fix: Correct minor typos
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52e6db99c6
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fd97a82926
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@ -7,24 +7,24 @@ use na::DefaultAllocator;
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use crate::traits::{Alloc, Number, Dimension};
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use crate::aliases::TVec;
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/// Component-wise approximate equality beween two vectors.
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/// Component-wise approximate equality between two vectors.
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pub fn epsilon_equal<T: Number, const D: usize>(x: &TVec<T, D>, y: &TVec<T, D>, epsilon: T) -> TVec<bool, D>
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where DefaultAllocator: Alloc<T, D> {
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x.zip_map(y, |x, y| abs_diff_eq!(x, y, epsilon = epsilon))
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}
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/// Component-wise approximate equality beween two scalars.
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/// Component-wise approximate equality between two scalars.
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pub fn epsilon_equal2<T: AbsDiffEq<Epsilon = T>>(x: T, y: T, epsilon: T) -> bool {
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abs_diff_eq!(x, y, epsilon = epsilon)
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}
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/// Component-wise approximate non-equality beween two vectors.
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/// Component-wise approximate non-equality between two vectors.
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pub fn epsilon_not_equal<T: Number, const D: usize>(x: &TVec<T, D>, y: &TVec<T, D>, epsilon: T) -> TVec<bool, D>
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where DefaultAllocator: Alloc<T, D> {
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x.zip_map(y, |x, y| abs_diff_ne!(x, y, epsilon = epsilon))
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}
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/// Component-wise approximate non-equality beween two scalars.
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/// Component-wise approximate non-equality between two scalars.
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pub fn epsilon_not_equal2<T: AbsDiffEq<Epsilon = T>>(x: T, y: T, epsilon: T) -> bool {
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abs_diff_ne!(x, y, epsilon = epsilon)
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}
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@ -80,7 +80,7 @@ pub fn quat_to_mat3<T: RealNumber>(x: &Qua<T>) -> TMat3<T> {
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.into_inner()
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}
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/// Converts a quaternion to a rotation matrix in homogenous coordinates.
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/// Converts a quaternion to a rotation matrix in homogeneous coordinates.
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pub fn quat_to_mat4<T: RealNumber>(x: &Qua<T>) -> TMat4<T> {
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UnitQuaternion::new_unchecked(*x).to_homogeneous()
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}
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@ -494,7 +494,7 @@ where
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assert_eq!(source_minor_indices.len(), values.len());
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let nnz = values.len();
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// Count the number of occurences of each minor index
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// Count the number of occurrences of each minor index
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let mut minor_counts = vec![0; minor_dim];
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for minor_idx in source_minor_indices {
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minor_counts[*minor_idx] += 1;
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@ -98,7 +98,7 @@ where
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/// Faster sparse-sparse matrix multiplication, `C <- beta * C + alpha * op(A) * op(B)`.
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/// This will not return an error even if the patterns don't match.
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/// Should be used for situations where pattern creation immediately preceeds multiplication.
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/// Should be used for situations where pattern creation immediately precedes multiplication.
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///
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/// Panics if the dimensions of the matrices involved are not compatible with the expression.
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pub fn spmm_csc_prealloc_unchecked<T>(
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@ -89,7 +89,7 @@ where
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/// Faster sparse-sparse matrix multiplication, `C <- beta * C + alpha * op(A) * op(B)`.
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/// This will not return an error even if the patterns don't match.
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/// Should be used for situations where pattern creation immediately preceeds multiplication.
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/// Should be used for situations where pattern creation immediately precedes multiplication.
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///
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/// Panics if the dimensions of the matrices involved are not compatible with the expression.
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pub fn spmm_csr_prealloc_unchecked<T>(
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@ -20,9 +20,9 @@ use std::mem::MaybeUninit;
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/// Every allocator must be both static and dynamic. Though not all implementations may share the
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/// same `Buffer` type.
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pub trait Allocator<T, R: Dim, C: Dim = U1>: Any + Sized {
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/// The type of buffer this allocator can instanciate.
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/// The type of buffer this allocator can instantiate.
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type Buffer: StorageMut<T, R, C> + IsContiguous + Clone + Debug;
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/// The type of buffer with uninitialized components this allocator can instanciate.
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/// The type of buffer with uninitialized components this allocator can instantiate.
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type BufferUninit: RawStorageMut<MaybeUninit<T>, R, C> + IsContiguous;
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/// Allocates a buffer with the given number of rows and columns without initializing its content.
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@ -1077,7 +1077,7 @@ where
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}
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// Move the elements of `data` in such a way that the matrix with
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// the rows `[i, i + nremove[` deleted is represented in a contigous
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// the rows `[i, i + nremove[` deleted is represented in a contiguous
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// way in `data` after this method completes.
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// Every deleted element are manually dropped by this method.
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unsafe fn compress_rows<T: Scalar>(
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@ -39,7 +39,7 @@ macro_rules! iterator {
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let ptr = storage.$ptr();
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// If we have a size of 0, 'ptr' must be
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// dangling. Howver, 'inner_offset' might
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// dangling. However, 'inner_offset' might
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// not be zero if only one dimension is zero, so
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// we don't want to call 'offset'.
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// This pointer will never actually get used
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@ -11,7 +11,7 @@ use crate::{
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use rayon::iter::plumbing::Producer;
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use rayon::{iter::plumbing::bridge, prelude::*};
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/// A rayon parallel iterator over the colums of a matrix. It is created
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/// A rayon parallel iterator over the columns of a matrix. It is created
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/// using the [`par_column_iter`] method of [`Matrix`].
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///
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/// *Only available if compiled with the feature `rayon`.*
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@ -89,7 +89,7 @@ pub struct ParColumnIterMut<
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}
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#[cfg_attr(doc_cfg, doc(cfg(feature = "rayon")))]
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/// *only availabe if compiled with the feature `rayon`*
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/// *only available if compiled with the feature `rayon`*
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impl<'a, T, R, Cols, S> ParColumnIterMut<'a, T, R, Cols, S>
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where
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R: Dim,
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@ -161,7 +161,7 @@ where
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S: Sync,
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{
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/// Iterate through the columns of the matrix in parallel using rayon.
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/// This iterates over *immutable* references ot the columns of the matrix,
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/// This iterates over *immutable* references to the columns of the matrix,
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/// if *mutable* access to the columns is required, use [`par_column_iter_mut`]
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/// instead.
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///
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@ -34,7 +34,7 @@ pub unsafe trait InitStatus<T>: Copy {
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/// A type implementing `InitStatus` indicating that the value is completely initialized.
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pub struct Init;
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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/// A type implementing `InitStatus` indicating that the value is completely unitialized.
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/// A type implementing `InitStatus` indicating that the value is completely uninitialized.
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pub struct Uninit;
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unsafe impl<T> InitStatus<T> for Init {
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@ -148,7 +148,7 @@ impl<T, R: Dim, C: Dim> VecStorage<T, R, C> {
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};
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// Avoid double-free by forgetting `self` because its data buffer has
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// been transfered to `new_data`.
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// been transferred to `new_data`.
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std::mem::forget(self);
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new_data
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}
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@ -42,7 +42,7 @@ impl<T: SimdRealField> Isometry3<T> {
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/// Attempts to interpolate between two isometries using a linear interpolation for the translation part,
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/// and a spherical interpolation for the rotation part.
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///
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/// Retuns `None` if the angle between both rotations is 180 degrees (in which case the interpolation
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/// Returns `None` if the angle between both rotations is 180 degrees (in which case the interpolation
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/// is not well-defined).
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///
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/// # Examples:
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@ -118,7 +118,7 @@ impl<T: SimdRealField> IsometryMatrix3<T> {
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/// Attempts to interpolate between two isometries using a linear interpolation for the translation part,
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/// and a spherical interpolation for the rotation part.
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///
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/// Retuns `None` if the angle between both rotations is 180 degrees (in which case the interpolation
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/// Returns `None` if the angle between both rotations is 180 degrees (in which case the interpolation
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/// is not well-defined).
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///
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/// # Examples:
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@ -1577,7 +1577,7 @@ where
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#[inline]
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#[must_use]
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pub fn inverse_transform_point(&self, pt: &Point3<T>) -> Point3<T> {
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// TODO: would it be useful performancewise not to call inverse explicitly (i-e. implement
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// TODO: would it be useful performance-wise not to call inverse explicitly (i-e. implement
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// the inverse transformation explicitly here) ?
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self.inverse() * pt
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}
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@ -122,7 +122,7 @@ macro_rules! category_mul_impl(
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)*}
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);
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// We require stability uppon multiplication.
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// We require stability upon multiplication.
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impl<T: TCategory> TCategoryMul<T> for T {
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type Representative = T;
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}
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@ -347,7 +347,7 @@ where
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#[inline]
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#[must_use]
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pub fn inverse_transform_point(&self, pt: &Point2<T>) -> Point2<T> {
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// TODO: would it be useful performancewise not to call inverse explicitly (i-e. implement
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// TODO: would it be useful performance-wise not to call inverse explicitly (i-e. implement
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// the inverse transformation explicitly here) ?
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self.inverse() * pt
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}
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@ -724,7 +724,7 @@ where
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/// Sort the estimated components of the SVD by its singular values in descending order.
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/// Such an ordering is often implicitly required when the decompositions are used for estimation or fitting purposes.
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/// Using this function is only required if `new_unordered` or `try_new_unorderd` were used and the specific sorting is required afterward.
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/// Using this function is only required if `new_unordered` or `try_new_unordered` were used and the specific sorting is required afterward.
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pub fn sort_by_singular_values(&mut self) {
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const VALUE_PROCESSED: usize = usize::MAX;
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@ -498,7 +498,7 @@ where
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}
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}
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// Remove dupliate entries on a sorted CsMatrix.
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// Remove duplicate entries on a sorted CsMatrix.
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pub(crate) fn dedup(&mut self)
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where
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T: Zero + ClosedAdd,
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@ -123,7 +123,7 @@ fn symmetric_eigen_singular_24x24() {
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//
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// /*
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// * NOTE: for the following tests, we use only upper-triangular matrices.
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// * Thes ensures the schur decomposition will work, and allows use to test the eigenvector
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// * This ensures the schur decomposition will work, and allows use to test the eigenvector
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// * computation.
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// */
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// fn eigen(n: usize) -> bool {
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@ -134,11 +134,11 @@ fn symmetric_eigen_singular_24x24() {
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// verify_eigenvectors(m, eig)
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// }
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//
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// fn eigen_with_adjascent_duplicate_diagonals(n: usize) -> bool {
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// fn eigen_with_adjacent_duplicate_diagonals(n: usize) -> bool {
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// let n = cmp::max(1, cmp::min(n, 10));
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// let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
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//
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// // Suplicate some adjascent diagonal elements.
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// // Suplicate some adjacent diagonal elements.
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// for i in 0 .. n / 2 {
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// m[(i * 2 + 1, i * 2 + 1)] = m[(i * 2, i * 2)];
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// }
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// verify_eigenvectors(m, eig)
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// }
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//
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// fn eigen_with_nonadjascent_duplicate_diagonals(n: usize) -> bool {
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// fn eigen_with_nonadjacent_duplicate_diagonals(n: usize) -> bool {
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// let n = cmp::max(3, cmp::min(n, 10));
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// let mut m = DMatrix::<f64>::new_random(n, n).upper_triangle();
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//
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