代码搜索：predict
找到约 2,271 项符合「predict」的源代码
代码结果 2,271
www.eeworm.com/read/138667/13226422
asv ssss.asv
p=[0.3 0.923;1.0 0.934;4 0.924;20 0.916;20 0.927;45 0.9555;12 0.952;23 0.9575;6.5 0.9585;1.6 0.948;0.5 0.948;0.5 0.9445;1.65 0.943;0.375 0.9455;2.0 0.919;20 0.921;27 0.9515;1.6 0.943;1.6 0.947]';
t=[
www.eeworm.com/read/138667/13226435
asv sss.asv
p=[0.3 1.0 4 20 20 45 12 23 6.5 1.6 0.5 0.5 1.65 0.375 ;
0.923 0.934 0.924 0.916 0.927 0.9555 0.952 0.9575 0.9585 0.948 0.948 0.9445 0.943 0.9455]';
t=[15.17 1
www.eeworm.com/read/138667/13226443
m ssss.m
p=[0.3 0.923;1.0 0.934;4 0.924;20 0.916;20 0.927;45 0.9555;12 0.952;23 0.9575;6.5 0.9585;1.6 0.948;0.5 0.948;0.5 0.9445;1.65 0.943;0.375 0.9455;2.0 0.919;20 0.921;27 0.9515;1.6 0.943;1.6 0.947]';
t=[
www.eeworm.com/read/310621/13648609
m adaboost.m
function [H,alpha]=AdaBoost(X,Y,C,T,WLearner)
% AdaBoost
% Train a strong classifier using several weak ones
%
% Input
% X - samples
% Y - label of samples -
% 1 - belong to
www.eeworm.com/read/483033/6607890
m ekf_nmcda_update.m
%EKF_NMCDA_PREDICT EKF/NMCDA Update step
%
% Syntax:
% [S,EV_STRS] = ekf_nmcda_update(S,Y,t,H,R,h,V,CP,CD,
% a_birth,l_birth,
%
www.eeworm.com/read/480116/6677182
namespace
##useDynLib(ada)
import(rpart)
export(ada,varplot,addtest)
S3method(summary,ada)
S3method(print,ada)
S3method(pairs,ada)
S3method(plot,ada)
S3method(predict,ada)
S3method(update,ada)
S3me
www.eeworm.com/read/477078/6745027
asv ssss.asv
p=[0.3 0.923;1.0 0.934;4 0.924;20 0.916;20 0.927;45 0.9555;12 0.952;23 0.9575;6.5 0.9585;1.6 0.948;0.5 0.948;0.5 0.9445;1.65 0.943;0.375 0.9455;2.0 0.919;20 0.921;27 0.9515;1.6 0.943;1.6 0.947]';
t=[
www.eeworm.com/read/477078/6745032
asv sss.asv
p=[0.3 1.0 4 20 20 45 12 23 6.5 1.6 0.5 0.5 1.65 0.375 ;
0.923 0.934 0.924 0.916 0.927 0.9555 0.952 0.9575 0.9585 0.948 0.948 0.9445 0.943 0.9455]';
t=[15.17 1
www.eeworm.com/read/477078/6745036
m ssss.m
p=[0.3 0.923;1.0 0.934;4 0.924;20 0.916;20 0.927;45 0.9555;12 0.952;23 0.9575;6.5 0.9585;1.6 0.948;0.5 0.948;0.5 0.9445;1.65 0.943;0.375 0.9455;2.0 0.919;20 0.921;27 0.9515;1.6 0.943;1.6 0.947]';
t=[
www.eeworm.com/read/471381/6892023
m adaboost.m
function [H,alpha]=AdaBoost(X,Y,C,T,WLearner)
% AdaBoost
% Train a strong classifier using several weak ones
%
% Input
% X - samples
% Y - label of samples -
% 1 - belong to