work/smartcore
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix needless-range and clippy::ptr_arg warnings. (#36)

Browse Source 
* Fix needless for loop range

* Do not ignore clippy::ptr_arg
This commit is contained in:
morenol
2020-12-11 16:52:39 -04:00
committed by GitHub
parent 2650416235
commit 53351b2ece
27 changed files with 208 additions and 219 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/linear/bg_solver.rs
+3 -3
View File
@@ -85,9 +85,9 @@ pub trait BiconjugateGradientSolver<T: RealNumber, M: Matrix<T>> {
let diag = Self::diag(a);
let n = diag.len();
for i in 0..n {
if diag[i] != T::zero() {
x.set(i, 0, b.get(i, 0) / diag[i]);
for (i, diag_i) in diag.iter().enumerate().take(n) {
if *diag_i != T::zero() {
x.set(i, 0, b.get(i, 0) / *diag_i);
} else {
x.set(i, 0, b.get(i, 0));
}
src/linear/lasso.rs
+6 -6
View File
@@ -120,14 +120,14 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
let mut w = optimizer.optimize(&scaled_x, y, &parameters)?;
for j in 0..p {
w.set(j, 0, w.get(j, 0) / col_std[j]);
for (j, col_std_j) in col_std.iter().enumerate().take(p) {
w.set(j, 0, w.get(j, 0) / *col_std_j);
}
let mut b = T::zero();
for i in 0..p {
b += w.get(i, 0) * col_mean[i];
for (i, col_mean_i) in col_mean.iter().enumerate().take(p) {
b += w.get(i, 0) * *col_mean_i;
}
b = y.mean() - b;
@@ -169,8 +169,8 @@ impl<T: RealNumber, M: Matrix<T>> Lasso<T, M> {
let col_mean = x.mean(0);
let col_std = x.std(0);
for i in 0..col_std.len() {
if (col_std[i] - T::zero()).abs() < T::epsilon() {
for (i, col_std_i) in col_std.iter().enumerate() {
if (*col_std_i - T::zero()).abs() < T::epsilon() {
return Err(Failed::fit(&format!(
"Cannot rescale constant column {}",
i
src/linear/logistic_regression.rs
+6 -6
View File
@@ -228,9 +228,9 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
let mut yi: Vec<usize> = vec![0; y_nrows];
for i in 0..y_nrows {
for (i, yi_i) in yi.iter_mut().enumerate().take(y_nrows) {
let yc = y_m.get(0, i);
yi[i] = classes.iter().position(|c| yc == *c).unwrap();
*yi_i = classes.iter().position(|c| yc == *c).unwrap();
}
match k.cmp(&2) {
@@ -291,11 +291,11 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
if self.num_classes == 2 {
let y_hat: Vec<T> = x.ab(false, &self.coefficients, true).get_col_as_vec(0);
let intercept = self.intercept.get(0, 0);
for i in 0..n {
for (i, y_hat_i) in y_hat.iter().enumerate().take(n) {
result.set(
0,
i,
self.classes[if (y_hat[i] + intercept).sigmoid() > T::half() {
self.classes[if (*y_hat_i + intercept).sigmoid() > T::half() {
1
} else {
0
@@ -310,8 +310,8 @@ impl<T: RealNumber, M: Matrix<T>> LogisticRegression<T, M> {
}
}
let class_idxs = y_hat.argmax();
for i in 0..n {
result.set(0, i, self.classes[class_idxs[i]]);
for (i, class_i) in class_idxs.iter().enumerate().take(n) {
result.set(0, i, self.classes[*class_i]);
}
}
Ok(result.to_row_vector())
src/linear/ridge_regression.rs
+6 -6
View File
@@ -155,14 +155,14 @@ impl<T: RealNumber, M: Matrix<T>> RidgeRegression<T, M> {
RidgeRegressionSolverName::SVD => x_t_x.svd_solve_mut(x_t_y)?,
};
for i in 0..p {
w.set(i, 0, w.get(i, 0) / col_std[i]);
for (i, col_std_i) in col_std.iter().enumerate().take(p) {
w.set(i, 0, w.get(i, 0) / *col_std_i);
}
let mut b = T::zero();
for i in 0..p {
b += w.get(i, 0) * col_mean[i];
for (i, col_mean_i) in col_mean.iter().enumerate().take(p) {
b += w.get(i, 0) * *col_mean_i;
}
let b = y.mean() - b;
@@ -196,8 +196,8 @@ impl<T: RealNumber, M: Matrix<T>> RidgeRegression<T, M> {
let col_mean = x.mean(0);
let col_std = x.std(0);
for i in 0..col_std.len() {
if (col_std[i] - T::zero()).abs() < T::epsilon() {
for (i, col_std_i) in col_std.iter().enumerate() {
if (*col_std_i - T::zero()).abs() < T::epsilon() {
return Err(Failed::fit(&format!(
"Cannot rescale constant column {}",
i