more doc updates

Author: morenol

src/linalg/traits/svd.rs

@@ -48,9 +48,7 @@ pub struct SVD<T: Number + RealNumber, M: SVDDecomposable<T>> {
     pub V: M,
     /// Singular values of the original matrix
     pub s: Vec<T>,
-    ///
     m: usize,
-    ///
     n: usize,
     /// Tolerance
     tol: T,

src/naive_bayes/bernoulli.rs

@@ -258,7 +258,7 @@ impl<TY: Number + Ord + Unsigned> BernoulliNBDistribution<TY> {
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
     /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    /// priors are adjusted according to the data.
+    ///     priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     /// * `binarize` - Threshold for binarizing.
     fn fit<TX: Number + PartialOrd, X: Array2<TX>, Y: Array1<TY>>(
@@ -402,10 +402,10 @@ impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 {
     /// Fits BernoulliNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    /// features.
+    ///   features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors, alpha for smoothing and
-    /// binarizing threshold.
+    ///   binarizing threshold.
     pub fn fit(x: &X, y: &Y, parameters: BernoulliNBParameters<TX>) -> Result<Self, Failed> {
         let distribution = if let Some(threshold) = parameters.binarize {
             BernoulliNBDistribution::fit(
@@ -427,6 +427,7 @@ impl<TX: Number + PartialOrd, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         if let Some(threshold) = self.binarize {

src/naive_bayes/categorical.rs

@@ -363,7 +363,7 @@ impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> Predictor<X, Y> for Categ
 impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> CategoricalNB<T, X, Y> {
     /// Fits CategoricalNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    /// features.
+    ///   features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like alpha for smoothing
     pub fn fit(x: &X, y: &Y, parameters: CategoricalNBParameters) -> Result<Self, Failed> {
@@ -375,6 +375,7 @@ impl<T: Number + Unsigned, X: Array2<T>, Y: Array1<T>> CategoricalNB<T, X, Y> {
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)

src/naive_bayes/gaussian.rs

@@ -175,7 +175,7 @@ impl<TY: Number + Ord + Unsigned> GaussianNBDistribution<TY> {
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
     /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    /// priors are adjusted according to the data.
+    ///     priors are adjusted according to the data.
     pub fn fit<TX: Number + RealNumber, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
         y: &Y,
@@ -317,7 +317,7 @@ impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 {
     /// Fits GaussianNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    /// features.
+    ///   features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors.
     pub fn fit(x: &X, y: &Y, parameters: GaussianNBParameters) -> Result<Self, Failed> {
@@ -328,6 +328,7 @@ impl<TX: Number + RealNumber, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Arr
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)

src/naive_bayes/mod.rs

@@ -89,6 +89,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: NBDistribution<TX,
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let y_classes = self.distribution.classes();

src/naive_bayes/multinomial.rs

@@ -208,7 +208,7 @@ impl<TY: Number + Ord + Unsigned> MultinomialNBDistribution<TY> {
     /// * `x` - training data.
     /// * `y` - vector with target values (classes) of length N.
     /// * `priors` - Optional vector with prior probabilities of the classes. If not defined,
-    /// priors are adjusted according to the data.
+    ///     priors are adjusted according to the data.
     /// * `alpha` - Additive (Laplace/Lidstone) smoothing parameter.
     pub fn fit<TX: Number + Unsigned, X: Array2<TX>, Y: Array1<TY>>(
         x: &X,
@@ -345,10 +345,10 @@ impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array
 {
     /// Fits MultinomialNB with given data
     /// * `x` - training data of size NxM where N is the number of samples and M is the number of
-    /// features.
+    ///   features.
     /// * `y` - vector with target values (classes) of length N.
     /// * `parameters` - additional parameters like class priors, alpha for smoothing and
-    /// binarizing threshold.
+    ///   binarizing threshold.
     pub fn fit(x: &X, y: &Y, parameters: MultinomialNBParameters) -> Result<Self, Failed> {
         let distribution =
             MultinomialNBDistribution::fit(x, y, parameters.alpha, parameters.priors)?;
@@ -358,6 +358,7 @@ impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         self.inner.as_ref().unwrap().predict(x)

src/neighbors/knn_classifier.rs

@@ -261,6 +261,7 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
 
     /// Estimates the class labels for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with class estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let mut result = Y::zeros(x.shape().0);

src/neighbors/knn_regressor.rs

@@ -246,6 +246,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
 
     /// Predict the target for the provided data.
     /// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
+    ///
     /// Returns a vector of size N with estimates.
     pub fn predict(&self, x: &X) -> Result<Y, Failed> {
         let mut result = Y::zeros(x.shape().0);

src/optimization/first_order/mod.rs

@@ -1,6 +1,6 @@
-///  
+/// Gradient descent optimization algorithm
 pub mod gradient_descent;
-///
+/// Limited-memory BFGS optimization algorithm
 pub mod lbfgs;
 
 use std::clone::Clone;