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Generate a single-precision floating-point Vandermonde matrix.
npm install @stdlib/blas-ext-base-svanderAlternatively,
- To load the package in a website via a
scripttag without installation and bundlers, use the ES Module available on theesmbranch (see README). - If you are using Deno, visit the
denobranch (see README for usage intructions). - For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the
umdbranch (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.
var svander = require( '@stdlib/blas-ext-base-svander' );Generates a single-precision floating-point Vandermonde matrix.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
var out = new Float32Array( 9 );
svander( 'row-major', -1, 3, 3, x, 1, out, 3 );
// out => <Float32Array>[ 1.0, 1.0, 1.0, 4.0, 2.0, 1.0, 9.0, 3.0, 1.0 ]The function has the following parameters:
- order: row-major (C-style) or column-major (Fortran-style) order.
- mode: mode. If
mode < 0, the function generates decreasing powers. Ifmode > 0, the function generates increasing powers. - M: number of rows in
out. - N: number of columns in
out. - x: input
Float32Array. - strideX: stride length for
x. - out: output matrix stored in linear memory as a
Float32Array. - ldo: stride between successive contiguous vectors of the matrix
out(a.k.a., leading dimension of the matrixout).
Note that indexing is relative to the first index. To introduce an offset, use typed array views.
var Float32Array = require( '@stdlib/array-float32' );
// Initial arrays:
var x0 = new Float32Array( [ 999.0, 1.0, 2.0, 3.0 ] );
var out0 = new Float32Array( 10 );
// Create offset views:
var x1 = new Float32Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var out1 = new Float32Array( out0.buffer, out0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
svander( 'row-major', 1, 3, 3, x1, 1, out1, 3 );
// out0 => <Float32Array>[ 0.0, 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 1.0, 3.0, 9.0 ]When the mode is positive, the matrix is generated such that
[
1 x_0^1 x_0^2 ... x_0^(N-1)
1 x_1^1 x_1^2 ... x_1^(N-1)
...
]
with increasing powers along the rows.
When the mode is negative, the matrix is generated such that
[
x_0^(N-1) ... x_0^2 x_0^1 1
x_1^(N-1) ... x_1^2 x_1^1 1
...
]
with decreasing powers along the rows.
Generates a single-precision floating-point Vandermonde matrix using alternative indexing semantics.
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 1.0, 2.0, 3.0 ] );
var out = new Float32Array( 9 );
svander.ndarray( -1, 3, 3, x, 1, 0, out, 3, 1, 0 );
// out => <Float32Array>[ 1.0, 1.0, 1.0, 4.0, 2.0, 1.0, 9.0, 3.0, 1.0 ]The function has the following additional parameters:
- offsetX: starting index for
x. - strideOut1: stride length for the first dimension of
out. - strideOut2: stride length for the second dimension of
out. - offsetOut: starting index for
out.
While typed array views mandate a view offset based on the underlying buffer, offset parameters support indexing semantics based on starting indices. For example, to use every other element from the input array starting from the second element:
var Float32Array = require( '@stdlib/array-float32' );
var x = new Float32Array( [ 0.0, 1.0, 0.0, 2.0, 0.0, 3.0 ] );
var out = new Float32Array( 9 );
svander.ndarray( 1, 3, 3, x, 2, 1, out, 3, 1, 0 );
// out => <Float32Array>[ 1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 1.0, 3.0, 9.0 ]- If
M <= 0orN <= 0, both functions return the output matrix unchanged.
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var Float32Array = require( '@stdlib/array-float32' );
var svander = require( '@stdlib/blas-ext-base-svander' );
var M = 3;
var N = 4;
var x = discreteUniform( M, 0, 10, {
'dtype': 'float32'
});
var out = new Float32Array( M*N );
svander( 'row-major', -1, M, N, x, 1, out, N );
console.log( out );#include "stdlib/blas/ext/base/svander.h"Generates a single-precision floating-point Vandermonde matrix.
#include "stdlib/blas/base/shared.h"
const float x[ 3 ] = { 1.0f, 2.0f, 3.0f };
float Out[ 3*3 ] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };
stdlib_strided_svander( CblasRowMajor, -1.0f, 3, 3, x, 1, Out, 3 );The function accepts the following arguments:
- order:
[in] CBLAS_LAYOUTstorage layout. - mode:
[in] floatmode. Ifmode < 0, the function generates decreasing powers. Ifmode > 0, the function generates increasing powers. - M:
[in] CBLAS_INTnumber of rows inOut. - N:
[in] CBLAS_INTnumber of columns inOut. - X:
[in] float*input array. - strideX:
[in] CBLAS_INTstride length forX. - Out:
[out] float*output matrix. - LDO:
[in] CBLAS_INTstride between successive contiguous vectors of the matrixOut(a.k.a., leading dimension of the matrixOut).
void API_SUFFIX(stdlib_strided_svander)( const CBLAS_LAYOUT order, const float mode, const CBLAS_INT M, const CBLAS_INT N, const float *X, const CBLAS_INT strideX, float *Out, const CBLAS_INT LDO );stdlib_strided_svander_ndarray( mode, M, N, *X, strideX, offsetX, *Out, strideOut1, strideOut2, offsetOut )
Generates a single-precision floating-point Vandermonde matrix using alternative indexing semantics.
#include "stdlib/blas/base/shared.h"
const float x[ 3 ] = { 1.0f, 2.0f, 3.0f };
float Out[ 3*3 ] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };
stdlib_strided_svander_ndarray( -1.0f, 3, 3, x, 1, 0, Out, 3, 1, 0 );The function accepts the following arguments:
- mode:
[in] floatmode. Ifmode < 0, the function generates decreasing powers. Ifmode > 0, the function generates increasing powers. - M:
[in] CBLAS_INTnumber of rows inOut. - N:
[in] CBLAS_INTnumber of columns inOut. - X:
[in] float*input array. - strideX:
[in] CBLAS_INTstride length forX. - offsetX:
[in] CBLAS_INTstarting index forX. - Out:
[out] float*output matrix. - strideOut1:
[in] CBLAS_INTstride length for the first dimension ofOut. - strideOut2:
[in] CBLAS_INTstride length for the second dimension ofOut. - offsetOut:
[in] CBLAS_INTstarting index forOut.
void API_SUFFIX(stdlib_strided_svander_ndarray)( const float mode, const CBLAS_INT M, const CBLAS_INT N, const float *X, const CBLAS_INT strideX, const CBLAS_INT offsetX, float *Out, const CBLAS_INT strideOut1, const CBLAS_INT strideOut2, const CBLAS_INT offsetOut );#include "stdlib/blas/ext/base/svander.h"
#include "stdlib/blas/base/shared.h"
#include <stdio.h>
int main( void ) {
// Define the input array:
const float x[ 3 ] = { 1.0f, 2.0f, 3.0f };
// Define a 3x3 output array stored in row-major order:
float Out[ 3*3 ] = { 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f };
// Specify the number of rows and columns:
const int M = 3;
const int N = 3;
// Perform operation:
stdlib_strided_svander( CblasRowMajor, -1.0f, M, N, x, 1, Out, N );
// Print the result:
for ( int i = 0; i < M; i++ ) {
for ( int j = 0; j < N; j++ ) {
printf( "Out[%i,%i] = %f\n", i, j, Out[ (i*N)+j ] );
}
}
}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.
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