Crate concision_core

concision-core

This library provides the core abstractions and utilities for the concision (cnc) machine learning framework.

Features

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

Re-exports

pub use rand;

pub use rand_distr;

pub use super::Activate;

pub use super::ActivateGradient;

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 is dedicated to activation function This module implements various activation functions for neural networks.

error

this module provides the base Error type for the library This module implements the core Error type for the framework and provides a Result type alias for convenience.

init

this module establishes generic random initialization routines for models, params, and tensors. This module works to provide the crate with various initialization methods suitable for machine-learning models.

loss

this module focuses on the loss functions used in training neural networks. This module provides various loss functions used in machine learning.

ops

This module provides the core operations for tensors, including filling, padding, reshaping, and tensor manipulation.

params

this module provides the ParamsBase type for the library, which is used to define the parameters of a neural network. Parameters for constructing neural network models. This module implements parameters using the ParamsBase struct and its associated types. The ParamsBase struct provides:

prelude

traits

This module provides the core traits for the library, such as Backward and Forward

utils

A suite of utilities tailored toward neural networks.

Structs

PadActionIter

An iterator over the variants of PadAction

Padding

Enums

Error

The Error type enumerates various errors that can occur within the framework.

PadAction

PadError

PadMode

UtilityError

Traits

ActivateExt

This trait extends the [Activate] trait with a number of additional activation functions and their derivatives. Note: this trait is automatically implemented for any type that implements the [Activate] trait eliminating the need to implement it manually.

ActivateMut

A trait for establishing a common mechanism to activate entities in-place.

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;

Biased

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

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

Gradient

The Gradient trait defines a common interface for all gradients

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.

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

The Pad trait defines a padding operation for tensors.

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

Weighted

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

floor_div

divide two values and round down to the nearest integer.

genspace

heavyside

Heaviside activation function

hstack

stack a 1D array into a 2D array by stacking them horizontally.

inverse

is_similar_pattern

Check if two patterns are similar enough to be considered duplicates

layer_norm

layer_norm_axis

linarr

pad

pad_to

relu

the relu activation function: $f(x) = \max(0, x)$

relu_derivative

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 function: $f(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}}$

softmax_axis

Softmax function along a specific axis: $f(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}}$

stack_iter

Creates a larger array from an iterator of smaller arrays.

tanh

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

tanh_derivative

the derivative of the tanh function

tril

Returns the lower triangular portion of a matrix.

triu

Returns the upper triangular portion of a matrix.

vstack

stack a 1D array into a 2D array by stacking them vertically.

Type Aliases

PadResult

Result

a type alias for a Result with a Error