function accumulator( x, y ) {
		if ( arguments.length === 0 ) {
			return model.coefficients;
		}
		if ( !isVectorLike( x ) ) {
			throw new TypeError( format( 'invalid argument. First argument must be a one-dimensional ndarray. Value: `%s`.', x ) );
		}
		if ( y !== -1 && y !== 1 ) {
			throw new TypeError( format( 'invalid argument. Second argument must be either +1 or -1. Value: `%s`.', y ) );
		}
		if ( x.shape[ 0 ] !== model.nfeatures ) {
			throw new TypeError( format( 'invalid argument. First argument must be a one-dimensional ndarray of length %u. Actual length: `%u`.', model.nfeatures, x.shape[ 0 ] ) );
		}
		model.update( x, y );
		return model.coefficients;
	}
If provided a feature vector and response value, the accumulator function updates a binary classification model; otherwise, the accumulator function returns the current binary classification model coefficients.

@private
@param {VectorLike} x - feature vector
@param {integer} y - response value
@throws {TypeError} first argument must be a one-dimensional ndarray
@throws {TypeError} first argument must be a one-dimensional ndarray whose length matches the number of model features
@throws {TypeError} second argument must be either `+1` or `-1`
@returns {ndarray} one-dimensional ndarray containing model coefficients

@example
var Float64Array = require( '@stdlib/array/float64' );
var array = require( '@stdlib/ndarray/array' );

// Create an accumulator:
var accumulator = incrBinaryClassification( 3 );

// ...

// Update the model:
var x = array( new Float64Array( [ 2.3, 1.0, 5.0 ] ) );
var coefs = accumulator( x, 1 );
// returns <ndarray>