Crate concision_core

This library provides the core abstractions and utilities for the Concision framework.

Features

• ParamsBase: A structure for defining the parameters within a neural network.
• Backward: This trait denotes a single backward pass through a layer of a neural network.
• Forward: This trait denotes a single forward pass through a layer of a neural network.

Re-exports

pub use super::Activate;

pub use super::ActivateGradient;

pub use super::BinaryAction;

pub use super::error::ParamsError;

pub use super::params::ParamsBase;

pub use super::Params;

pub use super::ParamsView;

pub use super::ParamsViewMut;

Modules

activate

This module implements various activation functions for neural networks.

error

init

This module works to provide the crate with various initialization methods suitable for machine-learning models.

loss

This module provides various loss functions used in machine learning.

ops

params

Parameters for constructing neural network models. This module implements parameters using the ParamsBase struct and its associated types. The ParamsBase struct provides:

prelude

traits

utils

A suite of utilities tailored toward neural networks.

Structs

FftDirectionIter

An iterator over the variants of FftDirection

FftModeIter

An iterator over the variants of FftMode

FftPlan

PadActionIter

An iterator over the variants of PadAction

Padding

Enums

C

Error

FftDirection

FftMode

PadAction

PadError

PadMode

R

UtilityError

Traits

Affine

apply an affine transformation to a tensor; affine transformation is defined as mul * self + add

ApplyGradient

A trait declaring basic gradient-related routines for a neural network

ApplyGradientExt

This trait extends the ApplyGradient trait by allowing for momentum-based optimization

ArrayLike

AsComplex

Backward

Backward propagate a delta through the system;

Clip

A trait denoting objects capable of being clipped between some minimum and some maximum.

ClipMut

This trait enables tensor clipping; it is implemented for ArrayBase

Codex

CrossEntropy

A trait for computing the cross-entropy loss of a tensor or array

DFT

Trait for computing the Discrete Fourier Transform (DFT) of a sequence.

Decode

Decode defines a standard interface for decoding data.

DecrementAxis

This trait enables an array to remove an axis from itself

DefaultLike

DropOut

[Dropout] randomly zeroizes elements with a given probability (p).

Encode

Encode defines a standard interface for encoding data.

FillLike

FloorDiv

Forward

This trait denotes entities capable of performing a single forward step

Heavyside

IncrementAxis

Init

A trait for creating custom initialization routines for models or other entities.

InitInplace

This trait enables models to implement custom, in-place initialization methods.

IntoAxis

IntoComplex

Trait for converting a type into a complex number.

Inverse

this trait enables the inversion of a matrix

IsSquare

L1Norm

a trait for computing the L1 norm of a tensor or array

L2Norm

a trait for computing the L2 norm of a tensor or array

LinearActivation

MaskFill

This trait is used to fill an array with a value based on a mask. The mask is a boolean array of the same shape as the array.

Matmul

A trait denoting objects capable of matrix multiplication.

Matpow

a trait denoting objects capable of matrix exponentiation

MeanAbsoluteError

Compute the mean absolute error (MAE) of the object.

MeanSquaredError

Compute the mean squared error (MSE) of the object.

NdActivate

NdActivateMut

NdLike

Norm

The Norm trait serves as a unified interface for various normalization routnines. At the moment, the trait provides L1 and L2 techniques.

Numerical

Numerical is a trait for all numerical types; implements a number of core operations

OnesLike

Pad

PercentDiff

Compute the percentage difference between two values. The percentage difference is defined as:

ReLU

Root

RoundTo

Scalar

The Scalar trait extends the Numerical trait to include additional mathematical operations for the purpose of reducing the number of overall traits required to complete various machine-learning tasks.

ScalarComplex

Sigmoid

Softmax

SoftmaxAxis

SummaryStatistics

This trait describes the fundamental methods of summary statistics. These include the mean, standard deviation, variance, and more.

Tanh

Tensor

Transpose

the trait denotes the ability to transpose a tensor

Unsqueeze

ZerosLike

Functions

calculate_pattern_similarity

Calculate similarity between two patterns

clip_gradient

Clip the gradient to a maximum value.

clip_inf_nan

concat_iter

Creates an n-dimensional array from an iterator of n dimensional arrays.

extract_patterns

Extract common patterns from historical sequences

fft

Computes the Fast Fourier Transform of a one-dimensional, complex-valued signal.

floor_div

genspace

heavyside

Heaviside activation function

hstack

ifft

Computes the Inverse Fast Fourier Transform of an one-dimensional, complex-valued signal.

inverse

irfft

Computes the Inverse Fast Fourier Transform of an one-dimensional, real-valued signal. TODO: Fix the function; currently fails to compute the correct result

is_similar_pattern

Check if two patterns are similar enough to be considered duplicates

layer_norm

layer_norm_axis

linarr

pad

pad_to

relu

relu_derivative

rfft

Computes the Fast Fourier Transform of an one-dimensional, real-valued signal. TODO: Optimize the function to avoid unnecessary computation.

round_to

Round the given value to the given number of decimal places.

sigmoid

the sigmoid activation function: $f(x) = \frac{1}{1 + e^{-x}}$

sigmoid_derivative

the derivative of the sigmoid function

softmax

softmax_axis

stack_iter

Creates a larger array from an iterator of smaller arrays.

tanh

tanh_derivative

tril

Returns the lower triangular portion of a matrix.

triu

Returns the upper triangular portion of a matrix.

vstack

Type Aliases

PadResult

Result

a type alias for a Result with a Error