Merge branch 'development' into ridge

src/linear/logistic_regression.rs

@@ -84,7 +84,7 @@ trait ObjectiveFunction<T: RealNumber, M: Matrix<T>> {
         let mut sum = T::zero();
         let p = x.shape().1;
         for i in 0..p {
-            sum = sum + x.get(m_row, i) * w.get(0, i + v_col);
+            sum += x.get(m_row, i) * w.get(0, i + v_col);
         }
 
         sum + w.get(0, p + v_col)
@@ -103,14 +103,14 @@ impl<T: RealNumber, M: Matrix<T>> PartialEq for LogisticRegression<T, M> {
             || self.num_attributes != other.num_attributes
             || self.classes.len() != other.classes.len()
         {
-            return false;
+            false
         } else {
             for i in 0..self.classes.len() {
                 if (self.classes[i] - other.classes[i]).abs() > T::epsilon() {
                     return false;
                 }
             }
-
+          
             return self.coefficients == other.coefficients && self.intercept == other.intercept;
         }
     }
@@ -125,7 +125,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> ObjectiveFunction<T, M>
 
         for i in 0..n {
             let wx = BinaryObjectiveFunction::partial_dot(w_bias, self.x, 0, i);
-            f = f + (wx.ln_1pe() - (T::from(self.y[i]).unwrap()) * wx);
+            f += wx.ln_1pe() - (T::from(self.y[i]).unwrap()) * wx;
         }
 
         f
@@ -171,7 +171,7 @@ impl<'a, T: RealNumber, M: Matrix<T>> ObjectiveFunction<T, M>
                 );
             }
             prob.softmax_mut();
-            f = f - prob.get(0, self.y[i]).ln();
+            f -= prob.get(0, self.y[i]).ln();
         }
 
         f
@@ -217,9 +217,9 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
         let (_, y_nrows) = y_m.shape();
 
         if x_nrows != y_nrows {
-            return Err(Failed::fit(&format!(
-                "Number of rows of X doesn't match number of rows of Y"
-            )));
+            return Err(Failed::fit(
+                &"Number of rows of X doesn\'t match number of rows of Y".to_string(),
+            ));
         }
 
         let classes = y_m.unique();
@@ -248,6 +248,7 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
                 };
 
                 let result = LogisticRegression::minimize(x0, objective);
+
                 let weights = result.x;
 
                 Ok(LogisticRegression {
@@ -269,7 +270,6 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
                 };
 
                 let result = LogisticRegression::minimize(x0, objective);
-
                 let weights = result.x.reshape(k, num_attributes + 1);
 
                 Ok(LogisticRegression {
@@ -332,8 +332,10 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
 
         let df = |g: &mut M, w: &M| objective.df(g, w);
 
-        let mut ls: Backtracking<T> = Default::default();
-        ls.order = FunctionOrder::THIRD;
+        let ls: Backtracking<T> = Backtracking {
+            order: FunctionOrder::THIRD,
+            ..Default::default()
+        };
         let optimizer: LBFGS<T> = Default::default();
 
         optimizer.optimize(&f, &df, &x0, &ls)
@@ -371,7 +373,7 @@ mod tests {
 
         let objective = MultiClassObjectiveFunction {
             x: &x,
-            y: y,
+            y,
             k: 3,
             phantom: PhantomData,
         };
@@ -420,7 +422,7 @@ mod tests {
 
         let objective = BinaryObjectiveFunction {
             x: &x,
-            y: y,
+            y,
             phantom: PhantomData,
         };