代码搜索：Probabilities
找到约 751 项符合「Probabilities」的源代码
代码结果 751
www.eeworm.com/read/255755/12058008
m genclass.m
%GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities (optional; default: equal prior probabilities)
%
% OUTPUT
% M C
www.eeworm.com/read/255755/12058013
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset
www.eeworm.com/read/150905/12248929
m getprior.m
%GETPRIOR Get class prior probabilities of dataset
%
% [PRIOR,LABLIST] = GETPRIOR(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% PRIOR Class prior probabilities
% LABLIST Label list
%
% DESC
www.eeworm.com/read/150905/12249327
m genclass.m
%GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities (optional; default: equal prior probabilities)
%
% OUTPUT
% M C
www.eeworm.com/read/150905/12249331
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset
www.eeworm.com/read/150214/12304681
html rand.html
Random Variate Generation Routines
Random Variate Generation Routines
This module provides facilities for basic pseudo-random number gene
www.eeworm.com/read/150214/12304809
c check.c
/* CHECK.C - Compute parity checks and other stats on decodings. */
/* Copyright (c) 2001 by Radford M. Neal
*
* Permission is granted for anyone to copy, use, or modify this program
* for purpo
www.eeworm.com/read/149739/12353265
m getprior.m
%GETPRIOR Get class prior probabilities of dataset
%
% [PRIOR,LABLIST] = GETPRIOR(A)
%
% INPUT
% A Dataset
%
% OUTPUT
% PRIOR Class prior probabilities
% LABLIST Label list
%
% DESC
www.eeworm.com/read/149739/12353602
m genclass.m
%GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities (optional; default: equal prior probabilities)
%
% OUTPUT
% M C
www.eeworm.com/read/149739/12353608
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = TREE_MAP(A,W)
%
% INPUT
% A Dataset
% W Decision tree mapping
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps the dataset