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/266534/11220832
txt greed.cc.txt
// Problem Huffman's Greed
// Algorithm Dynamic Programming
// Runtime O(n^3)
// Author Walter Guttmann
// Date 2003.12.07
#include
#include
#include
using
www.eeworm.com/read/431675/8661801
m classc.m
%CLASSC Convert mapping to classifier
%
% W = classc(W)
% W = W*classc
%
% The mapping W is converted into a classifier: outputs (distances to the map)
% are converted by the sigmoid function to proba
www.eeworm.com/read/431675/8662165
m tree_map.m
%TREE_MAP Map a dataset by binary decision tree
%
% F = tree_map(A,W)
%
% Maps the dataset A by the binary decision tree classfier W on the
% [0,1] interval for each of the classes W is trained on
www.eeworm.com/read/386050/8767456
m knn_map.m
%KNN_MAP Map a dataset on a K-NN classifier
%
% F = KNN_MAP(A,W)
%
% INPUT
% A Dataset
% W K-NN classifier trained by KNNC
%
% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps t
www.eeworm.com/read/386050/8767957
m getprior.m
%GETPRIOR Get class prior probabilities of dataset
%
% [PRIOR,LABLIST] = GETPRIOR(A,WARNING)
%
% INPUT
% A Dataset
% WARNING 1: Generate warning if priors are not set and should be
%
www.eeworm.com/read/386050/8768971
m genclass.m
%GENCLASS Generate class frequency distribution
%
% M = GENCLASS(N,P)
%
% INPUT
% N Number (scalar)
% P Prior probabilities
%
% OUTPUT
% M Class frequency distribution
%
% DESCRIPTION
% G
www.eeworm.com/read/386050/8768981
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/383950/8909261
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/160516/10523283
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