accumulateUnary

Perform a reduction over elements in an input ndarray.

Installation

npm install @stdlib/ndarray-base-unary-accumulate

Usage

var accumulateUnary = require( '@stdlib/ndarray-base-unary-accumulate' );

accumulateUnary( arrays, initial, clbk )

Performs a reduction over elements in an input ndarray.

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

function add( acc, x ) {
    return acc + x;
}

// Create a data buffer:
var xbuf = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Define the shape of the input array:
var shape = [ 3, 1, 2 ];

// Define the array strides:
var sx = [ 4, 4, 1 ];

// Define the index offset:
var ox = 1;

// Create the input ndarray-like object:
var x = {
    'dtype': 'float64',
    'data': xbuf,
    'shape': shape,
    'strides': sx,
    'offset': ox,
    'order': 'row-major'
};

// Compute the sum:
var v = accumulateUnary( [ x ], 0.0, add );
// returns 39.0

The function accepts the following arguments:

• arrays: array-like object containing one input ndarray.
• initial: initial value.
• clbk: callback function to apply.

Each provided ndarray should be an object with the following properties:

• dtype: data type.
• data: data buffer.
• shape: dimensions.
• strides: stride lengths.
• offset: index offset.
• order: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).

The callback is invoked with two arguments:

• acc: the current accumulated value. The first time the callback is invoked, acc is equal to the initial value.
• value: the current element.

After each callback invocation, the callback return value is subsequently used as the accumulated value for the next callback invocation.

Notes

• For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying an accumulator in order to achieve better performance.

Examples

var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var add = require( '@stdlib/number-float64-base-add' );
var accumulateUnary = require( '@stdlib/ndarray-base-unary-accumulate' );

var N = 10;
var x = {
    'dtype': 'generic',
    'data': discreteUniform( N, -100, 100, {
        'dtype': 'generic'
    }),
    'shape': [ 5, 2 ],
    'strides': [ 2, 1 ],
    'offset': 0,
    'order': 'row-major'
};

var sum = accumulateUnary( [ x ], 0.0, add );
console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );

console.log( 'sum: %d', sum );

C APIs

Character codes for data types:

• x: bool (boolean).
• c: complex64 (single-precision floating-point complex number).
• z: complex128 (double-precision floating-point complex number).
• f: float32 (single-precision floating-point number).
• d: float64 (double-precision floating-point number).
• k: int16 (signed 16-bit integer).
• i: int32 (signed 32-bit integer).
• s: int8 (signed 8-bit integer).
• t: uint16 (unsigned 16-bit integer).
• u: uint32 (unsigned 32-bit integer).
• b: uint8 (unsigned 8-bit integer).

Function name suffix naming convention:

stdlib_ndarray_<accumulation_data_type><input_data_type>_<output_data_type>[_as_<callback_arg1_data_type><callback_arg2_data_type>_<callback_return_data_type>]

For example,

void stdlib_ndarray_accumulate_dd_d(...) {...}

is a function which performs accumulation in double-precision and accepts one double-precision floating-point input ndarray and one double-precision floating-point output ndarray. In other words, the suffix encodes the function type signature.

To support callbacks whose input arguments and/or return values are of a different data type than the input and/or output ndarray data types, the naming convention supports appending an as suffix. For example,

void stdlib_ndarray_accumulate_ff_f_as_dd_d(...) {...}

is a function which performs accumulation in single-precision and accepts one single-precision floating-point input ndarray and one single-precision floating-point output ndarray. However, the callback accepts and returns double-precision floating-point numbers. Accordingly, the input and output values need to be cast using the following conversion sequence

// Convert the current accumulated value to double-precision:
double curr = (double)acc;

// Convert each input array element to double-precision:
double in1 = (double)x[ i ];

// Evaluate the callback:
double out = f( curr, in1 );

// Convert the callback return value to single-precision:
acc = (float)out;

The accumulation data type and the output ndarray data type should always be the same.

The callback is invoked with two arguments:

• acc: the current accumulated value. The first time the callback is invoked, this argument is equal to the initial value.
• value: the current element.

After each callback invocation, the callback return value is subsequently used as the accumulated value for the next callback invocation.

Usage

#include "stdlib/ndarray/base/unary_accumulate.h"

Notes

• The initial value and output ndarrays are assumed to be zero-dimensional ndarrays.

Examples

#include "stdlib/ndarray/base/unary_accumulate.h"
#include "stdlib/ndarray/dtypes.h"
#include "stdlib/ndarray/index_modes.h"
#include "stdlib/ndarray/orders.h"
#include "stdlib/ndarray/ctor.h"
#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>
#include <inttypes.h>

static void print_ndarray_contents( const struct ndarray *x ) {
    int64_t i;
    int8_t s;
    double v;

    for ( i = 0; i < stdlib_ndarray_length( x ); i++ ) {
        s = stdlib_ndarray_iget_float64( x, i, &v );
        if ( s != 0 ) {
            fprintf( stderr, "Unable to resolve data element.\n" );
            exit( EXIT_FAILURE );
        }
        fprintf( stdout, "data[%"PRId64"] = %lf\n", i, v );
    }
}

static double add( const double acc, const double x ) {
    return acc + x;
}

int main( void ) {
    // Define the ndarray data type:
    enum STDLIB_NDARRAY_DTYPE dtype = STDLIB_NDARRAY_FLOAT64;

    // Create underlying byte arrays:
    double xvalues[] = { 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0 };
    double ivalues[] = { 0.0 };
    double ovalues[] = { 0.0 };

    uint8_t *xbuf = (uint8_t *)xvalues;
    uint8_t *ibuf = (uint8_t *)ivalues;
    uint8_t *obuf = (uint8_t *)ovalues;

    // Define the number of dimensions:
    int64_t ndims = 3;

    // Define the array shapes:
    int64_t xsh[] = { 2, 2, 2 };
    int64_t ish[] = {};
    int64_t osh[] = {};

    // Define the strides:
    int64_t sx[] = { 32, 16, 8 };
    int64_t si[] = { 0 };
    int64_t so[] = { 0 };

    // Define the offsets:
    int64_t ox = 0;
    int64_t oi = 0;
    int64_t oo = 0;

    // Define the array order:
    enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;

    // Specify the index mode:
    enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;

    // Specify the subscript index modes:
    int8_t submodes[] = { imode };
    int64_t nsubmodes = 1;

    // Create an input ndarray:
    struct ndarray *x = stdlib_ndarray_allocate( dtype, xbuf, ndims, xsh, sx, ox, order, imode, nsubmodes, submodes );
    if ( x == NULL ) {
        fprintf( stderr, "Error allocating memory.\n" );
        exit( EXIT_FAILURE );
    }

    // Create an initial value zero-dimensional ndarray:
    struct ndarray *initial = stdlib_ndarray_allocate( dtype, ibuf, ndims, ish, si, oi, order, imode, nsubmodes, submodes );
    if ( initial == NULL ) {
        fprintf( stderr, "Error allocating memory.\n" );
        exit( EXIT_FAILURE );
    }

    // Create an output zero-dimensional ndarray:
    struct ndarray *out = stdlib_ndarray_allocate( dtype, obuf, ndims, osh, so, oo, order, imode, nsubmodes, submodes );
    if ( out == NULL ) {
        fprintf( stderr, "Error allocating memory.\n" );
        exit( EXIT_FAILURE );
    }

    // Define an array containing the ndarrays:
    struct ndarray *arrays[] = { x, initial, out };

    // Apply the callback:
    int8_t status = stdlib_ndarray_accumulate_dd_d( arrays, (void *)add );
    if ( status != 0 ) {
        fprintf( stderr, "Error during computation.\n" );
        exit( EXIT_FAILURE );
    }

    // Print the results:
    print_ndarray_contents( out );
    fprintf( stdout, "\n" );

    // Free allocated memory:
    stdlib_ndarray_free( x );
    stdlib_ndarray_free( initial );
    stdlib_ndarray_free( out );
}

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.