Lines Matching defs:d
72 d = repmat(NaN,size(D,1),size(CC.MEAN,1)); variable
78 d = exp(d-log(mean(sum(CC.N,1)))-log(2*pi)/2); variable
83 d = repmat(NaN,size(D,1),size(CC.MEAN,1)); variable
89 d = exp(d-log(mean(sum(CC.N,1)))-log(2*pi)/2); variable
94 d = repmat(NaN,size(D,1),size(CC.MEAN,1)); variable
100 d = exp(d-log(mean(sum(CC.N,1)))-log(2*pi)/2); variable
109 d = repmat(NaN,size(D,1),size(CC.MD,3)); variable
127 d = filtfilt(CC.FiltB,CC.FiltA,(D*CC.csp_w).^2); variable
129 d = R.output; variable
136 d = full(sparse(1:nr,cl,1,nr,CC.model.nr_class)); variable
141 d = repmat(NaN,size(D,1),size(CC.weights,2)); variable
150 d = r.output; variable
179 d = D*W; variable
194 d = D*W; variable
209 d = D*W; variable
211 d = repmat(NaN,size(D,1),length(CC.IR)); variable
216 d = repmat(NaN,size(D,1),length(CC.IR)); variable
220 d = -sqrt(d); variable
222 d = repmat(NaN,size(D,1),length(CC.IR)); variable
226 d = exp(-d/2); variable
228 d = repmat(NaN,size(D,1),length(CC.IR)); variable
232 d = exp(-d/2); variable
233 … d = d./repmat(sum(d,2),1,size(d,2)); % Zuordungswahrscheinlichkeit [1], p.601, equ (18.39) variable
235 d = repmat(NaN,size(D,1),length(CC.IR)); variable
241 d = repmat(NaN,size(D,1),length(CC.IR)); variable
247 d = repmat(NaN,size(D,1),length(CC.IR)); variable
251 d = exp(-sqrt(d)/2); variable
253 d = repmat(NaN,size(D,1),length(CC.IR)); variable
257 d = exp(-d); variable
259 d = repmat(NaN,size(D,1),length(CC.IR)); variable
263 d = -sqrt(1+d); variable
265 d = repmat(NaN,size(D,1),length(CC.IR)); variable
269 d = (1+d).^(-1/2); variable
271 d = repmat(NaN,size(D,1),length(CC.IR)); variable
275 d = 1./(1+d); variable