Added approximate counting in Rust

contents/approximate_counting/approximate_counting.md

@@ -366,6 +366,8 @@ As we do not have any objects to count, we will instead simulate the counting wi
 [import, lang:"cpp"](code/cpp/approximate_counting.cpp)
 {% sample lang="python" %}
 [import, lang:"python"](code/python/approximate_counting.py)
+{% sample lang="rs" %}
+[import, lang:"rust"](code/rust/approximate_counting.rs)
 {% sample lang="java" %}
 [import, lang:"java"](code/java/ApproximateCounting.java)
 {% endmethod %}

contents/approximate_counting/code/rust/Cargo.toml

@@ -0,0 +1,13 @@
+[package]
+name = "approximate_counting"
+version = "0.1.0"
+edition = "2021"
+
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+rand = "0.8.4"
+
+[[bin]]
+name = "main"
+path = "approximate_counting.rs"

contents/approximate_counting/code/rust/approximate_counting.rs

@@ -0,0 +1,67 @@
+// This function takes
+//     - v: value in register
+//     - a: a  scaling value for the logarithm based on Morris's paper
+// It returns n(v,a), the approximate count
+fn n(v: f64, a: f64) -> f64 {
+    a * ((1_f64 + 1_f64 / a).powf(v) - 1_f64)
+}
+
+
+// This function takes
+//    - v: value in register
+//    - a: a scaling value for the logarithm based on Morris's paper
+// It returns a new value for v
+fn increment(v: f64, a: f64) -> f64 {
+    // delta is the probability of incrementing our counter
+    let delta = 1_f64 / (n(v + 1_f64, a) - n(v, a));
+
+    if rand::random::<f64>() <= delta {
+        v + 1_f64
+    } else {
+        v
+    }
+}
+
+// This simulates counting and takes
+//     - n_items: number of items to count and loop over
+//     - a: a scaling value for the logarithm based on Morris's paper
+// It returns n(v,a), the approximate count
+fn approximate_count(n_items: usize, a: f64) -> f64 {
+    let mut v = 0_f64;
+
+    for _ in 0..n_items {
+        v = increment(v, a);
+    }
+
+    v
+}
+
+// This function takes
+//     - n_trials: the number of counting trials
+//     - n_items: the number of items to count to
+//     - a: a scaling value for the logarithm based on Morris's paper
+//     - threshold: the maximum percent error allowed
+// It returns a "passed" / "failed" test value
+fn test_approximate_count(n_trials: usize, n_items: usize, a: f64, threshold: f64) {
+    let avg = std::iter::from_fn(|| Some(approximate_count(n_items, a)))
+                        .take(n_trials)
+                        .sum::<f64>() / n_trials as f64;
+
+    let n_items_float = n_items as f64;
+
+    if ((avg - n_items_float) / n_items_float) < threshold {
+        println!("passed");
+    } else {
+        println!("failed");
+    }
+
+}
+
+fn main() {
+    println!("testing 1,000, a = 30, 10% error");
+    test_approximate_count(100, 1000, 30_f64, 0.1);
+    println!("testing 12,345, a = 10, 10% error");
+    test_approximate_count(100, 12345, 10_f64, 0.1);
+    println!("testing 222,222, a = 0.5, 20% error");
+    test_approximate_count(100, 222222, 0.5, 0.2);
+}