代码搜索:normal

找到约 10,000 项符合「normal」的源代码

代码结果 10,000
www.eeworm.com/read/353746/10423028

aliases pangox.aliases

# File defining aliases of PangoFontDescription to X font set # # family style variant weight stretch XLFD sans normal normal normal normal \ "-*-helvetica-medium-r-normal--*-*-*-*-*-*-*-*,\ -
www.eeworm.com/read/304660/13790162

aliases pangox.aliases

# File defining aliases of PangoFontDescription to X font set # # family style variant weight stretch XLFD sans normal normal normal normal \ "-*-helvetica-medium-r-normal--*-*-*-*-*-*-*-*,\ -
www.eeworm.com/read/195484/8150886

aliases pangox.aliases

# File defining aliases of PangoFontDescription to X font set # # family style variant weight stretch XLFD sans normal normal normal normal \ "-*-helvetica-medium-r-normal--*-*-*-*-*-*-*-*,\ -
www.eeworm.com/read/371256/9558920

m normalonetoone.m

%normal normalitzaci
www.eeworm.com/read/371256/9558949

m normalhartley.m

%normal normalitzaci
www.eeworm.com/read/386050/8767404

m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U) % % W = UDC(A) % W = A*UDC % % INPUT % A input dataset % % OUTPUT % W output mapping % % DESCRIPTION % Computation a
www.eeworm.com/read/299984/7139980

m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U) % % W = UDC(A) % W = A*UDC % % INPUT % A input dataset % % OUTPUT % W output mapping % % DESCRIPTION % Computation a
www.eeworm.com/read/460435/7250455

m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U) % % W = UDC(A) % W = A*UDC % % INPUT % A input dataset % % OUTPUT % W output mapping % % DESCRIPTION % Computation a
www.eeworm.com/read/441245/7672660

m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U) % % W = UDC(A) % W = A*UDC % % INPUT % A input dataset % % OUTPUT % W output mapping % % DESCRIPTION % Computation a
www.eeworm.com/read/400577/11572624

m udc.m

%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U) % % W = UDC(A) % W = A*UDC % % INPUT % A input dataset % % OUTPUT % W output mapping % % DESCRIPTION % Computation a