Crate concision_core

Expand description

§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