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Constructor for performing a reduction on an input ndarray.
import UnaryStrided1dDispatch from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-strided1d-dispatch@deno/mod.js';
Constructor for performing a reduction on an input ndarray.
import base from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-max@deno/mod.js';
var table = {
'default': base
};
var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
'output': 'same',
'casting': 'none'
};
var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );
The constructor has the following parameters:
-
table: strided reduction function dispatch table. Must have the following properties:
- default: default strided reduction function which should be invoked when provided ndarrays have data types which do not have a corresponding specialized implementation.
A dispatch table may have the following additional properties:
- types: one-dimensional list of ndarray data types describing specialized input ndarray argument signatures. Only the input ndarray argument data types should be specified. Output ndarray and additional input ndarray argument data types should be omitted and are not considered during dispatch. The length of
types
must equal the number of strided functions specified byfcns
(i.e., for every input ndarray data type, there must be a corresponding strided reduction function infcns
). - fcns: list of strided reduction functions which are specific to specialized input ndarray argument signatures.
-
idtypes: list containing lists of supported input data types for each input ndarray argument.
-
odtypes: list of supported output data types.
-
policies: dispatch policies. Must have the following properties:
Performs a reduction on a provided input ndarray.
import ndarray from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-ctor@deno/mod.js';
import base from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-max@deno/mod.js';
var table = {
'default': base
};
var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
'output': 'same',
'casting': 'none'
};
var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );
var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
var y = unary.apply( x );
// returns <ndarray>
var v = y.get();
// returns 2.0
The method has the following parameters:
- x: input ndarray.
- ...args: additional input ndarray arguments (optional).
- options: function options (optional).
The method accepts the following options:
- dims: list of dimensions over which to perform a reduction.
- dtype: output ndarray data type. Setting this option, overrides the output data type policy.
- keepdims: boolean indicating whether the reduced dimensions should be included in the returned ndarray as singleton dimensions. Default:
false
.
By default, the method returns an ndarray having a data type determined by the output data type policy. To override the default behavior, set the dtype
option.
import ndarray from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-ctor@deno/mod.js';
import base from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-max@deno/mod.js';
import getDType from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-dtype@deno/mod.js';
var table = {
'default': base
};
var dtypes = [ 'float64', 'float32', 'generic' ];
var policies = {
'output': 'same',
'casting': 'none'
};
var unary = new UnaryStrided1dDispatch( table, [ dtypes ], dtypes, policies );
var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
var y = unary.apply( x, {
'dtype': 'float64'
});
// returns <ndarray>
var dt = getDType( y );
// returns 'float64'
Performs a reduction on a provided input ndarray and assigns results to a provided output ndarray.
import base from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-max@deno/mod.js';
import dtypes from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-dtypes@deno/mod.js';
import ndarray from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-ctor@deno/mod.js';
var idt = dtypes( 'real_and_generic' );
var odt = idt;
var policies = {
'output': 'same',
'casting': 'none'
};
var table = {
'default': base
};
var unary = new UnaryStrided1dDispatch( table, [ idt ], odt, policies );
var xbuf = [ -1.0, 2.0, -3.0 ];
var x = new ndarray( 'generic', xbuf, [ xbuf.length ], [ 1 ], 0, 'row-major' );
var ybuf = [ 0.0 ];
var y = new ndarray( 'generic', ybuf, [], [ 0 ], 0, 'row-major' );
var out = unary.assign( x, y );
// returns <ndarray>
var v = out.get();
// returns 2.0
var bool = ( out === y );
// returns true
The method has the following parameters:
- x: input ndarray.
- args: additional input ndarray arguments (optional).
- out: output ndarray.
- options: function options (optional).
The method accepts the following options:
- dims: list of dimensions over which to perform a reduction.
-
A strided reduction function should have the following signature:
f( arrays )
where
- arrays: array containing an input ndarray, followed by any additional ndarray arguments.
-
The output data type policy only applies to the
apply
method. For theassign
method, the output ndarray is allowed to have any supported output data type.
import dmax from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-dmax@deno/mod.js';
import smax from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-smax@deno/mod.js';
import base from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-ndarray-max@deno/mod.js';
import uniform from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-array-uniform@deno/mod.js';
import dtypes from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-dtypes@deno/mod.js';
import dtype from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-dtype@deno/mod.js';
import ndarray2array from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-to-array@deno/mod.js';
import ndarray from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-ctor@deno/mod.js';
import UnaryStrided1dDispatch from 'https://cdn.jsdelivr.net/gh/stdlib-js/ndarray-base-unary-reduce-strided1d-dispatch@deno/mod.js';
// Define the supported input and output data types:
var idt = dtypes( 'real_and_generic' );
var odt = dtypes( 'real_and_generic' );
// Define dispatch policies:
var policies = {
'output': 'same',
'casting': 'none'
};
// Define a dispatch table:
var table = {
'types': [
'float64', // input
'float32' // input
],
'fcns': [
dmax,
smax
],
'default': base
};
// Create an interface for performing a reduction:
var max = new UnaryStrided1dDispatch( table, [ idt ], odt, policies );
// Generate an array of random numbers:
var xbuf = uniform( 100, -1.0, 1.0, {
'dtype': 'generic'
});
// Wrap in an ndarray:
var x = new ndarray( 'generic', xbuf, [ 10, 10 ], [ 10, 1 ], 0, 'row-major' );
// Perform a reduction:
var y = max.apply( x, {
'dims': [ 0 ]
});
// Resolve the output array data type:
var dt = dtype( y );
console.log( dt );
// Print the results:
console.log( ndarray2array( y ) );
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For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.
See LICENSE.
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