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/460435/7251022
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/460435/7251024
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/450608/7480320
m getprior.m
%GETPRIOR Get class prior probabilities of dataset
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% [PRIOR,LABLIST] = GETPRIOR(A)
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% INPUT
% A Dataset
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% OUTPUT
% PRIOR Class prior probabilities
% LABLIST Label list
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% DESC
www.eeworm.com/read/450608/7480437
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/450608/7480439
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/448535/7531374
m hmmabn.m
function [alphahat,betahat,f,c] = hmmabn(y,HMM)
%
% compute the normalized forward and backward probabilities for the model HMM
% and the output probabilities and the normalization factor
%
% fu
www.eeworm.com/read/441245/7672679
m knn_map.m
%KNN_MAP Map a dataset on a K-NN classifier
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% F = KNN_MAP(A,W)
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% INPUT
% A Dataset
% W K-NN classifier trained by KNNC
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% OUTPUT
% F Posterior probabilities
%
% DESCRIPTION
% Maps t
www.eeworm.com/read/441245/7672925
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/441245/7673240
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/441245/7673242
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