LostTech.TensorFlow : API Documentation

Type Uniform

Namespace tensorflow.distributions

Parent Distribution

Interfaces IUniform

Uniform distribution with `low` and `high` parameters.

#### Mathematical Details

The probability density function (pdf) is,

```none pdf(x; a, b) = I[a <= x < b] / Z Z = b - a ```


- `low = a`, - `high = b`, - `Z` is the normalizing constant, and - `I[predicate]` is the [indicator function]( https://en.wikipedia.org/wiki/Indicator_function) for `predicate`.

The parameters `low` and `high` must be shaped in a way that supports broadcasting (e.g., `high - low` is a valid operation).

#### Examples

Show Example
# Without broadcasting:
            u1 = Uniform(low=3.0, high=4.0)  # a single uniform distribution [3, 4]
            u2 = Uniform(low=[1.0, 2.0],
                         high=[3.0, 4.0])  # 2 distributions [1, 3], [2, 4]
            u3 = Uniform(low=[[1.0, 2.0],
                              [3.0, 4.0]],
                         high=[[1.5, 2.5],
                               [3.5, 4.5]])  # 4 distributions 



Public instance methods

double range(string name)

`high - low`.

object range_dyn(ImplicitContainer<T> name)

`high - low`.

Public properties

object allow_nan_stats get;

object allow_nan_stats_dyn get;

TensorShape batch_shape get;

object batch_shape_dyn get;

object dtype get;

object dtype_dyn get;

TensorShape event_shape get;

object event_shape_dyn get;

object high get;

Upper boundary of the output interval.

object high_dyn get;

Upper boundary of the output interval.

object low get;

Lower boundary of the output interval.

object low_dyn get;

Lower boundary of the output interval.

string name get;

object name_dyn get;

IDictionary<object, object> parameters get;

object parameters_dyn get;

object PythonObject get;

object reparameterization_type get;

object reparameterization_type_dyn get;

object validate_args get;

object validate_args_dyn get;