Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector Parameters ---------- X : numpy array of shape (n_samples, n_features) The input samples. y

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit the model using X as training data. Parameters ---------- X : array, shape (n_samples, n_features) Tr

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit the model to the data. Parameters ---------- X : numpy.ndarray Input data. y : None Igno

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector. y is ignored in unsupervised methods. Parameters ---------- X : numpy array of shape (n_samples, n_features)

Fit detector first and then predict whether a particular sample is an outlier or not. y is ignored in unsupervised models. Parameters

Fit the detector, predict on samples, and evaluate the model by predefined metrics, e.g., ROC. Parameters ----------

Fit the detector with labels, predict on samples, and evaluate the model by predefined metrics. Parameters ----------

Parameters ---------- x : Tensor, shape (batch, seq_len, d_model) Returns ------- out : Tensor, shap

Returns ------- x : Tensor (B, L, d_model) series_assoc : Tensor (B, H, L, L) prior_assoc : Tensor (B, H, L,

Parameters ---------- x : Tensor, shape (batch, window_size, n_channels) Returns ------- reconstruct

Forward pass through the model. Parameters ---------- x : torch.Tensor Input data. Returns -----

Forward pass to compute random Fourier features. Parameters ---------- x : torch.Tensor Input data. Retu

Get benchmark results (ADBench, NLP-ADBench, TSB-AD).

Return a protected member _centerer.

Return a protected member _get_kernel.

Toy encoder: hash-based random projection.

Batch generation for samples, alternating between positive and negative. Adjusted for use with PyTorch, handling data in tensors.

Constant in the decision function. Decorator for scikit-learn One class SVM attributes.

List available PyOD detectors. Args: data_type: Filter by data type (tabular, text, image, etc.). status: Filter by status (shipp

Estimated robust location. Decorator for scikit-learn MinCovDet attributes.

Lower bound value on the log-likelihood of the best fit of EM. Decorator for scikit-learn Gaussian Mixture Model attributes.

The actual number of samples. Decorator for scikit-learn Isolation Forest attributes.

Per-feature empirical mean, estimated from the training set. Decorator for scikit-learn PCA attributes.

The mean of each mixture component. Decorator for scikit-learn Gaussian Mixture Model attributes.

The number of features seen during the fit. Decorator for scikit-learn Isolation Forest attributes.

Number of step used by the best fit of EM to reach the convergence. Decorator for scikit-learn Gaussian Mixture Model attributes.

The actual number of neighbors used for kneighbors queries. Decorator for scikit-learn LOF attributes.

The estimated noise covariance following the Probabilistic PCA model from Tipping and Bishop 1999. See "Pattern Recognition and Machin

Offset used to define the decision function from the raw scores. Decorator for scikit-learn Isolation Forest attributes.

Plan an anomaly detection pipeline. Returns a DetectionPlan with detector, params, reason, and evidence. Args: data_profile: JSON st

Estimated pseudo inverse matrix. (stored only if store_precision is True) Decorator for scikit-learn MinCovDet attributes.

The precision matrices for each component in the mixture. Decorator for scikit-learn Gaussian Mixture Model attributes.

The cholesky decomposition of the precision matrices of each mixture component. Decorator for scikit-learn Gaussian Mixture Model a

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector). Raises ------ NotImplementedError

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector).

Not supported (transductive detector).