binom calculation part 1

Cargo.toml

@@ -9,18 +9,18 @@ authors = ["Felipe Contreras Salinas <felipe@bstr.cl>"]
 [dependencies]
 console_error_panic_hook = "0.1.7"
 console_log = "1.0.0"
-fluent-templates = "0.13.0"
+fluent-templates = "0.13.2"
-leptos = { version = "0.8.6", features = ["csr", "tracing"] }
+leptos = { version = "0.8.14", features = ["csr", "tracing"] }
-leptos-fluent = "0.2.16"
+leptos-fluent = "0.2.20"
 leptos_meta = { version = "0.8.5" }
-leptos_router = { version = "0.8.5" }
+leptos_router = { version = "0.8.10" }
-log = "0.4.27"
+log = "0.4.29"
 
 
 [dev-dependencies]
-wasm-bindgen = "0.2.100"
+wasm-bindgen = "0.2.106"
-wasm-bindgen-test = "0.3.50"
+wasm-bindgen-test = "0.3.56"
-web-sys = { version = "0.3.77", features = ["Document", "Window"] }
+web-sys = { version = "0.3.83", features = ["Document", "Window"] }
 
 
 [profile.release]

locales/en/main.ftl

@@ -6,7 +6,7 @@ population = Population Size
 successes = Successes in Population
 sample = Sample Size
 sample-successes = Successes in Sample
-hyper-description = Hypergeometric distribution measures the probability of getting a given amount of a certain type of elements from a sample on a population. Think on drawing
+hyper-description = Hypergeometric distribution measures the probability of getting a given amount of a certain type of elements from a sample on a population. Think on drawing a hand of 7 cards (sample size = 7) from a 52 cards deck (population size = 52) and wanting to know the probability of getting a given number of aces (successes in sample = X, successes in population = 4).
 success-probability = Success probability
 trials-number = Number of trials
 successes-number = Number of successes

locales/es/main.ftl

@@ -6,7 +6,7 @@ population = Tamaño población
 successes =  Éxitos en la población
 sample = Tamaño de la muestra
 sample-successes = Éxitos en la muestra
-hyper-description = Hypergeometric distribution measures the probability of getting a given amount of a certain type of elements from a sample on a population. Think on drawing
+hyper-description = La distribución hipergeométrica mide la probabilidad de obtener una cierta cantidad de elementos de cierto tipo en una muestra de una población. Por ejemplo, si tomamos una mano de 7 cartas (tamaño de la muestra = 7) de un mazo de 52 cartas (tamaño población = 52) y queremos saber la probabilidad de tener cierta cantidad de ases (éxitos en la muestra = X, éxitos en la población = 4).
 success-probability = Probabilidad de éxito
 trials-number = Número de intentos
 successes-number = Número de éxitos

public/styles.scss

@@ -25,9 +25,10 @@ header {
   display: flex;
   align-items: center;
   justify-content: space-between;
+  padding: 0.1em 1em;
 }
 
-header > select {
+header select {
   margin: 0 1em;
 }
 
@@ -92,3 +93,10 @@ div.results > span.right {
   grid-column: 4 / 5;
   text-align: left;
 }
+
+div.calc-description {
+  max-width: 55em;
+  text-align: left;
+  hyphens: auto;
+  margin-top: 2em;
+}

src/calc.rs

@@ -69,7 +69,13 @@ pub fn binomial(
     trials_number: u8,
     successes_number: u8,
 ) -> Option<BinomialProb> {
+    if successes_number > trials_number {
         None
+    } else {
+        let (p_powers, pc_powers) = powers(success_probability, trials_number);
+        let exactly = binom_exactly(success_probability, trials_number, successes_number);
+        None
+    }
 }
 
 /// Computes the probability of drawing exactly `sample_successes` in a sample of `sample_size`
@@ -140,6 +146,33 @@ fn hyper_geometric_exactly(
     top_product / bot_product
 }
 
+/// Computes the probability of getting exactly `successes_number` within `trials_number` given
+/// that the success probability is `success_probability`.
+///
+/// The formula is choose(successes_number, trials_number) * (success_probability)^successes_number
+/// * (1 - success_probability)^(trials_number - successes_number)
+fn binom_exactly(success_probability: f64, trials_number: u8, successes_number: u8) -> f64 {
+    0.0
+}
+
+fn powers(p: f64, N: u8) -> (Vec<f64>, Vec<f64>) {
+    let mut p_powers = Vec::with_capacity((N + 1) as usize);
+    let mut pc_powers = Vec::with_capacity((N + 1) as usize);
+
+    let mut p_power = 1.0;
+    let mut pc_power = 1.0;
+    for _ in 0..N + 1 {
+        p_powers.push(p_power);
+        pc_powers.push(pc_power);
+        p_power = p_power * p;
+        pc_power = pc_power * (1.0 - p)
+    }
+    (p_powers, pc_powers)
+}
+
+/// Simplify factors for a fraction.
+///
+/// This assumes factors are already prime factors.
 fn simplify(
     mut top_factors: HashMap<u8, u8>,
     mut bot_factors: HashMap<u8, u8>,
@@ -175,6 +208,8 @@ fn simplify(
 const PRIMES: &[u8] = &[2, 3, 5, 7, 11, 13];
 
 #[derive(Debug)]
+/// Iterator for the prime factors of a number. We obtain this iterator using the `factorize`
+/// function.
 struct FactorIter<'a> {
     /// remainder
     n: u8,
@@ -206,7 +241,7 @@ impl Iterator for FactorIter<'_> {
             }
             // If we stopped at a factor, we return it.
             // If we exhausted the factors, we return the remainder, as it will be a prime (we
-            // checked primes less than sqrt(MAX).
+            // checked primes less than sqrt(u8::MAX).
             match self.f {
                 Some(f) => Some(f),
                 None => Some(self.n),

src/components/calculator.rs

@@ -109,7 +109,7 @@ pub fn HyperCalculator() -> impl IntoView {
             </span>
             <span class="right">{move || display_rounded(result().greater_or_equal)}</span>
         </div>
-        <div>{move_tr!("hyper-description")}</div>
+        <div class="calc-description">{move_tr!("hyper-description")}</div>
     }
 }
 
@@ -199,7 +199,7 @@ pub fn BinomCalculator() -> impl IntoView {
             </span>
             <span class="right">{move || display_rounded(result().greater_or_equal)}</span>
         </div>
-        <div>{move_tr!("binom-description")}</div>
+        <div class="calc-description">{move_tr!("binom-description")}</div>
     }
 }