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feat: add C implementation of stats/base/ndarray/dmeanpw
Signed-off-by: Bhargav Dabhade <bhargava2005dabhade@gmail.com> --- type: pre_commit_static_analysis_report description: Results of running static analysis checks when committing changes. report: - task: lint_filenames status: passed - task: lint_editorconfig status: passed - task: lint_markdown status: passed - task: lint_package_json status: passed - task: lint_repl_help status: na - task: lint_javascript_src status: passed - task: lint_javascript_cli status: na - task: lint_javascript_examples status: na - task: lint_javascript_tests status: passed - task: lint_javascript_benchmarks status: passed - task: lint_python status: na - task: lint_r status: na - task: lint_c_src status: missing_dependencies - task: lint_c_examples status: missing_dependencies - task: lint_c_benchmarks status: missing_dependencies - task: lint_c_tests_fixtures status: na - task: lint_shell status: na - task: lint_typescript_declarations status: passed - task: lint_typescript_tests status: na - task: lint_license_headers status: passed ---
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‎lib/node_modules/@stdlib/stats/base/ndarray/dmeanpw/README.md‎

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<!-- /.examples -->
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<!-- C interface documentation. -->
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* * *
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<section class="c">
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## C APIs
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<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->
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<section class="intro">
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</section>
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<!-- /.intro -->
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<!-- C usage documentation. -->
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<section class="usage">
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### Usage
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```c
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#include "stdlib/stats/base/ndarray/dmeanpw.h"
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```
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#### stdlib_stats_dmeanpw( arrays )
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Computes the [arithmetic mean][arithmetic-mean] of a one-dimensional double-precision floating-point ndarray using pairwise summation.
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```c
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#include "stdlib/ndarray/ctor.h"
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#include "stdlib/ndarray/dtypes.h"
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#include "stdlib/ndarray/index_modes.h"
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#include "stdlib/ndarray/orders.h"
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#include "stdlib/ndarray/base/bytes_per_element.h"
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#include <stdint.h>
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// Create an ndarray:
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const double data[] = { 1.0, 2.0, 3.0, 4.0 };
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int64_t shape[] = { 4 };
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int64_t strides[] = { STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };
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int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
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struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT64, (uint8_t *)data, 1, shape, strides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );
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// Compute the arithmetic mean:
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const struct ndarray *arrays[] = { x };
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double v = stdlib_stats_dmeanpw( arrays );
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// returns ~2.5
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// Free allocated memory:
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stdlib_ndarray_free( x );
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```
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The function accepts the following arguments:
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- **arrays**: `[in] struct ndarray**` list containing a one-dimensional input ndarray.
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```c
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double stdlib_stats_dmeanpw( const struct ndarray *arrays[] );
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```
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</section>
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<!-- /.usage -->
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<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->
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<section class="notes">
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</section>
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<!-- /.notes -->
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<!-- C API usage examples. -->
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<section class="examples">
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### Examples
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```c
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#include "stdlib/stats/base/ndarray/dmeanpw.h"
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#include "stdlib/ndarray/ctor.h"
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#include "stdlib/ndarray/dtypes.h"
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#include "stdlib/ndarray/index_modes.h"
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#include "stdlib/ndarray/orders.h"
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#include "stdlib/ndarray/base/bytes_per_element.h"
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#include <stdint.h>
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#include <stdlib.h>
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#include <stdio.h>
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int main( void ) {
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// Create a data buffer:
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const double data[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };
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// Specify the number of array dimensions:
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const int64_t ndims = 1;
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// Specify the array shape:
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int64_t shape[] = { 4 };
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// Specify the array strides:
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int64_t strides[] = { 2*STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };
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// Specify the byte offset:
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const int64_t offset = 0;
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// Specify the array order:
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const enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;
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// Specify the index mode:
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const enum STDLIB_NDARRAY_INDEX_MODE imode = STDLIB_NDARRAY_INDEX_ERROR;
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// Specify the subscript index modes:
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int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };
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const int64_t nsubmodes = 1;
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// Create an ndarray:
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struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT64, (uint8_t *)data, ndims, shape, strides, offset, order, imode, nsubmodes, submodes );
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if ( x == NULL ) {
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fprintf( stderr, "Error allocating memory.\n" );
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exit( 1 );
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}
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// Define a list of ndarrays:
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const struct ndarray *arrays[] = { x };
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// Compute the arithmetic mean:
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double v = stdlib_stats_dmeanpw( arrays );
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// Print the result:
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printf( "mean: %lf\n", v );
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// Free allocated memory:
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stdlib_ndarray_free( x );
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}
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```
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</section>
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<!-- /.examples -->
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</section>
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<!-- /.c -->
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<section class="references">
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## References

‎lib/node_modules/@stdlib/stats/base/ndarray/dmeanpw/benchmark/benchmark.js‎

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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 ndarray = require( '@stdlib/ndarray/base/ctor' );
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var format = require( '@stdlib/string/format' );
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var pkg = require( './../package.json' ).name;
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var dmeanpw = require( './../lib' );
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var dmeanpw = require( './../lib/main.js' );
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// VARIABLES //
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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+':len='+len, f );
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bench( format( '%s:len=%d', pkg, len ), f );
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}
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}
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‎lib/node_modules/@stdlib/stats/base/ndarray/dmeanpw/benchmark/benchmark.native.js‎

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/**
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* @license Apache-2.0
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*
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* Copyright (c) 2026 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 resolve = require( 'path' ).resolve;
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var bench = require( '@stdlib/bench' );
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var uniform = require( '@stdlib/random/array/uniform' );
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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 ndarray = require( '@stdlib/ndarray/base/ctor' );
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var format = require( '@stdlib/string/format' );
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var tryRequire = require( '@stdlib/utils/try-require' );
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var pkg = require( './../package.json' ).name;
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// VARIABLES //
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var dmeanpw = tryRequire( resolve( __dirname, './../lib/native.js' ) );
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var opts = {
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'skip': ( dmeanpw instanceof Error )
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};
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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 xbuf;
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var x;
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xbuf = uniform( len, -10.0, 10.0, options );
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x = new ndarray( options.dtype, xbuf, [ len ], [ 1 ], 0, 'row-major' );
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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 v;
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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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v = dmeanpw( [ x ] );
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if ( isnan( v ) ) {
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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( v ) ) {
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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( format( '%s::native:len=%d', pkg, len ), opts, f );
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}
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}
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main();

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