Update Typescript definitions

Planeshifter · Planeshifter · commit 90bd20dcc37b · 2021-06-07T22:45:59.000-04:00
diff --git a/lib/node_modules/@stdlib/ml/incr/docs/types/index.d.ts b/lib/node_modules/@stdlib/ml/incr/docs/types/index.d.ts
@@ -21,12 +21,56 @@
 /* tslint:disable:max-line-length */
 /* tslint:disable:max-file-line-count */
 
+import incrBinaryClassification = require( '@stdlib/ml/incr/binary-classification' );
 import incrkmeans = require( '@stdlib/ml/incr/kmeans' );
+import incrSGDRegression = require( '@stdlib/ml/incr/sgd-regression' );
 
 /**
 * Interface describing the `incr` namespace.
 */
 interface Namespace {
+	/**
+	* Online learning for classification using stochastic gradient descent (SGD).
+	*
+	* ## Method
+	*
+	* The sub-gradient of the loss function is estimated for each datum and the classification model is updated incrementally, with a decreasing learning rate and regularization of the feature weights based on L2 regularization.
+	*
+	* ## References
+	*
+	* -   Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30. doi:10.1007/s10107-010-0420-4
+	*
+	* @param options - options object
+	* @param options.epsilon - insensitivity parameter (default: 0.1)
+	* @param options.eta0 - constant learning rate (default: 0.02)
+	* @param options.lambda - regularization parameter (default: 1e-3)
+	* @param options.learningRate - string denoting the learning rate to use. Can be `constant`, `pegasos`, or `basic` (default: 'basic')
+	* @param options.loss - string denoting the loss function to use. Can be `hinge`, `log`, `modifiedHuber`, `perceptron`, or `squaredHinge` (default: 'log')
+	* @param options.intercept - boolean indicating whether to include an intercept (default: true)
+	* @throws must provide valid options
+	* @returns classification model
+	*
+	* @example
+	* var ns.incrBinaryClassification = require( `@stdlib/streams/ml/incr/sgd-classification` );
+	*
+	* var accumulator = ns.incrBinaryClassification({
+	*    'intercept': true
+	*    'lambda': 1e-5
+	* });
+	*
+	* // Update model as observations come in:
+	* var y = -1;
+	* var x = [ 2.3, 1.0, 5.0 ];
+	* accumulator( x, y );
+	*
+	* // Predict new observation:
+	* var yHat = accumulator.predict( x );
+	*
+	* // Retrieve coefficients:
+	* var coefs = accumulator.coefs;
+	*/
+	incrBinaryClassification: typeof incrBinaryClassification;
+
 	/**
 	* Returns an accumulator function which incrementally partitions data into `k` clusters.
 	*
@@ -92,6 +136,48 @@ interface Namespace {
 	* // returns {...}
 	*/
 	incrkmeans: typeof incrkmeans;
+
+	/**
+	* Online learning for regression using stochastic gradient descent (SGD).
+	*
+	* ## Method
+	*
+	* The sub-gradient of the loss function is estimated for each datum and the regression model is updated incrementally, with a decreasing learning rate and regularization of the feature weights based on L2 regularization.
+	*
+	* ## References
+	*
+	* -   Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3–30. doi:10.1007/s10107-010-0420-4
+	*
+	* @param options - options object
+	* @param options.epsilon - insensitivity parameter (default: 0.1)
+	* @param options.eta0 - constant learning rate (default: 0.02)
+	* @param options.lambda - regularization parameter (default: 1e-3)
+	* @param options.learningRate - string denoting the learning rate to use. Can be `constant`, `pegasos`, or `basic` (default: 'basic')
+	* @param options.loss - string denoting the loss function to use. Can be `squaredError`, `epsilonInsensitive`, or `huber` (default: 'squaredError')
+	* @param options.intercept - boolean indicating whether to include an intercept (default: true)
+	* @throws must provide valid options
+	* @returns regression model
+	*
+	* @example
+	* var ns.incrSGDRegression = require( `@stdlib/streams/ml/incr/sgd-regression` );
+	*
+	* var accumulator = ns.incrSGDRegression({
+	*     'intercept': true
+	*     'lambda': 1e-5
+	* });
+	*
+	* // Update model as observations come in:
+	* var y = 3.5;
+	* var x = [ 2.3, 1.0, 5.0 ];
+	* accumulator( x, y );
+	*
+	* // Predict new observation:
+	* var yHat = accumulator.predict( x );
+	*
+	* // Retrieve coefficients:
+	* var coefs = accumulator.coefs;
+	*/
+	incrSGDRegression: typeof incrSGDRegression;
 }
 
 /**
