Struct NeuralPath 

pub struct NeuralPath {
    pub input_gradient: Vec<f32>,
    pub activations: Option<Vec<Vec<f32>>>,
    pub attention_weights: Option<Vec<Vec<f32>>>,
    pub integrated_gradients: Option<Vec<f32>>,
    pub prediction: f32,
    pub confidence: f32,
}

Decision path for neural networks (gradient-based)

Fields

input_gradient: Vec<f32>

Input gradient (saliency map)

activations: Option<Vec<Vec<f32>>>

Layer activations (optional, feature-gated for memory)

attention_weights: Option<Vec<Vec<f32>>>

Attention weights (for transformers)

integrated_gradients: Option<Vec<f32>>

Integrated gradients attribution

prediction: f32

Final prediction

confidence: f32

Confidence (softmax probability)

Implementations

impl NeuralPath

pub fn new(input_gradient: Vec<f32>, prediction: f32, confidence: f32) -> Self

Create a new neural path

pub fn with_activations(self, activations: Vec<Vec<f32>>) -> Self

Set layer activations

pub fn with_attention(self, attention: Vec<Vec<f32>>) -> Self

Set attention weights

pub fn with_integrated_gradients(self, ig: Vec<f32>) -> Self

Set integrated gradients

pub fn top_salient_features(&self, k: usize) -> Vec<(usize, f32)>

Get top salient features by absolute gradient

Trait Implementations

impl Clone for NeuralPath

fn clone(&self) -> NeuralPath

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for NeuralPath

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl DecisionPath for NeuralPath

fn explain(&self) -> String

Human-readable explanation

fn feature_contributions(&self) -> &[f32]

Feature importance scores (contribution of each feature)

fn confidence(&self) -> f32

Confidence in this decision (0.0 - 1.0)

fn to_bytes(&self) -> Vec<u8>

Compact binary representation

fn from_bytes(bytes: &[u8]) -> Result<Self, PathError>

Reconstruct from binary representation

impl<'de> Deserialize<'de> for NeuralPath

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for NeuralPath

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.