Struct numeric::tensor::Tensor [−] [src]

pub struct Tensor<T> { /* fields omitted */ }

An implementation of an N-dimensional matrix. A quick example:

use numeric::Tensor;
let t = Tensor::new(vec![1.0f64, 3.0, 2.0, 2.0]).reshape(&[2, 2]);
println!("t = {}", t);

Will output:

t =
 1 3
 2 2
[Tensor<f64> of shape 2x2]

Methods

`impl Tensor<f32>`
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`fn dot(&self, rhs: &Tensor<f32>) -> Tensor<f32>`

Takes the product of two tensors. If the tensors are both matrices (2D), then a matrix multiplication is taken. If the tensors are both vectors (1D), the scalar product is taken.

`impl Tensor<f64>`
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`fn dot(&self, rhs: &Tensor<f64>) -> Tensor<f64>`

Takes the product of two tensors. If the tensors are both matrices (2D), then a matrix multiplication is taken. If the tensors are both vectors (1D), the scalar product is taken.

`impl<T: TensorTrait + Add<Output = T>> Tensor<T>`
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`fn add_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + Sub<Output = T>> Tensor<T>`
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`fn sub_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + Mul<Output = T>> Tensor<T>`
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`fn mul_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + Div<Output = T>> Tensor<T>`
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`fn div_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + Rem<Output = T>> Tensor<T>`
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`fn rem_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + BitAnd<Output = T>> Tensor<T>`
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`fn bitand_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + BitOr<Output = T>> Tensor<T>`
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`fn bitor_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: TensorTrait + BitXor<Output = T>> Tensor<T>`
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`fn bitxor_with_out(&self, rhs: &Tensor<T>, out: &mut Tensor<T>)`

`impl<T: NumericTrait> Tensor<T>`
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`fn max(&self) -> T`

`fn min(&self) -> T`

`fn sum(&self) -> T`

`fn mean(&self) -> T`

`impl<T: TensorTrait + Add<Output = T>> Tensor<T>`
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`fn sum_axis(&self, axis: usize) -> Tensor<T>`

`impl<T: TensorTrait + Mul<Output = T>> Tensor<T>`
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`fn prod_axis(&self, axis: usize) -> Tensor<T>`

`impl<T: TensorTrait + BitAnd<Output = T>> Tensor<T>`
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`fn bitand_axis(&self, axis: usize) -> Tensor<T>`

`impl<T: TensorTrait + BitOr<Output = T>> Tensor<T>`
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`fn bitor_axis(&self, axis: usize) -> Tensor<T>`

`impl<T: TensorTrait + BitXor<Output = T>> Tensor<T>`
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`fn bitxor_axis(&self, axis: usize) -> Tensor<T>`

`impl<T: TensorTrait> Tensor<T>`
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`fn concat(lhs: &Tensor<T>, rhs: &Tensor<T>, axis: usize) -> Tensor<T>`

`impl<T: NumericTrait> Tensor<T>`
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`fn convert<D: NumericTrait>(&self) -> Tensor<D>`

Returns a new tensor with the elements converted to the selected type.

use numeric::Tensor;

let tdouble = tensor![1.0f64, 2.0, 3.0];
let tsingle = tdouble.convert::<f32>();

`fn to_f32(&self) -> Tensor<f32>`

Short-hand for convert::<f32>().

`fn to_f64(&self) -> Tensor<f64>`

Short-hand for convert::<f64>().

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_gt(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_ge(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_lt(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_le(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_eq(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl<T: TensorTrait + PartialOrd> Tensor<T>`
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Element-wise comparison.

`fn elem_ne(&self, rhs: &Tensor<T>) -> Tensor<bool>`

`impl Tensor<bool>`
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`fn all(&self) -> bool`

`fn any(&self) -> bool`

`impl<T: TensorTrait> Tensor<T>`
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`unsafe fn as_ptr(&self) -> *const T`

`unsafe fn as_mut_ptr(&mut self) -> *mut T`

`fn new(data: Vec<T>) -> Tensor<T>`

Creates a new tensor from a Vec object. It will take ownership of the vector.

`fn empty(shape: &[usize]) -> Tensor<T>`

Creates a zero-filled tensor of the specified shape.

`fn mem_slice(&self) -> &[T]`

`fn mem_slice_mut(&mut self) -> &mut [T]`

`fn slice(&self) -> &[T]`

Returns a flat slice of the tensor. Only works for canonical tensors.

`fn slice_mut(&mut self) -> &mut [T]`

Returns a mutable flat slice of the tensor. Only works for canonical tensors. Will make a copy of the underyling data if the tensor is not unique.

`fn iter(&self) -> TensorIterator<T>`

`fn scalar(value: T) -> Tensor<T>`

Creates a Tensor representing a scalar

`fn is_scalar(&self) -> bool`

`fn scalar_value(&self) -> T`

`fn filled(shape: &[usize], v: T) -> Tensor<T>`

Creates a new tensor of a given shape filled with the specified value.

`fn shape(&self) -> &Vec<usize>`

Returns the shape of the tensor.

`fn dim(&self, axis: usize) -> usize`

Returns length of single dimension.

`fn data(&self) -> &Vec<T>`

Returns a reference of the underlying data vector.

`fn flatten(&self) -> Tensor<T>`

Flattens the tensor to one-dimensional.

`fn canonize(&self) -> Tensor<T>`

Make a dense copy of the tensor. This means it will have default strides and no memory offset.

`fn canonize_inplace(&mut self)`

`fn size(&self) -> usize`

Returns number of elements in the tensor.

`fn ndim(&self) -> usize`

Returns the number of axes. This is the same as the length of the shape array.

`fn index(&self, selection: &[AxisIndex]) -> Tensor<T>`

Takes slices (subsets) of tensors and returns a tensor as a new object. Uses the AxisIndex enum to specify indexing for each axis.

use numeric::{DoubleTensor, Ellipsis, StridedSlice, Index, Full};

let t = DoubleTensor::ones(&[2, 3, 4]);

t.index(&[Ellipsis, StridedSlice(Some(1), Some(3), 1)]); // shape [2, 3, 2]
t.index(&[Index(-1)]); // shape [3, 4]
t.index(&[Full, StridedSlice(Some(1), None, 1), Index(1)]); // shape [2, 2]

`fn base(&self) -> Tensor<T>`

Returns the underlying memory as a vector.

`fn index_set(&mut self, selection: &[AxisIndex], other: &Tensor<T>)`

Similar to index, except this updates the tensor with other instead of returning them.

`fn bool_index(&self, indices: &Tensor<bool>) -> Tensor<T>`

`fn bool_index_set(&mut self, indices: &Tensor<bool>, values: &Tensor<T>)`

`fn unravel_index(&self, index: usize) -> Vec<usize>`

Takes a flatten index (if in row-major order) and returns a vector of the per-axis indices.

`fn ravel_index(&self, ii: &[usize]) -> usize`

Takes an array of per-axis indices and returns a flattened index (in row-major order).

`fn reshape(self, shape: &[isize]) -> Tensor<T>`

Reshapes the data. This moves the data, so no memory is allocated.

`fn set(&mut self, other: &Tensor<T>)`

Sets all the values according to another tensor.

`fn swapaxes(&self, axis1: usize, axis2: usize) -> Tensor<T>`

Swaps two axes.

`fn transpose(&self) -> Tensor<T>`

Transposes a matrix (for now, requires it to be 2D).

`impl<T: Copy + Zero> Tensor<T>`
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`fn zeros(shape: &[usize]) -> Tensor<T>`

Creates a zero-filled tensor of the specified shape.

`impl<T: Copy + One> Tensor<T>`
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`fn ones(shape: &[usize]) -> Tensor<T>`

Creates a one-filled tensor of the specified shape.

`impl<T: Copy + Zero + One> Tensor<T>`
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`fn eye(size: usize) -> Tensor<T>`

Creates an identity 2-D tensor (matrix). That is, all elements are zero except the diagonal which is filled with ones.

`impl<T: Copy + Add + Zero + One> Tensor<T>`
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`fn range(size: usize) -> Tensor<T>`

Creates a new vector with integer values starting at 0 and counting up:

use numeric::DoubleTensor;

let t = DoubleTensor::range(5); // [  0.00   1.00   2.00   3.00   4.00]

`impl<T: TensorTrait + Num + NumCast> Tensor<T>`
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`fn linspace(start: T, stop: T, num: usize) -> Tensor<T>`

Creates a new vector between two values at constant increments. The number of elements is specified.

`impl<T: NumericTrait> Tensor<T>`
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`fn fscalar(value: f64) -> Tensor<T>`

Creates a scalar specified as a f64 and internally casted to T

`impl Tensor<f64>`
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`fn solve(&self, b: &Tensor<f64>) -> Tensor<f64>`

Solves the linear equation Ax = b and returns x. The matrix A is self and must be a square matrix. The input b must be a vector.

Panics if matrix is singular.

`impl Tensor<f32>`
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`fn solve(&self, b: &Tensor<f32>) -> Tensor<f32>`

Solves the linear equation Ax = b and returns x. The matrix A is self and must be a square matrix. The input b must be a vector.

Panics if matrix is singular.

`impl Tensor<f64>`
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`fn svd(&self, full_matrices: bool) -> (Tensor<f64>, Tensor<f64>, Tensor<f64>)`

Performs a singular value decomposition on the matrix.

`impl Tensor<f32>`
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`fn svd(&self, full_matrices: bool) -> (Tensor<f32>, Tensor<f32>, Tensor<f32>)`

Performs a singular value decomposition on the matrix.

`impl Tensor<u8>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<u16>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<u32>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<u64>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<i8>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<i16>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<i32>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<i64>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<f32>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.

`impl Tensor<f64>`
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`fn save_hdf5(&self, path: &Path) -> Result<()>`

Saves tensor to an HDF5 file.

Warning: This function is not thread-safe (unless you compiled HDF5 to be thread-safe). Do no call this function concurrently from multiple threads.