 Algorithm - generating all combinations from items that must be chosen in sequence
By : user3782546
Date : March 29 2020, 07:55 AM
this one helps. It's a cartesian product, if exactly one item is chosen from each list/array, more precisely a list of the elements of the cartesian product. For lists, it's in Haskell's standard library: code :
Prelude> sequence ["abc","def","ghi"]
["adg","adh","adi","aeg","aeh","aei","afg","afh","afi","bdg","bdh","bdi","beg","beh"
,"bei","bfg","bfh","bfi","cdg","cdh","cdi","ceg","ceh","cei","cfg","cfh","cfi"]
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algorithm to get all combinations of splitting up N items into K bins
By : user3810183
Date : March 29 2020, 07:55 AM
Hope that helps A common approach is as follows. If you have, say, K bins, then add K-1 special values to your initial array. I will use the -1 value assuming that it never occurs in the initial array; you can choose a different value. code :
[-1,1,2,3,4] -> {{}, {1,2,3,4}}
[2,1,3,-1,4] -> {{2,3,4}, {4}}
[3,1,2,-1,4] -> {{3,1,2}, {4}}
[1,3,-1,2,4] -> {{1,3}, {2,4}}
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 Efficient algorithm to get the combinations of all items in object
By : JEGAN
Date : March 29 2020, 07:55 AM
hope this fix your issue Your algorithm is almost O(2^n), you can discard a lot of combinations, but the num of elements will be (n! * (n-x)!) / x!. To discard the useless combinations you can use an indexed array. code :
function combine(items, numSubItems) {
var result = [];
var indexes = new Array(numSubItems);
for (var i = 0 ; i < numSubItems; i++) {
indexes[i] = i;
}
while (indexes[0] < (items.length - numSubItems + 1)) {
var v = [];
for (var i = 0 ; i < numSubItems; i++) {
v.push(items[indexes[i]]);
}
result.push(v);
indexes[numSubItems - 1]++;
var l = numSubItems - 1; // reference always is the last position at beginning
while ( (indexes[numSubItems - 1] >= items.length) && (indexes[0] < items.length - numSubItems + 1)) {
l--; // the last position is reached
indexes[l]++;
for (var i = l +1 ; i < numSubItems; i++) {
indexes[i] = indexes[l] + (i - l);
}
}
}
return result;
}
var combinations = combine(["a", "b", "c", "d"], 3);
console.log(JSON.stringify(combinations));
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 Optimum algorithm to check various combinations of items when number of items is too large
By : OlegMihaylov
Date : March 29 2020, 07:55 AM
hop of those help? The idea is to replace the computation of alpha with the so-called discrimination for each item from classical test theory (CTT). The discrimination is the correlation of the item score with a "true score" (which we would assume to be the row sum). Let the data be code :
dat <- structure(list(CESD1 = c(1, 2, 2, 0, 1, 0, 0, 0, 0, 1), CESD2 = c(2, 3, 1, 0, 0, 1, 1, 1, 0, 1),
CESD3 = c(0, 3, 0, 1, 1, 0, 0, 0, 0, 0), CESD4 = c(1, 2, 0, 1, 0, 1, 1, 1, 0, 0),
CESD5 = c(0, 1, 0, 2, 1, 2, 2, 0, 0, 0), CESD6 = c(0, 3, 0, 1, 0, 0, 2, 0, 0, 0),
CESD7 = c(1, 2, 1, 1, 2, 0, 1, 0, 1, 0), CESD8 = c(1, 3, 1, 1, 0, 1, 0, 0, 1, 0),
CESD9 = c(0, 1, 0, 2, 0, 0, 1, 1, 0, 1), CESD10 = c(0, 1, 0, 2, 0, 0, 1, 1, 0, 1),
CESD11 = c(0, 2, 1, 1, 1, 1, 2, 3, 0, 0), CESD12 = c(0, 3, 1, 1, 1, 0, 2, 0, 0, 0),
CESD13 = c(0, 3, 0, 2, 1, 2, 1, 0, 1, 0), CESD14 = c(0, 3, 1, 2, 1, 1, 1, 0, 1, 1),
CESD15 = c(0, 2, 0, 1, 0, 1, 0, 1, 1, 0), CESD16 = c(0, 2, 2, 0, 0, 1, 1, 0, 0, 0),
CESD17 = c(0, 0, 0, 0, 0, 1, 1, 0, 0, 0), CESD18 = c(0, 2, 0, 0, 0, 0, 0, 0, 0, 1),
CESD19 = c(0, 3, 0, 0, 0, 0, 0, 1, 1, 0), CESD20 = c(0, 3, 0, 1, 0, 0, 0, 0, 0, 0)),
.Names = c("CESD1", "CESD2", "CESD3", "CESD4", "CESD5", "CESD6", "CESD7", "CESD8", "CESD9",
"CESD10", "CESD11", "CESD12", "CESD13", "CESD14", "CESD15", "CESD16", "CESD17",
"CESD18", "CESD19", "CESD20"), row.names = c(NA, -10L),
class = c("tbl_df", "tbl", "data.frame"))
stat <- t(sapply(1:ncol(dat), function(ii){
dd <- dat[, ii]
# discrimination is the correlation of the item to the rowsum
disc <- if(var(dd, na.rm = TRUE) > 0) cor(dd, rowSums(dat[, -ii]), use = "pairwise")
# alpha that would be obtained when we skip this item
alpha <- psych::alpha(dat[, -ii])$total$raw_alpha
c(disc, alpha)
}))
dimnames(stat) <- list(colnames(dat), c("disc", "alpha^I"))
stat <- data.frame(stat)
plot(stat, pch = 19)
stat <- stat[order(stat$disc), ]
this <- sapply(1:(nrow(stat)-2), function(ii){
ind <- match(rownames(stat)[1:ii], colnames(dat))
alpha <- psych::alpha(dat[, -ind, drop = FALSE])$total$raw_alpha
})
delete_these <- rownames(stat)[which(this > .9)]
psych::alpha(dat[, -match(delete_these, colnames(dat)), drop = FALSE])$total$raw_alpha
length(delete_these)
stat <- stat[order(stat$disc, decreasing = TRUE), ]
this <- sapply(1:(nrow(stat)-2), function(ii){
ind <- match(rownames(stat)[1:ii], colnames(dat))
alpha <- psych::alpha(dat[, -ind, drop = FALSE])$total$raw_alpha
})
delete_these <- rownames(stat)[which(this > .9)]
psych::alpha(dat[, -match(delete_these, colnames(dat)), drop = FALSE])$total$raw_alpha
length(delete_these)
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 After items selection in my ListView other items get randomly selected while scrolling Kotlin
By : user3196113
Date : March 29 2020, 07:55 AM
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» 
code :
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