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analysis.generative_functions

logger_config​

logger​

random_generative_samples_motif​

def random_generative_samples_motif(cfg: dict, model: torch.nn.Module,
                                    latent_vector: np.ndarray,
                                    labels: np.ndarray,
                                    n_clusters: int) -> plt.Figure

Generate random samples for motifs.

Parameters

  • cfg (dict): Configuration dictionary.
  • model (torch.nn.Module): PyTorch model.
  • latent_vector (np.ndarray): Latent vectors.
  • labels (np.ndarray): Labels.
  • n_clusters (int): Number of clusters.

Returns

  • plt.Figure: Figure of generated samples.

random_generative_samples​

def random_generative_samples(cfg: dict, model: torch.nn.Module,
                              latent_vector: np.ndarray) -> plt.Figure

Generate random generative samples.

Parameters

  • cfg (dict): Configuration dictionary.
  • model (torch.nn.Module): PyTorch model.
  • latent_vector (np.ndarray): Latent vectors.

Returns

  • plt.Figure: Figure of generated samples.

random_reconstruction_samples​

def random_reconstruction_samples(cfg: dict, model: torch.nn.Module,
                                  latent_vector: np.ndarray) -> plt.Figure

Generate random reconstruction samples.

Parameters

  • cfg (dict): Configuration dictionary.
  • model (torch.nn.Module): PyTorch model to use.
  • latent_vector (np.ndarray): Latent vectors.

Returns

  • plt.Figure: Figure of reconstructed samples.

visualize_cluster_center​

def visualize_cluster_center(cfg: dict, model: torch.nn.Module,
                             cluster_center: np.ndarray) -> plt.Figure

Visualize cluster centers.

Parameters

  • cfg (dict): Configuration dictionary.
  • model (torch.nn.Module): PyTorch model.
  • cluster_center (np.ndarray): Cluster centers.

Returns

  • plt.Figure: Figure of cluster centers.

generative_model​

@save_state(model=GenerativeModelFunctionSchema)
def generative_model(config: dict,
                     segmentation_algorithm: SegmentationAlgorithms,
                     mode: str = "sampling",
                     save_logs: bool = False) -> plt.Figure

Generative model.

Parameters

  • config (dict): Configuration dictionary.
  • mode (str, optional): Mode for generating samples. Defaults to "sampling".

Returns

  • plt.Figure: Plots of generated samples for each segmentation algorithm.