AI on Browser

JavaScript AI package and 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.
• 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 as shown below.

<script type="module">
    import dam from 'https://cdn.jsdelivr.net/npm/@ai-on-browser/data-analysis-models@0.2.0/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	k-means, k-means++, k-medois, k-medians, x-means, G-means, LBG, ISODATA, Soft k-means, Fuzzy c-means, Possibilistic c-means, Kernel k-means, Agglomerative (complete linkage, single linkage, group average, Ward's, centroid, weighted average, median), DIANA, Mean shift, DBSCAN, OPTICS, HDBSCAN, PAM, CLARA, CLARANS, BIRCH, CURE, ROCK, PLSA, Latent dirichlet allocation, GMM, VBGMM, Affinity propagation, Spectral clustering, Mountain, SOM, GTM, (Growing) Neural gas, Growing cell structures, LVQ, ART, SVC, CAST, NMF, Autoencoder
classification	Linear discriminant (FLD, LDA), Quadratic discriminant, Mixture discriminant, Least squares, Ridge, (Complement / Negation / Universal-set / Selective) Naive Bayes (gaussian), AODE, k-nearest neighbor, Radius neighbor, Fuzzy k-nearest neighbor, Nearest centroid, DANN, Decision tree, Random forest, GBDT, XGBoost, ALMA, ROMMA, Online gradient descent, Passive aggressive, RLS, Second order perceptron, AROW, NAROW, Confidence weighted, CELLIP, IELLIP, Normal herd, (Multinomial) Logistic regression, (Multinomial) Probit, SVM, Gaussian process, HMM, CRF, Bayesian Network, LVQ, Perceptron, ADALINE, MLP, LMNN
semi-supervised classification	k-nearest neighbor, Radius neighbor, Label propagation, Label spreading, k-means, GMM, 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, spline, 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, GBDT, XGBoost, SVR, MLP, GMR, Isotonic, Ramer Douglas Peucker, Theil-Sen, Passing-Bablok, Repeated median
interpolation	Nearest neighbor, IDW, Linear, Spherical linear, Brahmagupta, Logarithmic, Cosine, (Inverse) Smoothstep, Cubic, (Centripetal) Catmull-Rom, Hermit, Polynomial, Lagrange, Trigonometric, Spline, RBF Network, Akima, Natural neighbor, Delaunay
anomaly detection	Percentile, MAD, Tukey's fences, Grubbs's test, Thompson test, Tietjen Moore test, Generalized ESD, Hotelling, MT, MCD, k-nearest neighbor, LOF, PCA, OCSVM, KDE, GMM, Isolation forest, Autoencoder, GAN
dimensionality reduction	Random projection, (Dual/Kernel) PCA, Incremental PCA, Probabilistic PCA, GPLVM, LSA, MDS, Linear discriminant analysis, NCA, ICA, Principal curve, Sammon, FastMap, Sliced inverse regression, LLE, Laplacian eigenmaps, Isomap, Diffusion map, SNE, t-SNE, UMAP, SOM, GTM, NMF, MOD, 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, GMM, GBRBM, HMM, VAE, GAN, NICE
smoothing	Moving average (simple, linear weighted, triangular), Exponential average, Moving median, Cumulative moving average, 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, 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	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

Models (only in package)

type	model
clustering	k-modes, k-prototypes
classification	Categorical Naive Bayes
semi-supervised classification	Self-training, Co-training
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	ZIP
density ratio estimation	RuLSIF
	RANSAC

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
Titanic	Titanic data
UCI	Data from UCI Machine Learning Repository
ESL	Data from The Elements of Statistical Learning

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.
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, asin, asinh, atan, atanh, batch normalization, convolution, cos, cosh, ELU, exp, full, gaussian, Leaky ReLU, linear, log, negative, power, PReLU, ReLU, RReLU, sigmoid, sin, sinh, softmax, softplus, softsign, sqrt, square, tan, tanh
operator	add, sub, mult, div, matmul
recurrent	GRU, LSTM, Simple RNN
reduce	sum, mean, variance, argmax, softargmax
loss	Huber, MSE
other	concat, split, detach, clip, dropout, One-hot, reshape, flatten, transpose, reverse, sparce, conditional, less

Contact

Twitter : @mirasunimoni