# Source code for orion.algo.random

```# -*- coding: utf-8 -*-
"""
Random sampler as optimization algorithm
========================================

Draw and deliver samples from prior defined in problem's domain.

"""
import numpy

from orion.algo.base import BaseAlgorithm

[docs]class Random(BaseAlgorithm):
"""An algorithm that samples randomly from the problem's space.

Parameters
----------
space: `orion.algo.space.Space`
Optimisation space with priors for each dimension.
seed: None, int or sequence of int
Seed for the random number generator used to sample new trials.
Default: ``None``

"""

def __init__(self, space, seed=None):
super(Random, self).__init__(space, seed=seed)

[docs]    def seed_rng(self, seed):
"""Seed the state of the random number generator.

:param seed: Integer seed for the random number generator.
"""
self.rng = numpy.random.RandomState(seed)

@property
def state_dict(self):
"""Return a state dict that can be used to reset the state of the algorithm."""
_state_dict = super(Random, self).state_dict
_state_dict["rng_state"] = self.rng.get_state()
return _state_dict

[docs]    def set_state(self, state_dict):
"""Reset the state of the algorithm based on the given state_dict

:param state_dict: Dictionary representing state of an algorithm
"""
super(Random, self).set_state(state_dict)
self.seed_rng(0)
self.rng.set_state(state_dict["rng_state"])

[docs]    def suggest(self, num):
"""Suggest a `num` of new sets of parameters.

Randomly draw samples from the search space and return them.

Parameters
----------
num: int
Number of trials to suggest.

Returns
-------
List of unique trials suggested.
"""
trials = []
while len(trials) < num and not self.is_done:
seed = tuple(self.rng.randint(0, 1000000, size=3))
new_trial = self.format_trial(self.space.sample(1, seed=seed))
if not self.has_suggested(new_trial):
self.register(new_trial)
trials.append(new_trial)

return trials
```