Crate concision

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§concision (cnc)

concision aims to be a complete machine-learning toolkit written in Rust. The framework is designed to be performant, extensible, and easy to use while offering a wide range of features for building and training machine learning models.

The framework relies heavily on the ndarray crate for its n-dimensional arrays, which are essential for efficient data manipulation and mathematical operations.

§Features

data: Provides utilities for data loading, preprocessing, and augmentation.
derive: Custom derive macros for automatic implementation of traits
init: Enables various initialization strategies for model parameters.
macros: Procedural macros for simplifying common tasks in machine learning.
neural: A neural network module that includes layers, optimizers, and training utilities.

§Extensions

The crate is integrated with several optional externcal crates that are commonly used in Rust development; listed below are some of the most relevant of these extensions as they add additional functionality to the framework.

approx: Enables approximate equality checks for floating-point arithmetic, useful for testing and validation of model outputs.
json: Enables JSON serialization and deserialization for models and data.
rayon: Enables parallel processing for data loading and training.
serde: Enables the serde crate for the serialization and deserialization of models and data.
tracing: Enables the tracing crate for structured logging and diagnostics.

§Roadmap

DSL: Create a pseudo-DSL for defining machine learning models and training processes.
GPU: Support for GPU acceleration to speed up training and inference.
Interoperability: Integrate with other libraries and frameworks (TensorFlow, PyTorch)
Visualization: Utilities for visualizing model architectures and training progress
WASM: Native support for WebAssembly enabling models to be run in web browsers.

Modules§

activate: this module is dedicated to activation function This module implements various activation functions for neural networks.
data: this module contains various data loaders, preprocessors, and augmenters Datasets and data loaders for the Concision framework.
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.
loss: this module focuses on the loss functions used in training neural networks.
nn: this module defines various neural network abstractions, layers, and training utilities
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:
rand: Utilities for random number generation
rand_distr: Generating random samples from probability distributions.
tensor: the tensor module provides various traits and types for handling n-dimensional tensors. this module focuses on establishing a solid foundation for working with n-dimensional tensors.
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
ParamsBase: The ParamsBase struct is a generic container for a set of weights and biases for a model. The implementation is designed around the ArrayBase type from the ndarray crate, which allows for flexible and efficient storage of multi-dimensional arrays.
TensorBase: the TensorBase struct is the base type for all tensors in the library.

Enums§

Error: The Error type enumerates various errors that can occur within the framework.
PadAction
PadError
PadMode
ParamsError

Traits§

Activate: The Activate trait establishes a common interface for entities that can be activated according to some function
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
AsBiasDim: The AsBiasDim trait is used to define a type that can be used to get the bias dimension of the parameters.
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: The DecrementAxis trait defines a method enabling an axis to decrement 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 the gradient of a function, which is a function that takes an input and returns a delta, which is the change in the output with respect to the input.
Heavyside
IncrementAxis: The IncrementAxis trait defines a method enabling an axis to increment itself, effectively adding a new axis to the array.
IntoAxis: The IntoAxis trait is used to define a conversion routine that takes a type and wraps it in an Axis type.
Inverse: The Inverse trait generically establishes an interface for computing the inverse of a type, regardless of if its a tensor, scalar, or some other compatible type.
IsSquare: IsSquare is a trait for checking if the layout, or dimensionality, of a tensor is square.
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
Loss: The Loss trait defines a common interface for any custom loss function implementations. This trait requires the implementor to define their algorithm for calculating the loss between two values, lhs and rhs, which can be of different types, X and Y respectively. These terms are used generically to allow for flexibility in the allowed types, such as tensors, scalars, or other data structures while clearly defining the “order” in which the operations are performed. It is most common to expect the lhs to be the predicted output and the rhs to be the actual output, but this is not a strict requirement. The trait also defines an associated type Output, which represents the type of the loss value returned by the loss method. This allows for different loss functions to return different types of loss values, such as scalars or tensors, depending on the specific implementation of the loss function.
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: The MatMul trait defines an interface for matrix multiplication.
MatPow: The MatPow trait defines an interface for computing the exponentiation of a matrix.
MeanAbsoluteError: Compute the mean absolute error (MAE) of the object; more formally, we define the MAE as the average of the absolute differences between the predicted and actual values:
MeanSquaredError: The MeanSquaredError (MSE) is the average of the squared differences between the ($\hat{y_{i}}$) and actual values ($y_{i}$):
NdActivateMut
NdLike
NdTensor: The [Tensor] trait extends the RawTensor trait to provide additional functionality for tensors, such as creating tensors from shapes, applying functions, and iterating over elements. It is generic over the element type A and the dimension type `D
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:
RawDimension: the RawDimension trait is used to define a type that can be used as a raw dimension. This trait is primarily used to provide abstracted, generic interpretations of the dimensions of the ndarray crate to ensure long-term compatibility.
RawTensor: The RawTensor trait defines the base interface for all tensors,
ReLU
Root: The Root trait provides methods for computing the nth root of a number.
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.
Sigmoid
Softmax
SoftmaxAxis
SummaryStatistics: This trait describes the fundamental methods of summary statistics. These include the mean, standard deviation, variance, and more.
Tanh
Transpose: The Transpose trait generically establishes an interface for transposing a type
Unsqueeze: The Unsqueeze trait establishes an interface for a routine that unsqueezes an array, by inserting a new axis at a specified position. This is useful for reshaping arrays to meet specific dimensional requirements.
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§

ArcTensor: a type alias for a TensorBase with an owned representation
PadResult
Params: a type alias for owned parameters
ParamsView: a type alias for an immutable view of the parameters
ParamsViewMut: a type alias for a mutable view of the parameters
RawViewMutTensor: a type alias for a TensorBase with an owned representation
RawViewTensor: A type alias for a TensorBase with a raw pointer representation.
Result: a type alias for a Result with a Error
Tensor: a type alias for a TensorBase with an owned representation
TensorView: a type alias for a TensorBase with a view representation
TensorViewMut: a type alias for a TensorBase with a mutable view representation

Derive Macros§

Keyed: This macro generates a parameter struct and an enum of parameter keys.

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