代码搜索:Probability
找到约 4,670 项符合「Probability」的源代码
代码结果 4,670
www.eeworm.com/read/392168/8360483
pdf calculation of error probability for msk and oqpsk systems operating in a fading multipath environment.pdf
www.eeworm.com/read/301820/13847820
pdf introduction to probability models (sheldon m.ross , 9th edition).pdf
www.eeworm.com/read/188012/8579860
m bayes_classify.m
% Bayes分类器算法设计
close all
clear all
x=[-5:0.01:5];
%一个标准事件的样本值
x_sample=[
-3.9847 -3.5549 -1.2401 -0.9780 -0.7932 -2.8531...
-2.7605 -3.7287 -3.5414 -2.269
www.eeworm.com/read/150881/5688162
java tournamentselector.java
/*
* This file is part of JGAP.
*
* JGAP offers a dual license model containing the LGPL as well as the MPL.
*
* For licencing information please see the file license.txt included with JGAP
www.eeworm.com/read/254734/12121603
m observe_and_estimate.m
function [Sample_probability,Estimate,vx,vy,after_prop]=Observe_and_Estimate(Sample_Set,Estimate,Hx,Hy,target_histgram,new_sita,loop,after_prop,I,N)
Total_probability=0;
for i=1:1:N
Sample_hi
www.eeworm.com/read/254734/12121622
m initial_2.m
function [Sample_Set,Sample_probability,Estimate,target_histgram]=initial_2(x,y,Hx,Hy,vx,vy,I,N)
Estimate(1).x=x;
Estimate(1).y=y;
Estimate(1).prob=0.999;
for i=1:1:N
www.eeworm.com/read/254734/12121628
asv initial_2.asv
function [Sample_Set,Sample_probability,Estimate,target_histgram]=initial_2(x,y,Hx,Hy,vx,vy,I,N)
Estimate(1).x=x;
Estimate(1).y=y;
Estimate(1).prob=0.999;
for i=1:1:N
www.eeworm.com/read/254734/12121630
asv observe_and_estimate.asv
function [Sample_probability,Estimate,vx,vy,after_prop]=Observe_and_Estimate(Sample_Set,Estimate,Hx,Hy,target_histgram,new_sita,loop,after_prop,I,N)
Total_probability=0;
for i=1:1:N
Sample_hi
www.eeworm.com/read/192035/8410441
m mcbinom.m
function pb=mcbinom(M,p,n)
% MCBINOM - cumulative binomial distribution, in a form for MCSSA.
% Syntax: pb=mcbinom(M,p); pb=mcbinom(M,p,n);
%
% MCBINOM(M,p,n), where n is an integer, returns the
www.eeworm.com/read/191952/8414453
1 consult.1
.EN
.TH C4.5 1
.SH NAME
.PP
consult \- classify items using a decision tree
.SH SYNOPSIS
.PP
.B consult
[ \fB-f\fR FNS ]
[ \fB-t\fR ]
.SH DESCRIPTION
.PP
.I Consult
reads a decision tree produced by c