number (number) Number of clusters
Returns
Array<CURENode>: Cluster nodesReturns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDiscriminant adaptive nearest neighbor
new DiscriminantAdaptiveNearestNeighbor(k:
number?, iteration:
number?)
Parameters
k (number? = null) Number of neighborhoods
iteration (number? = 1) Iteration count
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesDistribution Based Clustering of LArge Spatial Databases
new DBCLASD()
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDensity-based spatial clustering of applications with noise
Parameters
eps (number? = 0.5) Radius to determine neighborhood
minPts (number? = 5) Minimum size of cluster
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDecision tree
new DecisionTree()
Instance Members
Depth of the tree
depth
Type: number
Initialize model.
Parameters
targets (Array<any>) Target values
Returns importances of the features.
Returns
Array<number>: ImportancesReturns predicted values.
Parameters
Returns
Array<number>: Predicted valuesDecision tree classifier
new DecisionTreeClassifier(method: ("ID3" | "CART"))
Extends DecisionTree
Parameters
method (("ID3" | "CART")) Method name
Instance Members
Returns probability of the datas.
Parameters
Returns
Array<number>: Predicted valuesReturns predicted values.
Parameters
Returns
Array<any>: Predicted valuesDecision tree regression
new DecisionTreeRegression()
Extends DecisionTree
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesDelaunay interpolation
new DelaunayInterpolation()
Instance Members
Returns probabilities of the datas.
Parameters
Returns
Array<number>: Predicted valuesDeming regression
new DemingRegression(d:
number)
Parameters
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesDENsity CLUstering
new DENCLUE(h:
number, version: (
1 |
2)?, kernel: (
"gaussian" | {name:
"gaussian"} | function (
Array<
number>):
number)?)
Parameters
h (number) Smoothing parameter for the kernel
version ((1 | 2)? = 1) Version number
kernel (("gaussian" | {name: "gaussian"} | function (Array<number>): number)? = 'gaussian') Kernel name
Instance Members
Number of clusters
size
Type: number
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesDIvisive ANAlysis Clustering
new DIANA()
Instance Members
Number of clusters
size
Type: number
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesDiffusion map
Parameters
Instance Members
Detecting Subspace cluster Hierarchies
Parameters
mu (number) Number of neighborhood
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDensity-based Optimal projective Clustering
Parameters
Instance Members
Returns predicted categories.
Returns
Array<number>: Predicted valuesFast Density-based Optimal projective Clustering
Parameters
maxiter (number) Maximum inner iteration
d0 (number) Threshold of selected dimension count
Instance Members
Returns predicted categories.
Returns
Array<number>: Predicted valuesDeep Q-Network agent
Parameters
resolution (number) Resolution of actions
layers (Array<LayerObject>) Network layers
optimizer (string) Optimizer of the network
Instance Members
DQN Method
method
Parameters
value (("DQN" | "DDQN")) New method name
▸ get_action(state, greedy_rate) Returns a action.
Parameters
state (Array<any>) Current states
greedy_rate (number = 0.002) Greedy rate
Returns
Array<any>: Action▸ update(action, state, next_state, reward, done, learning_rate, batch) Update model.
Parameters
action (Array<any>) Action
state (Array<any>) Current states
next_state (Array<any>) Next states
learning_rate (number) Learning rate
Drake's accelerated k-Means algorithm
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDelaunay triangulation-based spatial clustering of application with noise
Parameters
minPts (number? = 5) Minimum size of neighbors
threshold (number? = 1.0) Remove threshold score
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesDynamic programming agent
Parameters
resolution (number? = 20) Resolution
Instance Members
Returns a action.
Parameters
state (Array<any>) Current states
Returns
Array<any>: ActionUpdate model.
update(method: ("value" | "policy"))
Parameters
method (("value" | "policy")) Method name
Elastic net
new ElasticNet(lambda:
number?, alpha:
number?, method: (
"ISTA" |
"CD")?)
Parameters
lambda (number? = 0.1) Regularization strength
alpha (number? = 0.5) Mixing parameter
method (("ISTA" | "CD")? = 'CD') Method name
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns importances of the features.
Returns
Array<number>: ImportancesElkan's accelerated k-Means algorithm
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesElliptic filter
Parameters
ripple (number? = 1) Ripple factor
xi (number? = 1) Selectivity factor
Instance Members
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesExtreme learning machine classifier
new ELMClassifier(size:
Array<
number>, activation: (
"identity" |
"elu" |
"gaussian" |
"leaky_relu" |
"sigmoid" |
"softplus" |
"softsign" |
"tanh" | function (
number):
number)?)
Extends ELM
Parameters
activation (("identity" | "elu" | "gaussian" | "leaky_relu" | "sigmoid" | "softplus" | "softsign" | "tanh" | function (number): number)? = 'tanh') Activation name
Instance Members
Category list
categories
Type: Array<any>
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted probabilities.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted values.
Parameters
Returns
Array<any>: Predicted valuesExtreme learning machine regressor
new ELMRegressor(size:
Array<
number>, activation: (
"identity" |
"elu" |
"gaussian" |
"leaky_relu" |
"sigmoid" |
"softplus" |
"softsign" |
"tanh" | function (
number):
number)?)
Extends ELM
Parameters
activation (("identity" | "elu" | "gaussian" | "leaky_relu" | "sigmoid" | "softplus" | "softsign" | "tanh" | function (number): number)? = 'tanh') Activation name
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesExtended Natural Neighbor
Parameters
Instance Members
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesExtended Nearest Neighbor
Parameters
version ((0 | 1 | 2)? = 1) Version
k (number? = 5) Number of neighborhoods
Instance Members
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesBinaryModel
Type: object
Properties
fit (function (...any): void) : Fit model
Ensemble binary models
new EnsembleBinaryModel(model: any, type: (
"oneone" |
"onerest"), classes:
Array<any>?)
Parameters
type (("oneone" | "onerest")) Type name
classes (Array<any>?) Initial class labels
Instance Members
Initialize model.
Parameters
train_y (Array<any>) Target values
Fit model.
Parameters
y (Array<any>) Target values
args (...any) Arguments for fit
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesExponential moving average
new ExponentialMovingAverage(k:
number)
Parameters
k (number) Degree of weighting decrease
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesModified moving average
new ModifiedMovingAverage(k:
number)
Parameters
k (number) Degree of weighting decrease
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesBsae class for Extremely Randomized Trees
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 1.0) Sampling rate
Instance Members
Extra trees classifier
new ExtraTreesClassifier(tree_num:
number, sampling_rate:
number?)
Extends ExtraTrees
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 1.0) Sampling rate
Instance Members
Extra trees regressor
new ExtraTreesRegressor(tree_num:
number, sampling_rate:
number?)
Extends ExtraTrees
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 1.0) Sampling rate
Instance Members
FastMap
Parameters
Instance Members
a Fast and INtelligent subspace clustering algorithm using DImension voting
Parameters
minsize (number) Mininum size of clusters
mindist (number) Merge threshold
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted categoriesForgetron
new Forgetron(b:
number, kernel: any)
Parameters
kernel (any = 'gaussian')
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesFuzzy c-means
Parameters
m (number? = 2) Fuzziness factor
Instance Members
Initialize model.
Parameters
Fuzzy k-nearest neighbor
Parameters
k (number? = 5) Number of neighborhoods
m (number? = 2) Factor of weight for distance
Instance Members
Category list
categories
Type: Array<any>
Add a data.
Parameters
category (any?) Target value
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<Array<number>>: Predicted valuesGenerative adversarial networks
new GAN(noise_dim:
number, g_hidden:
Array<LayerObject>, d_hidden:
Array<LayerObject>, g_opt:
string, d_opt:
string, class_size: (
number | null), type: (
"" |
"conditional"))
Parameters
noise_dim (number) Number of noise dimension
g_hidden (Array<LayerObject>) Layers of generator
d_hidden (Array<LayerObject>) Layers of discriminator
g_opt (string) Optimizer of the generator network
d_opt (string) Optimizer of the discriminator network
class_size ((number | null)) Class size for conditional type
type (("" | "conditional")) Type name
Instance Members
▸ fit(x, y, step, gen_rate, dis_rate, batch) Fit model.
Parameters
gen_rate (number) Learning rate for generator
dis_rate (number) Learning rate for discriminator
Returns
{generatorLoss: number, discriminatorLoss: number}: Loss valueReturns probabilities of the data is true.
Parameters
y (any) Conditional values
Returns
Array<Array<number>>: Predicted valuesReturns generated data from the model.
Parameters
n (number) Number of generated data
Returns
Array<Array<number>>: Generated valuesGasser–Müller kernel estimator
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesGaussian process
new GaussianProcess(kernel:
"gaussian"?, beta:
number?)
Parameters
kernel ("gaussian"? = 'gaussian') Kernel name
beta (number? = 1) Precision parameter
Instance Members
Initialize model.
Parameters
Fit model.
Parameters
learning_rate (number = 0.1) Learning rate
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesGradient boosting decision tree
Parameters
maxdepth (number? = 1) Maximum depth of tree
srate (number? = 1.0) Sampling rate
lr (number? = 0) Learning rate
Instance Members
Number of trees
size
Type: number
Initialize model.
Parameters
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesGradient boosting decision tree classifier
Extends GBDT
Parameters
maxdepth (number? = 1) Maximum depth of tree
srate (number? = 1.0) Sampling rate
lr (number? = 0) Learning rate
Instance Members
Initialize model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesGeneralized extreme studentized deviate
Parameters
alpha (number) Significance level
r (number) Max number of outliers
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesGeneticModel
Type: object
Properties
run (function (...any): void) : Run model
mutation (function (): GeneticModel) : Returns mutated model
score (function (): number) : Returns a number how good the model is
Genetic algorithm
new GeneticAlgorithm(size:
number, model: any)
Parameters
size (number) Number of models per generation
Instance Members
Run for all models.
run(args: ...any)
Parameters
args (...any) Arguments for run
▸ next(mutation_rate = 0.001) Update models to new generation.
Parameters
mutation_rate (number? = 0.001) Mutation rate
Genetic algorithm generation
Parameters
size (number? = 100) Number of models per generation
resolution (number? = 20) Resolution
Instance Members
Returns the best score agent.
top_agent(): GeneticAlgorithmAgent
Returns
GeneticAlgorithmAgent: Best agentUpdate agent to new generation.
Parameters
mutation_rate (number = 0.001) Mutation rate
Genetic k-means model
Parameters
size (number) Number of models per generation
Instance Members
The best model.
bestModel
Type: GeneticKMeansModel
Initialize model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesG-means
new GMeans()
Instance Members
Number of clusters.
size
Type: number
Clear all clusters.
clear()
Fit model.
Parameters
iterations (number = -1) Iteration count
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesGaussian mixture model
new GMM()
Instance Members
Clear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesSemi-Supervised gaussian mixture model
new SemiSupervisedGMM()
Extends GMM
Instance Members
Categories
categories
Type: Array<any>
Initialize model.
Parameters
labels (Array<(any | null)>) Target values
Fit model.
Parameters
y (Array<(any | null)>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesGaussian mixture regression
new GMR()
Extends GMM
Instance Members
Clear all clusters.
clear()
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesGaussian Process Latent Variable Model
Parameters
alpha (number) Precision parameter
ez (number? = 1) Learning rate for z
ea (number? = 0.005) Learning rate for alpha
ep (number? = 0.2) Learning rate for kernel
kernel (any = 'gaussian')
Instance Members
Initialize model.
Parameters
Returns log likelihood.
Returns
number: Log likelihoodReturns reconstruct datas.
Parameters
Returns
Array<Array<number>>: Predicted valuesGrowing cell structures
new GrowingCellStructures()
Instance Members
Number of clusters
size
Type: number
Update parameter.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesGrowing neural gas
Parameters
m (number) Decreasing factor of
l Instance Members
Number of clusters
size
Type: number
Update parameter.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesGrowing Self-Organizing Map
Parameters
sf (number? = 0.1) Spread factor
lr (number? = 0.1) Learning rate
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesGenerative topographic mapping
Parameters
input_size (number) Input size
output_size (number) Output size
q (number? = 10) Grid size for basis function
Instance Members
Returns probabilities.
Parameters
Returns
Array<number>: Predicted valuesReturns responsibility.
Parameters
Returns
Matrix: ResponsibilityReturns best indexes.
Parameters
Returns
Array<number>: Predicted valuesReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesHamelry's accelerated k-Means algorithm
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesHampel filter
Parameters
k (number? = 3) Half window size
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesHartigan-Wong k-Means algorithm
new HartiganWongKMeans(k:
number)
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesHierarchical Density-based spatial clustering of applications with noise
Parameters
minClusterSize (number? = 5) Minimum number of clusters to be recognized as a cluster
minPts (number? = 5) Number of neighborhood with core distance
Instance Members
Number of clusters of last predicted
size
Type: number
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesHistogram
new Histogram(config:
object?)
Parameters
Instance Members
Returns histogram data.
Parameters
Returns
Array<any>: Predicted values. An array nested by the number of dimensions of the dataReturns predicted counted values.
Parameters
Returns
Array<number>: Predicted valuesHessian Locally Linear Embedding
Parameters
k (number) Number of neighborhoods
rd ((number | null)? = null) Reduced dimension
Instance Members
Hidden Markov model
Parameters
Instance Members
Returns probability of the datas.
Parameters
Returns
Matrix: Predicted valuesReturns best path of the datas.
Parameters
Returns
Matrix: Predicted pathHidden Markov model
Extends HMMBase
Parameters
Instance Members
Fit model.
Parameters
scaled (boolean = false) Do scaled calculation or not
Returns probability of the datas.
Parameters
Returns
Array<number>: Predicted valuesReturns best path of the datas.
Parameters
Returns
Array<Array<number>>: Predicted pathContinuous hidden Markov model
Extends HMMBase
Parameters
Instance Members
Fit model.
Parameters
scaled (boolean = false) Do scaled calculation or not
Returns probability of the datas.
Parameters
Returns
Array<number>: Predicted valuesReturns best path of the datas.
Parameters
Returns
Array<Array<number>>: Predicted pathHolt-Winters method
Parameters
a (number) Weight for last value
b (number? = 0) Weight for trend value
g (number? = 0) Weight for seasonal data
s (number? = 0) Length of season
Instance Members
Fit model and return predict values.
Parameters
Returns
Array<number>: Predicted valuesReturns predicted future values.
Parameters
Returns
Array<Array<number>>: Predicted valuesHopfield network
new HopfieldNetwork()
Instance Members
Return a energy value of the data.
Parameters
Returns
number: Energy valueReturns predicted values.
Parameters
Returns
Array<number>: Predicted valuesHotelling T-square Method
new Hotelling()
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesHuber regression
new HuberRegression(e:
number?, method: (
"rls" |
"gd")?, lr:
number?)
Parameters
e (number? = 1.35) Threshold of outliers
method (("rls" | "gd")? = 'rls') Method name
lr (number? = 1) Learning rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesIndependent component analysis
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Classical ellipsoid method
Parameters
gamma (number? = 0.1) Desired classification margin
a (number? = 0.5) Tradeoff parameter
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesImproved ellipsoid method
Parameters
b (number? = 0.9) Parameter controlling the memory of online learning
c (number? = 0.5) Parameter controlling the memory of online learning
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesLocally Informative K-Nearest Neighbor
Parameters
k (number) Number of neighbors
i (number) Number of informative points
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesIncremental principal component analysis
Parameters
f (number? = 0.95) Forgetting factor
rd ((number | null)? = null) Reduced dimension
Instance Members
Update parameters.
Parameters
Influenced Outlierness
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesInverse distance weighting
Parameters
k (number? = 5) Number of neighborhoods
p (number? = 2) Power parameter
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesInverse smoothstep interpolation
new InverseSmoothstepInterpolation()
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesIterative Self-Organizing Data Analysis Technique
Parameters
init_k (number) Initial cluster count
min_k (number) Minimum cluster count
max_k (number) Maximum cluster count
min_n (number) Minimum cluster size
split_std (number) Standard deviation as splid threshold
merge_dist (number) Merge distance
Instance Members
Number of clusters
size
Type: number
Initialize model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesIsolation forest
new IsolationForest(tree_num:
number?, sampling_rate:
number?)
Parameters
tree_num (number? = 100) Number of trees
sampling_rate (number? = 0.8) Sampling rate
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesIsomap
Parameters
neighbors (number? = 0) Number of neighborhoods
rd ((number | null)? = null) Reduced dimension
Instance Members
Isotonic regression
new IsotonicRegression()
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesKalman filter
new KalmanFilter()
Instance Members
Fit and returns smoothed values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted future values.
Parameters
Returns
Array<Array<number>>: Predicted valuesKernel Density Estimation Outlier Score
Parameters
kmin (number) Minimum number of neighborhoods
kmax (number) Maximum number of neighborhoods
kernel (("gaussian" | "epanechnikov" | {name: "gaussian"} | {name: "epanechnikov"} | function (number, number, number): number)? = 'gaussian') Kernel name
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesKernel density estimator
new KernelDensityEstimator(h:
number?, kernel: (
"gaussian" |
"rectangular" |
"triangular" |
"epanechnikov" |
"biweight" |
"triweight" | {name:
"gaussian"} | {name:
"rectangular"} | {name:
"triangular"} | {name:
"epanechnikov"} | {name:
"biweight"} | {name:
"triweight"} | function (
number):
number)?)
Parameters
h (number? = 0) Smoothing parameter for the kernel
kernel (("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "biweight" | "triweight" | {name: "gaussian"} | {name: "rectangular"} | {name: "triangular"} | {name: "epanechnikov"} | {name: "biweight"} | {name: "triweight"} | function (number): number)? = 'gaussian') Kernel name
Instance Members
Returns probabilities of the datas.
Parameters
Returns
Array<number>: Predicted valuesReturns probabilities of the datas.
Parameters
Returns
Array<number>: Predicted valuesKernel k-means
Parameters
k (number? = 3) Number of clusters
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesKernelized Primal Estimated sub-GrAdientSOlver for SVM
new KernelizedPegasos(rate:
number, kernel: any)
Parameters
kernel (any = 'gaussian')
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Fit model parameters.
fit()
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesKernelized perceptron
new KernelizedPerceptron(rate:
number?, kernel: any)
Parameters
rate (number? = 1) Learning rate
kernel (any = 'gaussian')
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesK-Harmonic Means
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesKullback-Leibler importance estimation procedure
Parameters
kernelNum (number) Number of kernels
Instance Members
Returns estimated values.
Parameters
Returns
Array<number>: Predicted valuesBsae class for k-means like model
new KMeansBase()
Instance Members
Number of clusters.
size
Type: number
Add a new cluster.
Parameters
Returns
Array<number>: Added centroidClear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesFit model and returns total distance the centroid has moved.
Parameters
Returns
number: Total distance the centroid has movedk-means model
new KMeans()
Extends KMeansBase
Instance Members
Returns a new centroid.
Parameters
Returns
Array<number>: Added centroid▸ _move(centroids, datas) Returns moved centroid positions.
Parameters
Returns
Array<Array<number>>: Moved centroidsk-means++ model
new KMeanspp()
Extends KMeans
Instance Members
Returns a new centroid.
Parameters
Returns
Array<number>: Added centroidk-medoids model
new KMedoids()
Extends KMeans
Instance Members
▸ _move(centroids, datas) Returns moved centroid positions.
Parameters
Returns
Array<Array<number>>: Moved centroidsk-medians model
new KMedians()
Extends KMeans
Instance Members
semi-supervised k-means model
new SemiSupervisedKMeansModel()
Extends KMeansBase
Instance Members
Categories
categories
Type: Array<any>
Initialize model.
Parameters
labels (Array<(any | null)>) Target values
Fit and returns total distance the centroid has moved.
Parameters
labels (Array<(any | null)>) Target values
Returns
number: Total distance the centroid has movedReturns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesk-modes model
new KModes()
Instance Members
Number of clusters.
size
Type: number
Add a new cluster.
Parameters
Clear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesFit model and returns total distance the modes has moved.
Parameters
Returns
number: Total distance the modes has movedBsae class for k-nearest neighbor models
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Add a data.
Parameters
category (any?) Target value
k-nearest neighbor
Extends KNNBase
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Add a data.
Parameters
category (any) Target value
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesk-nearest neighbor regression
Extends KNNBase
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Add a data.
Parameters
category (number) Target value
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesk-nearest neighbor anomaly detection
Extends KNNBase
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesk-nearest neighbor density estimation
Extends KNNBase
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSemi-supervised k-nearest neighbor
Extends KNNBase
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Add a data.
Parameters
category ((any | null)) Target value
Add datas.
Parameters
targets (Array<(any | null)>) Target values
Returns predicted values.
Returns
Array<any>: Predicted valuesk-prototypes model
Parameters
gamma (number) Weight for categorical data
Instance Members
Number of clusters.
size
Type: number
Add a new cluster.
Parameters
Clear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesFit model and returns total distance the modes has moved.
Parameters
Returns
number: Total distance the modes has movedk-SVD
Parameters
k (number? = m) Sparsity parameter
Instance Members
Fit model and returns error.
Returns
number: ErrorKolmogorov–Zurbenko filter
Parameters
k (number) Iteration count of a moving average
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesLabel propagation
new LabelPropagation(method: (
"rbf" |
"knn")?, sigma:
number?, k:
number?)
Parameters
method (("rbf" | "knn")? = 'rbf') Method name
sigma (number? = 0.1) Sigma of normal distribution
k (number? = Infinity) Number of neighborhoods
Instance Members
Initialize model.
Parameters
y (Array<(any | null)>) Target values
Returns predicted categories.
Returns
Array<any>: Predicted valuesLabel spreading
Parameters
alpha (number? = 0.2) Clamping factor
method (("rbf" | "knn")? = 'rbf') Method name
sigma (number? = 0.1) Sigma of normal distribution
k (number? = Infinity) Number of neighborhoods
Instance Members
Initialize model.
Parameters
y (Array<(any | null)>) Target values
Returns predicted categories.
Returns
Array<any>: Predicted valuesLadder network
Parameters
activation (string) Activation name
optimizer (string) Optimizer of the network
Instance Members
▸ fit(train_x, train_y, iteration, rate, batch) Fit model.
Parameters
train_y (Array<(any | null)>) Target values
iteration (number) Iteration count
Returns
{labeledLoss: number, unlabeledLoss: number}: Loss valueReturns predicted values.
Parameters
Returns
Array<any>: Predicted valuesLagrange interpolation
new LagrangeInterpolation(method: ("weighted" | "newton" | "")?)
Parameters
method (("weighted" | "newton" | "")? = 'weighted') Method name
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesLanczos interpolation
new LanczosInterpolation(n:
number)
Parameters
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesLaplacian edge detection
new Laplacian(th:
number, n: (
4 |
8)?)
Parameters
n ((4 | 8)? = 4) Number of neighborhoods
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Laplacian eigenmaps
new LaplacianEigenmaps(rd:
number, affinity: (
"rbf" |
"knn")?, k:
number?, sigma:
number?, laplacian: (
"unnormalized" |
"normalized")?)
Parameters
affinity (("rbf" | "knn")? = 'rbf') Affinity type name
k (number? = 10) Number of neighborhoods
sigma (number? = 1) Sigma of normal distribution
laplacian (("unnormalized" | "normalized")? = 'unnormalized') Normalized laplacian matrix or not
Instance Members
Least absolute shrinkage and selection operator
new Lasso(lambda:
number?, method: (
"CD" |
"ISTA" |
"LARS")?)
Parameters
lambda (number? = 0.1) Regularization strength
method (("CD" | "ISTA" | "LARS")? = 'CD') Method name
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns importances of the features.
Returns
Array<number>: ImportancesLatent dirichlet allocation
new LatentDirichletAllocation(t:
number?)
Parameters
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesLinde-Buzo-Gray algorithm
new LBG()
Instance Members
Number of clusters.
size
Type: number
Clear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesLinear discriminant analysis
new LinearDiscriminant()
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesFishers linear discriminant analysis
new FishersLinearDiscriminant()
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMulticlass linear discriminant analysis
new MulticlassLinearDiscriminant()
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesLinear discriminant analysis
new LinearDiscriminantAnalysis(rd: (
number | null)?)
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Local Density Factor
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesLocal Distance-based Outlier Factor
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesLeast absolute deviations
new LeastAbsolute()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLeast squares
new LeastSquares()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLinear interpolation
new LinearInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesLocally Linear Embedding
Parameters
k (number? = 1) Number of neighborhoods
rd ((number | null)? = null) Reduced dimension
Instance Members
Least median squares regression
new LeastMedianSquaresRegression(k:
number)
Parameters
Instance Members
Large Margin Nearest Neighbor
Parameters
gamma (number) Tuning parameter
lambda (number) Tuning parameter
Instance Members
Initialize model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesLocal Correlation Integral
Parameters
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesLocally estimated scatterplot smoothing
new LOESS()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLocal Outlier Factor
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesLaplacian of gaussian filter
Parameters
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Logarithmic interpolation
new LogarithmicInterpolation()
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesLogistic regression
new LogisticRegression()
Instance Members
▸ fit(x, y, iteration = 1, rate = 0.1, l1 = 0, l2 = 0) Fit model.
Parameters
y (Array<(1 | -1)>) Target values
iteration (number? = 1) Iteration count
rate (number? = 0.1) Learning rate
l1 (number? = 0) L1 regularization strength
l2 (number? = 0) L2 regularization strength
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMultinomial logistic regression
new MultinomialLogisticRegression(classes:
Array<
number>?)
Parameters
Instance Members
▸ fit(train_x, train_y, iteration = 1, rate = 0.1, l1 = 0, l2 = 0) Fit model.
Parameters
train_y (Array<any>) Target values
iteration (number? = 1) Iteration count
rate (number? = 0.1) Learning rate
l1 (number? = 0) L1 regularization strength
l2 (number? = 0) L2 regularization strength
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesLocal Outlier Probability
Parameters
k (number) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesLocally weighted scatter plot smooth
new LOWESS()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLowpass filter
Parameters
Instance Members
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesLp norm linear regression
new LpNormLinearRegression(p:
number?)
Parameters
p (number? = 2) Power parameter for norm
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLatent Semantic Analysis
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Least-squares density difference
Parameters
Instance Members
Returns estimated values.
Parameters
Returns
Array<number>: Predicted valuesLSDD for change point detection
Parameters
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesleast-squares importance fitting
Parameters
kernelNum (number) Number of kernels
Instance Members
Returns estimated values.
Parameters
Returns
Array<number>: Predicted valuesLeast trimmed squares
new LeastTrimmedSquaresRegression(h:
number?)
Parameters
h (number? = 0.9) Sampling rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesLocal Tangent Space Alignment
Parameters
k (number) Number of neighborhoods
rd ((number | null)? = null) Reduced dimension
Instance Members
Learning Vector Quantization clustering
Parameters
Instance Members
Fit model.
Parameters
lr (number = 0.1) Learning rate
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesLearning Vector Quantization classifier
new LVQClassifier(type: (1 | 2 | 3))
Parameters
type ((1 | 2 | 3)) Type number
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
lr (number = 0.1) Learning rate
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesMacQueen's k-Means algorithm
Parameters
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesMedian Absolute Deviation
new MAD()
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesMany Adaptive Linear Neuron model
Parameters
rule ((1 | 2 | 3)? = 2) Rule
Instance Members
Fit this model once.
Parameters
▸ outputLayers(data, from) Returns predicted datas.
Parameters
from (number = 0) Index of layers to calculate from
Returns
Array<Array<Array<number>>>: Predicted values for each layerReturns predicted datas.
Parameters
Returns
Array<Array<(1 | -1)>>: Predicted valuesMargin Perceptron
new MarginPerceptron(rate:
number)
Parameters
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMarkov switching
new MarkovSwitching(regime:
number)
Parameters
regime (number) Number of regime
Instance Members
Fit model.
Parameters
eps (number) Parameter update range
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesMultivariate Adaptive Regression Splines
new MultivariateAdaptiveRegressionSplines(mmax:
number)
Parameters
mmax (number) Maximum number of terms
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesMax absolute scaler
new MaxAbsScaler()
Instance Members
Maximum likelihood estimator
new MaximumLikelihoodEstimator(distribution: "normal"?)
Parameters
distribution ("normal"? = 'normal') Distribution name
Instance Members
Returns probability of the data.
Parameters
Returns
Array<number>: Predicted valuesReturns probability of the data.
Parameters
Returns
Array<number>: Predicted valuesMinimum Covariance Determinant
Parameters
sampling_rate (number) Sampling rate
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesMixture discriminant analysis
new MixtureDiscriminant(r:
number)
Parameters
r (number) Number of components
Instance Members
Initialize this model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesMulti-dimensional Scaling
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
▸ predict(x, dmat = false) Returns reduced values.
Parameters
dmat (boolean? = false) True if the
x is distance matrix.
Returns
Array<Array<number>>: Predicted valuesMean shift
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
Number of categories that last predicted
categories
Type: number
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesFit model.
Returns
boolean: true if any centroids has movedMetropolis-Hastings algorithm
Parameters
q ("gaussian"? = 'gaussian') Proposal density name
Instance Members
Returns sampled values.
Parameters
n (number) Number of generated data
t (number? = 100) Iteration count for each generation
Returns
Array<Array<number>>: Generated valuesMin-max normalization
Parameters
min (number? = 0) Minimum value
max (number? = 1) Maximum value
Instance Members
Margin Infused Relaxed Algorithm
new MIRA()
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesModified Locally Linear Embedding
Parameters
k (number) Number of neighborhoods
rd ((number | null)? = null) Reduced dimension
Instance Members
Multi layer perceptron classifier
new MLPClassifier(hidden_sizes:
Array<
number>, activation: (
"identity" |
"elu" |
"gaussian" |
"leaky_relu" |
"relu" |
"sigmoid" |
"softplus" |
"softsign" |
"tanh")?)
Parameters
activation (("identity" | "elu" | "gaussian" | "leaky_relu" | "relu" | "sigmoid" | "softplus" | "softsign" | "tanh")? = 'tanh') Activation name
Instance Members
Category list
categories
Type: Array<any>
Returns object representation.
toObject():
Array<LayerObject>
Returns
Array<LayerObject>: Object represented this neuralnetwork▸ fit(train_x, train_y, iteration, rate = 0.001, batch = 0) Fit model.
Parameters
train_y (Array<any>) Target values
iteration (number) Iteration count
rate (number? = 0.001) Learning rate
batch (number? = 0) Batch size
Returns
number: Loss valueReturns predicted probabilities.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted values.
Parameters
Returns
Array<any>: Predicted valuesMulti layer perceptron regressor
new MLPRegressor(hidden_sizes:
Array<
number>, activation: (
"identity" |
"elu" |
"gaussian" |
"leaky_relu" |
"relu" |
"sigmoid" |
"softplus" |
"softsign" |
"tanh")?)
Parameters
activation (("identity" | "elu" | "gaussian" | "leaky_relu" | "relu" | "sigmoid" | "softplus" | "softsign" | "tanh")? = 'tanh') Activation name
Instance Members
Returns object representation.
toObject():
Array<LayerObject>
Returns
Array<LayerObject>: Object represented this neuralnetwork▸ fit(train_x, train_y, iteration, rate = 0.001, batch = 0) Fit model.
Parameters
iteration (number) Iteration count
rate (number? = 0.001) Learning rate
batch (number? = 0) Batch size
Returns
number: Loss valueReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesMethod of Optimal Direction
Parameters
k (number? = m) Sparsity parameter
Instance Members
Fit model and returns reduced values.
Returns
Array<Array<number>>: Predicted valuesMONothetic Analysis Clustering
new MONA()
Instance Members
Number of clusters
size
Type: number
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesMonothetic Clustering
new MonotheticClustering()
Instance Members
Number of clusters
size
Type: number
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesMonte Carlo agent
Parameters
resolution (number? = 20) Resolution
Instance Members
▸ get_action(state, greedy_rate) Returns a action.
Parameters
state (Array<any>) Current states
greedy_rate (number = 0.5) Greedy rate
Returns
Array<any>: Action▸ update(action, state, reward, done) Update model.
Parameters
action (Array<any>) Action
state (Array<any>) Next state
Mountain method
Parameters
alpha (number) Tuning parameter
beta (number) Tuning parameter
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesSimple moving average
new SimpleMovingAverage(n:
number)
Parameters
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesLinear weighted moving average
new LinearWeightedMovingAverage(n:
number)
Parameters
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesTriangular moving average
new TriangularMovingAverage(k:
number)
Parameters
Instance Members
Returns smoothed values.
Parameters
Returns
Array<number>: Predicted valuesMoving median
Parameters
Instance Members
Mahalanobis Taguchi method
new MT()
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesMutual information feature selector
new MutualInformationFeatureSelection(k:
number)
Parameters
k (number) Number of selected features
Instance Members
Mutual k-nearest-neighbor model
Parameters
k (number? = 5) Number of neighborhoods
Instance Members
Number of clusters
size
Type: number
Returns predicted categories.
Returns
Array<number>: Predicted valuesn-cubic interpolation
new NCubicInterpolation()
Instance Members
Fit model parameters.
Parameters
values (Array<any>) Training data. Nested number array
Returns predicted interpolated values.
Parameters
Returns
Array<(number | null)>: Predicted valuesn-linear interpolation
new NLinearInterpolation()
Instance Members
Fit model parameters.
Parameters
values (Array<any>) Training data. Nested number array
Returns predicted interpolated values.
Parameters
Returns
Array<(number | null)>: Predicted valuesNadaraya–Watson kernel regression
new NadarayaWatson(s:
number?)
Parameters
s (number?) Sigmas of normal distribution
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesNaive bayes
new NaiveBayes(distribution: "gaussian"?)
Parameters
distribution ("gaussian"? = 'gaussian') Distribution name
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted probabilities.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesNaive bayes regression
Parameters
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesNarrow Adaptive Regularization Of Weights
Parameters
b (number? = 1) Tuning parameter
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesNatural neighbor interpolation
new NaturalNeighborInterpolation()
Instance Members
Returns probabilities of the datas.
Parameters
Returns
Array<number>: Predicted valuesNeighbourhood components analysis
new NeighbourhoodComponentsAnalysis(d:
number?, lr:
number?)
Parameters
d (number? = null) Reduced dimension
lr (number? = 0.1) Learning rate
Instance Members
Returns importances of the features.
Returns
Array<number>: ImportancesNearest centroid classifier
Parameters
Instance Members
Add a data.
Parameters
category (any) Target value
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesNegation Naive bayes
new NegationNaiveBayes(distribution: "gaussian"?)
Parameters
distribution ("gaussian"? = 'gaussian') Distribution name
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesNeural gas model
Parameters
l (number? = 1) Neughborhood range
m (number? = 0.99) Decreasing factor of
l Instance Members
Number of clusters.
size
Type: number
Add a new cluster.
Parameters
Returns
Array<number>: Added centroidClear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesFit model and returns total distance the centroid has moved.
Parameters
Returns
number: Total distance the centroid has movedException for neuralnetwork class
new NeuralnetworkException(message:
string, value: any)
Extends Error
Parameters
message (string) Error message
Neuralnetwork
new NeuralNetwork(graph:
ComputationalGraph, optimizer: (
"sgd" |
"adam" |
"momentum" |
"adagrad" |
"rmsprop" |
"adadelta" |
"rmspropgraves" |
"smorms3" |
"adamax" |
"nadam" |
"santae" |
"santasss" |
"amsgrad" |
"adabound" |
"amsbound" |
"adabelief")?)
Parameters
optimizer (("sgd" | "adam" | "momentum" | "adagrad" | "rmsprop" | "adadelta" | "rmspropgraves" | "smorms3" | "adamax" | "nadam" | "santae" | "santasss" | "amsgrad" | "adabound" | "amsbound" | "adabelief")? = 'sgd') Optimizer of the network
Static Members
▸ fromObject(layers, loss?, optimizer = 'sgd') Returns neuralnetwork.
fromObject(layers:
Array<LayerObject>, loss:
string?, optimizer: (
"sgd" |
"adam" |
"momentum" |
"adagrad" |
"rmsprop" |
"adadelta" |
"rmspropgraves" |
"smorms3" |
"adamax" |
"nadam")?):
NeuralNetworkParameters
layers (Array<LayerObject>) Network layers
optimizer (("sgd" | "adam" | "momentum" | "adagrad" | "rmsprop" | "adadelta" | "rmspropgraves" | "smorms3" | "adamax" | "nadam")? = 'sgd') Optimizer of the network
Returns
NeuralNetwork: Created NeuralnetworkInstance Members
Returns object representation.
toObject():
Array<LayerObject>
Returns
Array<LayerObject>: Object represented this neuralnetwork▸ calc(x, t?, out?, options = {}) Returns calculated values.
Parameters
out (Array<string>?) Name of node from which to get output
Returns gradient values.
Parameters
Returns
Matrix: Output of gradientUpdate model parameters.
Parameters
learning_rate (number) Learning rate
▸ fit(x, t, epoch = 1, learning_rate = 0.1, batch_size = null, options = {}) Fit model.
Parameters
epoch (number? = 1) Iteration count
learning_rate (number? = 0.1) Learning rate
batch_size (number? = null) Batch size
Returns
Array<number>: Loss valueReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesException for neuralnetwork layer class
new NeuralnetworkLayerException(message:
string, value: any)
Extends Error
Parameters
message (string) Error message
Neuralnetwork layer
Parameters
Static Members
Returns layer from JSON.
fromObject(obj: any):
LayerParameters
Returns
Layer: Layer▸ registLayer(name?, cls?) Regist layer class.
Parameters
name (string?) Name of the layer
Instance Members
Bind pre-condition values.
Parameters
values (object) Binding object
| Name | Description |
|---|
values.supervisor Matrix? | Supervisor data |
values.n number | Data count |
values.rest any | Some other values |
Update parameters.
Parameters
Returns object of this layer.
toObject()
Base class for Flow-based generative model
new FlowLayer()
Extends Layer
Instance Members
Returns determinant of the Jacobian.
Returns
number: Determinant of the JacobianNode
Type: object
Properties
name (string) : Name of the node
gradientValue (Array<Matrix>?) : Gradient value of this node from next layer
Computational graph for Neuralnetwork structure
new ComputationalGraph()
Static Members
Returns Graph.
Parameters
nodes (Array<LayerObject>) Array of object represented a graph
Returns
ComputationalGraph: GraphInstance Members
Number of nodes
size
Type: number
Returns object representation.
toObject():
Array<LayerObject>
Returns
Array<LayerObject>: Object represented this graphReturns a string of DOT format.
Returns
string: String of DOT format▸ add(layer, name?, inputs = undefined) Add a layer.
Parameters
layer (Layer) Added layer
Bind values to layers
Parameters
values (object) Binding values
Returns calculated values.
Parameters
Returns gradient values.
Parameters
Returns
Matrix: Output of gradientReturns a specific name node.
Parameters
Additive coupling layer
Extends FlowLayer
Parameters
| Name | Description |
|---|
$0.d any (default null) | |
$0.net any (default null) | |
$0.rest ...any | |
Adaptive piecewise linear layer
new AdaptivePiecewiseLinearLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.s any (default 2) | |
$0.a any (default 0.1) | |
$0.b any (default 0) | |
$0.rest ...any | |
Aranda layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.l any (default 2) | |
$0.rest ...any | |
Argmax layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Argmin layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Attention layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.dk any (default null) | |
$0.dv any (default null) | |
$0.wq any (default null) | |
$0.wk any (default null) | |
$0.wv any (default null) | |
$0.rest ...any | |
Average pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.kernel any | |
$0.stride any (default null) | |
$0.padding any (default null) | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Batch normalization layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.scale any (default 1) | |
$0.offset any (default 0) | |
$0.epsilon any (default 1.0e-12) | |
$0.channel_dim any (default -1) | |
$0.input_mean any | |
$0.input_var any | |
$0.rest ...any | |
Bimodal derivative adaptive activation layer
new BimodalDerivativeAdaptiveActivationLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.rest ...any | |
Bendable linear unit layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 0.1) | |
$0.rest ...any | |
Bounded ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.rest ...any | |
Continuously differentiable ELU layer
new ContinuouslyDifferentiableELULayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1.0) | |
$0.rest ...any | |
Clip layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.min any (default null) | |
$0.max any (default null) | |
$0.rest ...any | |
Concat layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default 1) | |
$0.rest ...any | |
Condition layer
new CondLayer()
Extends Layer
Constant layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.value any | |
$0.rest ...any | |
Convolutional layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.kernel any | |
$0.channel any (default null) | |
$0.stride any (default null) | |
$0.padding any (default null) | |
$0.w any (default null) | |
$0.activation any (default null) | |
$0.l2_decay any (default 0) | |
$0.l1_decay any (default 0) | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Concatenated ReLU layer
new ConcatenatedReLULayer()
Extends Layer
Dropout layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.drop_rate any (default 0.5) | |
$0.rest ...any | |
Elastic ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.k any (default 1) | |
$0.alpha any (default 1) | |
$0.beta any (default 1) | |
$0.rest ...any | |
ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.rest ...any | |
Embedding layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any (default 512) | |
$0.embeddings any (default {}) | |
$0.rest ...any | |
Elastic ReLU layer
new ElasticReLULayer($0:
Object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.rest ...any | |
E-swish layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 1) | |
$0.rest ...any | |
Fast ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.rest ...any | |
Flatten layer
new FlattenLayer()
Extends Layer
Flexible ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.b any (default 0) | |
$0.rest ...any | |
Fully connected layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.out_size any | |
$0.w any (default null) | |
$0.b any (default null) | |
$0.activation any (default null) | |
$0.l2_decay any (default 0) | |
$0.l1_decay any (default 0) | |
$0.rest ...any | |
Function layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.func any | |
$0.rest ...any | |
Gaussian layer
new GaussianLayer()
Extends Layer
Global average pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Global Lp pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.p any (default 2) | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Global max pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Graph convolutional layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.out_size any | |
$0.w any (default null) | |
$0.b any (default null) | |
$0.activation any (default null) | |
$0.l2_decay any (default 0) | |
$0.l1_decay any (default 0) | |
$0.rest ...any | |
Graph SAGE layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.out_size any | |
$0.aggregate any (default 'mean') | |
$0.w any (default null) | |
$0.b any (default null) | |
$0.activation any (default null) | |
$0.l2_decay any (default 0) | |
$0.l1_decay any (default 0) | |
$0.rest ...any | |
GRU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.return_sequences any (default false) | |
$0.w_z any (default null) | |
$0.w_r any (default null) | |
$0.w_h any (default null) | |
$0.u_z any (default null) | |
$0.u_r any (default null) | |
$0.u_h any (default null) | |
$0.b_z any (default null) | |
$0.b_r any (default null) | |
$0.b_h any (default null) | |
$0.sequence_dim any (default 1) | |
$0.rest ...any | |
Hard shrink layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.l any (default 0.5) | |
$0.rest ...any | |
Hard sigmoid layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 0.2) | |
$0.beta any (default 0.5) | |
$0.rest ...any | |
Hard tanh layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.v any (default 1) | |
$0.rest ...any | |
Hexpo layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.b any (default 1) | |
$0.c any (default 1) | |
$0.d any (default 1) | |
$0.rest ...any | |
Huber loss layer
new HuberLayer()
Extends Layer
Include layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.net any | |
$0.input_to any (default null) | |
$0.train any (default true) | |
$0.rest ...any | |
Input layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.name any (default null) | |
$0.rest ...any | |
Improved sigmoid layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.alpha any (default 1) | |
$0.rest ...any | |
Layer normalization layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.epsilon any (default 1.0e-12) | |
$0.scale any (default 1) | |
$0.offset any (default 0) | |
$0.rest ...any | |
Leaky ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 0.1) | |
$0.rest ...any | |
Log softmax layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.rest ...any | |
Lp pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.p any (default 2) | |
$0.kernel any | |
$0.stride any (default null) | |
$0.padding any (default null) | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
LRN layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 0.0001) | |
$0.beta any (default 0.75) | |
$0.k any (default 2) | |
$0.n any | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
LSTM layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.return_sequences any (default false) | |
$0.w_z any (default null) | |
$0.w_in any (default null) | |
$0.w_for any (default null) | |
$0.w_out any (default null) | |
$0.r_z any (default null) | |
$0.r_in any (default null) | |
$0.r_for any (default null) | |
$0.r_out any (default null) | |
$0.p_in any (default null) | |
$0.p_for any (default null) | |
$0.p_out any (default null) | |
$0.b_z any (default null) | |
$0.b_in any (default null) | |
$0.b_for any (default null) | |
$0.b_out any (default null) | |
$0.sequence_dim any (default 1) | |
$0.rest ...any | |
Matrix multiply layer
new MatmulLayer()
Extends Layer
Max pool layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.kernel any | |
$0.stride any (default null) | |
$0.padding any (default null) | |
$0.channel_dim any (default -1) | |
$0.rest ...any | |
Reduce mean layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Multiple parametric ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.beta any (default 1) | |
$0.rest ...any | |
MSE loss layer
new MSELayer()
Extends Layer
Multibin trainable linear unit layer
new MultibinTrainableLinearUnitLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.b any (default 0) | |
$0.c any (default null) | |
$0.k any (default 10) | |
$0.rest ...any | |
Natural logarithm ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 1) | |
$0.rest ...any | |
One-hot layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.class_size any (default null) | |
$0.values any (default []) | |
$0.rest ...any | |
Output layer
new OutputLayer()
Extends Layer
Pade activation unit layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.m any (default 2) | |
$0.n any (default 2) | |
$0.a any (default 0.1) | |
$0.b any (default 0) | |
$0.rest ...any | |
Parametric deformable ELU layer
new ParametricDeformableELULayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.t any (default 0.1) | |
$0.alpha any (default 1) | |
$0.rest ...any | |
Parametric ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.b any (default 1) | |
$0.rest ...any | |
Piecewise linear unit layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 0.1) | |
$0.c any (default 1) | |
$0.rest ...any | |
Parametric ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 0.25) | |
$0.rest ...any | |
Parametric rectified exponential unit layer
new ParametricRectifiedExponentialUnitLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.beta any (default 1) | |
$0.rest ...any | |
Reduce product layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Parametric sigmoid function layer
new ParametricSigmoidFunctionLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.m any (default 2) | |
$0.rest ...any | |
Penalized tanh layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 0.25) | |
$0.rest ...any | |
Parametric tanh linear unit layer
new ParametricTanhLinearUnitLayer($0:
Object, config:
object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.beta any (default 1) | |
$0.rest ...any | |
Random layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.mean any (default 0) | |
$0.variance any (default 1) | |
$0.rest ...any | |
Readout layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.method any (default 'mean') | |
$0.rest ...any | |
Reduce max layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Reduce min layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Rectified power unit layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.s any (default 2) | |
$0.rest ...any | |
Reshape layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.rest ...any | |
Simple RNN layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.activation any (default 'tanh') | |
$0.return_sequences any (default false) | |
$0.w_x any (default null) | |
$0.w_h any (default null) | |
$0.b_x any (default null) | |
$0.b_h any (default null) | |
$0.sequence_dim any (default 1) | |
$0.rest ...any | |
Randomized ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.l any (default 1.0/8) | |
$0.u any (default 1.0/3) | |
$0.rest ...any | |
Random translation ReLU layer
new RandomTranslationReLULayer($0:
Object)
Extends Layer
Parameters
| Name | Description |
|---|
$0.rest ...any | |
Scaled ELU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1.67326319217681884765625) | |
$0.g any (default 1.05070102214813232421875) | |
$0.rest ...any | |
Sigmoid layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.rest ...any | |
Self learnable AF layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.n any (default 3) | |
$0.a any (default 1) | |
$0.rest ...any | |
Softplus linear unit layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 1) | |
$0.beta any (default 1) | |
$0.gamma any (default 0) | |
$0.rest ...any | |
Soft shrink layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.l any (default 0.5) | |
$0.rest ...any | |
Softargmax layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 10000) | |
$0.rest ...any | |
Softmax layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.rest ...any | |
Softmin layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.rest ...any | |
Softplus layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 1) | |
$0.rest ...any | |
Sparse layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.rho any | |
$0.beta any | |
$0.rest ...any | |
Split layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default 1) | |
$0.size any | |
$0.rest ...any | |
Shifted ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.d any (default 0) | |
$0.rest ...any | |
Soft root sign layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.alpha any (default 3) | |
$0.beta any (default 2) | |
$0.rest ...any | |
Scaled tanh layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1) | |
$0.b any (default 1) | |
$0.rest ...any | |
Standard deviation layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Reduce sum layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
Supervisor layer
new SupervisorLayer()
Extends Layer
Swish layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.beta any (default 1) | |
$0.rest ...any | |
Trainable AF layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 0) | |
$0.b any (default 0) | |
$0.rest ...any | |
Thresholded ReLU layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.a any (default 1.0) | |
$0.rest ...any | |
Transpose layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any | |
$0.rest ...any | |
Variable layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.size any | |
$0.l2_decay any (default 0) | |
$0.l1_decay any (default 0) | |
$0.value any (default null) | |
$0.rest ...any | |
Variance layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default -1) | |
$0.keepdims any (default true) | |
$0.rest ...any | |
ONNX importer
new ONNXImporter()
Static Members
Load onnx model.
Parameters
Generated by JsPbCodeGenerator.
new AttributeProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.AttributeProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.AttributeProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.AttributeProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.AttributeProto, reader: !jspb.BinaryReader): !proto.onnx.AttributeProto
Parameters
msg (!proto.onnx.AttributeProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.AttributeProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.AttributeProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.AttributeProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string name = 1;
Returns
string:setName(value:
string): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string ref_attr_name = 21;
Returns
string:setRefAttrName(value:
string): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearRefAttrName(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string doc_string = 13;
Returns
string:setDocString(value:
string): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional AttributeType type = 20;
getType(): !proto.onnx.AttributeProto.AttributeType
Returns
!proto.onnx.AttributeProto.AttributeType:setType(value: !proto.onnx.AttributeProto.AttributeType): !proto.onnx.AttributeProto
Parameters
value (!proto.onnx.AttributeProto.AttributeType)
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearType(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional float f = 2;
Returns
number:setF(value:
number): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearF(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional int64 i = 3;
Returns
number:setI(value:
number): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearI(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional bytes s = 4; This is a type-conversion wrapper around getS()
Returns
string:Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the field making it undefined.
clearS(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional TensorProto t = 5;
getT(): proto.onnx.TensorProto?
Returns
proto.onnx.TensorProto?:setT(value: (proto.onnx.TensorProto? |
undefined)): !proto.onnx.AttributeProto
Parameters
value ((proto.onnx.TensorProto? | undefined)) Returns
!proto.onnx.AttributeProto: returns thisClears the message field making it undefined.
clearT(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional GraphProto g = 6;
getG(): proto.onnx.GraphProto?
Returns
proto.onnx.GraphProto?:setG(value: (proto.onnx.GraphProto? |
undefined)): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the message field making it undefined.
clearG(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional SparseTensorProto sparse_tensor = 22;
getSparseTensor(): proto.onnx.SparseTensorProto?
Returns
proto.onnx.SparseTensorProto?:setSparseTensor(value: (proto.onnx.SparseTensorProto? |
undefined)): !proto.onnx.AttributeProto
Parameters
value ((proto.onnx.SparseTensorProto? | undefined)) Returns
!proto.onnx.AttributeProto: returns thisClears the message field making it undefined.
clearSparseTensor(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:optional TypeProto tp = 14;
getTp(): proto.onnx.TypeProto?
Returns
proto.onnx.TypeProto?:setTp(value: (proto.onnx.TypeProto? |
undefined)): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the message field making it undefined.
clearTp(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisReturns whether this field is set.
Returns
boolean:setFloatsList(value: !
Array<
number>): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns this▸ addFloats(value, opt_index?) addFloats(value:
number, opt_index:
number?): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the list making it empty but non-null.
clearFloatsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thissetIntsList(value: !
Array<
number>): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns this▸ addInts(value, opt_index?) addInts(value:
number, opt_index:
number?): !proto.onnx.AttributeProto
Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the list making it empty but non-null.
clearIntsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisrepeated bytes strings = 9; This is a type-conversion wrapper around getStringsList()
Returns
!Array<string>:Parameters
Returns
!proto.onnx.AttributeProto: returns this▸ addStrings(value, opt_index?) Parameters
Returns
!proto.onnx.AttributeProto: returns thisClears the list making it empty but non-null.
clearStringsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisrepeated TensorProto tensors = 10;
getTensorsList(): !
Array<!proto.onnx.TensorProto>
Returns
!Array<!proto.onnx.TensorProto>:setTensorsList(value: !
Array<!proto.onnx.TensorProto>): !proto.onnx.AttributeProto
Parameters
value (!Array<!proto.onnx.TensorProto>) Returns
!proto.onnx.AttributeProto: returns this▸ addTensors(opt_value?, opt_index?) addTensors(opt_value: !proto.onnx.TensorProto?, opt_index:
number?): !proto.onnx.TensorProto
Parameters
opt_value (!proto.onnx.TensorProto?)
Returns
!proto.onnx.TensorProto:Clears the list making it empty but non-null.
clearTensorsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisrepeated GraphProto graphs = 11;
getGraphsList(): !
Array<!proto.onnx.GraphProto>
Returns
!Array<!proto.onnx.GraphProto>:setGraphsList(value: !
Array<!proto.onnx.GraphProto>): !proto.onnx.AttributeProto
Parameters
value (!Array<!proto.onnx.GraphProto>) Returns
!proto.onnx.AttributeProto: returns this▸ addGraphs(opt_value?, opt_index?) addGraphs(opt_value: !proto.onnx.GraphProto?, opt_index:
number?): !proto.onnx.GraphProto
Parameters
opt_value (!proto.onnx.GraphProto?)
Returns
!proto.onnx.GraphProto:Clears the list making it empty but non-null.
clearGraphsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisrepeated SparseTensorProto sparse_tensors = 23;
getSparseTensorsList(): !
Array<!proto.onnx.SparseTensorProto>
Returns
!Array<!proto.onnx.SparseTensorProto>:▸ setSparseTensorsList(value) setSparseTensorsList(value: !
Array<!proto.onnx.SparseTensorProto>): !proto.onnx.AttributeProto
Parameters
value (!Array<!proto.onnx.SparseTensorProto>) Returns
!proto.onnx.AttributeProto: returns this▸ addSparseTensors(opt_value?, opt_index?) addSparseTensors(opt_value: !proto.onnx.SparseTensorProto?, opt_index:
number?): !proto.onnx.SparseTensorProto
Parameters
opt_value (!proto.onnx.SparseTensorProto?)
Returns
!proto.onnx.SparseTensorProto:▸ clearSparseTensorsList() Clears the list making it empty but non-null.
clearSparseTensorsList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisrepeated TypeProto type_protos = 15;
getTypeProtosList(): !
Array<!proto.onnx.TypeProto>
Returns
!Array<!proto.onnx.TypeProto>:▸ setTypeProtosList(value) setTypeProtosList(value: !
Array<!proto.onnx.TypeProto>): !proto.onnx.AttributeProto
Parameters
value (!Array<!proto.onnx.TypeProto>) Returns
!proto.onnx.AttributeProto: returns this▸ addTypeProtos(opt_value?, opt_index?) addTypeProtos(opt_value: !proto.onnx.TypeProto?, opt_index:
number?): !proto.onnx.TypeProto
Parameters
opt_value (!proto.onnx.TypeProto?)
Returns
!proto.onnx.TypeProto:Clears the list making it empty but non-null.
clearTypeProtosList(): !proto.onnx.AttributeProto
Returns
!proto.onnx.AttributeProto: returns thisGenerated by JsPbCodeGenerator.
new ValueInfoProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.ValueInfoProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.ValueInfoProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.ValueInfoProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.ValueInfoProto, reader: !jspb.BinaryReader): !proto.onnx.ValueInfoProto
Parameters
msg (!proto.onnx.ValueInfoProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.ValueInfoProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.ValueInfoProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.ValueInfoProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string name = 1;
Returns
string:setName(value:
string): !proto.onnx.ValueInfoProto
Parameters
Returns
!proto.onnx.ValueInfoProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.ValueInfoProto
Returns
!proto.onnx.ValueInfoProto: returns thisReturns whether this field is set.
Returns
boolean:optional TypeProto type = 2;
getType(): proto.onnx.TypeProto?
Returns
proto.onnx.TypeProto?:setType(value: (proto.onnx.TypeProto? |
undefined)): !proto.onnx.ValueInfoProto
Parameters
Returns
!proto.onnx.ValueInfoProto: returns thisClears the message field making it undefined.
clearType(): !proto.onnx.ValueInfoProto
Returns
!proto.onnx.ValueInfoProto: returns thisReturns whether this field is set.
Returns
boolean:optional string doc_string = 3;
Returns
string:setDocString(value:
string): !proto.onnx.ValueInfoProto
Parameters
Returns
!proto.onnx.ValueInfoProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.ValueInfoProto
Returns
!proto.onnx.ValueInfoProto: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new NodeProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.NodeProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.NodeProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.NodeProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.NodeProto, reader: !jspb.BinaryReader): !proto.onnx.NodeProto
Parameters
msg (!proto.onnx.NodeProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.NodeProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.NodeProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.NodeProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:setOutputList(value: !
Array<
string>): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns this▸ addOutput(value, opt_index?) addOutput(value:
string, opt_index:
number?): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the list making it empty but non-null.
clearOutputList(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisoptional string name = 3;
Returns
string:setName(value:
string): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string op_type = 4;
Returns
string:setOpType(value:
string): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the field making it undefined.
clearOpType(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string domain = 7;
Returns
string:setDomain(value:
string): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the field making it undefined.
clearDomain(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string overload = 8;
Returns
string:setOverload(value:
string): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the field making it undefined.
clearOverload(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisReturns whether this field is set.
Returns
boolean:repeated AttributeProto attribute = 5;
getAttributeList(): !
Array<!proto.onnx.AttributeProto>
Returns
!Array<!proto.onnx.AttributeProto>:▸ setAttributeList(value) setAttributeList(value: !
Array<!proto.onnx.AttributeProto>): !proto.onnx.NodeProto
Parameters
value (!Array<!proto.onnx.AttributeProto>) Returns
!proto.onnx.NodeProto: returns this▸ addAttribute(opt_value?, opt_index?) addAttribute(opt_value: !proto.onnx.AttributeProto?, opt_index:
number?): !proto.onnx.AttributeProto
Parameters
opt_value (!proto.onnx.AttributeProto?)
Returns
!proto.onnx.AttributeProto:Clears the list making it empty but non-null.
clearAttributeList(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisoptional string doc_string = 6;
Returns
string:setDocString(value:
string): !proto.onnx.NodeProto
Parameters
Returns
!proto.onnx.NodeProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.NodeProto
Returns
!proto.onnx.NodeProto: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new TrainingInfoProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TrainingInfoProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TrainingInfoProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TrainingInfoProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TrainingInfoProto, reader: !jspb.BinaryReader): !proto.onnx.TrainingInfoProto
Parameters
msg (!proto.onnx.TrainingInfoProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TrainingInfoProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TrainingInfoProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TrainingInfoProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional GraphProto initialization = 1;
getInitialization(): proto.onnx.GraphProto?
Returns
proto.onnx.GraphProto?:▸ setInitialization(value) setInitialization(value: (proto.onnx.GraphProto? |
undefined)): !proto.onnx.TrainingInfoProto
Parameters
Returns
!proto.onnx.TrainingInfoProto: returns thisClears the message field making it undefined.
clearInitialization(): !proto.onnx.TrainingInfoProto
Returns
!proto.onnx.TrainingInfoProto: returns thisReturns whether this field is set.
Returns
boolean:optional GraphProto algorithm = 2;
getAlgorithm(): proto.onnx.GraphProto?
Returns
proto.onnx.GraphProto?:setAlgorithm(value: (proto.onnx.GraphProto? |
undefined)): !proto.onnx.TrainingInfoProto
Parameters
Returns
!proto.onnx.TrainingInfoProto: returns thisClears the message field making it undefined.
clearAlgorithm(): !proto.onnx.TrainingInfoProto
Returns
!proto.onnx.TrainingInfoProto: returns thisReturns whether this field is set.
Returns
boolean:▸ getInitializationBindingList() repeated StringStringEntryProto initialization_binding = 3;
getInitializationBindingList(): !
Array<!proto.onnx.StringStringEntryProto>
Returns
!Array<!proto.onnx.StringStringEntryProto>:▸ setInitializationBindingList(value) setInitializationBindingList(value: !
Array<!proto.onnx.StringStringEntryProto>): !proto.onnx.TrainingInfoProto
Parameters
value (!Array<!proto.onnx.StringStringEntryProto>) Returns
!proto.onnx.TrainingInfoProto: returns this▸ addInitializationBinding(opt_value?, opt_index?) addInitializationBinding(opt_value: !proto.onnx.StringStringEntryProto?, opt_index:
number?): !proto.onnx.StringStringEntryProto
Parameters
opt_value (!proto.onnx.StringStringEntryProto?)
Returns
!proto.onnx.StringStringEntryProto:▸ clearInitializationBindingList() Clears the list making it empty but non-null.
clearInitializationBindingList(): !proto.onnx.TrainingInfoProto
Returns
!proto.onnx.TrainingInfoProto: returns thisrepeated StringStringEntryProto update_binding = 4;
getUpdateBindingList(): !
Array<!proto.onnx.StringStringEntryProto>
Returns
!Array<!proto.onnx.StringStringEntryProto>:▸ setUpdateBindingList(value) setUpdateBindingList(value: !
Array<!proto.onnx.StringStringEntryProto>): !proto.onnx.TrainingInfoProto
Parameters
value (!Array<!proto.onnx.StringStringEntryProto>) Returns
!proto.onnx.TrainingInfoProto: returns this▸ addUpdateBinding(opt_value?, opt_index?) addUpdateBinding(opt_value: !proto.onnx.StringStringEntryProto?, opt_index:
number?): !proto.onnx.StringStringEntryProto
Parameters
opt_value (!proto.onnx.StringStringEntryProto?)
Returns
!proto.onnx.StringStringEntryProto:▸ clearUpdateBindingList() Clears the list making it empty but non-null.
clearUpdateBindingList(): !proto.onnx.TrainingInfoProto
Returns
!proto.onnx.TrainingInfoProto: returns thisGenerated by JsPbCodeGenerator.
new ModelProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.ModelProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.ModelProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.ModelProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.ModelProto, reader: !jspb.BinaryReader): !proto.onnx.ModelProto
Parameters
msg (!proto.onnx.ModelProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.ModelProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.ModelProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.ModelProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int64 ir_version = 1;
Returns
number:setIrVersion(value:
number): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearIrVersion(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:repeated OperatorSetIdProto opset_import = 8;
getOpsetImportList(): !
Array<!proto.onnx.OperatorSetIdProto>
Returns
!Array<!proto.onnx.OperatorSetIdProto>:▸ setOpsetImportList(value) setOpsetImportList(value: !
Array<!proto.onnx.OperatorSetIdProto>): !proto.onnx.ModelProto
Parameters
value (!Array<!proto.onnx.OperatorSetIdProto>) Returns
!proto.onnx.ModelProto: returns this▸ addOpsetImport(opt_value?, opt_index?) addOpsetImport(opt_value: !proto.onnx.OperatorSetIdProto?, opt_index:
number?): !proto.onnx.OperatorSetIdProto
Parameters
opt_value (!proto.onnx.OperatorSetIdProto?)
Returns
!proto.onnx.OperatorSetIdProto:Clears the list making it empty but non-null.
clearOpsetImportList(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisoptional string producer_name = 2;
Returns
string:setProducerName(value:
string): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearProducerName(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:optional string producer_version = 3;
Returns
string:▸ setProducerVersion(value) setProducerVersion(value:
string): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearProducerVersion(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:optional string domain = 4;
Returns
string:setDomain(value:
string): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearDomain(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:optional int64 model_version = 5;
Returns
number:setModelVersion(value:
number): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearModelVersion(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:optional string doc_string = 6;
Returns
string:setDocString(value:
string): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:optional GraphProto graph = 7;
getGraph(): proto.onnx.GraphProto?
Returns
proto.onnx.GraphProto?:setGraph(value: (proto.onnx.GraphProto? |
undefined)): !proto.onnx.ModelProto
Parameters
Returns
!proto.onnx.ModelProto: returns thisClears the message field making it undefined.
clearGraph(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisReturns whether this field is set.
Returns
boolean:repeated TrainingInfoProto training_info = 20;
getTrainingInfoList(): !
Array<!proto.onnx.TrainingInfoProto>
Returns
!Array<!proto.onnx.TrainingInfoProto>:▸ setTrainingInfoList(value) setTrainingInfoList(value: !
Array<!proto.onnx.TrainingInfoProto>): !proto.onnx.ModelProto
Parameters
value (!Array<!proto.onnx.TrainingInfoProto>) Returns
!proto.onnx.ModelProto: returns this▸ addTrainingInfo(opt_value?, opt_index?) addTrainingInfo(opt_value: !proto.onnx.TrainingInfoProto?, opt_index:
number?): !proto.onnx.TrainingInfoProto
Parameters
opt_value (!proto.onnx.TrainingInfoProto?)
Returns
!proto.onnx.TrainingInfoProto:▸ clearTrainingInfoList() Clears the list making it empty but non-null.
clearTrainingInfoList(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisrepeated FunctionProto functions = 25;
getFunctionsList(): !
Array<!proto.onnx.FunctionProto>
Returns
!Array<!proto.onnx.FunctionProto>:▸ setFunctionsList(value) setFunctionsList(value: !
Array<!proto.onnx.FunctionProto>): !proto.onnx.ModelProto
Parameters
value (!Array<!proto.onnx.FunctionProto>) Returns
!proto.onnx.ModelProto: returns this▸ addFunctions(opt_value?, opt_index?) addFunctions(opt_value: !proto.onnx.FunctionProto?, opt_index:
number?): !proto.onnx.FunctionProto
Parameters
opt_value (!proto.onnx.FunctionProto?)
Returns
!proto.onnx.FunctionProto:Clears the list making it empty but non-null.
clearFunctionsList(): !proto.onnx.ModelProto
Returns
!proto.onnx.ModelProto: returns thisGenerated by JsPbCodeGenerator.
new StringStringEntryProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.StringStringEntryProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.StringStringEntryProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.StringStringEntryProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.StringStringEntryProto, reader: !jspb.BinaryReader): !proto.onnx.StringStringEntryProto
Parameters
msg (!proto.onnx.StringStringEntryProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.StringStringEntryProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.StringStringEntryProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.StringStringEntryProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string key = 1;
Returns
string:setKey(value:
string): !proto.onnx.StringStringEntryProto
Parameters
Returns
!proto.onnx.StringStringEntryProto: returns thisClears the field making it undefined.
clearKey(): !proto.onnx.StringStringEntryProto
Returns
!proto.onnx.StringStringEntryProto: returns thisReturns whether this field is set.
Returns
boolean:optional string value = 2;
Returns
string:setValue(value:
string): !proto.onnx.StringStringEntryProto
Parameters
Returns
!proto.onnx.StringStringEntryProto: returns thisClears the field making it undefined.
clearValue(): !proto.onnx.StringStringEntryProto
Returns
!proto.onnx.StringStringEntryProto: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new TensorAnnotation(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TensorAnnotation) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TensorAnnotation
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TensorAnnotation:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TensorAnnotation, reader: !jspb.BinaryReader): !proto.onnx.TensorAnnotation
Parameters
msg (!proto.onnx.TensorAnnotation) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TensorAnnotation:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TensorAnnotation, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TensorAnnotation)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string tensor_name = 1;
Returns
string:setTensorName(value:
string): !proto.onnx.TensorAnnotation
Parameters
Returns
!proto.onnx.TensorAnnotation: returns thisClears the field making it undefined.
clearTensorName(): !proto.onnx.TensorAnnotation
Returns
!proto.onnx.TensorAnnotation: returns thisReturns whether this field is set.
Returns
boolean:▸ getQuantParameterTensorNamesList() repeated StringStringEntryProto quant_parameter_tensor_names = 2;
getQuantParameterTensorNamesList(): !
Array<!proto.onnx.StringStringEntryProto>
Returns
!Array<!proto.onnx.StringStringEntryProto>:▸ setQuantParameterTensorNamesList(value) setQuantParameterTensorNamesList(value: !
Array<!proto.onnx.StringStringEntryProto>): !proto.onnx.TensorAnnotation
Parameters
value (!Array<!proto.onnx.StringStringEntryProto>) Returns
!proto.onnx.TensorAnnotation: returns this▸ addQuantParameterTensorNames(opt_value?, opt_index?) addQuantParameterTensorNames(opt_value: !proto.onnx.StringStringEntryProto?, opt_index:
number?): !proto.onnx.StringStringEntryProto
Parameters
opt_value (!proto.onnx.StringStringEntryProto?)
Returns
!proto.onnx.StringStringEntryProto:▸ clearQuantParameterTensorNamesList() Clears the list making it empty but non-null.
clearQuantParameterTensorNamesList(): !proto.onnx.TensorAnnotation
Returns
!proto.onnx.TensorAnnotation: returns thisGenerated by JsPbCodeGenerator.
new GraphProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.GraphProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.GraphProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.GraphProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.GraphProto, reader: !jspb.BinaryReader): !proto.onnx.GraphProto
Parameters
msg (!proto.onnx.GraphProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.GraphProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.GraphProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.GraphProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:repeated NodeProto node = 1;
getNodeList(): !
Array<!proto.onnx.NodeProto>
Returns
!Array<!proto.onnx.NodeProto>:setNodeList(value: !
Array<!proto.onnx.NodeProto>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.NodeProto>) Returns
!proto.onnx.GraphProto: returns this▸ addNode(opt_value?, opt_index?) addNode(opt_value: !proto.onnx.NodeProto?, opt_index:
number?): !proto.onnx.NodeProto
Parameters
opt_value (!proto.onnx.NodeProto?)
Returns
!proto.onnx.NodeProto:Clears the list making it empty but non-null.
clearNodeList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisoptional string name = 2;
Returns
string:setName(value:
string): !proto.onnx.GraphProto
Parameters
Returns
!proto.onnx.GraphProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisReturns whether this field is set.
Returns
boolean:repeated TensorProto initializer = 5;
getInitializerList(): !
Array<!proto.onnx.TensorProto>
Returns
!Array<!proto.onnx.TensorProto>:▸ setInitializerList(value) setInitializerList(value: !
Array<!proto.onnx.TensorProto>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.TensorProto>) Returns
!proto.onnx.GraphProto: returns this▸ addInitializer(opt_value?, opt_index?) addInitializer(opt_value: !proto.onnx.TensorProto?, opt_index:
number?): !proto.onnx.TensorProto
Parameters
opt_value (!proto.onnx.TensorProto?)
Returns
!proto.onnx.TensorProto:Clears the list making it empty but non-null.
clearInitializerList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns this▸ getSparseInitializerList() repeated SparseTensorProto sparse_initializer = 15;
getSparseInitializerList(): !
Array<!proto.onnx.SparseTensorProto>
Returns
!Array<!proto.onnx.SparseTensorProto>:▸ setSparseInitializerList(value) setSparseInitializerList(value: !
Array<!proto.onnx.SparseTensorProto>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.SparseTensorProto>) Returns
!proto.onnx.GraphProto: returns this▸ addSparseInitializer(opt_value?, opt_index?) addSparseInitializer(opt_value: !proto.onnx.SparseTensorProto?, opt_index:
number?): !proto.onnx.SparseTensorProto
Parameters
opt_value (!proto.onnx.SparseTensorProto?)
Returns
!proto.onnx.SparseTensorProto:▸ clearSparseInitializerList() Clears the list making it empty but non-null.
clearSparseInitializerList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisoptional string doc_string = 10;
Returns
string:setDocString(value:
string): !proto.onnx.GraphProto
Parameters
Returns
!proto.onnx.GraphProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisReturns whether this field is set.
Returns
boolean:repeated ValueInfoProto output = 12;
getOutputList(): !
Array<!proto.onnx.ValueInfoProto>
Returns
!Array<!proto.onnx.ValueInfoProto>:setOutputList(value: !
Array<!proto.onnx.ValueInfoProto>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.ValueInfoProto>) Returns
!proto.onnx.GraphProto: returns this▸ addOutput(opt_value?, opt_index?) addOutput(opt_value: !proto.onnx.ValueInfoProto?, opt_index:
number?): !proto.onnx.ValueInfoProto
Parameters
opt_value (!proto.onnx.ValueInfoProto?)
Returns
!proto.onnx.ValueInfoProto:Clears the list making it empty but non-null.
clearOutputList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisrepeated ValueInfoProto value_info = 13;
getValueInfoList(): !
Array<!proto.onnx.ValueInfoProto>
Returns
!Array<!proto.onnx.ValueInfoProto>:▸ setValueInfoList(value) setValueInfoList(value: !
Array<!proto.onnx.ValueInfoProto>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.ValueInfoProto>) Returns
!proto.onnx.GraphProto: returns this▸ addValueInfo(opt_value?, opt_index?) addValueInfo(opt_value: !proto.onnx.ValueInfoProto?, opt_index:
number?): !proto.onnx.ValueInfoProto
Parameters
opt_value (!proto.onnx.ValueInfoProto?)
Returns
!proto.onnx.ValueInfoProto:Clears the list making it empty but non-null.
clearValueInfoList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns this▸ getQuantizationAnnotationList() repeated TensorAnnotation quantization_annotation = 14;
getQuantizationAnnotationList(): !
Array<!proto.onnx.TensorAnnotation>
Returns
!Array<!proto.onnx.TensorAnnotation>:▸ setQuantizationAnnotationList(value) setQuantizationAnnotationList(value: !
Array<!proto.onnx.TensorAnnotation>): !proto.onnx.GraphProto
Parameters
value (!Array<!proto.onnx.TensorAnnotation>) Returns
!proto.onnx.GraphProto: returns this▸ addQuantizationAnnotation(opt_value?, opt_index?) addQuantizationAnnotation(opt_value: !proto.onnx.TensorAnnotation?, opt_index:
number?): !proto.onnx.TensorAnnotation
Parameters
opt_value (!proto.onnx.TensorAnnotation?)
Returns
!proto.onnx.TensorAnnotation:▸ clearQuantizationAnnotationList() Clears the list making it empty but non-null.
clearQuantizationAnnotationList(): !proto.onnx.GraphProto
Returns
!proto.onnx.GraphProto: returns thisGenerated by JsPbCodeGenerator.
new TensorProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
Generated by JsPbCodeGenerator.
new Segment(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TensorProto.Segment) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TensorProto.Segment
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TensorProto.Segment:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TensorProto.Segment, reader: !jspb.BinaryReader): !proto.onnx.TensorProto.Segment
Parameters
msg (!proto.onnx.TensorProto.Segment) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TensorProto.Segment:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TensorProto.Segment, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TensorProto.Segment)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int64 begin = 1;
Returns
number:setBegin(value:
number): !proto.onnx.TensorProto.Segment
Parameters
Returns
!proto.onnx.TensorProto.Segment: returns thisClears the field making it undefined.
clearBegin(): !proto.onnx.TensorProto.Segment
Returns
!proto.onnx.TensorProto.Segment: returns thisReturns whether this field is set.
Returns
boolean:optional int64 end = 2;
Returns
number:setEnd(value:
number): !proto.onnx.TensorProto.Segment
Parameters
Returns
!proto.onnx.TensorProto.Segment: returns thisClears the field making it undefined.
clearEnd(): !proto.onnx.TensorProto.Segment
Returns
!proto.onnx.TensorProto.Segment: returns thisReturns whether this field is set.
Returns
boolean:▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TensorProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TensorProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TensorProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TensorProto, reader: !jspb.BinaryReader): !proto.onnx.TensorProto
Parameters
msg (!proto.onnx.TensorProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TensorProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TensorProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TensorProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:setDimsList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addDims(value, opt_index?) addDims(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearDimsList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisoptional int32 data_type = 2;
Returns
number:setDataType(value:
number): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the field making it undefined.
clearDataType(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:optional Segment segment = 3;
getSegment(): proto.onnx.TensorProto.Segment?
Returns
proto.onnx.TensorProto.Segment?:setSegment(value: (proto.onnx.TensorProto.Segment? |
undefined)): !proto.onnx.TensorProto
Parameters
value ((proto.onnx.TensorProto.Segment? | undefined)) Returns
!proto.onnx.TensorProto: returns thisClears the message field making it undefined.
clearSegment(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:▸ setFloatDataList(value) setFloatDataList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addFloatData(value, opt_index?) addFloatData(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearFloatDataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns this▸ setInt32DataList(value) setInt32DataList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addInt32Data(value, opt_index?) addInt32Data(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearInt32DataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns this▸ getStringDataList_asB64() repeated bytes string_data = 6; This is a type-conversion wrapper around getStringDataList()
Returns
!Array<string>:▸ getStringDataList_asU8() ▸ setStringDataList(value) Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addStringData(value, opt_index?) Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearStringDataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns this▸ setInt64DataList(value) setInt64DataList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addInt64Data(value, opt_index?) addInt64Data(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearInt64DataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisoptional string name = 8;
Returns
string:setName(value:
string): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:optional string doc_string = 12;
Returns
string:setDocString(value:
string): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:optional bytes raw_data = 9; This is a type-conversion wrapper around getRawData()
Returns
string:Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the field making it undefined.
clearRawData(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:repeated StringStringEntryProto external_data = 13;
getExternalDataList(): !
Array<!proto.onnx.StringStringEntryProto>
Returns
!Array<!proto.onnx.StringStringEntryProto>:▸ setExternalDataList(value) setExternalDataList(value: !
Array<!proto.onnx.StringStringEntryProto>): !proto.onnx.TensorProto
Parameters
value (!Array<!proto.onnx.StringStringEntryProto>) Returns
!proto.onnx.TensorProto: returns this▸ addExternalData(opt_value?, opt_index?) addExternalData(opt_value: !proto.onnx.StringStringEntryProto?, opt_index:
number?): !proto.onnx.StringStringEntryProto
Parameters
opt_value (!proto.onnx.StringStringEntryProto?)
Returns
!proto.onnx.StringStringEntryProto:▸ clearExternalDataList() Clears the list making it empty but non-null.
clearExternalDataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisoptional DataLocation data_location = 14;
getDataLocation(): !proto.onnx.TensorProto.DataLocation
Returns
!proto.onnx.TensorProto.DataLocation:setDataLocation(value: !proto.onnx.TensorProto.DataLocation): !proto.onnx.TensorProto
Parameters
value (!proto.onnx.TensorProto.DataLocation)
Returns
!proto.onnx.TensorProto: returns thisClears the field making it undefined.
clearDataLocation(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisReturns whether this field is set.
Returns
boolean:repeated double double_data = 10;
Returns
!Array<number>:▸ setDoubleDataList(value) setDoubleDataList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addDoubleData(value, opt_index?) addDoubleData(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearDoubleDataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisrepeated uint64 uint64_data = 11;
Returns
!Array<number>:▸ setUint64DataList(value) setUint64DataList(value: !
Array<
number>): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns this▸ addUint64Data(value, opt_index?) addUint64Data(value:
number, opt_index:
number?): !proto.onnx.TensorProto
Parameters
Returns
!proto.onnx.TensorProto: returns thisClears the list making it empty but non-null.
clearUint64DataList(): !proto.onnx.TensorProto
Returns
!proto.onnx.TensorProto: returns thisGenerated by JsPbCodeGenerator.
new SparseTensorProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.SparseTensorProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.SparseTensorProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.SparseTensorProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.SparseTensorProto, reader: !jspb.BinaryReader): !proto.onnx.SparseTensorProto
Parameters
msg (!proto.onnx.SparseTensorProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.SparseTensorProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.SparseTensorProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.SparseTensorProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional TensorProto values = 1;
getValues(): proto.onnx.TensorProto?
Returns
proto.onnx.TensorProto?:setValues(value: (proto.onnx.TensorProto? |
undefined)): !proto.onnx.SparseTensorProto
Parameters
value ((proto.onnx.TensorProto? | undefined)) Returns
!proto.onnx.SparseTensorProto: returns thisClears the message field making it undefined.
clearValues(): !proto.onnx.SparseTensorProto
Returns
!proto.onnx.SparseTensorProto: returns thisReturns whether this field is set.
Returns
boolean:optional TensorProto indices = 2;
getIndices(): proto.onnx.TensorProto?
Returns
proto.onnx.TensorProto?:setIndices(value: (proto.onnx.TensorProto? |
undefined)): !proto.onnx.SparseTensorProto
Parameters
value ((proto.onnx.TensorProto? | undefined)) Returns
!proto.onnx.SparseTensorProto: returns thisClears the message field making it undefined.
clearIndices(): !proto.onnx.SparseTensorProto
Returns
!proto.onnx.SparseTensorProto: returns thisReturns whether this field is set.
Returns
boolean:setDimsList(value: !
Array<
number>): !proto.onnx.SparseTensorProto
Parameters
Returns
!proto.onnx.SparseTensorProto: returns this▸ addDims(value, opt_index?) addDims(value:
number, opt_index:
number?): !proto.onnx.SparseTensorProto
Parameters
Returns
!proto.onnx.SparseTensorProto: returns thisClears the list making it empty but non-null.
clearDimsList(): !proto.onnx.SparseTensorProto
Returns
!proto.onnx.SparseTensorProto: returns thisGenerated by JsPbCodeGenerator.
new TensorShapeProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ new Dimension(opt_data?) Generated by JsPbCodeGenerator.
new Dimension(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TensorShapeProto.Dimension) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TensorShapeProto.Dimension
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TensorShapeProto.Dimension:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TensorShapeProto.Dimension, reader: !jspb.BinaryReader): !proto.onnx.TensorShapeProto.Dimension
Parameters
msg (!proto.onnx.TensorShapeProto.Dimension) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TensorShapeProto.Dimension:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TensorShapeProto.Dimension, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TensorShapeProto.Dimension)
writer (!jspb.BinaryWriter)
Instance Members
getValueCase(): proto.onnx.TensorShapeProto.Dimension.ValueCase
Returns
proto.onnx.TensorShapeProto.Dimension.ValueCase:▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int64 dim_value = 1;
Returns
number:setDimValue(value:
number): !proto.onnx.TensorShapeProto.Dimension
Parameters
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisClears the field making it undefined.
clearDimValue(): !proto.onnx.TensorShapeProto.Dimension
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisReturns whether this field is set.
Returns
boolean:optional string dim_param = 2;
Returns
string:setDimParam(value:
string): !proto.onnx.TensorShapeProto.Dimension
Parameters
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisClears the field making it undefined.
clearDimParam(): !proto.onnx.TensorShapeProto.Dimension
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisReturns whether this field is set.
Returns
boolean:optional string denotation = 3;
Returns
string:setDenotation(value:
string): !proto.onnx.TensorShapeProto.Dimension
Parameters
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisClears the field making it undefined.
clearDenotation(): !proto.onnx.TensorShapeProto.Dimension
Returns
!proto.onnx.TensorShapeProto.Dimension: returns thisReturns whether this field is set.
Returns
boolean:▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TensorShapeProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TensorShapeProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TensorShapeProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TensorShapeProto, reader: !jspb.BinaryReader): !proto.onnx.TensorShapeProto
Parameters
msg (!proto.onnx.TensorShapeProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TensorShapeProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TensorShapeProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TensorShapeProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:repeated Dimension dim = 1;
getDimList(): !
Array<!proto.onnx.TensorShapeProto.Dimension>
Returns
!Array<!proto.onnx.TensorShapeProto.Dimension>:setDimList(value: !
Array<!proto.onnx.TensorShapeProto.Dimension>): !proto.onnx.TensorShapeProto
Parameters
value (!Array<!proto.onnx.TensorShapeProto.Dimension>) Returns
!proto.onnx.TensorShapeProto: returns this▸ addDim(opt_value?, opt_index?) addDim(opt_value: !proto.onnx.TensorShapeProto.Dimension?, opt_index:
number?): !proto.onnx.TensorShapeProto.Dimension
Parameters
opt_value (!proto.onnx.TensorShapeProto.Dimension?)
Returns
!proto.onnx.TensorShapeProto.Dimension:Clears the list making it empty but non-null.
clearDimList(): !proto.onnx.TensorShapeProto
Returns
!proto.onnx.TensorShapeProto: returns thisGenerated by JsPbCodeGenerator.
new TypeProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
Generated by JsPbCodeGenerator.
new Tensor(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto.Tensor) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto.Tensor
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto.Tensor:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto.Tensor, reader: !jspb.BinaryReader): !proto.onnx.TypeProto.Tensor
Parameters
msg (!proto.onnx.TypeProto.Tensor) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto.Tensor:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto.Tensor, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto.Tensor)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int32 elem_type = 1;
Returns
number:setElemType(value:
number): !proto.onnx.TypeProto.Tensor
Parameters
Returns
!proto.onnx.TypeProto.Tensor: returns thisClears the field making it undefined.
clearElemType(): !proto.onnx.TypeProto.Tensor
Returns
!proto.onnx.TypeProto.Tensor: returns thisReturns whether this field is set.
Returns
boolean:optional TensorShapeProto shape = 2;
getShape(): proto.onnx.TensorShapeProto?
Returns
proto.onnx.TensorShapeProto?:setShape(value: (proto.onnx.TensorShapeProto? |
undefined)): !proto.onnx.TypeProto.Tensor
Parameters
value ((proto.onnx.TensorShapeProto? | undefined)) Returns
!proto.onnx.TypeProto.Tensor: returns thisClears the message field making it undefined.
clearShape(): !proto.onnx.TypeProto.Tensor
Returns
!proto.onnx.TypeProto.Tensor: returns thisReturns whether this field is set.
Returns
boolean:▸ new Sequence(opt_data?) Generated by JsPbCodeGenerator.
new Sequence(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto.Sequence) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto.Sequence
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto.Sequence:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto.Sequence, reader: !jspb.BinaryReader): !proto.onnx.TypeProto.Sequence
Parameters
msg (!proto.onnx.TypeProto.Sequence) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto.Sequence:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto.Sequence, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto.Sequence)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional TypeProto elem_type = 1;
getElemType(): proto.onnx.TypeProto?
Returns
proto.onnx.TypeProto?:setElemType(value: (proto.onnx.TypeProto? |
undefined)): !proto.onnx.TypeProto.Sequence
Parameters
Returns
!proto.onnx.TypeProto.Sequence: returns thisClears the message field making it undefined.
clearElemType(): !proto.onnx.TypeProto.Sequence
Returns
!proto.onnx.TypeProto.Sequence: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new Map(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto.Map) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto.Map
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto.Map:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto.Map, reader: !jspb.BinaryReader): !proto.onnx.TypeProto.Map
Parameters
msg (!proto.onnx.TypeProto.Map) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto.Map:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto.Map, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto.Map)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int32 key_type = 1;
Returns
number:setKeyType(value:
number): !proto.onnx.TypeProto.Map
Parameters
Returns
!proto.onnx.TypeProto.Map: returns thisClears the field making it undefined.
clearKeyType(): !proto.onnx.TypeProto.Map
Returns
!proto.onnx.TypeProto.Map: returns thisReturns whether this field is set.
Returns
boolean:optional TypeProto value_type = 2;
getValueType(): proto.onnx.TypeProto?
Returns
proto.onnx.TypeProto?:setValueType(value: (proto.onnx.TypeProto? |
undefined)): !proto.onnx.TypeProto.Map
Parameters
Returns
!proto.onnx.TypeProto.Map: returns thisClears the message field making it undefined.
clearValueType(): !proto.onnx.TypeProto.Map
Returns
!proto.onnx.TypeProto.Map: returns thisReturns whether this field is set.
Returns
boolean:▸ new Optional(opt_data?) Generated by JsPbCodeGenerator.
new Optional(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto.Optional) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto.Optional
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto.Optional:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto.Optional, reader: !jspb.BinaryReader): !proto.onnx.TypeProto.Optional
Parameters
msg (!proto.onnx.TypeProto.Optional) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto.Optional:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto.Optional, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto.Optional)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional TypeProto elem_type = 1;
getElemType(): proto.onnx.TypeProto?
Returns
proto.onnx.TypeProto?:setElemType(value: (proto.onnx.TypeProto? |
undefined)): !proto.onnx.TypeProto.Optional
Parameters
Returns
!proto.onnx.TypeProto.Optional: returns thisClears the message field making it undefined.
clearElemType(): !proto.onnx.TypeProto.Optional
Returns
!proto.onnx.TypeProto.Optional: returns thisReturns whether this field is set.
Returns
boolean:▸ new SparseTensor(opt_data?) Generated by JsPbCodeGenerator.
new SparseTensor(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto.SparseTensor) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto.SparseTensor
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto.SparseTensor:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto.SparseTensor, reader: !jspb.BinaryReader): !proto.onnx.TypeProto.SparseTensor
Parameters
msg (!proto.onnx.TypeProto.SparseTensor) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto.SparseTensor:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto.SparseTensor, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto.SparseTensor)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional int32 elem_type = 1;
Returns
number:setElemType(value:
number): !proto.onnx.TypeProto.SparseTensor
Parameters
Returns
!proto.onnx.TypeProto.SparseTensor: returns thisClears the field making it undefined.
clearElemType(): !proto.onnx.TypeProto.SparseTensor
Returns
!proto.onnx.TypeProto.SparseTensor: returns thisReturns whether this field is set.
Returns
boolean:optional TensorShapeProto shape = 2;
getShape(): proto.onnx.TensorShapeProto?
Returns
proto.onnx.TensorShapeProto?:setShape(value: (proto.onnx.TensorShapeProto? |
undefined)): !proto.onnx.TypeProto.SparseTensor
Parameters
value ((proto.onnx.TensorShapeProto? | undefined)) Returns
!proto.onnx.TypeProto.SparseTensor: returns thisClears the message field making it undefined.
clearShape(): !proto.onnx.TypeProto.SparseTensor
Returns
!proto.onnx.TypeProto.SparseTensor: returns thisReturns whether this field is set.
Returns
boolean:▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.TypeProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.TypeProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.TypeProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.TypeProto, reader: !jspb.BinaryReader): !proto.onnx.TypeProto
Parameters
msg (!proto.onnx.TypeProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.TypeProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.TypeProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.TypeProto)
writer (!jspb.BinaryWriter)
Instance Members
getValueCase(): proto.onnx.TypeProto.ValueCase
Returns
proto.onnx.TypeProto.ValueCase:▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional Tensor tensor_type = 1;
getTensorType(): proto.onnx.TypeProto.Tensor?
Returns
proto.onnx.TypeProto.Tensor?:setTensorType(value: (proto.onnx.TypeProto.Tensor? |
undefined)): !proto.onnx.TypeProto
Parameters
value ((proto.onnx.TypeProto.Tensor? | undefined)) Returns
!proto.onnx.TypeProto: returns thisClears the message field making it undefined.
clearTensorType(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:optional Sequence sequence_type = 4;
getSequenceType(): proto.onnx.TypeProto.Sequence?
Returns
proto.onnx.TypeProto.Sequence?:setSequenceType(value: (proto.onnx.TypeProto.Sequence? |
undefined)): !proto.onnx.TypeProto
Parameters
value ((proto.onnx.TypeProto.Sequence? | undefined)) Returns
!proto.onnx.TypeProto: returns thisClears the message field making it undefined.
clearSequenceType(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:optional Map map_type = 5;
getMapType(): proto.onnx.TypeProto.Map?
Returns
proto.onnx.TypeProto.Map?:setMapType(value: (proto.onnx.TypeProto.Map? |
undefined)): !proto.onnx.TypeProto
Parameters
value ((proto.onnx.TypeProto.Map? | undefined)) Returns
!proto.onnx.TypeProto: returns thisClears the message field making it undefined.
clearMapType(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:optional Optional optional_type = 9;
getOptionalType(): proto.onnx.TypeProto.Optional?
Returns
proto.onnx.TypeProto.Optional?:setOptionalType(value: (proto.onnx.TypeProto.Optional? |
undefined)): !proto.onnx.TypeProto
Parameters
value ((proto.onnx.TypeProto.Optional? | undefined)) Returns
!proto.onnx.TypeProto: returns thisClears the message field making it undefined.
clearOptionalType(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:optional SparseTensor sparse_tensor_type = 8;
getSparseTensorType(): proto.onnx.TypeProto.SparseTensor?
Returns
proto.onnx.TypeProto.SparseTensor?:▸ setSparseTensorType(value) setSparseTensorType(value: (proto.onnx.TypeProto.SparseTensor? |
undefined)): !proto.onnx.TypeProto
Parameters
value ((proto.onnx.TypeProto.SparseTensor? | undefined)) Returns
!proto.onnx.TypeProto: returns this▸ clearSparseTensorType() Clears the message field making it undefined.
clearSparseTensorType(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:optional string denotation = 6;
Returns
string:setDenotation(value:
string): !proto.onnx.TypeProto
Parameters
Returns
!proto.onnx.TypeProto: returns thisClears the field making it undefined.
clearDenotation(): !proto.onnx.TypeProto
Returns
!proto.onnx.TypeProto: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new OperatorSetIdProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.OperatorSetIdProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.OperatorSetIdProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.OperatorSetIdProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.OperatorSetIdProto, reader: !jspb.BinaryReader): !proto.onnx.OperatorSetIdProto
Parameters
msg (!proto.onnx.OperatorSetIdProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.OperatorSetIdProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.OperatorSetIdProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.OperatorSetIdProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string domain = 1;
Returns
string:setDomain(value:
string): !proto.onnx.OperatorSetIdProto
Parameters
Returns
!proto.onnx.OperatorSetIdProto: returns thisClears the field making it undefined.
clearDomain(): !proto.onnx.OperatorSetIdProto
Returns
!proto.onnx.OperatorSetIdProto: returns thisReturns whether this field is set.
Returns
boolean:optional int64 version = 2;
Returns
number:setVersion(value:
number): !proto.onnx.OperatorSetIdProto
Parameters
Returns
!proto.onnx.OperatorSetIdProto: returns thisClears the field making it undefined.
clearVersion(): !proto.onnx.OperatorSetIdProto
Returns
!proto.onnx.OperatorSetIdProto: returns thisReturns whether this field is set.
Returns
boolean:Generated by JsPbCodeGenerator.
new FunctionProto(opt_data:
Array?)
Extends jspb.Message
Parameters
opt_data (Array?) Optional initial data array, typically from a server response, or constructed directly in Javascript. The array is used in place and becomes part of the constructed object. It is not cloned. If no data is provided, the constructed object will be empty, but still valid.
Static Members
▸ toObject(includeInstance, msg) Static version of the {@see toObject} method.
Parameters
msg (!proto.onnx.FunctionProto) The msg instance to transform.
Returns
!Object:▸ deserializeBinary(bytes) Deserializes binary data (in protobuf wire format).
deserializeBinary(bytes: jspb.ByteSource): !proto.onnx.FunctionProto
Parameters
bytes (jspb.ByteSource) The bytes to deserialize.
Returns
!proto.onnx.FunctionProto:▸ deserializeBinaryFromReader(msg, reader) Deserializes binary data (in protobuf wire format) from the given reader into the given message object.
deserializeBinaryFromReader(msg: !proto.onnx.FunctionProto, reader: !jspb.BinaryReader): !proto.onnx.FunctionProto
Parameters
msg (!proto.onnx.FunctionProto) The message object to deserialize into.
reader (!jspb.BinaryReader) The BinaryReader to use.
Returns
!proto.onnx.FunctionProto:▸ serializeBinaryToWriter(message, writer) Serializes the given message to binary data (in protobuf wire format), writing to the given BinaryWriter.
serializeBinaryToWriter(message: !proto.onnx.FunctionProto, writer: !jspb.BinaryWriter)
Parameters
message (!proto.onnx.FunctionProto)
writer (!jspb.BinaryWriter)
Instance Members
▸ toObject(opt_includeInstance?) Creates an object representation of this proto. Field names that are reserved in JavaScript and will be renamed to pb_name. Optional fields that are not set will be set to undefined. To access a reserved field use, foo.pb_, eg, foo.pb_default. For the list of reserved names please see: net/proto2/compiler/js/internal/generator.cc#kKeyword.
Parameters
Returns
!Object:Serializes the message to binary data (in protobuf wire format).
Returns
!Uint8Array:optional string name = 1;
Returns
string:setName(value:
string): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the field making it undefined.
clearName(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisReturns whether this field is set.
Returns
boolean:setOutputList(value: !
Array<
string>): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns this▸ addOutput(value, opt_index?) addOutput(value:
string, opt_index:
number?): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the list making it empty but non-null.
clearOutputList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns this▸ setAttributeList(value) setAttributeList(value: !
Array<
string>): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns this▸ addAttribute(value, opt_index?) addAttribute(value:
string, opt_index:
number?): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the list making it empty but non-null.
clearAttributeList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns this▸ getAttributeProtoList() repeated AttributeProto attribute_proto = 11;
getAttributeProtoList(): !
Array<!proto.onnx.AttributeProto>
Returns
!Array<!proto.onnx.AttributeProto>:▸ setAttributeProtoList(value) setAttributeProtoList(value: !
Array<!proto.onnx.AttributeProto>): !proto.onnx.FunctionProto
Parameters
value (!Array<!proto.onnx.AttributeProto>) Returns
!proto.onnx.FunctionProto: returns this▸ addAttributeProto(opt_value?, opt_index?) addAttributeProto(opt_value: !proto.onnx.AttributeProto?, opt_index:
number?): !proto.onnx.AttributeProto
Parameters
opt_value (!proto.onnx.AttributeProto?)
Returns
!proto.onnx.AttributeProto:▸ clearAttributeProtoList() Clears the list making it empty but non-null.
clearAttributeProtoList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisrepeated NodeProto node = 7;
getNodeList(): !
Array<!proto.onnx.NodeProto>
Returns
!Array<!proto.onnx.NodeProto>:setNodeList(value: !
Array<!proto.onnx.NodeProto>): !proto.onnx.FunctionProto
Parameters
value (!Array<!proto.onnx.NodeProto>) Returns
!proto.onnx.FunctionProto: returns this▸ addNode(opt_value?, opt_index?) addNode(opt_value: !proto.onnx.NodeProto?, opt_index:
number?): !proto.onnx.NodeProto
Parameters
opt_value (!proto.onnx.NodeProto?)
Returns
!proto.onnx.NodeProto:Clears the list making it empty but non-null.
clearNodeList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisoptional string doc_string = 8;
Returns
string:setDocString(value:
string): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the field making it undefined.
clearDocString(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisReturns whether this field is set.
Returns
boolean:repeated OperatorSetIdProto opset_import = 9;
getOpsetImportList(): !
Array<!proto.onnx.OperatorSetIdProto>
Returns
!Array<!proto.onnx.OperatorSetIdProto>:▸ setOpsetImportList(value) setOpsetImportList(value: !
Array<!proto.onnx.OperatorSetIdProto>): !proto.onnx.FunctionProto
Parameters
value (!Array<!proto.onnx.OperatorSetIdProto>) Returns
!proto.onnx.FunctionProto: returns this▸ addOpsetImport(opt_value?, opt_index?) addOpsetImport(opt_value: !proto.onnx.OperatorSetIdProto?, opt_index:
number?): !proto.onnx.OperatorSetIdProto
Parameters
opt_value (!proto.onnx.OperatorSetIdProto?)
Returns
!proto.onnx.OperatorSetIdProto:Clears the list making it empty but non-null.
clearOpsetImportList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisoptional string domain = 10;
Returns
string:setDomain(value:
string): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the field making it undefined.
clearDomain(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisReturns whether this field is set.
Returns
boolean:optional string overload = 13;
Returns
string:setOverload(value:
string): !proto.onnx.FunctionProto
Parameters
Returns
!proto.onnx.FunctionProto: returns thisClears the field making it undefined.
clearOverload(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisReturns whether this field is set.
Returns
boolean:repeated ValueInfoProto value_info = 12;
getValueInfoList(): !
Array<!proto.onnx.ValueInfoProto>
Returns
!Array<!proto.onnx.ValueInfoProto>:▸ setValueInfoList(value) setValueInfoList(value: !
Array<!proto.onnx.ValueInfoProto>): !proto.onnx.FunctionProto
Parameters
value (!Array<!proto.onnx.ValueInfoProto>) Returns
!proto.onnx.FunctionProto: returns this▸ addValueInfo(opt_value?, opt_index?) addValueInfo(opt_value: !proto.onnx.ValueInfoProto?, opt_index:
number?): !proto.onnx.ValueInfoProto
Parameters
opt_value (!proto.onnx.ValueInfoProto?)
Returns
!proto.onnx.ValueInfoProto:Clears the list making it empty but non-null.
clearValueInfoList(): !proto.onnx.FunctionProto
Returns
!proto.onnx.FunctionProto: returns thisreadEnum
Type: !proto.onnx.AttributeProto.AttributeType
readEnum
Type: !proto.onnx.TensorProto.DataLocation
getField
Type: !proto.onnx.AttributeProto.AttributeType
getField
Type: !proto.onnx.TensorProto.DataLocation
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: !proto.onnx.AttributeProto.AttributeType
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: number
getFieldWithDefault
Type: number
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: !proto.onnx.TensorProto.DataLocation
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: number
getFieldWithDefault
Type: number
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: number
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFieldWithDefault
Type: string
getFloatingPointFieldWithDefault
Type: number
getWrapperField
Type: proto.onnx.TensorProto?
getWrapperField
Type: proto.onnx.GraphProto?
getWrapperField
Type: proto.onnx.SparseTensorProto?
getWrapperField
Type: proto.onnx.TypeProto?
getWrapperField
Type: proto.onnx.TypeProto?
getWrapperField
Type: proto.onnx.GraphProto?
getWrapperField
Type: proto.onnx.GraphProto?
getWrapperField
Type: proto.onnx.GraphProto?
getWrapperField
Type: proto.onnx.TensorProto.Segment?
getWrapperField
Type: proto.onnx.TensorProto?
getWrapperField
Type: proto.onnx.TensorProto?
getWrapperField
Type: proto.onnx.TensorShapeProto?
getWrapperField
Type: proto.onnx.TypeProto?
getWrapperField
Type: proto.onnx.TypeProto?
getWrapperField
Type: proto.onnx.TypeProto?
getWrapperField
Type: proto.onnx.TensorShapeProto?
getWrapperField
Type: proto.onnx.TypeProto.Tensor?
getWrapperField
Type: proto.onnx.TypeProto.Sequence?
getWrapperField
Type: proto.onnx.TypeProto.Map?
getWrapperField
Type: proto.onnx.TypeProto.Optional?
getWrapperField
Type: proto.onnx.TypeProto.SparseTensor?
getRepeatedWrapperField
Type: !Array<!proto.onnx.TensorProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.GraphProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.SparseTensorProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.TypeProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.AttributeProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.OperatorSetIdProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.TrainingInfoProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.FunctionProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.NodeProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.TensorProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.SparseTensorProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.ValueInfoProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.ValueInfoProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.ValueInfoProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.TensorAnnotation>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.TensorShapeProto.Dimension>
getRepeatedWrapperField
Type: !Array<!proto.onnx.AttributeProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.NodeProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.OperatorSetIdProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.ValueInfoProto>
getRepeatedWrapperField
Type: !Array<!proto.onnx.StringStringEntryProto>
computeOneofCase
Type: proto.onnx.TensorShapeProto.Dimension.ValueCase
computeOneofCase
Type: proto.onnx.TypeProto.ValueCase
Handle input node
HandleONNXInputNode
Static Members
Handle output node
HandleONNXOutputNode
Static Members
Import from onnx object.
import(model: onnx.ModelProto, node: onnx.ValueInfoProto):
Array<
object>
Parameters
model (onnx.ModelProto) Model object
node (onnx.ValueInfoProto) Node object
Returns
Array<object>: Objects represented a layerCreate const layers from initializer list.
requireTensor
Parameters
model (onnx.ModelProto) Model object
Returns
Array<object>: Require layer objectsNiblack thresholding
Parameters
n (number? = 3) Size of local range
k (number? = 0.1) Tuning parameter
Instance Members
Returns thresholded values.
Parameters
Returns
Array<Array<(0 | 1)>>: Predicted valuesFlow-based generative model non-linear independent component estimation
Parameters
layer_number (number) Number of layers
optimizer (string) Optimizer of the network
Instance Members
▸ fit(x, iteration, rate, batch_size) Fit model.
Parameters
iteration (number) Iteration count
batch_size (number) Batch size
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns generated values.
Parameters
Returns
Array<Array<number>>: Generated valuesReverse layer
Extends Layer
Parameters
| Name | Description |
|---|
$0.axis any (default 1) | |
$0.rest ...any | |
Non-local means filter
Parameters
n (number) Manhattan distance of the pixel to the nearest neighbor
h (number) Degree of filtering
Instance Members
Non-negative matrix factorization
new NMF()
Instance Members
Initialize model.
Parameters
rd (number? = 0) Reduced dimension
Natural Neighborhood Based Classification Algorithm
Parameters
Instance Members
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<(any | null)>: Predicted valuesNatural Outlier Factor
Parameters
k (number? = 0) Number of neighborhoods
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesNormal Herd
new NormalHERD(type: (
"full" |
"exact" |
"project" |
"drop")?, c:
number?)
Parameters
type (("full" | "exact" | "project" | "drop")? = 'exact') Method name
c (number? = 0.1) Tradeoff value between passiveness and aggressiveness
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesOnline Aggregate Prank-Bayes Point Machine
Parameters
n (number) Number of PRank models
tau (number) Probability to learn
rate (number? = 0.1) Learning rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesOne-class support vector machine
new OCSVM(nu:
number, kernel: any)
Parameters
Instance Members
Initialize model.
Parameters
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesOutlier Detection using Indegree Number
Parameters
k (number? = 5) Number of neighborhoods
t (number? = 0) Indegree threshold
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesOnline gradient descent
new OnlineGradientDescent(c:
number?, loss:
"zero_one"?)
Parameters
c (number? = 1) Tuning parameter
loss ("zero_one"? = 'zero_one') Loss type name
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesOrdering points to identify the clustering structure
Parameters
eps (number? = Infinity) Radius to determine neighborhood
minPts (number? = 5) Number of neighborhood with core distance
Instance Members
Returns predicted categories.
Returns
Array<number>: Predicted valuesarbitrarily ORiented projected CLUSter generation
Parameters
k0 (number) Number of begining seeds
l (number) Number of dimensions
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesOrdered logistic regression
new OrderedLogisticRegression(rate:
number?)
Parameters
rate (number? = 0.1) Learning rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesOrdered probit regression
new OrderedProbitRegression(rate:
number?)
Parameters
rate (number? = 0.001) Learning rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesOtus's thresholding
new OtsusThresholding()
Instance Members
Returns thresholded values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesPartitioning Around Medoids
Parameters
Instance Members
Initialize model.
Parameters
Fit model and returns true if any centroids has moved.
Returns
boolean: true if any centroids has movedReturns predicted categories.
Returns
Array<number>: Predicted valuesParticle filter
new ParticleFilter()
Instance Members
Fit and returns smoothed values.
Parameters
Returns
Array<Array<number>>: Predicted valuesPassing-Bablok method
new PassingBablok()
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesPassive Aggressive
new PA(v: (0 | 1 | 2)?)
Parameters
v ((0 | 1 | 2)? = 0) Version number
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesPerceptron Algorithm with Uneven Margins
Parameters
tp (number) Margin parameter for +1
tm (number) Margin parameter for -1
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesPrincipal component analysis
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Dual Principal component analysis
new DualPCA(rd: (
number | null)?)
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Kernel Principal component analysis
new KernelPCA(kernel: any, rd: (
number | null)?)
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Principal component analysis for anomaly detection
new AnomalyPCA()
Extends PCA
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesPossibilistic c-means
new PossibilisticCMeans(m:
number?)
Parameters
m (number? = 2) Fuzziness factor
Instance Members
Initialize model.
Parameters
Principal component regression
new PCR()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesPrimal Estimated sub-GrAdientSOlver for SVM
Parameters
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with some data.
Parameters
y (Array<(1 | -1)>) Target value
Fit model parameters.
fit()
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesPercentile anomaly detection
new PercentileAnormaly(percentile:
number, distribution: (
"data" |
"normal")?)
Parameters
percentile (number) Percentile value
distribution (("data" | "normal")? = 'data') Distribution name
Instance Members
Returns predicted anomaly flags.
Parameters
Returns
Array<boolean>: true if a data is anomaly.Perceptron
Parameters
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesAveraged perceptron
new AveragedPerceptron(rate:
number)
Parameters
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMulticlass perceptron
new MulticlassPerceptron(rate:
number)
Parameters
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesPhansalkar thresholding
Parameters
n (number? = 3) Size of local range
k (number? = 0.25) Tuning parameter
r (number? = 0.5) Tuning parameter
p (number? = 2) Tuning parameter
q (number? = 10) Tuning parameter
Instance Members
Returns thresholded values.
Parameters
Returns
Array<Array<(0 | 1)>>: Predicted valuesPartial least squares regression
Parameters
l (number) Limit on the number of latent factors
Instance Members
Initialize model.
Parameters
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesProbabilistic latent semantic analysis
Parameters
k (number? = 2) Number of clusters
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesPoisson regression
new PoissonRegression(rate:
number)
Parameters
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesPolicy gradient agent
Parameters
resolution (number? = 20) Resolution
Instance Members
Returns a action.
Parameters
state (Array<any>) Current states
Returns
Array<any>: Action▸ update(action, state, reward, done, learning_rate) Update model.
Parameters
action (Array<any>) Action
state (Array<any>) Next states
learning_rate (number) Learning rate
Polynomial histogram
Parameters
Instance Members
Returns predicted dencity.
Parameters
Returns
Array<number>: Predicted valuesPolynomial interpolation
new PolynomialInterpolation()
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesProjection pursuit regression
new ProjectionPursuit(r:
number?)
Parameters
r (number? = 5) Number of functions
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesPerceptron ranking
Parameters
rate (number? = 0.1) Learning rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesPrewitt edge detection
Parameters
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Priestley–Chao kernel estimator
Parameters
h (number) Smoothing parameter for the kernel
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesPrincipal curves
new PrincipalCurve()
Instance Members
Probabilistic Principal component analysis
new ProbabilisticPCA(method: (
"analysis" |
"em" |
"bayes")?, rd:
number)
Parameters
method (("analysis" | "em" | "bayes")? = 'analysis') Method name
Instance Members
ProbabilityModel
Type: object
Properties
Probability based classifier
new ProbabilityBasedClassifier(model: any)
Parameters
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<(any | null)>: Predicted valuesProbit
new Probit()
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Fit model parameters.
fit()
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesMultinomial probit
new MultinomialProbit()
Extends Probit
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesPROjected CLUStering algorithm
Parameters
a (number) Number to multiply the number of clusters for sample size
b (number) Number to multiply the number of clusters for final set size
minDeviation (number? = 0.1) Minimum deviation to check the medoid is bad
Instance Members
Initialize model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesReturns a list of the data predicted as outliers or not.
Returns
Array<boolean>: Predicted valuesProjectron
new Projectron(eta:
number?, kernel: any)
Parameters
kernel (any = 'gaussian')
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesProjectron++
new Projectronpp(eta:
number?, kernel: any)
Parameters
kernel (any = 'gaussian')
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesP-tile thresholding
Parameters
p (number? = 0.5) Percentile value
Instance Members
Returns thresholded values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesBase class for Q-table
Parameters
resolution (number? = 20) Resolution
Instance Members
Resolution
resolution
Type: number
Returns Q-table as array.
Returns
Array<any>: Nested arrayReturns the best action.
Parameters
state (Array<any>) Current states
Returns
Array<any>: ActionQ-learning agent
Parameters
resolution (number? = 20) Resolution
Instance Members
▸ get_action(state, greedy_rate) Returns a action.
Parameters
state (Array<any>) Current states
greedy_rate (number = 0.002) Greedy rate
Returns
Array<any>: Action▸ update(action, state, next_state, reward) Update model.
Parameters
action (Array<any>) Action
state (Array<any>) Current state
next_state (Array<any>) Next state
Quadratic discriminant analysis
new QuadraticDiscriminant()
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesQuantile regression
new QuantileRegression(tau:
number?)
Parameters
tau (number? = 0.5) Quantile value
Instance Members
▸ fit(x, y, learningRate = 0.1) Fit model.
Parameters
learningRate (number? = 0.1) Learning rate
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesBsae class for radius neighbor models
Parameters
r (number? = 1) Radius to determine neighborhood
Instance Members
Add a data.
Parameters
category (any?) Target value
radius neighbor
Extends RadiusNeighborBase
Parameters
r (number? = 1) Radius to determine neighborhood
Instance Members
Add a data.
Parameters
category (any) Target value
Add datas.
Parameters
targets (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesradius neighbor regression
Extends RadiusNeighborBase
Parameters
r (number? = 1) Radius to determine neighborhood
Instance Members
Add a data.
Parameters
category (number) Target value
Returns predicted values.
Parameters
Returns
Array<(number | null)>: Predicted valuesSemi-supervised radius neighbor
Extends RadiusNeighborBase
Parameters
k (number? = 5) Radius to determine neighborhood
Instance Members
Add a data.
Parameters
category ((any | null)) Target value
Add datas.
Parameters
targets (Array<(any | null)>) Target values
Returns predicted values.
Returns
Array<any>: Predicted valuesRamer-Douglas-Peucker algorithm
new RamerDouglasPeucker(e:
number?)
Parameters
e (number? = 0.1) Threshold of distance
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesBsae class for random forest models
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 0.8) Sampling rate
tree_class_args (Array<any>? = null) Arguments for constructor of tree class
Instance Members
The max depth among the trees.
depth
Type: number
Initialize model.
Parameters
targets (Array<any>) Target values
Random forest classifier
new RandomForestClassifier(tree_num:
number, sampling_rate:
number?, method: (
"ID3" |
"CART")?)
Extends RandomForest
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 0.8) Sampling rate
method (("ID3" | "CART")? = 'CART') Method name
Instance Members
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesRandom forest regressor
new RandomForestRegressor(tree_num:
number, sampling_rate:
number?)
Extends RandomForest
Parameters
tree_num (number) Number of trees
sampling_rate (number? = 0.8) Sampling rate
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesRandom projection
new RandomProjection(init: (
"uniform" |
"root3" |
"normal")?, rd: (
number | null)?)
Parameters
init (("uniform" | "root3" | "normal")? = 'uniform') Initialize method name
rd ((number | null)? = null) Reduced dimension
Instance Members
RankNet
Parameters
rate (number? = 0.01) Learning rate
Instance Members
Fit model.
Parameters
comp (Array<(-1 | 0 | 1)>) Sign of (data 1 rank - data 2 rank). If data 1 rank is bigger than data 2, corresponding value is 1.
Returns
number: lossReturns predicted values.
Parameters
Returns
Array<number>: Predicted valuesRANSACSubModel
Type: object
Properties
score (function (Array<any>, Array<any>): number?) : Returns a number how accurate the prediction is
Random sample consensus
new RANSAC(model: any, sample: (
number | null)?)
Parameters
sample ((number | null)? = null) Sampling rate
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesRadial basis function network
new RadialBasisFunctionNetwork(rbf: (
"linear" |
"gaussian" |
"multiquadric" |
"inverse quadratic" |
"inverse multiquadric" |
"thin plate" |
"bump")?, e:
number?, l:
number?)
Parameters
rbf (("linear" | "gaussian" | "multiquadric" | "inverse quadratic" | "inverse multiquadric" | "thin plate" | "bump")? = 'linear') RBF name
e (number? = 1) Tuning parameter
l (number? = 0) Regularization parameter
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesRestricted Boltzmann machine
Parameters
hiddenSize (number) Size of hidden layer
lr (number? = 0.01) Learning rate
Instance Members
Return a energy value of the data.
Parameters
Returns
number: Energy valueReturns predicted values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesGaussian-Bernouili Restricted Boltzmann machine
Parameters
hiddenSize (number) Size of hidden layer
lr (number? = 0.01) Learning rate
fixSigma (boolean? = false) Do not learn sigma or not
Instance Members
Return a energy value of the data.
Parameters
Returns
number: Energy valueReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesRandomized Budget Perceptron
Parameters
b (number) Number of support vectors
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesRelative Density Factor
Parameters
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesRelative Density-based Outlier Score
Parameters
k (number) Number of neighborhoods
kernel (("gaussian" | {name: "gaussian"} | function (Array<number>): number)? = 'gaussian') Kernel name
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesRidge regressioin
Parameters
lambda (number? = 0.1) Regularization strength
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns importances of the features.
Returns
Array<number>: ImportancesMulticlass ridge regressioin
new MulticlassRidge(lambda:
number?)
Parameters
lambda (number? = 0.1) Regularization strength
Instance Members
Category list
categories
Type: Array<any>
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted values.
Parameters
Returns
Array<any>: Predicted valuesReturns importances of the features.
Returns
Array<number>: ImportancesKernel ridge regression
new KernelRidge(lambda:
number?, kernel: any)
Parameters
lambda (number? = 0.1) Regularization strength
kernel (any = 'gaussian')
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns importances of the features.
Returns
Array<number>: ImportancesRobust Kernel-based Outlier Factor
Parameters
k (number) Number of neighborhoods
h (number) Smoothing parameter
alpha (number) Sensitivity parameter
kernel (any = 'gaussian')
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesRecursive least squares
new RecursiveLeastSquares()
Instance Members
Update model parameters with one data.
Parameters
Returns predicted datas.
Parameters
Returns
Array<number>: Predicted valuesRepeated median regression
new RepeatedMedianRegression()
Instance Members
Recurrent neuralnetwork
Parameters
method (("rnn" | "lstm" | "gru")? = 'lstm') Method name
window (number? = 10) Window size
unit (number? = 10) Size of recurrent unit
out_size (number? = 1) Output size
optimizer (string? = 'adam') Optimizer of the network
Instance Members
Method
method
Type: ("rnn" | "lstm" | "gru")
▸ fit(train_x, train_y, iteration, rate, batch) Fit model.
Parameters
iteration (number) Iteration count
Returns
number: Loss valueReturns predicted future values.
Parameters
Returns
Array<Array<number>>: Predicted valuesRoberts cross
Parameters
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: true if a pixel is edge.Robust scaler
new RobustScaler()
Instance Members
ROCKNode
Type: object
Properties
index (number?) : Data index of leaf node
g (number) : Number of leaf nodes
distance (number?) : Distance between children nodes
RObust Clustering using linKs
Parameters
Instance Members
Returns the specified number of clusters.
Parameters
number (number) Number of clusters
Returns
Array<ROCKNode>: Cluster nodesReturns predicted categories.
Returns
Array<number>: Predicted valuesRelaxed Online Maximum Margin Algorithm
new ROMMA()
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesAggressive Relaxed Online Maximum Margin Algorithm
new AggressiveROMMA()
Extends ROMMA
Relevance vector machine
new RVM()
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSemi-Supervised Support Vector Machine
new S3VM(kernel: any)
Parameters
Instance Members
Initialize model.
Parameters
y (Array<(1 | -1 | null)>) Target values
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSammon mapping
Parameters
Instance Members
Fit model and returns reduced values.
Returns
Array<Array<number>>: Predicted valuesSARSA agent
Parameters
resolution (number? = 20) Resolution
Instance Members
▸ get_action(state, greedy_rate) Returns a action.
Parameters
state (Array<any>) Current states
greedy_rate (number = 0.002) Greedy rate
Returns
Array<any>: Action▸ update(action, state, next_state, reward) Update model.
Parameters
action (Array<any>) Action
state (Array<any>) Current states
next_state (Array<any>) Next states
sauvola thresholding
Parameters
n (number? = 3) Size of local range
k (number? = 0.1) Tuning parameter
r (number? = 1) Tuning parameter
Instance Members
Returns thresholded values.
Parameters
Returns
Array<Array<(0 | 1)>>: Predicted valuesSavitzky-Golay filter
new SavitzkyGolayFilter(k:
number)
Parameters
k (number) Number of coefficients
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSequentially Discounting Autoregressive model
Deprecated: Does not work properly
Parameters
r (number? = 0.8) Forgetting factor
Instance Members
Returns probability of the datas.
Parameters
Returns
Array<number>: Predicted valuesReturns predicted future values.
Parameters
Returns
Array<number>: Predicted valuesSegmented regression
new SegmentedRegression(seg:
number?)
Parameters
seg (number? = 3) Number of segments
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesSelective Naive bayes
new SelectiveNaiveBayes(distribution: "gaussian"?)
Parameters
distribution ("gaussian"? = 'gaussian') Distribution name
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesSelective sampling Perceptron
Parameters
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSelective sampling Perceptron with adaptive parameter
new SelectiveSamplingAdaptivePerceptron(beta:
number, rate:
number)
Parameters
beta (number) Smooth parameter
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSelective sampling second-order Perceptron
new SelectiveSamplingSOP(b:
number)
Parameters
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSelective sampling Winnow
Parameters
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSelf-training
Parameters
Instance Members
Initialize model.
Parameters
y (Array<(any | null)>) Target values
Returns predicted categories.
predict():
Array<(any | null)>
Returns
Array<(any | null)>: Predicted valuesSemi-supervised naive bayes
new SemiSupervisedNaiveBayes(lambda:
number?)
Parameters
lambda (number? = 1) Weight applied to the contribution of the unlabeled data
Instance Members
Initialize model.
Parameters
labels (Array<(any | null)>) Target values
Returns predicted probabilities.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted categories.
Returns
number: Log likelihood valueReturns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesSezan's thresholding
Parameters
gamma (number? = 0.5) Tradeoff value between black and white
sigma (number? = 5) Sigma of normal distribution
Instance Members
Returns thresholded values.
Parameters
Returns
Array<(0 | 1)>: Predicted valuesShifting Perceptron Algorithm
new ShiftingPerceptron(lambda:
number)
Parameters
lambda (number) Rate of weight decay
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesImplicit online Learning with Kernels
new ILK(eta:
number?, lambda:
number?, c:
number?, kernel: any, loss: (
"square" |
"hinge" |
"logistic")?)
Parameters
eta (number? = 1) Learning rate
lambda (number? = 1) Regularization constant
c (number? = 1) Penalty imposed on point prediction violations.
kernel (any = 'gaussian')
loss (("square" | "hinge" | "logistic")? = 'hinge') Loss type name
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSparse Implicit online Learning with Kernels
new SILK(eta:
number?, lambda:
number?, c:
number?, w:
number?, kernel: any, loss: (
"square" |
"hinge" |
"graph" |
"logistic")?)
Extends ILK
Parameters
eta (number? = 1) Learning rate
lambda (number? = 1) Regularization constant
c (number? = 1) Penalty imposed on point prediction violations.
kernel (any = 'gaussian')
loss (("square" | "hinge" | "graph" | "logistic")? = 'hinge') Loss type name
Instance Members
Update model parameters with one data.
Parameters
y ((1 | -1)) Target value
Sinc interpolation
new SincInterpolation()
Instance Members
Fit model parameters.
Parameters
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesSliced inverse regression
new SlicedInverseRegression(s:
number, rd: (
number | null)?)
Parameters
rd ((number | null)? = null) Reduced dimension
Instance Members
Spherical linear interpolation
Parameters
o (number? = 1) Angle subtended by the arc
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesslice sampling
Parameters
Instance Members
Returns sampled values.
Parameters
n (number) Number of generated data
Returns
Array<Array<number>>: Generated valuesStandardizes Major Axis regression
new SMARegression()
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSmirnovGrubbs test
new SmirnovGrubbs(alpha:
number)
Parameters
alpha (number) Significance level
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesSmoothstep interpolation
new SmoothstepInterpolation(n:
number?)
Parameters
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesSnakes (active contour model)
Parameters
alpha (number) Penalty for length
beta (number) Penalty for curvature
gamma (number) Penalty for conformity with image
k (number? = 100) Number of vertices
Instance Members
Initialize model.
Parameters
Returns predicted edge flags.
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Sobel edge detection
Parameters
Instance Members
Returns predicted edge flags.
Parameters
Returns
Array<Array<boolean>>: Predicted values. true if a pixel is edge.Soft k-means
Parameters
beta (number? = 1) Tuning parameter
Instance Members
Initialize model.
Parameters
Returns predicted responsibilities.
Returns
Array<Array<number>>: Predicted valuesSelf-Organizing Map
Parameters
input_size (number) Input size
output_size (number) Output size
resolution (number? = 20) Resolution of output
Instance Members
Returns predicted categories.
Parameters
Returns
Array<Array<number>>: Predicted valuesSecond order perceptron
new SecondOrderPerceptron(a:
number?)
Parameters
a (number? = 1) Tuning parameter
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Update model parameters with one data.
update(x:
Matrix, y: (
1 |
-1))
Parameters
y ((1 | -1)) Target value
Fit model parameters.
fit()
Returns predicted datas.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSpectral clustering
new SpectralClustering(affinity: (
"rbf" |
"knn")?, param:
object?)
Parameters
affinity (("rbf" | "knn")? = 'rbf') Affinity type name
Instance Members
Number of clusters.
size
Type: number
Initialize model.
Parameters
Clear all clusters.
clear()
Returns predicted categories.
Returns
Array<number>: Predicted valuesFit and returns total distance the centroid has moved.
Returns
number: Total distance the centroid has movedSpline smoothing
new SmoothingSpline(l:
number)
Parameters
l (number) Smoothing parameter
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSpline interpolation
new SplineInterpolation()
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesSplit and merge segmentation
new SplitAndMerge(method: (
"variance" |
"uniformity")?, threshold:
number?)
Parameters
method (("variance" | "uniformity")? = 'variance') Method name
threshold (number? = 0.1) Threshold
Instance Members
Returns predicted segments.
Parameters
Returns
Array<Array<number>>: Predicted valuesSquared-loss Mutual information change point detection
Parameters
model (object) Density ratio estimation model
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesSingular-spectrum transformation
Parameters
Instance Members
Returns anomaly degrees.
Parameters
Returns
Array<number>: Predicted valuesStandardization
new Standardization(ddof:
number?)
Parameters
ddof (number? = 0) Delta Degrees of Freedom
Instance Members
Statistical Region Merging
new StatisticalRegionMerging(t:
number)
Parameters
Instance Members
STatistical INformation Grid-based method
new STING()
Deprecated: Not implemented
Instance Members
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesStoptron
new Stoptron(n:
number?, kernel: any)
Parameters
kernel (any = 'gaussian')
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesSupport vector clustering
new SVC(kernel: any)
Parameters
Instance Members
Number of clusters
size
Type: number
Initialize this model.
Parameters
Returns predicted categories.
Returns
Array<number>: Predicted valuesSupport vector machine
new SVM(kernel: any)
Parameters
Instance Members
Initialize this model.
Parameters
train_y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesSupport vector regression
new SVR(kernel: any)
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesTheil-Sen regression
new TheilSenRegression()
Instance Members
Returns predicted values.
Parameters
Returns
Array<number>: Predicted valuesThompson test
Parameters
alpha (number) Significance level
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesTietjen-Moore Test
Parameters
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesTighter Budget Perceptron
new TighterPerceptron(beta:
number?, p:
number?, update: (
"perceptron" |
"mira" |
"nobias")?)
Parameters
update (("perceptron" | "mira" | "nobias")? = 'perceptron') Update rule
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesTightest Perceptron
new TightestPerceptron(b:
number?, kernel: any, accuracyLoss: (
"zero_one" |
"hinge")?)
Parameters
kernel (any = 'gaussian')
accuracyLoss (("zero_one" | "hinge")? = 'hinge') Accuracy loss type name
Instance Members
Fit model parameters.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesTrigonometric interpolation
new TrigonometricInterpolation()
Instance Members
Returns predicted interpolated values.
Parameters
Returns
Array<number>: Predicted valuesStochastic Neighbor Embedding
Parameters
rd (number? = 1) Reduced dimension
perplexity (number? = 30) Perplexity
Instance Members
Fit model and returns reduced values.
Returns
Array<Array<number>>: Predicted valuesT-distributed Stochastic Neighbor Embedding
Parameters
rd (number? = 1) Reduced dimension
perplexity (number? = 30) Perplexity
Instance Members
Fit model and returns reduced values.
Returns
Array<Array<number>>: Predicted valuesTukey regression
new TukeyRegression(e:
number)
Parameters
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesTukey's fences
Parameters
Instance Members
Returns a list of the data predicted as outliers or not.
Parameters
Returns
Array<boolean>: Predicted valuesRelative unconstrained Least-Squares Importance Fitting
Parameters
alpha (number) Relative parameter
kernelNum (number) Number of kernels
Instance Members
Returns estimated values.
Parameters
Returns
Array<number>: Predicted valuesunconstrained Least-Squares Importance Fitting
Extends RuLSIF
Parameters
kernelNum (number) Number of kernels
Uniform Manifold Approximation and Projection
Parameters
n (number? = 10) Number of neighborhoods
min_dist (number? = 0.1) Minimum distance
Instance Members
Fit model and returns reduced values.
Returns
Array<Array<number>>: Predicted valuesUniversal-set Naive bayes
new UniversalSetNaiveBayes(distribution: "gaussian"?)
Parameters
distribution ("gaussian"? = 'gaussian') Distribution name
Instance Members
Fit model.
Parameters
labels (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesVariational Autoencoder
new VAE(in_size:
number, noise_dim:
number, enc_layers:
Array<LayerObject>, dec_layers:
Array<LayerObject>, optimizer:
string, class_size: (
number | null), type: (
"" |
"conditional"))
Parameters
noise_dim (number) Number of noise dimension
enc_layers (Array<LayerObject>) Layers of encoder
dec_layers (Array<LayerObject>) Layers of decoder
optimizer (string) Optimizer of the network
class_size ((number | null)) Class size for conditional type
type (("" | "conditional")) Type name
Instance Members
▸ fit(x, y, iteration, rate, batch) Fit model.
Parameters
iteration (number) Iteration count
Returns
number: Loss valueReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesVector Autoregressive model
Parameters
Instance Members
Returns predicted future values.
Parameters
Returns
Array<Array<number>>: Predicted valuesVariational Gaussian Mixture Model
Parameters
k (number) Initial number of clusters
Instance Members
Initialize model.
Parameters
Returns probability of the datas.
Parameters
Returns
Matrix: Predicted valuesReturns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesVoted-perceptron
new VotedPerceptron(rate:
number?)
Parameters
rate (number? = 1) Learning rate
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesWeighted k-means model
new WeightedKMeans(beta:
number)
Parameters
beta (number) Tuning parameter
Instance Members
Number of clusters.
size
Type: number
Add a new cluster.
Parameters
Returns
Array<number>: Added centroidClear all clusters.
clear()
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesFit model and returns total distance the centroid has moved.
Parameters
Returns
number: Total distance the centroid has movedWeighted K-Nearest Neighbor
new WeightedKNN(k:
number, metric: (
"euclid" |
"manhattan" |
"chebyshev" |
"minkowski" | function (
Array<
number>,
Array<
number>):
number)?, weight: (
"gaussian" |
"rectangular" |
"triangular" |
"epanechnikov" |
"quartic" |
"triweight" |
"cosine" |
"inversion")?)
Parameters
k (number) Number of neighbors
weight (("gaussian" | "rectangular" | "triangular" | "epanechnikov" | "quartic" | "triweight" | "cosine" | "inversion")? = 'gaussian') Weighting scheme name
Instance Members
Fit model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesWeighted least squares
new WeightedLeastSquares()
Instance Members
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valuesWinnow
new Winnow(alpha:
boolean?, threshold:
number?, version: (
1 |
2)?)
Parameters
threshold (number? = null) Threshold
version ((1 | 2)? = 1) Version of model
Instance Members
Fit model.
Parameters
y (Array<(1 | -1)>) Target values
Returns predicted values.
Parameters
Returns
Array<(1 | -1)>: Predicted valuesWord2Vec
Parameters
method (("CBOW" | "skip-gram")) Method name
n (number) Number of how many adjacent words to learn
reduce_size (number) Reduced dimension
optimizer (string) Optimizer of the network
Instance Members
▸ fit(words, iteration, rate, batch) Fit model.
Parameters
iteration (number) Iteration count
Returns
number: Loss valueReturns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valueseXtreme Gradient Boosting regression
Parameters
maxdepth (number? = 1) Maximum depth of tree
srate (number? = 1.0) Sampling rate
lambda (number? = 0.1) Regularization parameter
lr (number? = 0.5) Learning rate
Instance Members
Number of trees
size
Type: number
Initialize model.
Parameters
Returns predicted values.
Parameters
Returns
Array<Array<number>>: Predicted valueseXtreme Gradient Boosting classifier
Extends XGBoost
Parameters
maxdepth (number? = 1) Maximum depth of tree
srate (number? = 1.0) Sampling rate
lambda (number? = 0.1) Regularization parameter
lr (number? = 0) Learning rate
Instance Members
Initialize model.
Parameters
y (Array<any>) Target values
Returns predicted categories.
Parameters
Returns
Array<any>: Predicted valuesx-means
new XMeans()
Instance Members
Number of clusters.
size
Type: number
Clear all clusters.
clear()
Fit model.
Parameters
iterations (number = -1) Iteration count
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesYeo-Johnson power transformation
new YeoJohnson(lambda:
number?)
Parameters
lambda (number? = null) Lambda
Instance Members
Yinyang k-Means algorithm
Parameters
Instance Members
Initialize this model.
Parameters
Returns predicted categories.
Parameters
Returns
Array<number>: Predicted valuesZero-inflated negative binomial
new ZeroInflatedNegativeBinomial()
Instance Members
Returns predicted probabilities.
Parameters
Returns
Array<number>: Predicted valuesZero-inflated poisson
new ZeroInflatedPoisson(method: ("moments" | "maximum_likelihood")?)
Parameters
method (("moments" | "maximum_likelihood")? = 'maximum_likelihood') Method name
Instance Members
Returns predicted probabilities.
Parameters
Returns
Array<number>: Predicted valuesZero-truncated poisson
new ZeroTruncatedPoisson()
Instance Members
Returns predicted probabilities.
Parameters
Returns
Array<number>: Predicted valuesReal number range state/actioin
Parameters
Instance Members
Returns spaces.
Parameters
resolution (number) Resolution value
Returns
Array<number>: Representative valueReturns array.
Parameters
resolution (number) Resolution value
Returns
Array<number>: Array of center values▸ indexOf(value, resolution) Returns index of the value.
Parameters
resolution (number) Resolution value
Returns
number: Index of the valueInteger number range state/actioin
Parameters
Instance Members
Returns array.
Parameters
resolution (number) Resolution value
Returns
Array<number>: Representative value▸ indexOf(value, resolution) Returns index of the value.
Parameters
resolution (number) Resolution value
Returns
number: Index of the valueBase class for reinforcement learning environment
new RLEnvironmentBase()
Properties
Instance Members
Reset environment.
reset()
Returns current state.
state(agent: any):
Array<any>
Parameters
Returns
Array<any>: Current stateSet new state.
setState(state:
Array<any>, agent: any)
Parameters
state (Array<any>) New state
Do action and returns new state.
step(action:
Array<any>, agent: any)
Parameters
action (Array<any>) Actions to be performed by the agent
▸ test(state, action, agent) Do actioin without changing environment and returns new state.
test(state:
Array<any>, action:
Array<any>, agent: any)
Parameters
state (Array<any>) Environment state
action (Array<any>) Actions to be performed by the agent
Sample an action.
sample_action(agent: any):
Array<any>
Parameters
Returns
Array<any>: Sampled actionReturns accuracy.
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: AccuracyReturns precision with macro average.
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: PrecisionReturns recall with macro average.
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: RecallReturns F-score with macro average.
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
beta (number? = 1) Positive real factor. Recall is considered
beta times as important as precision.
Returns
number: F-scoreReturns Cohen's kappa coefficient.
Parameters
pred (Array<any>) Predicted classes
t (Array<any>) True classes
Returns
number: Cohen's kappa coefficientReturns Davies-Bouldin index.
Parameters
pred (Array<any>) Predicted categories
Returns
number: Davies-Bouldin indexReturns Silhouette coefficient.
Parameters
pred (Array<any>) Predicted categories
Returns
Array<number>: Silhouette coefficientReturns Dunn index.
Parameters
pred (Array<any>) Predicted categories
intra_d (("max" | "mean" | "centroid") = 'max') Intra-cluster distance type
inter_d ("centroid" = 'centroid') Inter-cluster distance type
Returns
number: Dunn indexReturns Purity.
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: PurityReturns Rand index.
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Rank indexReturns Dice index.
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
beta (number? = 1) Positive real factor. Recall is considered
beta times as important as precision.
Returns
number: Dice indexReturns Jaccard index.
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Jaccard indexReturns Fowlkes-Mallows index.
Parameters
pred (Array<any>) Predicted categories
t (Array<any>) True categories
Returns
number: Fowlkes-Mallows indexReturns Co-Ranking Matrix.
Parameters
Returns
number: Co-Ranking Matrix valueReturns MSE (Mean Squared Error).
Parameters
Returns
(number | Array<number>): Mean Squared ErrorReturns RMSE (Root Mean Squared Error).
Parameters
Returns
(number | Array<number>): Root Mean Squared ErrorReturns MAE (Mean Absolute Error).
Parameters
Returns
(number | Array<number>): Mean Absolute ErrorReturns MAD (Median Absolute Deviation).
Parameters
Returns
(number | Array<number>): Median Absolute DeviationReturns RMSPE (Root Mean Squared Percentage Error).
Parameters
Returns
(number | Array<number>): Root Mean Squared Percentage ErrorReturns MAPE (Mean Absolute Percentage Error).
Parameters
Returns
(number | Array<number>): Mean Absolute Percentage ErrorReturns MSLE (Mean Squared Logarithmic Error).
Parameters
Returns
(number | Array<number>): Mean Squared Logarithmic ErrorReturns RMSLE (Root Mean Squared Logarithmic Error).
Parameters
Returns
(number | Array<number>): RootMean Squared Logarithmic ErrorReturns R2 (coefficient of determination).
Parameters
Returns
(number | Array<number>): Coefficient of determination