analytics~ActiveLearner

Example

// load libs
var qm = require('qminer');
var la = qm.la;
// create model
var al = new qm.analytics.ActiveLearner();

// set data (4 labelled and 2 unlabelled examples)
var X = new la.Matrix([
    [-2, 1],
    [-2, 0],
    [-2, -1],
    [0, 1],
    [-0.9, 0],
    [0, -1]
]).transpose(); // column examples
al.setX(X);

var y = [-1, 0, -1, 1, 0, 1];
al.sety(y);
// get the array containing 1 index of the unlabelled example
// that is the closest to the hyperplane
var qidx = al.getQueryIdx(1);
console.log(qidx); // 4

Parameter

_assertLabel()

Asserts if a label and the index are valid, given number of examples cols

_assertLabelSet()

Asserts if a Map with labels is valid

_assertMatrix()

Asserts if the object is a la matrix

_getDefaultSettings()

Returns default settings

_getLabArr()

Returns an array of label values corresponding to the labelled examples

_getLabelMapFromArr()

Transforms an Array of labels to a Map from indices to labels

_getLabIdxArr()

Returns an array of indices of labelled examples

_getQueryIdx(X, y, SVC[, num]) → Array of number

Return an array of indices where the model has the highest uncertainty (1 element by default)

Returns

Array of numberB - array of indices of unlabelled examples

_getUnlabIdxArr()

Returns an array of indices of unlabelled examples

_retrain(X, y, SVC)

Retrains the SVC model

getQueryIdx([num]) → Array of number

Returns an array of 0 or more example indices sorted by uncertainty (first element is the closest to the hyperplane)

Returns

Array of numberB array of unlabelled example indices

getSVC() → module:analytics.SVC

Returns the SVC model

Returns

module:analytics.SVCB SVC model

getX() → (module:la.Matrix or module:la.SparseMatrix)

Returns the data matrix (column examples)

Returns

(module:la.Matrix or module:la.SparseMatrix)B data matrix (column examples)

gety() → Map

Returns the Map from example indices to labels (-1 or 1)

Returns

MapB label map

load(input)

Loads instance matrix, labels and the model from the input stream

retrain()

Retrains the SVC model

save(output)

Saves the instance matrix, labels and the model to the input stream

setLabel(idx, label)

Sets the label

setX(X)

Sets the data matrix (column examples)

sety(_y)

Sets the labels