Computes a score from the sum of three objective functions multiplied by their respective weights. The score is used to determine the best set of indices subsampled by the acLHS algorithm, where lower is better.
score_samples(
var_samples,
df,
num_samples,
quantile_ind,
corrs,
min_val,
vario_dep,
vario_params,
weights
)Returns the summed score of the weighted objective functions
Subsampled indices to test
A dataframe with three columns of data
The number of subsamples
The quantile of the independent variable in df
A vector of three correlations of the two variables in df
The minimum time or distance between two points in df
The computed Variogram of the data
The parameters to set for computing a Variogram
A vector of three weights for each objective function