Uniform¶
- class paddle.distribution. Uniform ( low, high, name=None ) [source]
-
Uniform distribution with low and high parameters.
Mathematical Details
The probability density function (pdf) is
\[pdf(x; a, b) = \frac{1}{Z}, \ a <=x <b\]\[Z = b - a\]In the above equation:
\(low = a\),
\(high = b\),
\(Z\): is the normalizing constant.
The parameters low and high must be shaped in a way that supports Boardcasting (e.g., high - low is a valid operation).
Note
If you want know more about broadcasting, please refer to Introduction to Tensor .
- Parameters
-
low (int|float|list|tuple|numpy.ndarray|Tensor) – The lower boundary of uniform distribution.The data type is float32 and float64.
high (int|float|list|tuple|numpy.ndarray|Tensor) – The higher boundary of uniform distribution.The data type is float32 and float64.
name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.
Examples
import paddle from paddle.distribution import Uniform # Without broadcasting, a single uniform distribution [3, 4]: u1 = Uniform(low=3.0, high=4.0) # 2 distributions [1, 3], [2, 4] u2 = Uniform(low=[1.0, 2.0], high=[3.0, 4.0]) # 4 distributions u3 = Uniform(low=[[1.0, 2.0], [3.0, 4.0]], high=[[1.5, 2.5], [3.5, 4.5]]) # With broadcasting: u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0]) # Complete example value_tensor = paddle.to_tensor([0.8], dtype="float32") uniform = Uniform([0.], [2.]) sample = uniform.sample([2]) # a random tensor created by uniform distribution with shape: [2, 1] entropy = uniform.entropy() # [0.6931472] with shape: [1] lp = uniform.log_prob(value_tensor) # [-0.6931472] with shape: [1] p = uniform.probs(value_tensor) # [0.5] with shape: [1]
- property batch_shape
-
Returns batch shape of distribution
- Returns
-
batch shape
- Return type
-
Sequence[int]
- property event_shape
-
Returns event shape of distribution
- Returns
-
event shape
- Return type
-
Sequence[int]
-
kl_divergence
(
other
)
[source]
kl_divergence¶
-
The KL-divergence between self distributions and other.
- property mean
-
Mean of distribution
-
prob
(
value
)
prob¶
-
Probability density/mass function evaluated at value.
- Parameters
-
value (Tensor) – value which will be evaluated
-
rsample
(
shape=()
)
rsample¶
-
reparameterized sample
- property variance
-
Variance of distribution
-
sample
(
shape,
seed=0
)
sample¶
-
Generate samples of the specified shape.
- Parameters
-
shape (list) – 1D int32. Shape of the generated samples.
seed (int) – Python integer number.
- Returns
-
Tensor, A tensor with prepended dimensions shape. The data type is float32.
-
log_prob
(
value
)
log_prob¶
-
Log probability density/mass function.
- Parameters
-
value (Tensor) – The input tensor.
- Returns
-
Tensor, log probability.The data type is same with value.
-
probs
(
value
)
probs¶
-
Probability density/mass function.
- Parameters
-
value (Tensor) – The input tensor.
- Returns
-
Tensor, probability. The data type is same with value.
-
entropy
(
)
entropy¶
-
Shannon entropy in nats.
The entropy is
\[\begin{split}entropy(low, high) = \\log (high - low)\end{split}\]- Returns
-
Tensor, Shannon entropy of uniform distribution.The data type is float32.