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dmskmax

Calculate the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask.

Installation

npm install @stdlib/stats-base-ndarray-dmskmax

Alternatively,

• To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
• If you are using Deno, visit the deno branch (see README for usage intructions).
• For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var dmskmax = require( '@stdlib/stats-base-ndarray-dmskmax' );

dmskmax( arrays )

Computes the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask.

var Float64Vector = require( '@stdlib/ndarray-vector-float64' );
var Uint8Vector = require( '@stdlib/ndarray-vector-uint8' );

var x = new Float64Vector( [ 1.0, -2.0, 4.0, 2.0 ] );
var mask = new Uint8Vector( [ 0, 0, 1, 0 ] );

var v = dmskmax( [ x, mask ] );
// returns 2.0

The function has the following parameters:

• arrays: array-like object containing the following ndarrays:

  • a one-dimensional input ndarray.
  • a one-dimensional mask ndarray.

Notes

• If a mask array element is 0, the corresponding element in the input ndarray is considered valid and included in computation. If a mask array element is 1, the corresponding element in the input ndarray is considered invalid/missing and excluded from computation.
• If provided an empty ndarray or a mask with all elements set to 1, the function returns NaN.

Examples

var uniform = require( '@stdlib/random-uniform' );
var bernoulli = require( '@stdlib/random-bernoulli' );
var ndarray2array = require( '@stdlib/ndarray-to-array' );
var dmskmax = require( '@stdlib/stats-base-ndarray-dmskmax' );

var opts = {
    'dtype': 'float64'
};

var x = uniform( [ 10 ], -50.0, 50.0, opts );
console.log( ndarray2array( x ) );

var mask = bernoulli( [ 10 ], 0.2, {
    'dtype': 'uint8'
});
console.log( ndarray2array( mask ) );

var v = dmskmax( [ x, mask ] );
console.log( v );

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C APIs

Usage

#include "stdlib/stats/base/ndarray/dmskmax.h"

stdlib_stats_dmskmax( arrays )

Computes the maximum value of a one-dimensional double-precision floating-point ndarray according to a mask.

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

// Create an ndarray:
const double data[] = { 1.0, 2.0, 3.0, 4.0 };
int64_t shape[] = { 4 };
int64_t strides[] = { STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };
int8_t submodes[] = { STDLIB_NDARRAY_INDEX_ERROR };

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 );

// Create a mask ndarray:
const uint8_t mdata[] = { 0, 0, 1, 0 };
int64_t mstrides[] = { STDLIB_NDARRAY_UINT8_BYTES_PER_ELEMENT };

struct ndarray *mask = stdlib_ndarray_allocate( STDLIB_NDARRAY_UINT8, mdata, 1, shape, mstrides, 0, STDLIB_NDARRAY_ROW_MAJOR, STDLIB_NDARRAY_INDEX_ERROR, 1, submodes );

// Compute the maximum value:
const struct ndarray *arrays[] = { x, mask };
double v = stdlib_stats_dmskmax( arrays );
// returns 4.0

// Free allocated memory:
stdlib_ndarray_free( x );
stdlib_ndarray_free( mask );

The function accepts the following arguments:

• arrays: [in] struct ndarray** list containing the following ndarrays:

  • [in] struct ndarray* a one-dimensional input ndarray.
  • [in] struct ndarray* a one-dimensional mask ndarray.

double stdlib_stats_dmskmax( const struct ndarray *arrays[] );

Examples

#include "stdlib/stats/base/ndarray/dmskmax.h"
#include "stdlib/ndarray/ctor.h"
#include "stdlib/ndarray/dtypes.h"
#include "stdlib/ndarray/index_modes.h"
#include "stdlib/ndarray/orders.h"
#include "stdlib/ndarray/base/bytes_per_element.h"
#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>

int main( void ) {
   // Create a data buffer:
   const double data[] = { 1.0, -2.0, 3.0, -4.0, 5.0, -6.0, 7.0, -8.0 };

   // Specify the number of array dimensions:
   const int64_t ndims = 1;

   // Specify the array shape:
   int64_t shape[] = { 4 };

   // Specify the array strides:
   int64_t strides[] = { 2*STDLIB_NDARRAY_FLOAT64_BYTES_PER_ELEMENT };

   // Specify the byte offset:
   const int64_t offset = 0;

   // Specify the array order:
   const enum STDLIB_NDARRAY_ORDER order = STDLIB_NDARRAY_ROW_MAJOR;

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

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

   // Create an ndarray:
   struct ndarray *x = stdlib_ndarray_allocate( STDLIB_NDARRAY_FLOAT64, (uint8_t *)data, ndims, shape, strides, offset, order, imode, nsubmodes, submodes );
   if ( x == NULL ) {
      fprintf( stderr, "Error allocating memory.\n" );
      exit( 1 );
   }

   // Create a mask ndarray:
   const uint8_t mdata[] = { 0, 0, 1, 0 };
   int64_t mstrides[] = { STDLIB_NDARRAY_UINT8_BYTES_PER_ELEMENT };
   struct ndarray *mask = stdlib_ndarray_allocate( STDLIB_NDARRAY_UINT8, mdata, ndims, shape, mstrides, offset, order, imode, nsubmodes, submodes );
   if ( mask == NULL ) {
      fprintf( stderr, "Error allocating memory.\n" );
      exit( 1 );
   }

   // Define a list of ndarrays:
   const struct ndarray *arrays[] = { x, mask };

   // Compute the maximum value:
   double v = stdlib_stats_dmskmax( arrays );

   // Print the result:
   printf( "max: %lf\n", v );

   // Free allocated memory:
   stdlib_ndarray_free( x );
   stdlib_ndarray_free( mask );
}

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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

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License

See LICENSE.

Copyright

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