代码搜索：corresponding
找到约 4,250 项符合「corresponding」的源代码
代码结果 4,250
www.eeworm.com/read/377727/9263940
m distance.m
function d = distance(inputcities)
% DISTANCE
% d = DISTANCE(inputcities) calculates the distance between n cities as
% required in a Traveling Salesman Problem. The input argument has two rows
%
www.eeworm.com/read/377572/9270695
hh route_db.hh
// -*- c-basic-offset: 4; tab-width: 8; indent-tabs-mode: t -*-
// vim:set sts=4 ts=8:
// Copyright (c) 2001-2008 XORP, Inc.
//
// Permission is hereby granted, free of charge, to any person obtaini
www.eeworm.com/read/376212/9324776
m help_io.m
function help_io(self)
% Coastline data consist of counter-clockwise closed contours,
% separated by NaNs. The geographic positions must be stored in a
% Matlab mat-file, using the variable names
www.eeworm.com/read/179612/9349437
h wlfsim.h
/*
*
* Copyright (C) 1996-2005, OFFIS
*
* This software and supporting documentation were developed by
*
* Kuratorium OFFIS e.V.
* Healthcare Information and Communication Systems
* Escherw
www.eeworm.com/read/178529/9393786
m spec.m
function [freq, a] = spec(X, N)
%%X = data of length N
%%gives frequencies and corresponding spectral estimate
%%(modulus squared of fft of the data); frequency from
%%[0, 0.5]
freq=(0:(N-1))./(2
www.eeworm.com/read/374010/9423656
m nniol.m
function [W1f,W2f,W1g,W2g,PI_vector,iteration,lambda]=nniol(NetDeff,NetDefg,...
NN,W1f,W2f,W1g,W2g,trparms,Y,U)
% NNIOL
% -----
% Train a
www.eeworm.com/read/177674/9442411
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by s
www.eeworm.com/read/177674/9442486
m netunpak.m
function net = netunpak(net, w)
%NETUNPAK Separates weights vector into weight and bias matrices.
%
% Description
% NET = NETUNPAK(NET, W) takes an net network data structure NET and a
% weight vect
www.eeworm.com/read/177674/9442674
m mlpprior.m
function prior = mlpprior(nin, nhidden, nout, aw1, ab1, aw2, ab2)
%MLPPRIOR Create Gaussian prior for mlp.
%
% Description
% PRIOR = MLPPRIOR(NIN, NHIDDEN, NOUT, AW1, AB1, AW2, AB2) generates a
% dat
www.eeworm.com/read/176823/9483117
m netinit.m
function net = netinit(net, prior)
%NETINIT Initialise the weights in a network.
%
% Description
%
% NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
% the weights and biases by s