chore: fix clippy (#283)
* chore: fix clippy Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>
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@@ -261,6 +261,7 @@ impl<TX: Number, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec
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/// Estimates the class labels for the provided data.
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/// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
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///
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/// Returns a vector of size N with class estimates.
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pub fn predict(&self, x: &X) -> Result<Y, Failed> {
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let mut result = Y::zeros(x.shape().0);
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@@ -88,25 +88,21 @@ pub struct KNNRegressor<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D:
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impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
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KNNRegressor<TX, TY, X, Y, D>
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{
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///
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fn y(&self) -> &Y {
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self.y.as_ref().unwrap()
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}
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///
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fn knn_algorithm(&self) -> &KNNAlgorithm<TX, D> {
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self.knn_algorithm
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.as_ref()
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.expect("Missing parameter: KNNAlgorithm")
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}
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///
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fn weight(&self) -> &KNNWeightFunction {
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self.weight.as_ref().expect("Missing parameter: weight")
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}
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#[allow(dead_code)]
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///
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fn k(&self) -> usize {
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self.k.unwrap()
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}
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@@ -250,6 +246,7 @@ impl<TX: Number, TY: Number, X: Array2<TX>, Y: Array1<TY>, D: Distance<Vec<TX>>>
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/// Predict the target for the provided data.
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/// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
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///
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/// Returns a vector of size N with estimates.
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pub fn predict(&self, x: &X) -> Result<Y, Failed> {
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let mut result = Y::zeros(x.shape().0);
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@@ -312,7 +309,7 @@ mod tests {
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let y_hat = knn.predict(&x).unwrap();
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assert_eq!(5, Vec::len(&y_hat));
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for i in 0..y_hat.len() {
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assert!((y_hat[i] - y_exp[i]).abs() < std::f64::EPSILON);
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assert!((y_hat[i] - y_exp[i]).abs() < f64::EPSILON);
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}
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}
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