代码搜索：Boltzmann

找到约 199 项符合「Boltzmann」的源代码

代码结果 199
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www.eeworm.com/read/169382/9864421

txt boltzmann.txt

Dim w(1 To 99, 1 To 99) As Double Dim pointcount As Integer Dim initialPoint As Integer Dim x(1 To 99) As Integer Dim yuZhi(1 To 99) As Double Dim E As Double Dim initialT As Double Dim lastT A
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www.eeworm.com/read/449744/7497444

m boltzmann.m

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www.eeworm.com/read/338524/12295069

m boltzmann.m

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www.eeworm.com/read/212314/15159930

m boltzmann.m

function [AcceptChange] = Boltzmann(Value,iter,MLKP); if upper(MLKP.BoltzmannMode)=='EXP' BoltzmannFact=exp(MLKP.BoltzmannK*(Value-2)*iter/MLKP.MaxIter); if (BoltzmannFact > rand)
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www.eeworm.com/read/163233/5511105

ps boltzmann.ps

%! %%BoundingBox: (atend) %%Pages: (atend) %%DocumentFonts: (atend) %%EndComments % % FrameMaker PostScript Prolog 3.0, for use with FrameMaker 3.0 % Copyright (c) 1986,87,89,90,91 by Frame Technology
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www.eeworm.com/read/191902/8417086

m deterministic_boltzmann.m

function D = Deterministic_Boltzmann(train_features, train_targets, params, region); % Classify using the deterministic Boltzmann algorithm % Inputs: % features - Train features % targets - Tra
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www.eeworm.com/read/191902/8417194

m hmm_boltzmann.m

function [a, b] = HMM_Boltzmann(Nh, No, V, init_state) % Find the probability transition matrices a,b from sample data using the Boltzmann network algorithm % % Inputs: % Nh - Number of hidd
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www.eeworm.com/read/286662/8751681

m deterministic_boltzmann.m

function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params); % Classify using the deterministic Boltzmann algorithm % Inputs: % train_pattern
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www.eeworm.com/read/286662/8751806

m hmm_boltzmann.m

function [a, b] = HMM_Boltzmann(Nh, No, V, init_state) % Find the probability transition matrices a,b from sample data using the Boltzmann network algorithm % % Inputs: % Nh - Number of hidd
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www.eeworm.com/read/177129/9468772

m deterministic_boltzmann.m

function D = Deterministic_Boltzmann(train_features, train_targets, params, region); % Classify using the deterministic Boltzmann algorithm % Inputs: % features - Train features % targets - Tra
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