@ai-on-browser/data-analysis-models

AI on Browser

Machine learning and data analysis package implemented in JavaScript and its online demo.

Features

• Most of the models are completed in a single file and implemented in a simple way.
• The machine learning part of the code does not use any external libraries, except for the loading part of the ONNX file.
• All processing in the demo is done in client-side JavaScript.

Links

• Online demo
• Package document
• Package release notes

Caution

• The code is not practical in terms of speed, memory usage, etc.
• There is no single compact file, and each model file exists only separately. However, it is possible to use them from the default import as shown in Example.

Install

npm

npm install --save @ai-on-browser/data-analysis-models

HTML

Download from the CDN.

<script type="module">
    import dam from 'https://cdn.jsdelivr.net/npm/@ai-on-browser/data-analysis-models@0.20.0/lib/index.min.js';
    // Do something
</script>

Examples

Ridge

import dam from '@ai-on-browser/data-analysis-models';

const x = dam.Matrix.randn(100, 3);
const y = x.sum(1);

const model = new dam.models.Ridge(0.1);
model.fit(x.toArray(), y.toArray());

const predict = model.predict(x.toArray());
const error = dam.evaluate.rmse(predict, y.toArray());
console.log(error);

NeuralNetwork

import dam from '@ai-on-browser/data-analysis-models';

const x = dam.Matrix.randn(100, 3);
const y = x.sum(1);

const layers = [
    { type: 'input' },
    { type: 'full', out_size: 5 },
    { type: 'tanh' },
    { type: 'full', out_size: 1 },
];
const model = dam.models.NeuralNetwork.fromObject(layers, 'mse', 'adam');
for (let i = 0; i < 100; i++) {
    model.fit(x.toArray(), y.toArray());
}

const predict = model.predict(x.toArray());
const error = dam.evaluate.rmse(predict, y.toArray());
console.log(error);

Q-learning

import dam from '@ai-on-browser/data-analysis-models';

const env = new dam.rl.CartPoleRLEnvironment();
const agent = new dam.models.QAgent(env, 6);

const n = 1.0e+4;
const totalRewards = []
for (let i = 0; i < n; i++) {
    let curState = env.reset();
    totalRewards[i] = 0;
    while (true) {
        const action = agent.get_action(curState, Math.max(0.01, 1 - i / 2000));
        const { state, reward, done } = env.step(action);
        agent.update(action, curState, state, reward);
        totalRewards[i] += reward;
        curState = state;
        if (done) {
            break;
        }
    }

    if (totalRewards.length >= 10 && totalRewards.slice(-10).reduce((s, v) => s + v, 0) / 10 > 150) {
        console.log(i, totalRewards[totalRewards.length - 1]);
        break;
    }
}

Models (with demo)

task	model
clustering	(Soft / Kernel / Genetic / Weighted / Bisecting) k-means, k-means++, k-medois, k-medians, x-means, G-means, LBG, ISODATA, Fuzzy c-means, Possibilistic c-means, k-harmonic means, MacQueen, Hartigan-Wong, Elkan, Hamelry, Drake, Yinyang, Agglomerative (complete linkage, single linkage, group average, Ward's, centroid, weighted average, median), DIANA, Monothetic, Mutual kNN, Mean shift, DBSCAN, OPTICS, HDBSCAN, DENCLUE, DBCLASD, BRIDGE, CLUES, PAM, CLARA, CLARANS, BIRCH, CURE, ROCK, C2P, PLSA, Latent dirichlet allocation, GMM, VBGMM, Affinity propagation, Spectral clustering, Mountain, (Growing) SOM, GTM, (Growing) Neural gas, Growing cell structures, LVQ, ART, SVC, CAST, CHAMELEON, COLL, CLIQUE, PROCLUS, ORCLUS, FINDIT, NMF, Autoencoder
classification	(Fisher's) Linear discriminant, Quadratic discriminant, Mixture discriminant, Least squares, (Multiclass / Kernel) Ridge, (Complement / Negation / Universal-set / Selective) Naive Bayes (gaussian), AODE, (Fuzzy / Weighted) k-nearest neighbor, Radius neighbor, Nearest centroid, ENN, ENaN, NNBCA, ADAMENN, DANN, IKNN, Decision tree, Random forest, Extra trees, GBDT, XGBoost, ALMA, (Aggressive) ROMMA, (Bounded) Online gradient descent, (Budgeted online) Passive aggressive, RLS, (Selective-sampling) Second order perceptron, AROW, NAROW, Confidence weighted, CELLIP, IELLIP, Normal herd, Stoptron, (Kernelized) Pegasos, MIRA, Forgetron, Projectron, Projectron++, Banditron, Ballseptron, (Multiclass) BSGD, ILK, SILK, (Multinomial) Logistic regression, (Multinomial) Probit, SVM, Gaussian process, HMM, CRF, Bayesian Network, LVQ, (Average / Multiclass / Voted / Kernelized / Selective-sampling / Margin / Shifting / Budget / Tighter / Tightest) Perceptron, PAUM, RBP, ADALINE, MADALINE, MLP, LMNN
semi-supervised classification	k-nearest neighbor, Radius neighbor, Label propagation, Label spreading, k-means, GMM, S3VM, Ladder network
regression	Least squares, Ridge, Lasso, Elastic net, RLS, Bayesian linear, Poisson, Least absolute deviations, Huber, Tukey, Least trimmed squares, Least median squares, Lp norm linear, SMA, Deming, Segmented, LOWESS, LOESS, spline, Naive Bayes, Gaussian process, Principal components, Partial least squares, Projection pursuit, Quantile regression, k-nearest neighbor, Radius neighbor, IDW, Nadaraya Watson, Priestley Chao, Gasser Muller, RBF Network, RVM, Decision tree, Random forest, Extra trees, GBDT, XGBoost, SVR, MLP, GMR, Isotonic, Ramer Douglas Peucker, Theil-Sen, Passing-Bablok, Repeated median
interpolation	Nearest neighbor, IDW, (Spherical) Linear, Brahmagupta, Logarithmic, Cosine, (Inverse) Smoothstep, Cubic, (Centripetal) Catmull-Rom, Hermit, Polynomial, Lagrange, Trigonometric, Spline, RBF Network, Akima, Natural neighbor, Delaunay
learning to rank	Ordered logistic, Ordered probit, PRank, OAP-BPM, RankNet
anomaly detection	Percentile, MAD, Tukey's fences, Grubbs's test, Thompson test, Tietjen Moore test, Generalized ESD, Hotelling, MT, MCD, k-nearest neighbor, LOF, COF, ODIN, LDOF, INFLO, LOCI, LoOP, RDF, LDF, KDEOS, RDOS, NOF, RKOF, ABOD, PCA, OCSVM, KDE, GMM, Isolation forest, Autoencoder, GAN
dimensionality reduction	Random projection, (Dual / Kernel / Incremental / Probabilistic) PCA, GPLVM, LSA, MDS, Linear discriminant analysis, NCA, ICA, Principal curve, Sammon, FastMap, Sliced inverse regression, LLE, HLLE, MLLE, Laplacian eigenmaps, Isomap, LTSA, Diffusion map, SNE, t-SNE, UMAP, SOM, GTM, NMF, MOD, K-SVD, Autoencoder, VAE
feature selection	Mutual information, Ridge, Lasso, Elastic net, Decision tree, NCA
transformation	Box-Cox, Yeo-Johnson
density estimation	Histogram, Average shifted histogram, Polynomial histogram, Maximum likelihood, Kernel density estimation, k-nearest neighbor, Naive Bayes, GMM, HMM
generate	MH, Slice sampling, GMM, GBRBM, HMM, VAE, GAN, NICE
smoothing	(Linear weighted / Triangular / Cumulative) Moving average, Exponential average, Moving median, KZ filter, Savitzky Golay filter, Hampel filter, Kalman filter, Particle filter, Lowpass filter, Bessel filter, Butterworth filter, Chebyshev filter, Elliptic filter
timeseries prediction	Holt winters, AR, ARMA, SDAR, VAR, Kalman filter, MLP, RNN
change point detection	Cumulative sum, k-nearest neighbor, LOF, COF, SST, KLIEP, LSIF, uLSIF, LSDD, HMM, Markov switching
segmentation	P-Tile, Automatic thresholding, Balanced histogram thresholding, Otsu's method, Sezan, Adaptive thresholding, Bernsen, Niblack, Sauvola, Phansalkar, Split and merge, Statistical Region Merging, Mean shift
denoising	NL-means, Hopfield network, RBM, GBRBM
edge detection	Roberts cross, Sobel, Prewitt, Laplacian, LoG, Canny, Snakes
word embedding	Word2Vec
recommendation	association analysis
markov decision process	Dynamic programming, Monte carlo, Q learning, SARSA, Policy gradient, DQN, DDQN, A2C, Genetic algorithm
game	DQN, DDQN

Models (only in package)

type	model
clustering	k-modes, k-prototypes, MONA
classification	Categorical Naive Bayes, (Selective-sampling) Winnow
semi-supervised classification	Semi-supervised Naive Bayes
regression	Weighted least squares
interpolation	Cubic convolution, Sinc, Lanczos, Bilinear, n-linear, n-cubic
scaling	Max absolute scaler, Minmax normalization, Robust scaler, Standardization
density estimation	ZINB, ZIP, ZTP
density ratio estimation	RuLSIF

Models (meta)

type	model
classification	Binary ensemble, Probability based, RANSAC
semi-supervised classification	Self-training, Co-training
regression	RANSAC
change point detection	Squared-loss Mutual information

Datas

name	description
manual	Create 2D or 1D data manually.
text	Create text data manually.
function	Create from a expression like exp(-(x ^ 2 + y ^ 2) / 2).
camera	Images taken with a web camera
capture	Images captured from a window
microphone	Audio recorded with a microphone
upload	Uploaded Text/CSV/Image file
Air passenger	Famous 1D time series data
HR Diagram	The Hertzsprung-Russell Diagram of the Star Cluster CYG OB1
Titanic	Titanic data
UCI	Data from UCI Machine Learning Repository
ESL	Data from The Elements of Statistical Learning
StatLib	Data from StatLib---Datasets Archive
MNIST	handwritten digits
e-Stat	Data from Statistics Dashboard (https://dashboard.e-stat.go.jp/en/)
Pokémon	Pokémon data (https://pokeapi.co/)

Reinforcement learning environment

name	description
grid	A simple maze on 2D grid world.
cartpole	Stand the pole on the cart.
mountain car	Drive the car up the hill.
acrobot	Lift the double pendulum.
pendulum	Lift the pendulum.
maze	A maze on a fine grid plane.
waterball	Moving amidst the drift of bait and poison.
blackjack	Blackjack game.
draughts	Draughts game.
reversi	Reversi game.
gomoku	Gomoku game.
breaker	Breaker game.

NeuralNetwork layers

type	name
basic	input, output, supervisor, include, const, random, variable, activation
function	absolute, acos, acosh, APL, Aranda, asin, asinh, atan, atanh, attention, batch normalization, BDAA, Bent identity, BLU, BReLU, ceil, CELU, cloglog, cloglogm, cos, cosh, CReLU, EELU, (hard) ELiSH, Elliott, ELU, embedding, EReLU, erf, ESwish, exp, FELU, full, floor, FReLU, gaussian, GELU, Hard shrink, Hexpo, identity, ISigmoid, layer normalization, Leaky ReLU, LiSHT, log, loglog, logsigmoid, mish, MPELU, MTLU, negative, NLReLU, PAU, PDELU, PELU, PLU, PReLU, PREU, PSF, pTanh, PTELU, reciprocal, ReLU, RePU, ReSech, REU, rootsig, round, RReLU, RTReLU, SELU, (hard) sigmoid, sign, SiLU, sin, sinh, SLAF, SLU, softmax, softplus, Soft shrink, softsign, sqrt, square, SReLU, SRS, sSigmoid, sTanh, (hard) Swish, TAF, tan, (hard) tanh, tanhExp, tanShrink, Thresholded ReLU
operator	add, sub, mult, div, mod, matmul, power, max, min
logical	and, bitwise and, bitwise not, bitwise or, bitwise xor, equal, greater, greater or equal, is inf, is nan, left bitshift, less, less or equal, not, or, right bitshift, xor
convolute	convolution, (Global) MaxPool, (Global) AveragePool, (Global) LpPool, LRN
recurrent	GRU, LSTM, Simple RNN
reduce	sum, mean, prod, variance, std, reduce max, reduce min, argmax, softargmax
graph	convolutional, SAGE, readout
loss	Huber, MSE
other	concat, split, detach, clip, dropout, One-hot, reshape, flatten, transpose, reverse, sparce, conditional, function

Contact

Twitter : @mirasunimoni