| |
| |
| ``mx.symbol.sample_multinomial`` |
| ================================================================ |
| |
| Description |
| ---------------------- |
| |
| Concurrent sampling from multiple multinomial distributions. |
| |
| *data* is an *n* dimensional array whose last dimension has length *k*, where |
| *k* is the number of possible outcomes of each multinomial distribution. This |
| operator will draw *shape* samples from each distribution. If shape is empty |
| one sample will be drawn from each distribution. |
| |
| If *get_prob* is true, a second array containing log likelihood of the drawn |
| samples will also be returned. This is usually used for reinforcement learning |
| where you can provide reward as head gradient for this array to estimate |
| gradient. |
| |
| Note that the input distribution must be normalized, i.e. *data* must sum to |
| 1 along its last axis. |
| |
| |
| **Example**:: |
| |
| |
| probs = [[0, 0.1, 0.2, 0.3, 0.4], [0.4, 0.3, 0.2, 0.1, 0]] |
| |
| // Draw a single sample for each distribution |
| sample_multinomial(probs) = [3, 0] |
| |
| // Draw a vector containing two samples for each distribution |
| sample_multinomial(probs, shape=(2)) = [[4, 2], |
| [0, 0]] |
| |
| // requests log likelihood |
| sample_multinomial(probs, get_prob=True) = [2, 1], [0.2, 0.3] |
| |
| Usage |
| ---------- |
| |
| .. code:: r |
| |
| mx.symbol.sample_multinomial(...) |
| |
| Arguments |
| ------------------ |
| |
| +----------------------------------------+------------------------------------------------------------+ |
| | Argument | Description | |
| +========================================+============================================================+ |
| | ``data`` | NDArray-or-Symbol. | |
| | | | |
| | | Distribution probabilities. Must sum to one on the last | |
| | | axis. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``shape`` | Shape(tuple), optional, default=[]. | |
| | | | |
| | | Shape to be sampled from each random distribution. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``get.prob`` | boolean, optional, default=0. | |
| | | | |
| | | Whether to also return the log probability of sampled | |
| | | result. This is usually used for differentiating through | |
| | | stochastic variables, e.g. in reinforcement | |
| | | learning. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``dtype`` | {'float16', 'float32', 'float64', 'int32', | |
| | | 'uint8'},optional, | |
| | | default='int32'. | |
| | | | |
| | | DType of the output in case this can't be inferred. | |
| +----------------------------------------+------------------------------------------------------------+ |
| | ``name`` | string, optional. | |
| | | | |
| | | Name of the resulting symbol. | |
| +----------------------------------------+------------------------------------------------------------+ |
| |
| Value |
| ---------- |
| |
| ``out`` The result mx.symbol |
| |
| |
