cmf_krr_ecvr_pred_mem: To make predictions using ecvr results in memory

In conmolfields: Continuous molecular fields

Description

To make predictions using ecvr results in memory

Usage

cmf_krr_ecvr_pred_mem(ecvr, kernels, kernels_pred, y_exp, print_pred = TRUE, plot_pred = TRUE, ...)

Arguments

ecvr
kernels
kernels_pred
y_exp
print_pred
plot_pred
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (ecvr, kernels, kernels_pred, y_exp, print_pred = TRUE, 
    plot_pred = TRUE, ...) 
{
    ntrain <- dim(kernels_pred[[2]][[1]])[2]
    npred <- dim(kernels_pred[[2]][[1]])[1]
    nmodels <- ecvr$nmodels
    models <- ecvr$models
    permutations <- ecvr$permutations
    indexes <- ecvr$indexes
    mfields <- ecvr$mfields
    YPred <- matrix(0, npred, nmodels)
    for (imod in 1:nmodels) {
        model <- models[[imod]]
        permutation <- permutations[[imod]]
        ind_train <- indexes[[imod]]
        kernels_perm <- cmf_permute_kernels(kernels, permutation, 
            mfields)
        kernels_pred_perm <- cmf_permute_kernels(kernels_pred, 
            permutation, mfields, for_pred = TRUE)
        kernels_perm_ind <- cmf_extract_subkernels(kernels_perm, 
            ind_train, ind_train, mfields)
        kernels_pred_perm_ind <- cmf_extract_subkernels(kernels_pred_perm, 
            1:npred, ind_train, mfields)
        y_pred <- cmf_krr_pred_mem(model = model, kernels = kernels_perm_ind, 
            kernels_pred = kernels_pred_perm_ind, y_exp = y_exp, 
            print_pred = FALSE, plot_pred = FALSE, ...)
        YPred[, imod] <- y_pred
    }
    YPredMean <- rowMeans(YPred)
    YPredSD <- double(npred)
    for (i in 1:npred) YPredSD[i] <- sd(YPred[i, ])
    if (!is.na(y_exp[1])) {
        if (print_pred) {
            dif <- y_exp - YPredMean
            cat("No.   Prediction   Experiment  Difference\n")
            for (ipred in 1:npred) {
                cat(sprintf("%3d  %.3f +- %.3f   %.3f    %6.3f\n", 
                  ipred, YPredMean[ipred], YPredSD[ipred] * 2, 
                  y_exp[ipred], dif[ipred]))
            }
            if (npred > 1) {
                regr <- regr_param(YPredMean, y_exp)
                cat(sprintf("R2pred=%g RMSEpred=%g (%g%%)\n", 
                  regr$R2, regr$RMSE, regr$RMSE_pc))
            }
            flush.console()
        }
        if (plot_pred) {
            cinf_plotxy(YPredMean, y_exp, xlab = "Predicted", 
                ylab = "Experiment", main = "Scatter Plot for Prediction")
            abline(coef = c(0, 1))
        }
    }
    if (plot_pred) {
        cinf_plotxy(YPredMean, y_exp, xlab = "Predicted", ylab = "Experiment", 
            main = "Scatter Plot for Prediction")
        abline(coef = c(0, 1))
    }
    list(YPred = YPred, YPredMean = YPredMean, YPredSD = YPredSD, 
        y_exp = y_exp)
  }

conmolfields documentation built on May 2, 2019, 4:18 p.m.