代码搜索：Normalized
找到约 4,216 项符合「Normalized」的源代码
代码结果 4,216
www.eeworm.com/read/150905/12248402
m classc.m
%CLASSC Convert mapping to classifier
%
% W = CLASSC(W)
% W = W*CLASSC
%
% INPUT
% W Any mapping or dataset
%
% OUTPUT
% W Classifier mapping or normalized dataset: outputs/features sum to 1
%
www.eeworm.com/read/149739/12352760
m classc.m
%CLASSC Convert mapping to classifier
%
% W = CLASSC(W)
% W = W*CLASSC
%
% INPUT
% W Any mapping or dataset
%
% OUTPUT
% W Classifier mapping or normalized dataset: outputs/features sum to 1
%
www.eeworm.com/read/170938/5402570
m wt04fig11.m
%CAPTION
fprintf('\n');
disp('Figure 4.11')
disp('Window 1: Normalized scalogram of the signal shown in Figure 4.3,')
disp('calculated from the modulus of an analytic wavelet transform.')
disp('D
www.eeworm.com/read/309003/3708460
m wt04fig11.m
%CAPTION
fprintf('\n');
disp('Figure 4.11')
disp('Window 1: Normalized scalogram of the signal shown in Figure 4.3,')
disp('calculated from the modulus of an analytic wavelet transform.')
disp('D
www.eeworm.com/read/309003/3708852
m splineuchoose.m
function [xh,c] = SplineUChoose(y,k)
% SplineUChoose -- Estimate Optimal Bandwidth for Spline Kernel
% Usage
% [xhat,risk] = SplineUChoose(y,k)
% Inputs
% y Noisy Data Normalized to Noise Le
www.eeworm.com/read/427864/1964796
m wt04fig11.m
%CAPTION
fprintf('\n');
disp('Figure 4.11')
disp('Window 1: Normalized scalogram of the signal shown in Figure 4.3,')
disp('calculated from the modulus of an analytic wavelet transform.')
disp('D
www.eeworm.com/read/384673/2598535
m wt04fig11.m
%CAPTION
fprintf('\n');
disp('Figure 4.11')
disp('Window 1: Normalized scalogram of the signal shown in Figure 4.3,')
disp('calculated from the modulus of an analytic wavelet transform.')
disp('D
www.eeworm.com/read/293183/8310290
m subsm.m
%SUBSM Find subspace map
%
% [W,alf] = subsm(A,n)
%
% A n-dimensional subspace map for the dataset A is found using PCA,
% such that it contains the origin. All object in A are normalized
% first on u
www.eeworm.com/read/174160/9605636
m simparetonrm.m
function [sample] = simparetonrm(M, N, alpha, gamma)
% SIMPARETONRM Generate a matrix of random numbers from the
% normalized Pareto distribution with
%
% pdf f(x) = alpha*gamma/(1+gamma*x)
www.eeworm.com/read/413630/11149144
csv example2_crossval.csv
// these are data with normalized inputs (centered around zero)
// -0.8 means male; 0.8 means female
SpecimenNumber;Species;FrontalLip;RearWidth;Length;Width;Depth;Sex
-40.5;0.5;-4.783;-3.2385;-9.6