function reduce( x, initial, fcn, thisArg ) {
	var xbuf;
	var ordx;
	var acc;
	var len;
	var get;
	var ref;
	var shx;
	var sx;
	var ox;
	var ix;
	var i;

	// Cache the total number of elements over which to iterate:
	len = x.length;

	// Cache the input array shape:
	shx = x.shape;

	// Cache reference to the input ndarray data buffer:
	xbuf = x.data;

	// Cache reference to the stride array:
	sx = x.strides;

	// Cache the index of the first indexed element:
	ox = x.offset;

	// Cache the array order:
	ordx = x.order;

	// Cache the element accessor:
	get = x.accessors[ 0 ];

	// Cache the reference to the original input array:
	ref = x.ref;

	// Check for a zero-dimensional array...
	if ( shx.length === 0 ) {
		return fcn.call( thisArg, initial, get( xbuf, ox ), 0, ref );
	}
	// Iterate over each element based on the linear **view** index, regardless as to how the data is stored in memory (note: this has negative performance implications for non-contiguous ndarrays due to a lack of data locality)...
	acc = initial;
	for ( i = 0; i < len; i++ ) {
		ix = vind2bind( shx, sx, ox, ordx, i, MODE );
		acc = fcn.call( thisArg, acc, get( xbuf, ix ), i, ref );
	}
	return acc;
}
Applies a function against an accumulator and each element in an array and returns the accumulated result.

@private
@param {Object} x - object containing input ndarray meta data
@param {string} x.ref - reference to original input ndarray-like object
@param {string} x.dtype - data type
@param {Collection} x.data - data buffer
@param {NonNegativeInteger} x.length - number of elements
@param {NonNegativeIntegerArray} x.shape - dimensions
@param {IntegerArray} x.strides - stride lengths
@param {NonNegativeInteger} x.offset - index offset
@param {string} x.order - specifies whether `x` is row-major (C-style) or column-major (Fortran-style)
@param {Array<Function>} x.accessors - accessors for accessing data buffer elements
@param {*} initial - initial value
@param {Function} fcn - function to apply
@param {*} thisArg - function execution context
@returns {*} accumulated result

@example
var Complex64Array = require( '@stdlib/array/complex64' );
var Complex64 = require( '@stdlib/complex/float32/ctor' );
var realf = require( '@stdlib/complex/float32/real' );
var imagf = require( '@stdlib/complex/float32/imag' );
var cadd = require( '@stdlib/complex/float64/base/add' );
var naryFunction = require( '@stdlib/utils/nary-function' );

// Create a data buffer:
var xbuf = new Complex64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 ] );

// Define the shape of the input array:
var shape = [ 2, 2 ];

// Define the array strides:
var sx = [ 2, 1 ];

// Define the index offset:
var ox = 0;

// Define a getter:
function getter( buf, idx ) {
    return buf.get( idx );
}

// Create the input ndarray-like object:
var x = {
    'ref': null,
    'dtype': 'complex64',
    'data': xbuf,
    'length': 4,
    'shape': shape,
    'strides': sx,
    'offset': ox,
    'order': 'row-major',
    'accessors': [ getter ]
};
x.ref = x;

// Compute the sum:
var v = reduce( x, new Complex64( 0.0, 0.0 ), naryFunction( cadd, 2 ) );

var re = realf( v );
// returns 16.0

var im = imagf( v );
// returns 20.0