Exponential
- class paddle.distribution. Exponential ( rate: float | Tensor ) [source]
-
Exponential distribution parameterized by
rate.The probability density function (pdf) is
\[f(x; \theta) = \theta e^{- \theta x }, (x \ge 0) $$\]In the above equation:
\(rate = \theta\): is the rate parameter.
- Parameters
-
rate (float|Tensor) – Rate parameter. The value of rate must be positive.
Example
>>> import paddle >>> expon = paddle.distribution.Exponential(paddle.to_tensor([0.5])) >>> print(expon.mean) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [2.]) >>> print(expon.variance) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [4.]) >>> print(expon.entropy()) Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True, [1.69314718])
- property mean : Tensor
-
Mean of exponential distribution.
- Returns
-
mean value.
- Return type
-
Tensor
- property variance : Tensor
-
Variance of exponential distribution.
- Returns
-
variance value.
- Return type
-
Tensor
-
sample
(
shape: Sequence[int] = []
)
Tensor
sample¶
-
Generate samples of the specified shape.
- Parameters
-
shape (Sequence[int], optional) – Shape of the generated samples.
- Returns
-
Tensor, A tensor with prepended dimensions shape. The data type is float32.
-
rsample
(
shape: Sequence[int] = []
)
Tensor
rsample¶
-
Generate reparameterized samples of the specified shape.
- Parameters
-
shape (Sequence[int], optional) – Shape of the generated samples.
- Returns
-
A tensor with prepended dimensions shape. The data type is float32.
- Return type
-
Tensor
-
prob
(
value: float | Tensor
)
Tensor
prob¶
-
Probability density function evaluated at value.
\[{ f(x; \theta) = \theta e^{- \theta x}, (x \ge 0 ) }\]- Parameters
-
value (float|Tensor) – Value to be evaluated.
- Returns
-
Probability.
- Return type
-
Tensor
-
log_prob
(
value: float | Tensor
)
Tensor
log_prob¶
-
Log probability density function evaluated at value.
- Parameters
-
value (float|Tensor) – Value to be evaluated
- Returns
-
Log probability.
- Return type
-
Tensor
-
entropy
(
)
Tensor
entropy¶
-
Entropy of exponential distribution.
- Returns
-
Entropy.
- Return type
-
Tensor
-
cdf
(
value: float | Tensor
)
Tensor
cdf¶
-
Cumulative distribution function(CDF) evaluated at value.
\[{ cdf(x; \theta) = 1 - e^{- \theta x }, (x \ge 0) }\]- Parameters
-
value (float|Tensor) – Value to be evaluated.
- Returns
-
CDF evaluated at value.
- Return type
-
Tensor
- property batch_shape : Sequence[int]
-
Returns batch shape of distribution
- Returns
-
batch shape
- Return type
-
Sequence[int]
- property event_shape : Sequence[int]
-
Returns event shape of distribution
- Returns
-
event shape
- Return type
-
Sequence[int]
-
icdf
(
value: float | Tensor
)
Tensor
icdf¶
-
Inverse cumulative distribution function(CDF) evaluated at value.
\[{ icdf(x; \theta) = -\frac{ 1 }{ \theta } ln(1 + x), (x \ge 0) }\]- Parameters
-
value (float|Tensor) – Value to be evaluated.
- Returns
-
CDF evaluated at value.
- Return type
-
Tensor
-
probs
(
value: Tensor
)
Tensor
probs¶
-
Probability density/mass function.
Note
This method will be deprecated in the future, please use prob instead.