feat: add plots for n m k trends

example/stats_basic_bloom_filter.cpp

@@ -1,68 +0,0 @@
-#include "bloom_filter/bloom_filter.hpp"
-#include <cmath>
-#include <cstdint>
-#include <cstdio>
-#include <iostream>
-#include <random>
-#include <set>
-#include <string>
-
-int main(int argc, char *argv[]) {
-    if (argc != 4) { // REM: 0th ars it the program name
-        std::cerr << "need args n(number of keys inserted) m(filter size in "
-                     "num bits) k(num hash funcs)";
-        return 1;
-    }
-
-    uint n = std::stoul(argv[1]);
-    uint m = std::stoul(argv[2]);
-    uint k = std::stoul(argv[3]);
-
-    // std::random_device rd; you can uncomment and pass it in here to randomise
-    std::mt19937_64 rng{}; // no seed so this is deterministic
-    const uint TOTAL_TRIALS = 20;
-
-    double sum_theorital_approx_fp_rate = 0;
-    double sum_filter_approx_fp_rate = 0;
-    double sum_actual_fp_rate = 0;
-
-    for (int _t = 0; _t < TOTAL_TRIALS; _t++) {
-        uint64_t seed1 = rng(), seed2 = rng();
-
-        BloomFilter filter{m, k, seed1, seed2};
-
-        std::set<uint64_t> actual_inserts;
-        for (int i = 0; i < n; i++) {
-            uint64_t num = rng();
-            actual_inserts.insert(num);
-            filter.put(&num, sizeof(num));
-        }
-
-        const uint ELEMS_CHECK = 10000;
-        uint fp_count = 0;
-        for (int i = 0; i < ELEMS_CHECK; i++) {
-            uint64_t test = rng();
-            bool actually_present = actual_inserts.count(test);
-            bool filter_contains = filter.may_contain(&test, sizeof(test));
-            fp_count += !actually_present && filter_contains;
-        }
-
-        double theoretical_approx_fp_rate =
-            std::pow(1 - std::exp(-static_cast<double>(k) * n / m), k);
-        double filter_approx_fp_rate = filter.false_positive_probability();
-        double actual_fp_rate = static_cast<double>(fp_count) / ELEMS_CHECK;
-
-        sum_theorital_approx_fp_rate += theoretical_approx_fp_rate;
-        sum_filter_approx_fp_rate += filter_approx_fp_rate;
-        sum_actual_fp_rate += actual_fp_rate;
-    }
-
-    std::printf("simple bloom filter with n = %d, m = %d, k = %d\n", n, m, k);
-    std::printf("theoretical fp probs : %.8f\n",
-                sum_theorital_approx_fp_rate / TOTAL_TRIALS);
-    std::printf("filter reported fp probs : %.8f\n",
-                sum_filter_approx_fp_rate / TOTAL_TRIALS);
-    std::printf("actual fp rate : %.8f\n", sum_actual_fp_rate / TOTAL_TRIALS);
-
-    return 0;
-}