# MixtureSameFamily

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

```python
class tensorplay.distributions.MixtureSameFamily(mixture_distribution: Categorical, component_distribution: Distribution, validate_args: bool | None = None)
```

The MixtureSameFamily distribution implements a (batch of) mixture
distribution where all components are from different parameterizations of
the same distribution type. It is parameterized by a Categorical
“selecting distribution” (over k components) and a component
distribution, i.e., a Distribution with a rightmost batch shape
(equal to [k]) which indexes each (batch of) component.

Examples:

```
>>> # xdoctest: +SKIP("undefined vars")
>>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally
>>> # weighted normal distributions
>>> mix = D.Categorical(tensorplay.ones(5,))
>>> comp = D.Normal(tensorplay.randn(5,), tensorplay.rand(5,))
>>> gmm = MixtureSameFamily(mix, comp)

>>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally
>>> # weighted bivariate normal distributions
>>> mix = D.Categorical(tensorplay.ones(5,))
>>> comp = D.Independent(D.Normal(
...          tensorplay.randn(5,2), tensorplay.rand(5,2)), 1)
>>> gmm = MixtureSameFamily(mix, comp)

>>> # Construct a batch of 3 Gaussian Mixture Models in 2D each
>>> # consisting of 5 random weighted bivariate normal distributions
>>> mix = D.Categorical(tensorplay.rand(3,5))
>>> comp = D.Independent(D.Normal(
...         tensorplay.randn(3,5,2), tensorplay.rand(3,5,2)), 1)
>>> gmm = MixtureSameFamily(mix, comp)
```

Parameters:

- mixture_distribution – tensorplay.distributions.Categorical-like instance. Manages the probability of selecting components. The number of categories must match the rightmost batch dimension of the component_distribution. Must have either scalar batch_shape or batch_shape matching component_distribution.batch_shape[:-1]

- component_distribution – tensorplay.distributions.Distribution-like instance. Right-most batch dimension indexes component.

```python
property batch_shape: Size
```

Returns the shape over which parameters are batched.

```python
entropy() → Tensor
```

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

```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
property mode: Tensor
```

Returns the mode of the distribution.

```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.