# Source code for mars.tensor.random.geometric

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from ... import opcodes as OperandDef
from ...serialization.serializables import AnyField
from ..utils import gen_random_seeds
from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
class TensorGeometric(TensorDistribution, TensorRandomOperandMixin):
_input_fields_ = ["p"]
_op_type_ = OperandDef.RAND_GEOMETRIC
_fields_ = "p", "size"
p = AnyField("p")
_func_name = "geometric"
def __call__(self, p, chunk_size=None):
return self.new_tensor([p], None, raw_chunk_size=chunk_size)
[docs]def geometric(random_state, p, size=None, chunk_size=None, gpu=None, dtype=None):
"""
Draw samples from the geometric distribution.
Bernoulli trials are experiments with one of two outcomes:
success or failure (an example of such an experiment is flipping
a coin). The geometric distribution models the number of trials
that must be run in order to achieve success. It is therefore
supported on the positive integers, ``k = 1, 2, ...``.
The probability mass function of the geometric distribution is
.. math:: f(k) = (1 - p)^{k - 1} p
where `p` is the probability of success of an individual trial.
Parameters
----------
p : float or array_like of floats
The probability of success of an individual trial.
size : int or tuple of ints, optional
Output shape. If the given shape is, e.g., ``(m, n, k)``, then
``m * n * k`` samples are drawn. If size is ``None`` (default),
a single value is returned if ``p`` is a scalar. Otherwise,
``mt.array(p).size`` samples are drawn.
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
dtype : data-type, optional
Data-type of the returned tensor.
Returns
-------
out : Tensor or scalar
Drawn samples from the parameterized geometric distribution.
Examples
--------
Draw ten thousand values from the geometric distribution,
with the probability of an individual success equal to 0.35:
>>> import mars.tensor as mt
>>> z = mt.random.geometric(p=0.35, size=10000)
How many trials succeeded after a single run?
>>> ((z == 1).sum() / 10000.).execute()
0.34889999999999999 #random
"""
if dtype is None:
dtype = np.random.RandomState().geometric(handle_array(p), size=(0,)).dtype
size = random_state._handle_size(size)
seed = gen_random_seeds(1, random_state.to_numpy())[0]
op = TensorGeometric(seed=seed, size=size, gpu=gpu, dtype=dtype)
return op(p, chunk_size=chunk_size)
```