Merge pull request avhz#130 from chammika-become/typos-fixes

spelling fixes with typos

README.md

@@ -408,7 +408,7 @@ fn main() {
 <details>
 <summary>
 <h3>:chart_with_upwards_trend: Stochastic Processes and Short Rate Models <a name="stochastics"></a></h3>
-<br> Can generate Brownian Motion (standard, arithmetric and geometric) and various short-rate models (CIR, OU, Vasicek, Hull-White, etc). <br>
+<br> Can generate Brownian Motion (standard, arithmetic and geometric) and various short-rate models (CIR, OU, Vasicek, Hull-White, etc). <br>
 </summary>
 
 The following is a list of stochastic processes that can be generated.

examples/logistic_regression.rs

@@ -16,7 +16,7 @@ fn main() {
     //          from surface of the unit sphere S_(K-1)  and a bias from uniform(-0.5,0.5);
     //          arrange as DVector "coefs"
     //      3. Compute vector of probabilities(target=1) as sigmoid(X_ext * coefs)
-    //      4. Compute target values:for each sample i draw from Bernouilli(prob_i)
+    //      4. Compute target values:for each sample i draw from Bernoulli(prob_i)
 
     use rand::prelude::*;
     use rand_distr::{Bernoulli, StandardNormal, Uniform};

src/autodiff/overloading/add.rs

@@ -33,7 +33,7 @@ impl<'v> AddAssign<f64> for Variable<'v> {
     }
 }
 
-/// AddAsign: f64 += Variable<'v>
+/// AddAssign: f64 += Variable<'v>
 impl<'v> AddAssign<Variable<'v>> for f64 {
     #[inline]
     fn add_assign(&mut self, other: Variable<'v>) {

src/interactive/app.rs

@@ -92,7 +92,7 @@ impl App {
                 }
             }
         } else {
-            warn!("No action accociated to {}", key);
+            warn!("No action associated to {}", key);
             AppReturn::Continue
         }
     }

src/interactive/state.rs

@@ -41,7 +41,7 @@ pub enum AppState {
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 impl AppState {
-    /// Initilised app
+    /// Initialised app
     pub fn initialized() -> Self {
         let duration = Duration::from_secs(1);
         let counter_sleep = 0;

src/ml/regression/logistic.rs

@@ -365,7 +365,7 @@ mod tests_logistic_regression {
         // - For each of the N samples (train/test) draw K feature values each from a uniform distribution over (-1.,1.) and arrange as design matrix "X".
         // - For the coefficients of the generating distribution draw K values from surface of the unit sphere S_(K-1)  and a bias from uniform(-0.7,0.7); arrange as DVector "coefs"
         // - compute vector of probabilities(target=1) as sigmoid(X_ext * coefs)
-        // - compute target values:for each sample i draw from Bernouilli(prob_i)
+        // - compute target values:for each sample i draw from Bernoulli(prob_i)
         use crate::statistics::distributions::{Bernoulli, Distribution, Gaussian, Uniform};
 
         let N_train = 200; //Number of training samples

src/money/iso_currencies.rs

@@ -830,7 +830,7 @@ pub const KES: Currency = Currency {
     fractions: 100,
 };
 
-/// Kyrgyzstani som (KGS)
+/// Kyrgyzstani Som (KGS)
 pub const KGS: Currency = Currency {
     name: "Kyrgyzstani Som",
     symbol: "лв",
@@ -1756,7 +1756,7 @@ pub const UYU: Currency = Currency {
     fractions: 100,
 };
 
-/// Uzbekistan som (UZS)
+/// Uzbekistan Som (UZS)
 pub const UZS: Currency = Currency {
     name: "Uzbekistan Som",
     symbol: "лв",

src/statistics/distributions/binomial.rs

@@ -24,7 +24,7 @@ pub struct Binomial {
 }
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// IMPLMENTATIONS
+// IMPLEMENTATIONS
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 impl Default for Binomial {

src/statistics/distributions/gaussian.rs

@@ -97,7 +97,7 @@ impl Distribution for Gaussian {
         (-0.5 * ((x - self.mean) / self.variance).powi(2)).exp() / (2.0 * PI * self.variance).sqrt()
     }
 
-    /// Probability mass function for the Gaussian distribution (continous) is not defined.
+    /// Probability mass function for the Gaussian distribution (continuous) is not defined.
     /// Using this method will call `self.pdf()` instead.
     /// # Examples
     /// ```

src/stochastics/arithmetic_brownian_motion.rs

@@ -9,7 +9,7 @@
 
 use crate::stochastics::*;
 
-/// Struct containin the Arithmetic Brownian Motion parameters.
+/// Struct containing the Arithmetic Brownian Motion parameters.
 pub struct ArithmeticBrownianMotion {
     /// The drift ($\mu$) in percentage.
     pub mu: f64,

src/stochastics/brownian_motion.rs

@@ -9,7 +9,7 @@
 
 use crate::stochastics::*;
 
-/// Struct containin the Geometric Brownian Motion parameters.
+/// Struct containing the Geometric Brownian Motion parameters.
 #[derive(Debug)]
 pub struct BrownianMotion {}
 

src/stochastics/fractional_brownian_motion.rs

@@ -15,7 +15,7 @@ use rand::{rngs::StdRng, SeedableRng};
 use rand_distr::StandardNormal;
 use rayon::prelude::*;
 
-/// Struct containin the Fractional Brownian Motion parameters.
+/// Struct containing the Fractional Brownian Motion parameters.
 #[derive(Debug)]
 pub struct FractionalBrownianMotion {
     /// Hurst parameter of the process.

src/stochastics/geometric_brownian_motion.rs

@@ -9,7 +9,7 @@
 
 use crate::stochastics::*;
 
-/// Struct containin the Geometric Brownian Motion parameters.
+/// Struct containing the Geometric Brownian Motion parameters.
 pub struct GeometricBrownianMotion {
     /// The drift ($\mu$) in percentage.
     pub mu: f64,

src/trading/limit_order_book.rs

@@ -154,7 +154,7 @@ impl Book {
     /// `shares` number of shares to buy.
     /// `is_buy` buy or sell shares.
     /// Returns tuple:
-    /// First item is a bool, if false, not enough volume to fufill order. If true, bought all
+    /// First item is a bool, if false, not enough volume to fulfill order. If true, bought all
     /// shares.
     /// Second item is vector of tuples. First item in tuple is price executed, second item is
     /// number of shares executed at price.