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/*!
* Generic matrix type.
*
* Matrices are generic over some type `T`. If `T` is [Numeric](super::numeric) then
* the matrix can be used in a mathematical way.
*/
use std::ops::{Add, Div, Mul, Sub};
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};
mod errors;
pub mod iterators;
pub mod operations;
pub mod slices;
pub mod views;
pub use errors::ScalarConversionError;
use crate::linear_algebra;
use crate::matrices::iterators::*;
use crate::matrices::slices::Slice2D;
use crate::matrices::views::{MatrixPart, MatrixQuadrants, MatrixView};
use crate::numeric::extra::{Real, RealRef};
use crate::numeric::{Numeric, NumericRef};
/**
* A general purpose matrix of some type. This type may implement
* no traits, in which case the matrix will be rather useless. If the
* type implements [`Clone`](std::clone::Clone)
* most storage and accessor methods are defined and if the type implements
* [`Numeric`](super::numeric) then the matrix can be used in
* a mathematical way.
*
* When doing numeric operations with Matrices you should be careful to not
* consume a matrix by accidentally using it by value. All the operations are
* also defined on references to matrices so you should favor `&x * &y` style
* notation for matrices you intend to continue using. There are also convenience
* operations defined for a matrix and a scalar.
*
* # Matrix size invariants
*
* Matrices must always be at least 1x1. You cannot construct a matrix with no rows or
* no columns, and any function that resizes matrices will error if you try to use it
* in a way that would construct a 0x1, 1x0, or 0x0 matrix. The maximum size of a matrix
* is dependent on the platform's `std::usize::MAX` value. Matrices with dimensions NxM
* such that N * M < `std::usize::MAX` should not cause any errors in this library, but
* attempting to expand their size further may cause panics and or errors. At the time of
* writing it is no longer possible to construct or use matrices where the product of their
* number of rows and columns exceed `std::usize::MAX`, but some constructor methods may be used
* to attempt this. Concerned readers should note that on a 64 bit computer this maximum
* value is 18,446,744,073,709,551,615 so running out of memory is likely to occur first.
*
* # Matrix layout and iterator performance
*
* [See iterators submodule for Matrix layout and iterator performance](iterators#matrix-layout-and-iterator-performance)
*
* # Matrix operations
*
* [See operations submodule](operations)
*/
#[derive(Debug)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct Matrix<T> {
data: Vec<T>,
rows: Row,
columns: Column,
}
/// The maximum row and column lengths are usize, due to the internal storage being backed by Vec
pub type Row = usize;
/// The maximum row and column lengths are usize, due to the internal storage being backed by Vec
pub type Column = usize;
/**
* Methods for matrices of any type, including non numerical types such as bool.
*/
impl<T> Matrix<T> {
/**
* Creates a 1x1 matrix from some scalar
*/
pub fn from_scalar(value: T) -> Matrix<T> {
Matrix {
data: vec![value],
rows: 1,
columns: 1,
}
}
/**
* Creates a row vector (1xN) from a list
*
* # Panics
*
* Panics if no values are provided. Note: this method erroneously did not validate its inputs
* in Easy ML versions up to and including 1.7.0
*/
#[track_caller]
pub fn row(values: Vec<T>) -> Matrix<T> {
assert!(!values.is_empty(), "No values provided");
Matrix {
columns: values.len(),
data: values,
rows: 1,
}
}
/**
* Creates a column vector (Nx1) from a list
*
* # Panics
*
* Panics if no values are provided. Note: this method erroneously did not validate its inputs
* in Easy ML versions up to and including 1.7.0
*/
#[track_caller]
pub fn column(values: Vec<T>) -> Matrix<T> {
assert!(!values.is_empty(), "No values provided");
Matrix {
rows: values.len(),
data: values,
columns: 1,
}
}
/**
* Creates a matrix from a nested array of values, each inner vector
* being a row, and hence the outer vector containing all rows in sequence, the
* same way as when writing matrices in mathematics.
*
* Example of a 2 x 3 matrix in both notations:
* ```ignore
* [
* 1, 2, 4
* 8, 9, 3
* ]
* ```
* ```
* use easy_ml::matrices::Matrix;
* Matrix::from(vec![
* vec![ 1, 2, 4 ],
* vec![ 8, 9, 3 ]]);
* ```
*
* # Panics
*
* Panics if the input is jagged or rows or column length is 0.
*/
#[track_caller]
pub fn from(mut values: Vec<Vec<T>>) -> Matrix<T> {
assert!(!values.is_empty(), "No rows defined");
// check length of first row is > 1
assert!(!values[0].is_empty(), "No column defined");
// check length of each row is the same
assert!(
values.iter().map(|x| x.len()).all(|x| x == values[0].len()),
"Inconsistent size"
);
// flatten the data into a row major layout
let rows = values.len();
let columns = values[0].len();
let mut data = Vec::with_capacity(rows * columns);
let mut value_stream = values.drain(..);
for _ in 0..rows {
let mut value_row_stream = value_stream.next().unwrap();
let mut row_of_values = value_row_stream.drain(..);
for _ in 0..columns {
data.push(row_of_values.next().unwrap());
}
}
Matrix {
data,
rows,
columns,
}
}
/**
* Creates a matrix with the specified size from a row major vec of data.
* The length of the vec must match the size of the matrix or the constructor
* will panic.
*
* Example of a 2 x 3 matrix in both notations:
* ```ignore
* [
* 1, 2, 4
* 8, 9, 3
* ]
* ```
* ```
* use easy_ml::matrices::Matrix;
* Matrix::from_flat_row_major((2, 3), vec![
* 1, 2, 4,
* 8, 9, 3]);
* ```
*
* This method is more efficient than [`Matrix::from`](Matrix::from())
* but requires specifying the size explicitly and manually keeping track of where rows
* start and stop.
*
* # Panics
*
* Panics if the length of the vec does not match the size of the matrix, or no values are
* provided. Note: this method erroneously did not validate its inputs were not empty in
* Easy ML versions up to and including 1.7.0
*/
#[track_caller]
pub fn from_flat_row_major(size: (Row, Column), values: Vec<T>) -> Matrix<T> {
assert!(size.0 * size.1 == values.len(),
"Inconsistent size, attempted to construct a {}x{} matrix but provided with {} elements.",
size.0, size.1, values.len());
assert!(!values.is_empty(), "No values provided");
Matrix {
data: values,
rows: size.0,
columns: size.1,
}
}
#[deprecated(
since = "1.1.0",
note = "Incorrect use of terminology, a unit matrix is another term for an identity matrix, please use `from_scalar` instead"
)]
pub fn unit(value: T) -> Matrix<T> {
Matrix::from_scalar(value)
}
/**
* Returns the dimensionality of this matrix in Row, Column format
*/
pub fn size(&self) -> (Row, Column) {
(self.rows, self.columns)
}
/**
* Gets the number of rows in this matrix.
*/
pub fn rows(&self) -> Row {
self.rows
}
/**
* Gets the number of columns in this matrix.
*/
pub fn columns(&self) -> Column {
self.columns
}
/**
* Matrix data is stored as row major, so each row is stored as
* adjacent items going through the different columns. Therefore,
* to index this flattened representation we jump down in row sized
* blocks to reach the correct row, and then jump further equal to
* the column. The confusing thing is that the number of columns
* this matrix has is the length of each of the rows in this matrix,
* and vice versa.
*/
fn get_index(&self, row: Row, column: Column) -> usize {
column + (row * self.columns())
}
/**
* The reverse of [get_index], converts from the flattened storage
* in memory into the row and column to index at this position.
*
* Matrix data is stored as row major, so each multiple of the number
* of columns starts a new row, and each index modulo the columns
* gives the column.
*/
#[allow(dead_code)]
fn get_row_column(&self, index: usize) -> (Row, Column) {
(index / self.columns(), index % self.columns())
}
/**
* Gets a reference to the value at this row and column. Rows and Columns are 0 indexed.
*
* # Panics
*
* Panics if the index is out of range.
*/
#[track_caller]
pub fn get_reference(&self, row: Row, column: Column) -> &T {
assert!(row < self.rows(), "Row out of index");
assert!(column < self.columns(), "Column out of index");
&self.data[self.get_index(row, column)]
}
/**
* Gets a mutable reference to the value at this row and column.
* Rows and Columns are 0 indexed.
*
* # Panics
*
* Panics if the index is out of range.
*/
#[track_caller]
pub fn get_reference_mut(&mut self, row: Row, column: Column) -> &mut T {
assert!(row < self.rows(), "Row out of index");
assert!(column < self.columns(), "Column out of index");
let index = self.get_index(row, column);
// borrow for get_index ends
&mut self.data[index]
}
/**
* Not public API because don't want to name clash with the method on MatrixRef
* that calls this.
*/
pub(crate) fn _try_get_reference(&self, row: Row, column: Column) -> Option<&T> {
if row < self.rows() || column < self.columns() {
Some(&self.data[self.get_index(row, column)])
} else {
None
}
}
/**
* Not public API because don't want to name clash with the method on MatrixRef
* that calls this.
*/
pub(crate) unsafe fn _get_reference_unchecked(&self, row: Row, column: Column) -> &T {
self.data.get_unchecked(self.get_index(row, column))
}
/**
* Sets a new value to this row and column. Rows and Columns are 0 indexed.
*
* # Panics
*
* Panics if the index is out of range.
*/
#[track_caller]
pub fn set(&mut self, row: Row, column: Column, value: T) {
assert!(row < self.rows(), "Row out of index");
assert!(column < self.columns(), "Column out of index");
let index = self.get_index(row, column);
// borrow for get_index ends
self.data[index] = value;
}
/**
* Not public API because don't want to name clash with the method on MatrixMut
* that calls this.
*/
pub(crate) fn _try_get_reference_mut(&mut self, row: Row, column: Column) -> Option<&mut T> {
if row < self.rows() || column < self.columns() {
let index = self.get_index(row, column);
// borrow for get_index ends
Some(&mut self.data[index])
} else {
None
}
}
/**
* Not public API because don't want to name clash with the method on MatrixMut
* that calls this.
*/
pub(crate) unsafe fn _get_reference_unchecked_mut(
&mut self,
row: Row,
column: Column,
) -> &mut T {
let index = self.get_index(row, column);
// borrow for get_index ends
self.data.get_unchecked_mut(index)
}
/**
* Removes a row from this Matrix, shifting all other rows to the left.
* Rows are 0 indexed.
*
* # Panics
*
* This will panic if the row does not exist or the matrix only has one row.
*/
#[track_caller]
pub fn remove_row(&mut self, row: Row) {
assert!(self.rows() > 1);
let mut r = 0;
let mut c = 0;
// drop the values at the specified row
let columns = self.columns();
self.data.retain(|_| {
let keep = r != row;
if c < (columns - 1) {
c += 1;
} else {
r += 1;
c = 0;
}
keep
});
self.rows -= 1;
}
/**
* Removes a column from this Matrix, shifting all other columns to the left.
* Columns are 0 indexed.
*
* # Panics
*
* This will panic if the column does not exist or the matrix only has one column.
*/
#[track_caller]
pub fn remove_column(&mut self, column: Column) {
assert!(self.columns() > 1);
let mut r = 0;
let mut c = 0;
// drop the values at the specified column
let columns = self.columns();
self.data.retain(|_| {
let keep = c != column;
if c < (columns - 1) {
c += 1;
} else {
r += 1;
c = 0;
}
keep
});
self.columns -= 1;
}
/**
* Returns an iterator over references to a column vector in this matrix.
* Columns are 0 indexed.
*
* # Panics
*
* Panics if the column does not exist in this matrix.
*/
#[track_caller]
pub fn column_reference_iter(&self, column: Column) -> ColumnReferenceIterator<T> {
ColumnReferenceIterator::new(self, column)
}
/**
* Returns an iterator over references to a row vector in this matrix.
* Rows are 0 indexed.
*
* # Panics
*
* Panics if the row does not exist in this matrix.
*/
#[track_caller]
pub fn row_reference_iter(&self, row: Row) -> RowReferenceIterator<T> {
RowReferenceIterator::new(self, row)
}
/**
* Returns an iterator over mutable references to a column vector in this matrix.
* Columns are 0 indexed.
*
* # Panics
*
* Panics if the column does not exist in this matrix.
*/
#[track_caller]
pub fn column_reference_mut_iter(&mut self, column: Column) -> ColumnReferenceMutIterator<T> {
ColumnReferenceMutIterator::new(self, column)
}
/**
* Returns an iterator over mutable references to a row vector in this matrix.
* Rows are 0 indexed.
*
* # Panics
*
* Panics if the row does not exist in this matrix.
*/
#[track_caller]
pub fn row_reference_mut_iter(&mut self, row: Row) -> RowReferenceMutIterator<T> {
RowReferenceMutIterator::new(self, row)
}
/**
* Returns a column major iterator over references to all values in this matrix,
* proceeding through each column in order.
*/
pub fn column_major_reference_iter(&self) -> ColumnMajorReferenceIterator<T> {
ColumnMajorReferenceIterator::new(self)
}
/**
* Returns a row major iterator over references to all values in this matrix,
* proceeding through each row in order.
*/
pub fn row_major_reference_iter(&self) -> RowMajorReferenceIterator<T> {
RowMajorReferenceIterator::new(self)
}
// Non public row major reference iterator since we don't want to expose our implementation
// details to public API since then we could never change them.
pub(crate) fn direct_row_major_reference_iter(&self) -> std::slice::Iter<T> {
self.data.iter()
}
/**
* Returns a column major iterator over mutable references to all values in this matrix,
* proceeding through each column in order.
*/
pub fn column_major_reference_mut_iter(&mut self) -> ColumnMajorReferenceMutIterator<T> {
ColumnMajorReferenceMutIterator::new(self)
}
/**
* Returns a row major iterator over mutable references to all values in this matrix,
* proceeding through each row in order.
*/
pub fn row_major_reference_mut_iter(&mut self) -> RowMajorReferenceMutIterator<T> {
RowMajorReferenceMutIterator::new(self)
}
/**
* Returns an iterator over references to the main diagonal in this matrix.
*/
pub fn diagonal_reference_iter(&self) -> DiagonalReferenceIterator<T> {
DiagonalReferenceIterator::new(self)
}
/**
* Returns an iterator over mutable references to the main diagonal in this matrix.
*/
pub fn diagonal_reference_mut_iter(&mut self) -> DiagonalReferenceMutIterator<T> {
DiagonalReferenceMutIterator::new(self)
}
/**
* Shrinks this matrix down from its current MxN size down to
* some new size OxP where O and P are determined by the kind of
* slice given and 1 <= O <= M and 1 <= P <= N.
*
* Only rows and columns specified by the slice will be retained, so for
* instance if the Slice is constructed by
* `Slice2D::new().rows(Slice::Range(0..2)).columns(Slice::Range(0..3))` then the
* modified matrix will be no bigger than 2x3 and contain up to the first two
* rows and first three columns that it previously had.
*
* See [Slice](slices::Slice) for constructing slices.
*
* # Panics
*
* This function will panic if the slice would delete all rows or all columns
* from this matrix, ie the resulting matrix must be at least 1x1.
*/
#[track_caller]
pub fn retain_mut(&mut self, slice: Slice2D) {
let mut r = 0;
let mut c = 0;
// drop the values rejected by the slice
let columns = self.columns();
self.data.retain(|_| {
let keep = slice.accepts(r, c);
if c < (columns - 1) {
c += 1;
} else {
r += 1;
c = 0;
}
keep
});
// work out the resulting size of this matrix by using the non
// public fields of the Slice2D to handle each row and column
// seperately.
let remaining_rows = {
let mut accepted = 0;
for i in 0..self.rows() {
if slice.rows.accepts(i) {
accepted += 1;
}
}
accepted
};
let remaining_columns = {
let mut accepted = 0;
for i in 0..self.columns() {
if slice.columns.accepts(i) {
accepted += 1;
}
}
accepted
};
assert!(
remaining_rows > 0,
"Provided slice must leave at least 1 row in the retained matrix"
);
assert!(
remaining_columns > 0,
"Provided slice must leave at least 1 column in the retained matrix"
);
assert!(
!self.data.is_empty(),
"Provided slice must leave at least 1 row and 1 column in the retained matrix"
);
self.rows = remaining_rows;
self.columns = remaining_columns
// By construction jagged slices should be impossible, if this
// invariant later changes by accident it would be possible to break the
// rectangle shape invariant on a matrix object
// As Slice2D should prevent the construction of jagged slices no
// check is here to detect if all rows are still the same length
}
/**
* Consumes a 1x1 matrix and converts it into a scalar without copying the data.
*
* # Example
*
* ```
* use easy_ml::matrices::Matrix;
* # fn main() -> Result<(), Box<dyn std::error::Error>> {
* let x = Matrix::column(vec![ 1.0, 2.0, 3.0 ]);
* let sum_of_squares: f64 = (x.transpose() * x).try_into_scalar()?;
* # Ok(())
* # }
* ```
*/
pub fn try_into_scalar(self) -> Result<T, ScalarConversionError> {
if self.size() == (1, 1) {
Ok(self.data.into_iter().next().unwrap())
} else {
Err(ScalarConversionError {})
}
}
/**
* Partition a matrix into an arbitary number of non overlapping parts.
*
* **This function is much like a hammer you should be careful to not overuse. If you don't need
* to mutate the parts of the matrix data individually it will be much easier and less error
* prone to create immutable views into the matrix using [MatrixRange](views::MatrixRange)
* instead.**
*
* Parts are returned in row major order, forming a grid of slices into the Matrix data that
* can be mutated independently.
*
* # Panics
*
* Panics if any row or column index is greater than the number of rows or columns in the
* matrix. Each list of row partitions and column partitions must also be in ascending order.
*
* # Further Info
*
* The partitions form the boundries between each slice of matrix data. Hence, for each
* dimension, each partition may range between 0 and the length of the dimension inclusive.
*
* For one dimension of length 5, you can supply 0 up to 6 partitions,
* `[0,1,2,3,4,5]` would split that dimension into 7, 0 to 0, 0 to 1, 1 to 2,
* 2 to 3, 3 to 4, 4 to 5 and 5 to 5. 0 to 0 and 5 to 5 would of course be empty and the
* 5 parts in between would each be of length 1 along that dimension.
* `[2,4]` would instead split that dimension into three parts of 0 to 2, 2 to 4, and 4 to 5.
* `[]` would not split that dimension at all, and give a single part of 0 to 5.
*
* `partition` does this along both dimensions, and returns the parts in row major order, so
* you will receive a list of R+1 * C+1 length where R is the length of the row partitions
* provided and C is the length of the column partitions provided. If you just want to split
* a matrix into a 2x2 grid see [`partition_quadrants`](Matrix::partition_quadrants) which
* provides a dedicated API with more ergonomics for extracting the parts.
*/
#[track_caller]
pub fn partition(
&mut self,
row_partitions: &[Row],
column_partitions: &[Column],
) -> Vec<MatrixView<T, MatrixPart<T>>> {
let rows = self.rows();
let columns = self.columns();
fn check_axis(partitions: &[usize], length: usize) {
let mut previous: Option<usize> = None;
for &index in partitions {
assert!(index <= length);
previous = match previous {
None => Some(index),
Some(i) => {
assert!(index > i, "{:?} must be ascending", partitions);
Some(i)
}
}
}
}
check_axis(row_partitions, rows);
check_axis(column_partitions, columns);
// There will be one more slice than partitions, since partitions are the boundries
// between slices.
let row_slices = row_partitions.len() + 1;
let column_slices = column_partitions.len() + 1;
let total_slices = row_slices * column_slices;
let mut slices: Vec<Vec<&mut [T]>> = Vec::with_capacity(total_slices);
let (_, mut data) = self.data.split_at_mut(0);
let mut index = 0;
for r in 0..row_slices {
let row_index = row_partitions.get(r).cloned().unwrap_or(rows);
// Determine how many rows of our matrix we need for the next set of row slices
let rows_included = row_index - index;
for _ in 0..column_slices {
slices.push(Vec::with_capacity(rows_included));
}
index = row_index;
for _ in 0..rows_included {
// Partition the next row of our matrix along the columns
let mut index = 0;
for c in 0..column_slices {
let column_index = column_partitions.get(c).cloned().unwrap_or(columns);
let columns_included = column_index - index;
index = column_index;
// Split off as many elements as included in this column slice
let (slice, rest) = data.split_at_mut(columns_included);
// Insert the slice into the slices, we'll push `rows_included` times into
// each slice Vec.
slices[(r * column_slices) + c].push(slice);
data = rest;
}
}
}
// rest is now empty, so we can ignore it.
slices
.into_iter()
.map(|slices| {
let rows = slices.len();
let columns = slices.get(0).map(|columns| columns.len()).unwrap_or(0);
if columns == 0 {
// We may have allocated N rows but if each column in that row has no size
// our actual size is 0x0
MatrixView::from(MatrixPart::new(slices, 0, 0))
} else {
MatrixView::from(MatrixPart::new(slices, rows, columns))
}
})
.collect()
}
/**
* Partition a matrix into 4 non overlapping quadrants. Top left starts at 0,0 until
* exclusive of row and column, bottom right starts at row and column to the end of the matrix.
*
* # Panics
*
* Panics if the row or column are greater than the number of rows or columns in the matrix.
*
* # Examples
*
* ```
* use easy_ml::matrices::Matrix;
* let mut matrix = Matrix::from(vec![
* vec![ 0, 1, 2 ],
* vec![ 3, 4, 5 ],
* vec![ 6, 7, 8 ]
* ]);
* // Split the matrix at the second row and first column giving 2x1, 2x2, 1x1 and 2x1
* // quadrants.
* // 0 | 1 2
* // 3 | 4 5
* // -------
* // 6 | 7 8
* let mut parts = matrix.partition_quadrants(2, 1);
* assert_eq!(parts.top_left, Matrix::column(vec![ 0, 3 ]));
* assert_eq!(parts.top_right, Matrix::from(vec![vec![ 1, 2 ], vec![ 4, 5 ]]));
* assert_eq!(parts.bottom_left, Matrix::column(vec![ 6 ]));
* assert_eq!(parts.bottom_right, Matrix::row(vec![ 7, 8 ]));
* // Modify the matrix data independently without worrying about the borrow checker
* parts.top_right.map_mut(|x| x + 10);
* parts.bottom_left.map_mut(|x| x - 10);
* // Drop MatrixQuadrants so we can use the matrix directly again
* std::mem::drop(parts);
* assert_eq!(matrix, Matrix::from(vec![
* vec![ 0, 11, 12 ],
* vec![ 3, 14, 15 ],
* vec![ -4, 7, 8 ]
* ]));
* ```
*/
#[track_caller]
#[allow(clippy::needless_lifetimes)] // false positive?
pub fn partition_quadrants<'a>(
&'a mut self,
row: Row,
column: Column,
) -> MatrixQuadrants<'a, T> {
let mut parts = self.partition(&[row], &[column]).into_iter();
// We know there will be exactly 4 parts returned by the partition since we provided
// 1 row and 1 column to partition ourself into 4 with.
MatrixQuadrants {
top_left: parts.next().unwrap(),
top_right: parts.next().unwrap(),
bottom_left: parts.next().unwrap(),
bottom_right: parts.next().unwrap(),
}
}
}
/**
* Methods for matrices with types that can be copied, but still not neccessarily numerical.
*/
impl<T: Clone> Matrix<T> {
/**
* Computes and returns the transpose of this matrix
*
* ```
* use easy_ml::matrices::Matrix;
* let x = Matrix::from(vec![
* vec![ 1, 2 ],
* vec![ 3, 4 ]]);
* let y = Matrix::from(vec![
* vec![ 1, 3 ],
* vec![ 2, 4 ]]);
* assert_eq!(x.transpose(), y);
* ```
*/
pub fn transpose(&self) -> Matrix<T> {
let mut result = Matrix::empty(self.get(0, 0), (self.columns(), self.rows()));
for i in 0..self.columns() {
for j in 0..self.rows() {
result.set(i, j, self.get(j, i).clone());
}
}
result
}
/**
* Transposes the matrix in place (if it is square).
*
* ```
* use easy_ml::matrices::Matrix;
* let mut x = Matrix::from(vec![
* vec![ 1, 2 ],
* vec![ 3, 4 ]]);
* x.transpose_mut();
* let y = Matrix::from(vec![
* vec![ 1, 3 ],
* vec![ 2, 4 ]]);
* assert_eq!(x, y);
* ```
*
* Note: None square matrices were erroneously not supported in previous versions (1.8.0) and
* could be incorrectly mutated. This method will now correctly transpose non square matrices
* by not attempting to transpose them in place.
*/
pub fn transpose_mut(&mut self) {
if self.rows() != self.columns() {
let transposed = self.transpose();
self.data = transposed.data;
self.rows = transposed.rows;
self.columns = transposed.columns;
} else {
for i in 0..self.rows() {
for j in 0..self.columns() {
if i > j {
continue;
}
let temp = self.get(i, j);
self.set(i, j, self.get(j, i));
self.set(j, i, temp);
}
}
}
}
/**
* Returns an iterator over a column vector in this matrix. Columns are 0 indexed.
*
* If you have a matrix such as:
* ```ignore
* [
* 1, 2, 3
* 4, 5, 6
* 7, 8, 9
* ]
* ```
* then a column of 0, 1, and 2 will yield [1, 4, 7], [2, 5, 8] and [3, 6, 9]
* respectively. If you do not need to copy the elements use
* [`column_reference_iter`](Matrix::column_reference_iter) instead.
*
* # Panics
*
* Panics if the column does not exist in this matrix.
*/
#[track_caller]
pub fn column_iter(&self, column: Column) -> ColumnIterator<T> {
ColumnIterator::new(self, column)
}
/**
* Returns an iterator over a row vector in this matrix. Rows are 0 indexed.
*
* If you have a matrix such as:
* ```ignore
* [
* 1, 2, 3
* 4, 5, 6
* 7, 8, 9
* ]
* ```
* then a row of 0, 1, and 2 will yield [1, 2, 3], [4, 5, 6] and [7, 8, 9]
* respectively. If you do not need to copy the elements use
* [`row_reference_iter`](Matrix::row_reference_iter) instead.
*
* # Panics
*
* Panics if the row does not exist in this matrix.
*/
#[track_caller]
pub fn row_iter(&self, row: Row) -> RowIterator<T> {
RowIterator::new(self, row)
}
/**
* Returns a column major iterator over all values in this matrix, proceeding through each
* column in order.
*
* If you have a matrix such as:
* ```ignore
* [
* 1, 2
* 3, 4
* ]
* ```
* then the iterator will yield [1, 3, 2, 4]. If you do not need to copy the
* elements use [`column_major_reference_iter`](Matrix::column_major_reference_iter) instead.
*/
pub fn column_major_iter(&self) -> ColumnMajorIterator<T> {
ColumnMajorIterator::new(self)
}
/**
* Returns a row major iterator over all values in this matrix, proceeding through each
* row in order.
*
* If you have a matrix such as:
* ```ignore
* [
* 1, 2
* 3, 4
* ]
* ```
* then the iterator will yield [1, 2, 3, 4]. If you do not need to copy the
* elements use [`row_major_reference_iter`](Matrix::row_major_reference_iter) instead.
*/
pub fn row_major_iter(&self) -> RowMajorIterator<T> {
RowMajorIterator::new(self)
}
/**
* Returns a iterator over the main diagonal of this matrix.
*
* If you have a matrix such as:
* ```ignore
* [
* 1, 2
* 3, 4
* ]
* ```
* then the iterator will yield [1, 4]. If you do not need to copy the
* elements use [`diagonal_reference_iter`](Matrix::diagonal_reference_iter) instead.
*
* # Examples
*
* Computing a [trace](https://en.wikipedia.org/wiki/Trace_(linear_algebra))
* ```
* use easy_ml::matrices::Matrix;
* let matrix = Matrix::from(vec![
* vec![ 1, 2, 3 ],
* vec![ 4, 5, 6 ],
* vec![ 7, 8, 9 ],
* ]);
* let trace: i32 = matrix.diagonal_iter().sum();
* assert_eq!(trace, 1 + 5 + 9);
* ```
*/
pub fn diagonal_iter(&self) -> DiagonalIterator<T> {
DiagonalIterator::new(self)
}
/**
* Creates a matrix of the provided size with all elements initialised to the provided value
*
* # Panics
*
* Panics if no values are provided. Note: this method erroneously did not validate its inputs
* in Easy ML versions up to and including 1.7.0
*/
#[track_caller]
pub fn empty(value: T, size: (Row, Column)) -> Matrix<T> {
assert!(size.0 > 0 && size.1 > 0, "Size must be at least 1x1");
Matrix {
data: vec![value; size.0 * size.1],
rows: size.0,
columns: size.1,
}
}
/**
* Gets a copy of the value at this row and column. Rows and Columns are 0 indexed.
*
* # Panics
*
* Panics if the index is out of range.
*/
#[track_caller]
pub fn get(&self, row: Row, column: Column) -> T {
assert!(
row < self.rows(),
"Row out of index, only have {} rows",
self.rows()
);
assert!(
column < self.columns(),
"Column out of index, only have {} columns",
self.columns()
);
self.data[self.get_index(row, column)].clone()
}
/**
* Similar to matrix.get(0, 0) in that this returns the element in the first
* row and first column, except that this method will panic if the matrix is
* not 1x1.
*
* This is provided as a convenience function when you want to convert a unit matrix
* to a scalar, such as after taking a dot product of two vectors.
*
* # Example
*
* ```
* use easy_ml::matrices::Matrix;
* let x = Matrix::column(vec![ 1.0, 2.0, 3.0 ]);
* let sum_of_squares: f64 = (x.transpose() * x).scalar();
* ```
*
* # Panics
*
* Panics if the matrix is not 1x1
*/
#[track_caller]
pub fn scalar(&self) -> T {
assert!(
self.rows() == 1,
"Cannot treat matrix as scalar as it has more than one row"
);
assert!(
self.columns() == 1,
"Cannot treat matrix as scalar as it has more than one column"
);
self.get(0, 0)
}
/**
* Applies a function to all values in the matrix, modifying
* the matrix in place.
*/
pub fn map_mut(&mut self, mapping_function: impl Fn(T) -> T) {
for value in self.data.iter_mut() {
*value = mapping_function(value.clone());
}
}
/**
* Applies a function to all values and each value's index in the
* matrix, modifying the matrix in place.
*/
pub fn map_mut_with_index(&mut self, mapping_function: impl Fn(T, Row, Column) -> T) {
self.row_major_reference_mut_iter()
.with_index()
.for_each(|((i, j), x)| {
*x = mapping_function(x.clone(), i, j);
});
}
/**
* Creates and returns a new matrix with all values from the original with the
* function applied to each. This can be used to change the type of the matrix
* such as creating a mask:
* ```
* use easy_ml::matrices::Matrix;
* let x = Matrix::from(vec![
* vec![ 0.0, 1.2 ],
* vec![ 5.8, 6.9 ]]);
* let y = x.map(|element| element > 2.0);
* let result = Matrix::from(vec![
* vec![ false, false ],
* vec![ true, true ]]);
* assert_eq!(&y, &result);
* ```
*/
pub fn map<U>(&self, mapping_function: impl Fn(T) -> U) -> Matrix<U>
where
U: Clone,
{
let mapped = self
.data
.iter()
.map(|x| mapping_function(x.clone()))
.collect();
Matrix::from_flat_row_major(self.size(), mapped)
}
/**
* Creates and returns a new matrix with all values from the original
* and the index of each value mapped by a function. This can be used
* to perform elementwise operations that are not defined on the
* Matrix type itself.
*
* # Exmples
*
* Matrix elementwise division:
*
* ```
* use easy_ml::matrices::Matrix;
* let x = Matrix::from(vec![
* vec![ 9.0, 2.0 ],
* vec![ 4.0, 3.0 ]]);
* let y = Matrix::from(vec![
* vec![ 3.0, 2.0 ],
* vec![ 1.0, 3.0 ]]);
* let z = x.map_with_index(|x, row, column| x / y.get(row, column));
* let result = Matrix::from(vec![
* vec![ 3.0, 1.0 ],
* vec![ 4.0, 1.0 ]]);
* assert_eq!(&z, &result);
* ```
*/
pub fn map_with_index<U>(&self, mapping_function: impl Fn(T, Row, Column) -> U) -> Matrix<U>
where
U: Clone,
{
let mapped = self
.row_major_iter()
.with_index()
.map(|((i, j), x)| mapping_function(x, i, j))
.collect();
Matrix::from_flat_row_major(self.size(), mapped)
}
/**
* Inserts a new row into the Matrix at the provided index,
* shifting other rows to the right and filling all entries with the
* provided value. Rows are 0 indexed.
*
* # Panics
*
* This will panic if the row is greater than the number of rows in the matrix.
*/
#[track_caller]
pub fn insert_row(&mut self, row: Row, value: T) {
assert!(
row <= self.rows(),
"Row to insert must be <= to {}",
self.rows()
);
for column in 0..self.columns() {
self.data.insert(self.get_index(row, column), value.clone());
}
self.rows += 1;
}
/**
* Inserts a new row into the Matrix at the provided index, shifting other rows
* to the right and filling all entries with the values from the iterator in sequence.
* Rows are 0 indexed.
*
* # Panics
*
* This will panic if the row is greater than the number of rows in the matrix,
* or if the iterator has fewer elements than `self.columns()`.
*
* Example of duplicating a row:
* ```
* use easy_ml::matrices::Matrix;
* let x: Matrix<u8> = Matrix::row(vec![ 1, 2, 3 ]);
* let mut y = x.clone();
* // duplicate the first row as the second row
* y.insert_row_with(1, x.row_iter(0));
* assert_eq!((2, 3), y.size());
* let mut values = y.column_major_iter();
* assert_eq!(Some(1), values.next());
* assert_eq!(Some(1), values.next());
* assert_eq!(Some(2), values.next());
* assert_eq!(Some(2), values.next());
* assert_eq!(Some(3), values.next());
* assert_eq!(Some(3), values.next());
* assert_eq!(None, values.next());
* ```
*/
#[track_caller]
pub fn insert_row_with<I>(&mut self, row: Row, mut values: I)
where
I: Iterator<Item = T>,
{
assert!(
row <= self.rows(),
"Row to insert must be <= to {}",
self.rows()
);
for column in 0..self.columns() {
self.data.insert(
self.get_index(row, column),
values.next().unwrap_or_else(|| {
panic!("At least {} values must be provided", self.columns())
}),
);
}
self.rows += 1;
}
/**
* Inserts a new column into the Matrix at the provided index, shifting other
* columns to the right and filling all entries with the provided value.
* Columns are 0 indexed.
*
* # Panics
*
* This will panic if the column is greater than the number of columns in the matrix.
*/
#[track_caller]
pub fn insert_column(&mut self, column: Column, value: T) {
assert!(
column <= self.columns(),
"Column to insert must be <= to {}",
self.columns()
);
for row in (0..self.rows()).rev() {
self.data.insert(self.get_index(row, column), value.clone());
}
self.columns += 1;
}
/**
* Inserts a new column into the Matrix at the provided index, shifting other columns
* to the right and filling all entries with the values from the iterator in sequence.
* Columns are 0 indexed.
*
* # Panics
*
* This will panic if the column is greater than the number of columns in the matrix,
* or if the iterator has fewer elements than `self.rows()`.
*
* Example of duplicating a column:
* ```
* use easy_ml::matrices::Matrix;
* let x: Matrix<u8> = Matrix::column(vec![ 1, 2, 3 ]);
* let mut y = x.clone();
* // duplicate the first column as the second column
* y.insert_column_with(1, x.column_iter(0));
* assert_eq!((3, 2), y.size());
* let mut values = y.column_major_iter();
* assert_eq!(Some(1), values.next());
* assert_eq!(Some(2), values.next());
* assert_eq!(Some(3), values.next());
* assert_eq!(Some(1), values.next());
* assert_eq!(Some(2), values.next());
* assert_eq!(Some(3), values.next());
* assert_eq!(None, values.next());
* ```
*/
#[track_caller]
pub fn insert_column_with<I>(&mut self, column: Column, values: I)
where
I: Iterator<Item = T>,
{
assert!(
column <= self.columns(),
"Column to insert must be <= to {}",
self.columns()
);
let mut array_values = values.collect::<Vec<T>>();
assert!(
array_values.len() >= self.rows(),
"At least {} values must be provided",
self.rows()
);
for row in (0..self.rows()).rev() {
self.data
.insert(self.get_index(row, column), array_values.pop().unwrap());
}
self.columns += 1;
}
/**
* Makes a copy of this matrix shrunk down in size according to the slice. See
* [retain_mut](Matrix::retain_mut()).
*/
pub fn retain(&self, slice: Slice2D) -> Matrix<T> {
let mut retained = self.clone();
retained.retain_mut(slice);
retained
}
}
/**
* Any matrix of a Cloneable type implements Clone.
*/
impl<T: Clone> Clone for Matrix<T> {
fn clone(&self) -> Self {
self.map(|element| element)
}
}
/**
* Any matrix of a Displayable type implements Display
*/
impl<T: std::fmt::Display> std::fmt::Display for Matrix<T> {
fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
crate::matrices::views::format_view(self, f)
}
}
/**
* Methods for matrices with numerical types, such as f32 or f64.
*
* Note that unsigned integers are not Numeric because they do not
* implement [Neg](std::ops::Neg). You must first
* wrap unsigned integers via [Wrapping](std::num::Wrapping).
*
* While these methods will all be defined on signed integer types as well, such as i16 or i32,
* in many cases integers cannot be used sensibly in these computations. If you
* have a matrix of type i8 for example, you should consider mapping it into a floating
* type before doing heavy linear algebra maths on it.
*
* Determinants can be computed without loss of precision using sufficiently large signed
* integers because the only operations performed on the elements are addition, subtraction
* and mulitplication. However the inverse of a matrix such as
*
* ```ignore
* [
* 4, 7
* 2, 8
* ]
* ```
*
* is
*
* ```ignore
* [
* 0.6, -0.7,
* -0.2, 0.4
* ]
* ```
*
* which requires a type that supports decimals to accurately represent.
*
* Mapping matrix type example:
* ```
* use easy_ml::matrices::Matrix;
* use std::num::Wrapping;
*
* let matrix: Matrix<u8> = Matrix::from(vec![
* vec![ 2, 3 ],
* vec![ 6, 0 ]
* ]);
* // determinant is not defined on this matrix because u8 is not Numeric
* // println!("{:?}", matrix.determinant()); // won't compile
* // however Wrapping<u8> is numeric
* let matrix = matrix.map(|element| Wrapping(element));
* println!("{:?}", matrix.determinant()); // -> 238 (overflow)
* println!("{:?}", matrix.map(|element| element.0 as i16).determinant()); // -> -18
* println!("{:?}", matrix.map(|element| element.0 as f32).determinant()); // -> -18.0
* ```
*/
impl<T: Numeric> Matrix<T>
where
for<'a> &'a T: NumericRef<T>,
{
/**
* Returns the determinant of this square matrix, or None if the matrix
* does not have a determinant. See [`linear_algebra`](super::linear_algebra::determinant())
*/
pub fn determinant(&self) -> Option<T> {
linear_algebra::determinant::<T>(self)
}
/**
* Computes the inverse of a matrix provided that it exists. To have an inverse a
* matrix must be square (same number of rows and columns) and it must also have a
* non zero determinant. See [`linear_algebra`](super::linear_algebra::inverse())
*/
pub fn inverse(&self) -> Option<Matrix<T>>
where
T: Add<Output = T> + Mul<Output = T> + Sub<Output = T> + Div<Output = T>,
{
linear_algebra::inverse::<T>(self)
}
/**
* Computes the covariance matrix for this NxM feature matrix, in which
* each N'th row has M features to find the covariance and variance of. See
* [`linear_algebra`](super::linear_algebra::covariance_column_features())
*/
pub fn covariance_column_features(&self) -> Matrix<T> {
linear_algebra::covariance_column_features::<T>(self)
}
/**
* Computes the covariance matrix for this NxM feature matrix, in which
* each M'th column has N features to find the covariance and variance of. See
* [`linear_algebra`](super::linear_algebra::covariance_row_features())
*/
pub fn covariance_row_features(&self) -> Matrix<T> {
linear_algebra::covariance_row_features::<T>(self)
}
}
/**
* Methods for matrices with numerical real valued types, such as f32 or f64.
*
* This excludes signed and unsigned integers as they do not support decimal
* precision and hence can't be used for operations like square roots.
*
* Third party fixed precision and infinite precision decimal types should
* be able to implement all of the methods for [Real](super::numeric::extra::Real)
* and then utilise these functions.
*/
impl<T: Numeric + Real> Matrix<T>
where
for<'a> &'a T: NumericRef<T> + RealRef<T>,
{
/**
* Computes the [L2 norm](https://en.wikipedia.org/wiki/Euclidean_vector#Length)
* of this row or column vector, also referred to as the length or magnitude,
* and written as ||x||, or sometimes |x|.
*
* ||**a**|| = sqrt(a<sub>1</sub><sup>2</sup> + a<sub>2</sub><sup>2</sup> + a<sub>3</sub><sup>2</sup>...) = sqrt(**a**<sup>T</sup> * **a**)
*
* This is a shorthand for `(x.transpose() * x).scalar().sqrt()` for
* column vectors and `(x * x.transpose()).scalar().sqrt()` for row vectors, ie
* the square root of the dot product of a vector with itself.
*
* The euclidean length can be used to compute a
* [unit vector](https://en.wikipedia.org/wiki/Unit_vector), that is, a
* vector with length of 1. This should not be confused with a unit matrix,
* which is another name for an identity matrix.
*
* ```
* use easy_ml::matrices::Matrix;
* let a = Matrix::column(vec![ 1.0, 2.0, 3.0 ]);
* let length = a.euclidean_length(); // (1^2 + 2^2 + 3^2)^0.5
* let unit = a / length;
* assert_eq!(unit.euclidean_length(), 1.0);
* ```
*
* # Panics
*
* If the matrix is not a vector, ie if it has more than one row and more than one
* column.
*/
#[track_caller]
pub fn euclidean_length(&self) -> T {
if self.columns() == 1 {
// column vector
(self.transpose() * self).scalar().sqrt()
} else if self.rows() == 1 {
// row vector
(self * self.transpose()).scalar().sqrt()
} else {
panic!(
"Cannot compute unit vector of a non vector, rows: {}, columns: {}",
self.rows(),
self.columns()
);
}
}
}
// FIXME: want this to be callable in the main numeric impl block
impl<T: Numeric> Matrix<T> {
/**
* Creates a diagonal matrix of the provided size with the diagonal elements
* set to the provided value and all other elements in the matrix set to 0.
* A diagonal matrix is always square.
*
* The size is still taken as a tuple to facilitate creating a diagonal matrix
* from the dimensionality of an existing one. If the provided value is 1 then
* this will create an identity matrix.
*
* A 3 x 3 identity matrix:
* ```ignore
* [
* 1, 0, 0
* 0, 1, 0
* 0, 0, 1
* ]
* ```
*
* # Panics
*
* If the provided size is not square.
*/
#[track_caller]
pub fn diagonal(value: T, size: (Row, Column)) -> Matrix<T> {
assert!(size.0 == size.1);
let mut matrix = Matrix::empty(T::zero(), size);
for i in 0..size.0 {
matrix.set(i, i, value.clone());
}
matrix
}
/**
* Creates a diagonal matrix with the elements along the diagonal set to the
* provided values and all other elements in the matrix set to 0.
* A diagonal matrix is always square.
*
* Examples
*
* ```
* use easy_ml::matrices::Matrix;
* let matrix = Matrix::from_diagonal(vec![ 1, 1, 1 ]);
* assert_eq!(matrix.size(), (3, 3));
* let copy = Matrix::from_diagonal(matrix.diagonal_iter().collect());
* assert_eq!(matrix, copy);
* assert_eq!(matrix, Matrix::from(vec![
* vec![ 1, 0, 0 ],
* vec![ 0, 1, 0 ],
* vec![ 0, 0, 1 ],
* ]))
* ```
*/
pub fn from_diagonal(values: Vec<T>) -> Matrix<T> {
let mut matrix = Matrix::empty(T::zero(), (values.len(), values.len()));
for (i, element) in values.into_iter().enumerate() {
matrix.set(i, i, element);
}
matrix
}
}
/**
* PartialEq is implemented as two matrices are equal if and only if all their elements
* are equal and they have the same size.
*/
impl<T: PartialEq> PartialEq for Matrix<T> {
#[inline]
fn eq(&self, other: &Self) -> bool {
if self.rows() != other.rows() {
return false;
}
if self.columns() != other.columns() {
return false;
}
// perform elementwise check, return true only if every element in
// each matrix is the same
self.data.iter().zip(other.data.iter()).all(|(x, y)| x == y)
}
}
#[test]
fn test_sync() {
fn assert_sync<T: Sync>() {}
assert_sync::<Matrix<f64>>();
}
#[test]
fn test_send() {
fn assert_send<T: Send>() {}
assert_send::<Matrix<f64>>();
}
#[cfg(feature = "serde")]
#[test]
fn test_serialize() {
fn assert_serialize<T: Serialize>() {}
assert_serialize::<Matrix<f64>>();
}
#[cfg(feature = "serde")]
#[test]
fn test_deserialize() {
fn assert_deserialize<'de, T: Deserialize<'de>>() {}
assert_deserialize::<Matrix<f64>>();
}
#[test]
fn test_indexing() {
let a = Matrix::from(vec![vec![1, 2], vec![3, 4]]);
assert_eq!(a.get_index(0, 1), 1);
assert_eq!(a.get_row_column(1), (0, 1));
assert_eq!(a.get(0, 1), 2);
let b = Matrix::from(vec![vec![1, 2, 3], vec![5, 6, 7]]);
assert_eq!(b.get_index(1, 2), 5);
assert_eq!(b.get_row_column(5), (1, 2));
assert_eq!(b.get(1, 2), 7);
assert_eq!(
Matrix::from(vec![vec![0, 0], vec![0, 0], vec![0, 0]])
.map_with_index(|_, r, c| format!("{:?}x{:?}", r, c)),
Matrix::from(vec![
vec!["0x0", "0x1"],
vec!["1x0", "1x1"],
vec!["2x0", "2x1"]
])
.map(|x| x.to_owned())
);
}