代码搜索：deviation
找到约 1,443 项符合「deviation」的源代码
代码结果 1,443
www.eeworm.com/read/137160/13341890
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/314653/13562250
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/309349/13673607
ex-ldpc36-5000a
#!/bin/sh
# Example of a (10000,5000) LDPC code with 3 checks per bit and 6 bits per
# check, tested on Additive White Gaussian Noise channels with noise standard
# deviations varying from 0.80 to
www.eeworm.com/read/309349/13673650
ex-ldpc36-1000a
#!/bin/sh
# Example of a (2000,1000) LDPC code with 3 checks per bit and 6 bits per
# check, tested on Additive White Gaussian Noise channels with noise standard
# deviations varying from 0.80 to 0
www.eeworm.com/read/130105/5964619
txt hmm.txt
5
3 6 6 6 3
3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
9
5
-0.100083 -2.351375 -10000000000.0
www.eeworm.com/read/493294/6399960
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/481859/6631685
m gaussianhighpass.m
clear all
clc
a=imread('Fig0457(a)(thumb_print).tif');
a=double(a);
c=size(a);
N=c(1)
D0=input('Enter the cutoff frequency/standard deviation ');
for u=1:1:c(1)
for v=1:1:c(2)
Dx=
www.eeworm.com/read/255755/12057325
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer (default: 10)
% S Standard deviation of weights in an input lay
www.eeworm.com/read/255577/12072204
m f_randg.m
function A = f_randg (m,n,mu,sigma)
%F_RANDG: Construct Gaussian random matrix
%
% Usage: A = f_randg (m,n,mu,sigma)
%
% Inputs:
% m = number of rows of A
% n = numbe
www.eeworm.com/read/150905/12248387
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer (default: 10)
% S Standard deviation of weights in an input lay