def binomial(self, shape, counts, probs, dtype=dtypes.int32, name=None):
"""Outputs random values from a binomial distribution.
The generated values follow a binomial distribution with specified count and
probability of success parameters.
Example:
```python
counts = [10., 20.]
# Probability of success.
probs = [0.8]
rng = tf.random.experimental.Generator.from_seed(seed=234)
binomial_samples = rng.binomial(shape=[2], counts=counts, probs=probs)
counts = ... # Shape [3, 1, 2]
probs = ... # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
rng = tf.random.experimental.Generator.from_seed(seed=1717)
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = rng.binomial(shape=shape, counts=counts, probs=probs)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
counts: Tensor. The counts of the binomial distribution. Must be
broadcastable with `probs`, and broadcastable with the rightmost
dimensions of `shape`.
probs: Tensor. The probability of success for the
binomial distribution. Must be broadcastable with `counts` and
broadcastable with the rightmost dimensions of `shape`.
dtype: The type of the output. Default: tf.int32
name: A name for the operation (optional).
Returns:
samples: A Tensor of the specified shape filled with random binomial
values. For each i, each samples[i, ...] is an independent draw from
the binomial distribution on counts[i] trials with probability of
success probs[i].
"""
dtype = dtypes.as_dtype(dtype)
with ops.name_scope(name, "binomial", [shape, counts, probs]) as name:
counts = ops.convert_to_tensor(counts, name="counts")
probs = ops.convert_to_tensor(probs, name="probs")
shape_tensor = _shape_tensor(shape)
return gen_stateful_random_ops.stateful_random_binomial(
self.state.handle,
self.algorithm,
shape=shape_tensor,
counts=counts,
probs=probs,
dtype=dtype,
name=name)
def binomial(self, shape, counts, probs, dtype=dtypes.int32, name=None):
"""Outputs random values from a binomial distribution.
The generated values follow a binomial distribution with specified count and
probability of success parameters.
Example:
```python
counts = [10., 20.]
# Probability of success.
probs = [0.8, 0.9]
rng = tf.random.experimental.Generator(seed=234)
binomial_samples = rng.binomial(shape=[2], counts=counts, probs=probs)
```
Args:
shape: A 1-D integer Tensor or Python array. The shape of the output
tensor.
counts: A 0/1-D Tensor or Python value`. The counts of the binomial
distribution.
probs: A 0/1-D Tensor or Python value`. The probability of success for the
binomial distribution.
dtype: The type of the output. Default: tf.int32
name: A name for the operation (optional).
Returns:
A tensor of the specified shape filled with random binomial values.
"""
dtype = dtypes.as_dtype(dtype)
with ops.name_scope(name, "binomial", [shape, counts, probs]) as name:
counts = ops.convert_to_tensor(counts, name="counts")
probs = ops.convert_to_tensor(probs, name="probs")
shape_tensor = _shape_tensor(shape)
return gen_stateful_random_ops.stateful_random_binomial(
self.state.handle,
self.algorithm,
shape=shape_tensor,
counts=counts,
probs=probs,
dtype=dtype,
name=name)
