yobx.sklearn.covariance.ledoit_wolf#

yobx.sklearn.covariance.ledoit_wolf.sklearn_ledoit_wolf(g: GraphBuilderExtendedProtocol, sts: Dict, outputs: List[str], estimator: LedoitWolf, X: str, name: str = 'ledoit_wolf') → str[source]#

Converts a sklearn.covariance.LedoitWolf into ONNX.

The converter maps to mahalanobis(), returning the squared Mahalanobis distance from the fitted distribution for each observation:

$d^2(x) = (x - \mu)^\top \, \Sigma^{-1} \, (x - \mu) = \sum_j \bigl[(x - \mu) \Sigma^{-1}\bigr]_j \cdot (x - \mu)_j$

where $\mu$ is location_ and $\Sigma^{-1}$ is precision_.

Full graph structure:

X (N, F)
  │
  └─ Sub(location_) ──► X_centered (N, F)
       │
       ├─ MatMul(precision_) ──► X_prec (N, F)
       │                              │
       └──────────────────── Mul ──► X_prec_centered (N, F)
                                      │
                           ReduceSum(axis=1) ──► mahal_sq (N,)  [output]

Parameters:

g – the graph builder to add nodes to
sts – shapes defined by scikit-learn
outputs – desired output names; outputs[0] receives the squared Mahalanobis distances
estimator – a fitted LedoitWolf
X – input tensor name
name – prefix for added node names

Returns:

output tensor name

yobx.sklearn.covariance.ledoit_wolf#

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