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feat: add blas/ext/cusum
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‎lib/node_modules/@stdlib/blas/ext/cusum/README.md

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<!--
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@license Apache-2.0
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Copyright (c) 2025 The Stdlib Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# cusum
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> Compute the cumulative sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions.
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<section class="usage">
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## Usage
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```javascript
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var cusum = require( '@stdlib/blas/ext/cusum' );
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```
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#### cusum( x\[, initial]\[, options] )
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Computes the cumulative sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions.
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```javascript
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var xbuf = [ -1.0, 2.0, -3.0 ];
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var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
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var y = cusum( x );
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// returns <ndarray>
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var arr = ndarray2array( y );
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// returns [ -1.0, 1.0, -2.0 ]
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```
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The function has the following parameters:
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- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a numeric or "generic" [data type][@stdlib/ndarray/dtypes].
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- **initial**: initial value for the cumulative sum (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] which [promotes][@stdlib/ndarray/promotion-rules] to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided a scalar value, the value is cast to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] initial value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] initial value must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the initial value is the additive identity (i.e., zero).
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- **options**: function options (_optional_).
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The function accepts the following options:
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- **dims**: list of dimensions over which to perform operation. If not provided, the function performs the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
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- **dtype**: output ndarray [data type][@stdlib/ndarray/dtypes]. Must be a numeric or "generic" [data type][@stdlib/ndarray/dtypes].
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By default, the function uses the additive identity when computing the cumulative sum. To begin summing from a different value, provide an `initial` argument.
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```javascript
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var xbuf = [ -1.0, 2.0, -3.0 ];
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var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
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var y = cusum( x, 10.0 );
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// returns <ndarray>
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var arr = ndarray2array( y );
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// returns [ 9.0, 11.0, 8.0 ]
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```
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By default, the function performs the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor]. To perform the operation over specific dimensions, provide a `dims` option.
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```javascript
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var xbuf = [ -1.0, 2.0, -3.0, 4.0 ];
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var x = new ndarray( 'generic', xbuf, [ 2, 2 ], [ 2, 1 ], 0, 'row-major' );
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var y = cusum( x, {
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'dims': [ 0 ]
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});
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// returns <ndarray>
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var v = ndarray2array( y );
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// returns [ [ -1.0, 2.0 ], [ -4.0, 6.0 ] ]
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y = cusum( x, {
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'dims': [ 1 ]
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});
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// returns <ndarray>
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v = ndarray2array( y );
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// returns [ [ -1.0, 1.0 ], [ -3.0, 1.0 ] ]
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y = cusum( x, {
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'dims': [ 0, 1 ]
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});
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// returns <ndarray>
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v = ndarray2array( y );
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// returns [ [ -1.0, 1.0 ], [ -2.0, 2.0 ] ]
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```
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By default, the function returns an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] determined by the function's output data type [policy][@stdlib/ndarray/output-dtype-policies]. To override the default behavior, set the `dtype` option.
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```javascript
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var dtype = require( '@stdlib/ndarray/dtype' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var xbuf = [ -1.0, 2.0, -3.0 ];
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var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
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var y = cusum( x, {
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'dtype': 'float64'
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});
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// returns <ndarray>
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var dt = dtype( y );
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// returns 'float64'
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```
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#### cusum.assign( x\[, initial], out\[, options] )
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Computes the cumulative sum along one or more [ndarray][@stdlib/ndarray/ctor] dimensions and assigns results to a provided output [ndarray][@stdlib/ndarray/ctor].
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```javascript
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var xbuf = [ -1.0, 2.0, -3.0 ];
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var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
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var ybuf = [ 0.0, 0.0, 0.0 ];
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var y = new ndarray( 'generic', ybuf, [ ybuf.length ], [ 1 ], 0, 'row-major' );
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var out = cusum.assign( x, y );
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// returns <ndarray>
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var v = ndarray2array( out );
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// returns [ -1.0, 1.0, -2.0 ]
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var bool = ( out === y );
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// returns true
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```
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The method has the following parameters:
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- **x**: input [ndarray][@stdlib/ndarray/ctor]. Must have a numeric or generic [data type][@stdlib/ndarray/dtypes].
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- **initial**: initial value for the cumulative sum (_optional_). May be either a scalar value or an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] which [promotes][@stdlib/ndarray/promotion-rules] to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided a scalar value, the value is cast to the data type of the input [ndarray][@stdlib/ndarray/ctor]. If provided an [ndarray][@stdlib/ndarray/ctor], the value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the complement of the shape defined by `options.dims`. For example, given the input shape `[2, 3, 4]` and `options.dims=[0]`, an [ndarray][@stdlib/ndarray/ctor] initial value must have a shape which is [broadcast-compatible][@stdlib/ndarray/base/broadcast-shapes] with the shape `[3, 4]`. Similarly, when performing the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor], an [ndarray][@stdlib/ndarray/ctor] initial value must be a zero-dimensional [ndarray][@stdlib/ndarray/ctor]. By default, the initial value is the additive identity (i.e., zero).
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- **out**: output [ndarray][@stdlib/ndarray/ctor].
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- **options**: function options (_optional_).
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The method accepts the following options:
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- **dims**: list of dimensions over which to perform operation. If not provided, the function performs the operation over all elements in a provided input [ndarray][@stdlib/ndarray/ctor].
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</section>
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<!-- /.usage -->
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<section class="notes">
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## Notes
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- Both functions iterate over [ndarray][@stdlib/ndarray/ctor] elements according to the memory layout of the input [ndarray][@stdlib/ndarray/ctor]. Accordingly, performance degradation is possible when operating over multiple dimensions of a large non-contiguous multi-dimensional input [ndarray][@stdlib/ndarray/ctor]. In such scenarios, one may want to copy an input [ndarray][@stdlib/ndarray/ctor] to contiguous memory before computing the cumulative sum.
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- The output data type [policy][@stdlib/ndarray/output-dtype-policies] only applies to the main function and specifies that, by default, in order to avoid issues arising from integer overflow, the function must return an [ndarray][@stdlib/ndarray/ctor] having a [data type][@stdlib/ndarray/dtypes] amenable to accumulation. This means that, for integer data types having small value ranges (e.g., `int8`, `uint8`, etc), the main function returns an [ndarray][@stdlib/ndarray/ctor] having at least a 32-bit integer data type. By default, if an input [ndarray][@stdlib/ndarray/ctor] has a floating-point data type, the main function returns an [ndarray][@stdlib/ndarray/ctor] having the same data type. For the `assign` method, the output [ndarray][@stdlib/ndarray/ctor] is allowed to have any [data type][@stdlib/ndarray/dtypes].
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</section>
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<!-- /.notes -->
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<section class="examples">
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## Examples
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<!-- eslint no-undef: "error" -->
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```javascript
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var dtype = require( '@stdlib/ndarray/dtype' );
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var ndarray2array = require( '@stdlib/ndarray/to-array' );
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var ndarray = require( '@stdlib/ndarray/ctor' );
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var cusum = require( '@stdlib/blas/ext/cusum' );
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// Generate an array of random numbers:
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var xbuf = discreteUniform( 25, 0, 20, {
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'dtype': 'generic'
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});
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// Wrap in an ndarray:
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var x = new ndarray( 'generic', xbuf, [ 5, 5 ], [ 5, 1 ], 0, 'row-major' );
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console.log( ndarray2array( x ) );
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// Perform operation:
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var y = cusum( x, 100.0, {
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'dims': [ 0 ]
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});
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// Resolve the output array data type:
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var dt = dtype( y );
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console.log( dt );
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// Print the results:
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console.log( ndarray2array( y ) );
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```
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</section>
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<!-- /.examples -->
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<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->
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<section class="related">
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</section>
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<!-- /.related -->
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<!-- Section for all links. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="links">
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[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/ctor
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[@stdlib/ndarray/dtypes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/dtypes
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[@stdlib/ndarray/promotion-rules]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/promotion-rules
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[@stdlib/ndarray/output-dtype-policies]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/output-dtype-policies
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[@stdlib/ndarray/base/broadcast-shapes]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/base/broadcast-shapes
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</section>
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<!-- /.links -->

‎lib/node_modules/@stdlib/blas/ext/cusum/benchmark/benchmark.assign.js

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2025 The Stdlib Authors.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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'use strict';
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// MODULES //
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var bench = require( '@stdlib/bench' );
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var isnan = require( '@stdlib/math/base/assert/is-nan' );
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var pow = require( '@stdlib/math/base/special/pow' );
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var uniform = require( '@stdlib/random/array/uniform' );
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var zerosLike = require( '@stdlib/ndarray/zeros-like' );
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var ndarray = require( '@stdlib/ndarray/base/ctor' );
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var pkg = require( './../package.json' ).name;
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var cusum = require( './../lib' );
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// VARIABLES //
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var options = {
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'dtype': 'float64'
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};
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// FUNCTIONS //
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/**
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* Creates a benchmark function.
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*
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* @private
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* @param {PositiveInteger} len - array length
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len ) {
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var out;
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var x;
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x = uniform( len, -50.0, 50.0, options );
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x = new ndarray( options.dtype, x, [ len ], [ 1 ], 0, 'row-major' );
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out = zerosLike( x );
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return benchmark;
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/**
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* Benchmark function.
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*
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* @private
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* @param {Benchmark} b - benchmark instance
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*/
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function benchmark( b ) {
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var o;
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var i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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o = cusum.assign( x, out );
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if ( typeof o !== 'object' ) {
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b.fail( 'should return an ndarray' );
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}
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}
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b.toc();
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if ( isnan( o.get( i%len ) ) ) {
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b.fail( 'should not return NaN' );
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}
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b.pass( 'benchmark finished' );
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b.end();
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}
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}
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// MAIN //
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/**
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* Main execution sequence.
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*
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* @private
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*/
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function main() {
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var len;
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var min;
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var max;
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var f;
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var i;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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f = createBenchmark( len );
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bench( pkg+':assign:dtype='+options.dtype+',len='+len, f );
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}
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}
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main();

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