代码搜索：intelligence
找到约 675 项符合「intelligence」的源代码
代码结果 675
www.eeworm.com/read/108511/15585220
merchants
150
161
train:HP
train:Strength
train:Dexterity
train:Constitution
train:Dodge
end
23
2
train:Strength
train:Strength
train:SP
train:HP
end
153
161
train:Intelligence
train:Wisdom
t
www.eeworm.com/read/389844/8496324
m elm_train.m
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% ELM_train(network,data) - train MNN model with ELM (standard mode) .
%
% Parameters: network - neural network w
www.eeworm.com/read/386050/8768247
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/182833/9188941
authors
This software was developed at the MIT Computer Science and Artificial
Intelligence Laboratory in the Parallel and Distributed Operating Systems
Group.
The following people contributed to this implem
www.eeworm.com/read/423094/10588681
authors
This software was developed at the MIT Computer Science and Artificial
Intelligence Laboratory in the Parallel and Distributed Operating Systems
Group.
The following people contributed to this implem
www.eeworm.com/read/299984/7140346
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/460435/7250821
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/441245/7673035
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K
www.eeworm.com/read/400577/11572999
m gentrunk.m
%GENTRUNK Generation of Trunk's classification problem of 2 Gaussian classes
%
% A = GENTRUNK(N,K)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K