1d:T703,# Selects a random neighbour for a subset of non-zero pigments that are outside the min and max bounds for the simulated annealing algorithm, specifically handling Prochlorococcus pigments. ```r Prochloro_Random_Neighbour( f_new, Temp, chlv, chlvp, N, place, S, S_weights, minF, maxF ) ``` ## Arguments - `f_new`: F matrix of pigment ratios - `Temp`: Temperature of the annealing - `chlv`: Chlorophyll-a column - `chlvp`: Dvchla column - `N`: Indexs of pigment ratios to be changed - `place`: Indexes in F matrix where values are non-zero - `S`: S matrix of samples - `S_weights`: Weights for NNLS algorithm - `minF`: Minimum bounds for each phytoplankton group and pigments - `maxF`: Maximum bounds for each phytoplankton group and pigments ## Returns A list containing: - **F matrix**: The new F matrix with randomly modified values - **RMSE**: Root mean square error of the new solution - **C matrix**: The concentration matrix from NNLS ## Examples ```r Fmat <- as.matrix(phytoclass::Fp) S <- as.matrix(phytoclass::Sp) S_weights <- as.numeric(phytoclass:::Bounded_weights(S)) place <- which(Fmat[, seq(ncol(Fmat) - 2)] > 0) # Get min_max from package data min_max_mat <- phytoclass:::Default_min_max(phytoclass::min_max, Fmat[, -ncol(Fmat)]) # Get bounds using Prochloro_Wrangling as in simulated_annealing_Prochloro f_c <- Fmat W0 <- phytoclass:::Prochloro_Wrangling(f_c, min_max_mat[[1]], min_max_mat[[2]]) minF <- W0[[1]] maxF <- W0[[2]] # Extract chlv and chlvp from the matrix chlv <- f_c[, ncol(f_c)] # Chl a (Tchla) chlvp <- f_c[, ncol(f_c) - 1] # dvChl a Temp <- 0.5 N <- place # Run Prochloro_Random_Neighbour result <- phytoclass:::Prochloro_Random_Neighbour( f_c, Temp, chlv, chlvp, N, place, S, S_weights, minF, maxF ) ```1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302 14.2222 5.70302 14.0299C5.70302 13.8576 5.69679 13.4019 5.69323 12.797C3.67661 13.235 3.25112 11.825 3.25112 11.825C2.

Prochloro_Random_Neighbour function