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‎.github/workflows/publish.yml

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# Publish package to npm:
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- name: 'Publish package to npm'
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# Pin action to full length commit SHA
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uses: JS-DevTools/npm-publish@4b07b26a2f6e0a51846e1870223e545bae91c552 # v3.0.1
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uses: JS-DevTools/npm-publish@19c28f1ef146469e409470805ea4279d47c3d35c # v3.1.1
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with:
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token: ${{ secrets.NPM_TOKEN }}
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access: public

‎.npmrc

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# Disable automatically "saving" dependencies on install:
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save = false
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# Generate provenance metadata:
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provenance = true
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# Do not generate provenance metadata:
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provenance = false

‎CHANGELOG.md

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‎base/for-each/README.md

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<!--
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@license Apache-2.0
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Copyright (c) 2024 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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# forEach
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> Invoke a callback function once for each ndarray element.
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<section class="intro">
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</section>
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<!-- /.intro -->
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<section class="usage">
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## Usage
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```javascript
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var forEach = require( '@stdlib/ndarray/base/for-each' );
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```
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#### forEach( arrays, fcn\[, thisArg] )
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Invokes a callback function once for each ndarray element.
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```javascript
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var Float64Array = require( '@stdlib/array/float64' );
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var naryFunction = require( '@stdlib/utils/nary-function' );
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var log = require( '@stdlib/console/log' );
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// Create data buffers:
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var xbuf = new Float64Array( 12 );
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// Define the shape of the array:
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var shape = [ 3, 1, 2 ];
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// Define the array strides:
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var sx = [ 4, 4, 1 ];
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// Define the index offset:
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var ox = 1;
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// Create an ndarray-like object:
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var x = {
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'dtype': 'float64',
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'data': xbuf,
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'shape': shape,
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'strides': sx,
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'offset': ox,
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'order': 'row-major'
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};
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// Apply the callback function:
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forEach( [ x ], naryFunction( log, 1 ) );
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```
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The function accepts the following arguments:
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- **arrays**: array-like object containing an ndarray.
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- **fcn**: callback to apply.
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- **thisArg**: callback execution context.
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The callback function is provided the following arguments:
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- **value**: current array element.
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- **indices**: current array element indices.
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- **arr**: the input ndarray.
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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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- The provided ndarray should be an `object` with the following properties:
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- **dtype**: data type.
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- **data**: data buffer.
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- **shape**: dimensions.
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- **strides**: stride lengths.
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- **offset**: index offset.
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- **order**: specifies whether an ndarray is row-major (C-style) or column major (Fortran-style).
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- For very high-dimensional ndarrays which are non-contiguous, one should consider copying the underlying data to contiguous memory before applying a callback function in order to achieve better performance.
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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 ndarray2array = require( '@stdlib/ndarray/base/to-array' );
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var zeroTo = require( '@stdlib/array/base/zero-to' );
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var naryFunction = require( '@stdlib/utils/nary-function' );
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var log = require( '@stdlib/console/log' );
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var forEach = require( '@stdlib/ndarray/base/for-each' );
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var x = {
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'dtype': 'generic',
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'data': zeroTo( 10 ),
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'shape': [ 5, 2 ],
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'strides': [ -2, 1 ],
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'offset': 8,
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'order': 'row-major'
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};
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log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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forEach( [ x ], naryFunction( log, 2 ) );
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x = {
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'dtype': 'generic',
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'data': zeroTo( 10 ),
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'shape': [ 5, 2 ],
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'strides': [ 1, -5 ],
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'offset': 5,
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'order': 'column-major'
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};
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log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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forEach( [ x ], naryFunction( log, 2 ) );
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x = {
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'dtype': 'generic',
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'data': zeroTo( 18 ),
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'shape': [ 2, 3, 3 ],
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'strides': [ 9, 3, 1 ],
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'offset': 0,
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'order': 'row-major'
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};
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log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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forEach( [ x ], naryFunction( log, 2 ) );
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x = {
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'dtype': 'generic',
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'data': zeroTo( 18 ),
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'shape': [ 2, 3, 3 ],
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'strides': [ -1, -2, -6 ],
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'offset': 17,
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'order': 'column-major'
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};
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log( ndarray2array( x.data, x.shape, x.strides, x.offset, x.order ) );
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forEach( [ x ], naryFunction( log, 2 ) );
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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 class="links">
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<!-- <related-links> -->
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<!-- </related-links> -->
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</section>
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<!-- /.links -->

‎base/for-each/benchmark/benchmark.10d_blocked_columnmajor.js

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2024 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 floor = require( '@stdlib/math/base/special/floor' );
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var discreteUniform = require( '@stdlib/random/array/discrete-uniform' );
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var shape2strides = require( './../../../base/shape2strides' );
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var pkg = require( './../package.json' ).name;
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var forEach = require( './../lib/10d_blocked.js' );
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// VARIABLES //
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var types = [ 'float64' ];
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var order = 'column-major';
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// FUNCTIONS //
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/**
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* Callback invoked for each ndarray element.
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*
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* @private
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* @param {number} value - array element
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* @throws {Error} unexpected error
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*/
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function fcn( value ) {
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if ( isnan( value ) ) {
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throw new Error( 'unexpected error' );
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}
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}
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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 - ndarray length
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* @param {NonNegativeIntegerArray} shape - ndarray shape
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* @param {string} xtype - output ndarray data type
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* @returns {Function} benchmark function
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*/
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function createBenchmark( len, shape, xtype ) {
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var x;
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x = discreteUniform( len, -100, 100 );
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x = {
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'dtype': xtype,
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'data': x,
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'shape': shape,
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'strides': shape2strides( shape, order ),
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'offset': 0,
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'order': order
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};
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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 i;
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b.tic();
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for ( i = 0; i < b.iterations; i++ ) {
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forEach( x, fcn );
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if ( isnan( x.data[ i%len ] ) ) {
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b.fail( 'should not return NaN' );
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}
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}
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b.toc();
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if ( isnan( x.data[ 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 sh;
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var t1;
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var f;
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var i;
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var j;
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min = 1; // 10^min
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max = 6; // 10^max
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for ( j = 0; j < types.length; j++ ) {
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t1 = types[ j ];
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for ( i = min; i <= max; i++ ) {
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len = pow( 10, i );
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sh = [ len/2, 2, 1, 1, 1, 1, 1, 1, 1, 1 ];
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f = createBenchmark( len, sh, t1 );
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bench( pkg+'::blocked:ndims='+sh.length+',len='+len+',shape=['+sh.join(',')+'],xorder='+order+',xtype='+t1, f );
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sh = [ 1, 1, 1, 1, 1, 1, 1, 1, 2, len/2 ];
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f = createBenchmark( len, sh, t1 );
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bench( pkg+'::blocked:ndims='+sh.length+',len='+len+',shape=['+sh.join(',')+'],xorder='+order+',xtype='+t1, f );
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len = floor( pow( len, 1.0/10.0 ) );
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sh = [ len, len, len, len, len, len, len, len, len, len ];
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len *= pow( len, 9 );
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f = createBenchmark( len, sh, t1 );
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bench( pkg+'::blocked:ndims='+sh.length+',len='+len+',shape=['+sh.join(',')+'],xorder='+order+',xtype='+t1, f );
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}
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}
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}
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main();

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