[ad_1] If different identifiers can have same values, your third map will keep the last parsed one. E.g. : File 1 : localId1 => “aaaa” localId2 => “bbbb” localId3 => “cccc” localId4 => “aaaa” File 2: localId1 => “1111” localId2 => “2222” localId3 => “3333” localId4 => “4444” Your first and second maps will store … Read more
[ad_1] An approach should consider breaking the OP’s entire task into two separate ones. Within an intermediate step one firstly does reduce the list of material items into a map of (grouped) material items. The final step does a map–reduce on the values of the intermediate result, where the reduce task is responsible for summing … Read more
[ad_1] You could use a simple character array: static const char symbols[] = {‘@’, ‘#’, ‘^’, ‘*’}; char c; std::cin >> c; unsigned int value; for (value = 0; value < sizeof(symbols); ++value) { if (c == symbols[value]) { break; } } if (value >= sizeof(symbols)) { // Symbol not found. } else { value … Read more
[ad_1] The spatstat package makes this easy. Since I don’t know what your data looks like, I’ll start by creating some demo data. x <- rnorm(327) y <- runif(327) Now convert the data into a geospatial format using the ppp() function from spatstat. library(spatstat) dta <- ppp(x, y, window = owin(c(-5, 5), c(-5, 5))) Finally, … Read more