Notoriously Psychedelic Modules

    gaussianMixture

    0.9.0 • Public • Published

    Gaussian Mixture

    Build Status

    This module implements a 1D Gaussian Mixture class that allows to fit a distribution of points along a one-dimensional axis.

    image

    Install

    npm install gaussianMixture

    Require

    var GMM = require('gaussianMixture');

    GMM

    index.js:26-37

    Instantiate a new GMM.

    Parameters

    • nComponents Number number of components in the mixture
    • weights Array array of weights for each component in the mixture, must sum to 1
    • means Array array of means for each component
    • vars Array array of variances of each component
    • options Object an object that can define the variancePrior, separationPrior, variancePriorRelevance and separationPriorRelevance. The priors are taken into account when the GMM is optimized given some data. The relevance parameters should be non-negative numbers, 1 meaning that the prior has equal weight as the result of the optimal GMM in each EM step, 0 meaning no influence, and Infinity means a fixed variance (resp. separation).

    Examples

    var gmm = new GMM(3, [0.3, 0.2, 0.5], [1, 2, 3], [1, 1, 0.5]);

    Returns GMM a gmm object

    sample

    index.js:57-71

    Randomly sample from the GMM's distribution.

    Parameters

    • nSamples Number desired number of samples

    Returns Array An array of randomly sampled numbers that follow the GMM's distribution

    memberships

    index.js:79-86

    Given an array of data, determine their memberships for each component of the GMM.

    Parameters

    • data Array array of numbers representing the samples to score under the model
    • gaussians Array (optional) an Array of length nComponents that contains the gaussians for the GMM

    Returns Array (data.length * this.nComponents) matrix with membership weights

    membership

    index.js:116-127

    Given a datapoint, determine its memberships for each component of the GMM.

    Parameters

    • x Number number representing the sample to score under the model
    • gaussians Array (optional) an Array of length nComponents that contains the gaussians for the GMM

    Returns Array an array of length this.nComponents with membership weights, i.e the probabilities that this datapoint was drawn from the each component

    logLikelihood

    index.js:252-257

    Compute the log-likelihood for the GMM given data.

    Parameters

    Returns Number the log-likelihood

    optimize

    index.js:345-350

    Compute the optimal GMM components given an array of data. If options has a true flag for initialize, the optimization will begin with a K-means++ initialization. This allows to have a data-dependent initialization and should converge quicker and to a better model. The initialization is agnostic to the other priors that the options might contain. The initialize flag is unavailable with the histogram version of this function

    Parameters

    • data (Array | Histogram) the data array or histogram
    • maxIterations Number? maximum number of expectation-maximization steps (optional, default 200)
    • logLikelihoodTol Number? tolerance for the log-likelihood to determine if we reached the optimum (optional, default 0.0000001)

    Returns Number the number of steps to reach the converged solution

    initialize

    index.js:428-470

    Initialize the GMM given data with the K-means++ initialization algorithm. The k-means++ algorithm choses datapoints amongst the data at random, while ensuring that the chosen seeds are far from each other. The resulting seeds are returned sorted.

    Parameters

    • data Array array of numbers representing the samples to use to optimize the model

    Examples

    var gmm = new GMM(3, [0.3, .04, 0.3], [1, 5, 10]);
    var data = [1.2, 1.3, 7.4, 1.4, 14.3, 15.3, 1.0, 7.2];
    gmm.initialize(data); // updates the means of the GMM with the K-means++ initialization algorithm, returns something like [1.3, 7.4, 14.3]

    Returns Array an array of length nComponents that contains the means for the initialization.

    model

    index.js:492-499

    Return the model for the GMM as a raw JavaScript Object.

    Returns Object the model, with keys nComponents, weights, means, vars.

    fromModel

    index.js:511-519

    Instantiate a GMM from an Object model and options.

    Parameters

    • model
    • options

    Examples

    var gmm = GMM.fromModel({
    nComponents: 3,
    weights: [0.3, 0.2, 0.5],
    means: [1, 2, 3],
    vars: [1, 1, 0.5]
    });

    Returns GMM the GMM corresponding to the given model

    Histogram

    index.js:533-538

    Instantiate a new Histogram.

    Parameters

    • h Object? an object with keys 'counts' and 'bins'. Both are optional. An observation x will be counted for the key i if bins[i][0] <= x < bins[i][1]. If bins are not specified, the bins will be corresponding to one unit in the scale of the data. The keys of the 'counts' hash will be stringified integers. (optional, default {})

    Examples

    var h = new Histogram({counts: {'a': 3, 'b': 2, 'c': 5}, bins: {'a': [0, 2], 'b': [2, 4], 'c': [4, 7]}});
    var h = new Histogram({counts: {'1': 3, '2': 2, '3': 5}});
    var h = new Histogram();

    Returns Histogram a histogram object. It has keys 'bins' (possibly null) and 'counts'.

    add

    index.js:577-587

    Add an observation to an histogram.

    Parameters

    • x Array observation to add tos the histogram

    Returns Histogram the histogram with added value.

    flatten

    index.js:593-608

    Return a data array from a histogram.

    Returns Array an array of observations derived from the histogram counts.

    value

    index.js:638-645

    Return the median value for the given key, derived from the bins.

    Parameters

    • key

    Returns Number the value for the provided key.

    fromData

    index.js:624-632

    Instantiate a new Histogram.

    Parameters

    • data Array? array of observations to include in the histogram. Observations that do not correspond to any bin will be discarded. (optional, default [])
    • bins Object? a map from key to range (a range being an array of two elements) An observation x will be counted for the key i if bins[i][0] <= x < bins[i][1]. If not specified, the bins will be corresponding to one unit in the scale of the data. (optional, default {})

    Examples

    var h = Histogram.fromData([1, 2, 2, 2, 5, 5], {A: [0, 1], B: [1, 5], C: [5, 10]});
    // {bins: {A: [0, 1], B: [1, 5], C: [5, 10]}, counts: {A: 0, B: 4, C: 2}}
    var h = Histogram.fromData([1, 2, 2, 2, 2.4, 2.5, 5, 5]);
    // {counts: {'1': 1, '2': 4, '3': 1, '5': 2}}

    Returns Histogram a histogram object It has keys 'bins' (possibly null) and 'counts'.

    Install

    npm i gaussianMixture

    DownloadsWeekly Downloads

    22

    Version

    0.9.0

    License

    MIT

    Last publish

    Collaborators

    • benjamintd