代码搜索：Approximation
找到约 1,542 项符合「Approximation」的源代码
代码结果 1,542
www.eeworm.com/read/215323/15065214
tm vegas.tm
:Evaluate: BeginPackage["Cuba`"]
:Evaluate: Vegas::usage = "Vegas[f, {x, xmin, xmax}..] computes a numerical approximation to the integral of the real scalar or vector function f.
The output is a li
www.eeworm.com/read/481260/1297496
m newton copy.m
function A = newton(A,paramlist)
% newton.m A Newton-Raphson procedure for computing solutions
% to naemodel-derived objects. The method uses a
% finite-difference approximation
www.eeworm.com/read/210914/4946890
m cumquad.m
function ci = cumquad(y,x)
% Function computes the numerical approximation to the indefinite
% integral y dx (corresponding to cumsum)
% y ordinates
% x abscissas
% If only one input argume
www.eeworm.com/read/175689/5343554
m contents.m
% Kernel feature extraction.
%
% gda - Generalized Discriminant Analysis.
% greedyappx - Kernel greedy data approximation.
% greedykpca - Greedy kernel PCA.
% kpca - Kernel Principal
www.eeworm.com/read/300084/3848184
h funcs.h
#ifndef _FUNCS_INCLUDED
#define _FUNCS_INCLUDED
#include "..\..\..\MTParserLib\MTParserPrivate.h"
#include "..\..\..\MTParserLib\MTParser.h"
// Numerical Approximation: Derivative
class Deriv
www.eeworm.com/read/270087/4241846
rsg hoap2.rsg
; -*- mode: lisp; -*-
;
; approximation of the Fujitsu Hoap-2 robot
;
(RSG 0 1)
(
;
; define constants for the robot parts
;
; feet
(def $FootLength 0.6)
(def $FootWidth 0.
www.eeworm.com/read/428780/1954228
m contents.m
% Kernel feature extraction.
%
% gda - Generalized Discriminant Analysis.
% greedyappx - Kernel greedy data approximation.
% greedykpca - Greedy kernel PCA.
% kpca - Kernel Principal
www.eeworm.com/read/389864/2535109
rsg hoap2.rsg
; -*- mode: lisp; -*-
;
; approximation of the Fujitsu Hoap-2 robot
;
(RSG 0 1)
(
;
; define constants for the robot parts
;
; feet
(def $FootLength 0.6)
(def $FootWidth 0.
www.eeworm.com/read/388600/2549347
tex abstract.tex
\begin{abstract}
Seismic imaging based on single-scattering approximation
is based on analysis of the match between the
source and receiver wavefields at every image location.
Wavefields at depth ar
www.eeworm.com/read/376881/2706611
m contents.m
% Kernel feature extraction.
%
% gda - Generalized Discriminant Analysis.
% greedyappx - Kernel greedy data approximation.
% greedykpca - Greedy kernel PCA.
% kpca - Kernel Principal