cellflow.solvers.GENOT.predict

GENOT.predict(x, condition=None, rng=None, rng_genot=None, **kwargs)[source]

Generate the push-forward of x under condition condition.

This function solves the ODE learnt with the ConditionalVelocityField.

Parameters:

  • x (ndarray[tuple[Any, ...], dtype[float64]]) – Input data of shape [batch_size, …].

  • condition (dict[str, ndarray[tuple[Any, ...], dtype[float64]]] | None) – Condition of the input data of shape [batch_size, …].

  • rng (ndarray[tuple[Any, ...], dtype[float64]] | None) – Random number generator to sample from the latent distribution, only used if condition_mode='stochastic'. If None, the mean embedding is used.

  • rng_genot (ndarray[tuple[Any, ...], dtype[float64]] | None) – Random generate used to sample from the latent distribution in cell space.

  • kwargs (Any) – Keyword arguments for diffrax.diffeqsolve().

Return type:

ndarray[tuple[Any, ...], dtype[float64]] | tuple[ndarray[tuple[Any, ...], dtype[float64]], Solution]

Returns:

: The push-forward distribution of x under condition condition.