Package Exports
- @stdlib/ndarray-base-unary
- @stdlib/ndarray-base-unary/lib/index.js
This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@stdlib/ndarray-base-unary) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.
Readme
Unary
Apply a unary callback to elements in an input ndarray and assign results to elements in an output ndarray.
Installation
npm install @stdlib/ndarray-base-unaryUsage
var unary = require( '@stdlib/ndarray-base-unary' );unary( arrays, fcn )
Applies a unary callback to elements in an input ndarray and assigns results to elements in an output ndarray.
var Float64Array = require( '@stdlib/array-float64' );
function scale( x ) {
return x * 10.0;
}
// Create data buffers:
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 ] );
var ybuf = new Float64Array( 6 );
// Define the shape of the input and output arrays:
var shape = [ 3, 1, 2 ];
// Define the array strides:
var sx = [ 4, 4, 1 ];
var sy = [ 2, 2, 1 ];
// Define the index offsets:
var ox = 1;
var oy = 0;
// Create the input and output ndarray-like objects:
var x = {
'dtype': 'float64',
'data': xbuf,
'shape': shape,
'strides': sx,
'offset': ox,
'order': 'row-major'
};
var y = {
'dtype': 'float64',
'data': ybuf,
'shape': shape,
'strides': sy,
'offset': oy,
'order': 'row-major'
};
// Apply the unary function:
unary( [ x, y ], scale );
console.log( y.data );
// => <Float64Array>[ 20.0, 30.0, 60.0, 70.0, 100.0, 110.0 ]The function accepts the following arguments:
- arrays: array-like object containing one input ndarray and one output ndarray.
- fcn: unary 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).
Notes
- For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a unary function in order to achieve better performance.
Examples
var discreteUniform = require( '@stdlib/random-base-discrete-uniform' ).factory;
var filledarray = require( '@stdlib/array-filled' );
var filledarrayBy = require( '@stdlib/array-filled-by' );
var shape2strides = require( '@stdlib/ndarray-base-shape2strides' );
var ndarray2array = require( '@stdlib/ndarray-base-to-array' );
var unary = require( '@stdlib/ndarray-base-unary' );
function scale( x ) {
return x * 10;
}
var N = 10;
var x = {
'dtype': 'generic',
'data': filledarrayBy( N, 'generic', discreteUniform( -100, 100 ) ),
'shape': [ 5, 2 ],
'strides': [ 2, 1 ],
'offset': 0,
'order': 'row-major'
};
var y = {
'dtype': 'generic',
'data': filledarray( 0, N, 'generic' ),
'shape': x.shape.slice(),
'strides': shape2strides( x.shape, 'column-major' ),
'offset': 0,
'order': 'column-major'
};
unary( [ x, y ], scale );
console.log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
console.log( ndarray2array( y.data, y.shape, y.strides, y.offset, y.order ) );C APIs
Character codes for data types:
- d:
float64(double-precision floating-point number). - f:
float32(single-precision floating-point number). - c:
complex64(single-precision floating-point complex number). - z:
complex128(double-precision floating-point complex number). - s:
int8(signed 8-bit integer). - b:
uint8(unsigned 8-bit integer). - k:
int16(signed 16-bit integer). - t:
uint16(unsigned 16-bit integer). - i:
int32(signed 32-bit integer). - u:
uint32(unsigned 32-bit integer). - l:
int64(signed 64-bit integer). - v:
uint64(unsigned 64-bit integer). - x:
boolean.
Function name suffix naming convention:
stdlib_ndarray_<input_data_type>_<output_data_type>[_as_<callback_arg_data_type>_<callback_return_data_type>]For example,
void stdlib_ndarray_d_d(...) {...}is a function which 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_f_f_as_d_d(...) {...}is a function which 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 each input array element to double-precision:
double dxi = (double)fx[ i ];
// Evaluate the callback:
double dyi = f( dxi );
// Convert the callback return value to single-precision:
fy[ i ] = (float)dyi;Usage
#include "stdlib/ndarray/base/unary.h"FIXME: add docs for the loop interfaces
Examples
// FIXME: add exampleNotice
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 © 2016-2022. The Stdlib Authors.