random

zfit.z.random.counts_multinomial(total_count: Union[int, tensorflow.python.framework.ops.Tensor], probs: Iterable[Union[float, tensorflow.python.framework.ops.Tensor]] = None, logits: Iterable[Union[float, tensorflow.python.framework.ops.Tensor]] = None, dtype=tf.int32) → tensorflow.python.framework.ops.Tensor[source]

Get the number of counts for different classes with given probs/logits.

Parameters
  • total_count (int) – The total number of draws.

  • probs – Length k (number of classes) object where the k-1th entry contains the probability to get a single draw from the class k. Have to be from [0, 1] and sum up to 1.

  • logits – Same as probs but from [-inf, inf] (will be transformet to [0, 1])

Returns

py:class.`tf.Tensor`: shape (k,) tensor containing the number of draws.