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Compute the tangent of a double-precision floating-point number on
[-π/4, π/4]
.
npm install @stdlib/math-base-special-kernel-tan
Alternatively,
- To load the package in a website via a
script
tag without installation and bundlers, use the ES Module available on theesm
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.
var kernelTan = require( '@stdlib/math-base-special-kernel-tan' );
Computes the tangent of a double-precision floating-point number on [-π/4, π/4]
.
var out = kernelTan( 3.141592653589793/4.0, 0.0, 1 );
// returns ~1.0
out = kernelTan( 3.141592653589793/6.0, 0.0, 1 );
// returns ~0.577
out = kernelTan( 0.664, 5.288e-17, 1 );
// returns ~0.783
If k = 1
, the function returns tan(x+y)
. To return the negative inverse -1/tan(x+y)
, set k = -1
.
var out = kernelTan( 3.141592653589793/4.0, 0.0, -1 );
// returns ~-1.0
If either x
or y
is NaN
, the function returns NaN
.
var out = kernelTan( NaN, 0.0, 1 );
// returns NaN
out = kernelTan( 3.0, NaN, 1 );
// returns NaN
out = kernelTan( NaN, NaN, 1 );
// returns NaN
-
For increased accuracy, the number for which the tangent should be evaluated can be supplied as a double-double number (i.e., a non-evaluated sum of two double-precision floating-point numbers
x
andy
). -
As components of a double-double number, the two double-precision floating-point numbers
x
andy
must satisfywhere
ulp
stands for units in the last place.
var linspace = require( '@stdlib/array-base-linspace' );
var binomial = require( '@stdlib/random-base-binomial' ).factory;
var PI = require( '@stdlib/constants-float64-pi' );
var kernelTan = require( '@stdlib/math-base-special-kernel-tan' );
var x = linspace( -PI/4.0, PI/4.0, 100 );
var rbinom = binomial( 1, 0.5 );
var descr;
var i;
var k;
for ( i = 0; i < x.length; i++ ) {
k = rbinom();
descr = ( k === 1 ) ? 'tan(%d) = %d' : '-1/tan(%d) = %d';
console.log( descr, x[ i ], kernelTan( x[ i ], 0.0, k ) );
}
#include "stdlib/math/base/special/kernel_tan.h"
Computes the tangent of a double-precision floating-point number on [-π/4, π/4]
.
double out = stdlib_base_kernel_tan( 3.141592653589793/4.0, 0.0, 1 );
// returns ~1.0
out = stdlib_base_kernel_tan( 3.141592653589793/6.0, 0.0, 1 );
// returns ~0.577
The function accepts the following arguments:
- x:
[in] double
input value (in radians, assumed to be bounded by~pi/4
in magnitude). - y:
[in] double
tail ofx
. - k:
[in] int32_t
indicates whethertan(x+y)
(ifk = 1
) or-1/tan(x+y)
(ifk = -1
) is returned.
double stdlib_base_kernel_tan( const double x, const double y, const int32_t k );
- For increased accuracy, the number for which the tangent should be evaluated can be supplied as a double-double number (i.e., a non-evaluated sum of two double-precision floating-point numbers
x
andy
).
#include "stdlib/math/base/special/kernel_tan.h"
#include <stdio.h>
int main( void ) {
const double x[] = { -0.7853981633974483, -0.6108652381980153, -0.4363323129985824, -0.26179938779914946, -0.08726646259971649, 0.08726646259971649, 0.26179938779914935, 0.43633231299858233, 0.6108652381980153, 0.7853981633974483 };
double out;
int i;
for ( i = 0; i < 10; i++ ) {
out = stdlib_base_kernel_tan( x[ i ], 0.0, 1 );
printf( "tan(%lf) = %lf\n", x[ i ], out );
}
}
@stdlib/math-base/special/kernel-cos
: compute the cosine of a double-precision floating-point number on [-π/4, π/4].@stdlib/math-base/special/kernel-sin
: compute the sine of a double-precision floating-point number on [-π/4, π/4].@stdlib/math-base/special/tan
: evaluate the tangent of a number.
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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