代码搜索：Sampling
找到约 3,969 项符合「Sampling」的源代码
代码结果 3,969
www.eeworm.com/read/247001/12693016
c dsysfixp.c
/* Model: dsys
* Sampling period = 0.100000 seconds
*
* Input scaling: [2]
* State scaling: [0.5 4]
* Output scaling: [2]
*
* Generated at 19:17:18 on 26-Feb-2003
*/
/*
www.eeworm.com/read/143706/12850011
m demprior.m
function demprior(action);
%DEMPRIOR Demonstrate sampling from a multi-parameter Gaussian prior.
%
% Description
% This function plots the functions represented by a multi-layer
% perceptron network w
www.eeworm.com/read/140851/13058992
m demhmc1.m
%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%
% Description
% The problem consists of generating data from a mixture of two
% Gaussians in two dimensions using a
www.eeworm.com/read/140851/13059144
m~ demhmc1.m~
%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%
% Description
% The problem consists of generating data from a mixture of two
% Gaussians in two dimensions using a
www.eeworm.com/read/327456/13076969
java samplingndi.java
package datamining;
import java.io.*;
import java.util.*;
/**
* Class for finding frequent itemsets using sampling
* with the NDI algorithm.
*
* @author Michael Holler
* @version 0.1
www.eeworm.com/read/241192/13164248
m demo2_02.m
%
% Demonstrates relative performance of Wiener filter (fixed-gain)
% and Kalman filter (time-varying gain) on random walk estimation
%
% Applied to random walk process with gaussian sampling nois
www.eeworm.com/read/138798/13212025
m demhmc1.m
%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%
% Description
% The problem consists of generating data from a mixture of two
% Gaussians in two dimensions using a
www.eeworm.com/read/138798/13212213
m~ demhmc1.m~
%DEMHMC1 Demonstrate Hybrid Monte Carlo sampling on mixture of two Gaussians.
%
% Description
% The problem consists of generating data from a mixture of two
% Gaussians in two dimensions using a
www.eeworm.com/read/318840/13471256
m demo2_02.m
%
% Demonstrates relative performance of Wiener filter (fixed-gain)
% and Kalman filter (time-varying gain) on random walk estimation
%
% Applied to random walk process with gaussian sampling nois
www.eeworm.com/read/314385/13568714
m demo2_02.m
%
% Demonstrates relative performance of Wiener filter (fixed-gain)
% and Kalman filter (time-varying gain) on random walk estimation
%
% Applied to random walk process with gaussian sampling nois