// Generated automatically from src/optional/wren_opt_random.wren. Do not edit. static const char* randomModuleSource = "foreign class Random {\n" " construct new() {\n" " seed_()\n" " }\n" "\n" " construct new(seed) {\n" " if (seed is Num) {\n" " seed_(seed)\n" " } else if (seed is Sequence) {\n" " if (seed.isEmpty) Fiber.abort(\"Sequence cannot be empty.\")\n" "\n" " // TODO: Empty sequence.\n" " var seeds = []\n" " for (element in seed) {\n" " if (!(element is Num)) Fiber.abort(\"Sequence elements must all be numbers.\")\n" "\n" " seeds.add(element)\n" " if (seeds.count == 16) break\n" " }\n" "\n" " // Cycle the values to fill in any missing slots.\n" " var i = 0\n" " while (seeds.count < 16) {\n" " seeds.add(seeds[i])\n" " i = i + 1\n" " }\n" "\n" " seed_(\n" " seeds[0], seeds[1], seeds[2], seeds[3],\n" " seeds[4], seeds[5], seeds[6], seeds[7],\n" " seeds[8], seeds[9], seeds[10], seeds[11],\n" " seeds[12], seeds[13], seeds[14], seeds[15])\n" " } else {\n" " Fiber.abort(\"Seed must be a number or a sequence of numbers.\")\n" " }\n" " }\n" "\n" " foreign seed_()\n" " foreign seed_(seed)\n" " foreign seed_(n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13, n14, n15, n16)\n" "\n" " foreign float()\n" " float(end) { float() * end }\n" " float(start, end) { float() * (end - start) + start }\n" "\n" " foreign int()\n" " int(end) { (float() * end).floor }\n" " int(start, end) { (float() * (end - start)).floor + start }\n" "\n" " sample(list) { sample(list, 1)[0] }\n" " sample(list, count) {\n" " if (count > list.count) Fiber.abort(\"Not enough elements to sample.\")\n" "\n" " // There at (at least) two simple algorithms for choosing a number of\n" " // samples from a list without replacement -- where we don't pick the same\n" " // element more than once.\n" " //\n" " // The first is faster when the number of samples is small relative to the\n" " // size of the collection. In many cases, it avoids scanning the entire\n" " // list. In the common case of just wanting one sample, it's a single\n" " // random index lookup.\n" " //\n" " // However, its performance degrades badly as the sample size increases.\n" " // Vitter's algorithm always scans the entire list, but it's also always\n" " // O(n).\n" " //\n" " // The cutoff point between the two follows a quadratic curve on the same\n" " // size. Based on some empirical testing, scaling that by 5 seems to fit\n" " // pretty closely and chooses the fastest one for the given sample and\n" " // collection size.\n" " if (count * count * 5 < list.count) {\n" " // Pick random elements and retry if you hit a previously chosen one.\n" " var picked = {}\n" " var result = []\n" " for (i in 0...count) {\n" " // Find an index that we haven't already selected.\n" " var index\n" " while (true) {\n" " index = int(count)\n" " if (!picked.containsKey(index)) break\n" " }\n" "\n" " picked[index] = true\n" " result.add(list[index])\n" " }\n" "\n" " return result\n" " } else {\n" " // Jeffrey Vitter's Algorithm R.\n" "\n" " // Fill the reservoir with the first elements in the list.\n" " var result = list[0...count]\n" "\n" " // We want to ensure the results are always in random order, so shuffle\n" " // them. In cases where the sample size is the entire collection, this\n" " // devolves to running Fisher-Yates on a copy of the list.\n" " shuffle(result)\n" "\n" " // Now walk the rest of the list. For each element, randomly consider\n" " // replacing one of the reservoir elements with it. The probability here\n" " // works out such that it does this uniformly.\n" " for (i in count...list.count) {\n" " var slot = int(0, i + 1)\n" " if (slot < count) result[slot] = list[i]\n" " }\n" "\n" " return result\n" " }\n" " }\n" "\n" " shuffle(list) {\n" " if (list.isEmpty) return\n" "\n" " // Fisher-Yates shuffle.\n" " for (i in 0...list.count - 1) {\n" " var from = int(i, list.count)\n" " var temp = list[from]\n" " list[from] = list[i]\n" " list[i] = temp\n" " }\n" " }\n" "}\n";