# Geometric

Source: https://www.tensorplay.cn/docs/generated/tensorplay.distributions.Geometric.html

```python
class tensorplay.distributions.Geometric(probs: Tensor | bool | int | float | None = None, logits: Tensor | bool | int | float | None = None, validate_args: bool | None = None)
```

Creates a Geometric distribution parameterized by probs,
where probs is the probability of success of Bernoulli trials.

$$P(X=k) = (1-p)^{k} p, k = 0, 1, ...$$

> **Note**
>
> tensorplay.distributions.geometric.Geometric() $(k+1)$-th trial is the first success hence draws samples in $\{0, 1, \ldots\}$, whereas tensorplay.Tensor.geometric_() k-th trial is the first success hence draws samples in $\{1, 2, \ldots\}$.

Example:

```
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Geometric(tensorplay.tensor([0.3]))
>>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
tensor([ 2.])
```

Parameters:

- probs (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the probability of sampling 1. Must be in range (0, 1]

- logits (Number, [Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the log-odds of sampling 1.

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```python
cdf(value: Tensor) → Tensor
```

Returns the cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
enumerate_support(expand: bool = True) → Tensor
```

Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be (cardinality,) + batch_shape + event_shape
(where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step
[[0, 0], [1, 1], …]. With expand=False, enumeration happens
along dim 0, but with the remaining batch dimensions being
singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use
itertools.product(m.enumerate_support()).

Parameters:

expand ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – whether to expand the support over the
batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

```python
property event_shape: Size
```

Returns the shape of a single sample (without batching).

```python
icdf(value: Tensor) → Tensor
```

Returns the inverse cumulative density/mass function evaluated at
value.

Parameters:

value ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor))

```python
perplexity() → Tensor
```

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```python
rsample(sample_shape: Size | Sequence[int] = ()) → Tensor
```

Generates a sample_shape shaped reparameterized sample or sample_shape
shaped batch of reparameterized samples if the distribution parameters
are batched.

```python
sample_n(n: int) → Tensor
```

Generates n samples or n batches of samples if the distribution
parameters are batched.

```python
static set_default_validate_args(value: bool) → None
```

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation
is on by default, but is disabled if Python is run in optimized mode
(via python -O). Validation may be expensive, so you may want to
disable it once a model is working.

Parameters:

value ([bool](https://docs.python.org/3/builtins/functions.html#bool)) – Whether to enable validation.

```python
property stddev: Tensor
```

Returns the standard deviation of the distribution.