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use crate::{
core::prelude::*,
errors::prelude::*,
numeric::prelude::*,
validators::prelude::*,
};
/// `ArrayTrait` - Array Create functions
pub trait ArrayCreateFrom<N: Numeric> where Array<N>: Sized + Clone {
// matrices
/// Extract a diagonal or construct a diagonal array
///
/// # Arguments
///
/// * `k` - chosen diagonal. optional, defaults to 0
///
/// # Examples
///
/// ```
/// use arr_rs::prelude::*;
///
/// let expected = array!(i32, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]).unwrap();
/// let arr = Array::diag(&array!(i32, [1, 2, 3, 4]).unwrap(), None).unwrap();
/// assert_eq!(expected, arr);
///
/// let expected = array!(i32, [1, 2, 3, 4]).unwrap();
/// let arr = Array::diag(&array!(i32, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]).unwrap(), None).unwrap();
/// assert_eq!(expected, arr);
///
/// let expected = array!(i32, [0, 0, 0]).unwrap();
/// let arr = Array::diag(&array!(i32, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]).unwrap(), Some(1)).unwrap();
/// assert_eq!(expected, arr);
/// ```
///
/// # Errors
///
/// may returns `ArrayError`
fn diag(&self, k: Option<isize>) -> Result<Array<N>, ArrayError>;
/// Construct a diagonal array for flattened input
///
/// # Arguments
///
/// * `data` - input array
/// * `k` - chosen diagonal. optional, defaults to 0
///
/// # Examples
///
/// ```
/// use arr_rs::prelude::*;
///
/// let expected = array!(i32, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]).unwrap();
/// let arr = Array::diagflat(&array!(i32, [1, 2, 3, 4]).unwrap(), None).unwrap();
/// assert_eq!(expected, arr);
///
/// let expected = array!(i32, [[1, 0, 0, 0], [0, 2, 0, 0], [0, 0, 3, 0], [0, 0, 0, 4]]).unwrap();
/// let arr = Array::diagflat(&array!(i32, [[1, 2], [3, 4]]).unwrap(), None).unwrap();
/// assert_eq!(expected, arr);
/// ```
///
/// # Errors
///
/// may returns `ArrayError`
fn diagflat(&self, k: Option<isize>) -> Result<Array<N>, ArrayError>;
/// Return a copy of an array with elements above the k-th diagonal zeroed.
/// For arrays with ndim exceeding 2, tril will apply to the final two axes.
///
/// # Arguments
///
/// * `k` - chosen diagonal. optional, defaults to 0
///
/// # Examples
///
/// ```
/// use arr_rs::prelude::*;
///
/// let arr = array_arange!(i32, 1, 8).reshape(&[2, 4]).unwrap();
/// let expected = array!(i32, [[1, 0, 0, 0], [5, 6, 0, 0]]).unwrap();
/// assert_eq!(expected, arr.tril(None).unwrap());
///
/// let arr = array_arange!(i32, 1, 8).reshape(&[2, 2, 2]).unwrap();
/// let expected = array!(i32, [[[1, 0], [3, 4]], [[5, 0], [7, 8]]]).unwrap();
/// assert_eq!(expected, arr.tril(None).unwrap());
/// ```
///
/// # Errors
///
/// may returns `ArrayError`
fn tril(&self, k: Option<isize>) -> Result<Array<N>, ArrayError>;
/// Return a copy of an array with elements below the k-th diagonal zeroed.
/// For arrays with ndim exceeding 2, triu will apply to the final two axes.
///
/// # Arguments
///
/// * `k` - chosen diagonal. optional, defaults to 0
///
/// # Examples
///
/// ```
/// use arr_rs::prelude::*;
///
/// let arr = array_arange!(i32, 1, 8).reshape(&[2, 4]).unwrap();
/// let expected = array!(i32, [[1, 2, 3, 4], [0, 6, 7, 8]]).unwrap();
/// assert_eq!(expected, arr.triu(None).unwrap());
///
/// let arr = array_arange!(i32, 1, 8).reshape(&[2, 2, 2]).unwrap();
/// let expected = array!(i32, [[[1, 2], [0, 4]], [[5, 6], [0, 8]]]).unwrap();
/// assert_eq!(expected, arr.triu(None).unwrap());
/// ```
///
/// # Errors
///
/// may returns `ArrayError`
fn triu(&self, k: Option<isize>) -> Result<Array<N>, ArrayError>;
/// Generate a Vandermonde matrix
///
/// # Arguments
///
/// * `n` - number of columns in the output. optional, by default square array is returned
/// * `increasing` - order of the powers of the columns. optional, defaults to false
/// if true, the powers increase from left to right, if false, they are reversed.
///
/// # Examples
///
/// ```
/// use arr_rs::prelude::*;
///
/// let arr = array!(i32, [1, 2, 3, 4]).unwrap();
/// let expected = array!(i32, [[1, 1, 1, 1], [8, 4, 2, 1], [27, 9, 3, 1], [64, 16, 4, 1]]).unwrap();
/// assert_eq!(expected, arr.vander(None, Some(false)).unwrap());
///
/// let arr = array!(i32, [1, 2, 3, 4]).unwrap();
/// let expected = array!(i32, [[1, 1, 1, 1], [1, 2, 4, 8], [1, 3, 9, 27], [1, 4, 16, 64]]).unwrap();
/// assert_eq!(expected, arr.vander(None, Some(true)).unwrap());
/// ```
///
/// # Errors
///
/// may returns `ArrayError`
fn vander(&self, n: Option<usize>, increasing: Option<bool>) -> Result<Self, ArrayError> where Self: Sized;
}
impl <N: Numeric> ArrayCreateFrom<N> for Array<N> {
// ==== matrices
fn diag(&self, k: Option<isize>) -> Result<Self, ArrayError> {
fn diag_1d<N: Numeric>(data: &Array<N>, k: isize) -> Result<Array<N>, ArrayError> {
let size = data.get_shape()?[0];
let abs_k = k.unsigned_abs();
let new_shape = vec![size + abs_k, size + abs_k];
let data_elements = data.get_elements()?;
let elements = (0..new_shape[0] * new_shape[1])
.map(|idx| {
let (i, j) = (idx / new_shape[1], idx % new_shape[1]);
if k >= 0 && j == i + k.to_usize() {
if i < size { data_elements[i] }
else { N::zero() }
} else if k < 0 && i == j + abs_k {
if j < size { data_elements[j] }
else { N::zero() }
} else {
N::zero()
}
})
.collect();
Array::new(elements, new_shape)
}
fn diag_2d<N: Numeric>(data: &Array<N>, k: isize) -> Result<Array<N>, ArrayError> {
let rows = data.get_shape()?[0];
let cols = data.get_shape()?[1];
let (start_row, start_col) =
if k >= 0 { (0, k.to_usize()) }
else { ((-k).to_usize(), 0) };
let data_elements = data.get_elements()?;
let elements = (start_row..rows)
.zip(start_col..cols)
.map(|(i, j)| data_elements[i * cols + j])
.collect::<Vec<N>>();
Array::new(elements.clone(), vec![elements.len()])
}
self.is_dim_supported(&[1, 2])?;
let k = k.unwrap_or(0);
if self.ndim()? == 1 { diag_1d(self, k) }
else { diag_2d(self, k) }
}
fn diagflat(&self, k: Option<isize>) -> Result<Self, ArrayError> {
self.ravel()?.diag(k)
}
fn tril(&self, k: Option<isize>) -> Result<Self, ArrayError> {
let k = k.unwrap_or(0);
self.apply_triangular(k, |j, i, k| j > i + k)
}
fn triu(&self, k: Option<isize>) -> Result<Self, ArrayError> {
let k = k.unwrap_or(0);
self.apply_triangular(k, |j, i, k| j < i + k)
}
fn vander(&self, n: Option<usize>, increasing: Option<bool>) -> Result<Self, ArrayError> {
self.is_dim_supported(&[1])?;
let size = self.shape[0];
let increasing = increasing.unwrap_or(false);
let n_columns = n.unwrap_or(size);
let mut elements = Vec::with_capacity(size * n_columns);
for item in self {
for i in 0..n_columns {
let power = if increasing { i } else { n_columns - i - 1 }.to_f64();
elements.push(N::from(item.to_f64().powf(power)));
}
}
Self::new(elements, vec![size, n_columns])
}
}
impl <N: Numeric> Array<N> {
fn apply_triangular<F>(&self, k: isize, compare: F) -> Result<Self, ArrayError>
where F: Fn(isize, isize, isize) -> bool {
let last_dim = self.shape.len() - 1;
let second_last_dim = self.shape.len() - 2;
let chunk_size = self.shape[last_dim] * self.shape[second_last_dim];
let elements = self.elements
.chunks(chunk_size)
.flat_map(|chunk| {
chunk
.iter()
.enumerate()
.map(|(idx, &value)| {
let i = (idx / self.shape[last_dim]) % self.shape[second_last_dim];
let j = idx % self.shape[last_dim];
if compare(j.to_isize(), i.to_isize(), k) { N::zero() } else { value }
})
})
.collect();
Self::new(elements, self.shape.clone())
}
}