scipy.stats.qmc.QMCEngine#
- class scipy.stats.qmc.QMCEngine(d, *, seed=None)[source]#
A generic Quasi-Monte Carlo sampler class meant for subclassing.
QMCEngine is a base class to construct a specific Quasi-Monte Carlo sampler. It cannot be used directly as a sampler.
- Parameters
- dint
Dimension of the parameter space.
- seed{None, int,
numpy.random.Generator
}, optional If seed is None the
numpy.random.Generator
singleton is used. If seed is an int, a newGenerator
instance is used, seeded with seed. If seed is already aGenerator
instance then that instance is used.
Notes
By convention samples are distributed over the half-open interval
[0, 1)
. Instances of the class can access the attributes:d
for the dimension; andrng
for the random number generator (used for theseed
).Subclassing
When subclassing
QMCEngine
to create a new sampler,__init__
andrandom
must be redefined.__init__(d, seed=None)
: at least fix the dimension. If the sampler does not take advantage of aseed
(deterministic methods like Halton), this parameter can be omitted.random(n)
: drawn
from the engine and increase the counternum_generated
byn
.
Optionally, two other methods can be overwritten by subclasses:
reset
: Reset the engine to its original state.fast_forward
: If the sequence is deterministic (like Halton sequence), thenfast_forward(n)
is skipping then
first draw.
Examples
To create a random sampler based on
np.random.random
, we would do the following:>>> from scipy.stats import qmc >>> class RandomEngine(qmc.QMCEngine): ... def __init__(self, d, seed=None): ... super().__init__(d=d, seed=seed) ... ... ... def random(self, n=1): ... self.num_generated += n ... return self.rng.random((n, self.d)) ... ... ... def reset(self): ... super().__init__(d=self.d, seed=self.rng_seed) ... return self ... ... ... def fast_forward(self, n): ... self.random(n) ... return self
After subclassing
QMCEngine
to define the sampling strategy we want to use, we can create an instance to sample from.>>> engine = RandomEngine(2) >>> engine.random(5) array([[0.22733602, 0.31675834], # random [0.79736546, 0.67625467], [0.39110955, 0.33281393], [0.59830875, 0.18673419], [0.67275604, 0.94180287]])
We can also reset the state of the generator and resample again.
>>> _ = engine.reset() >>> engine.random(5) array([[0.22733602, 0.31675834], # random [0.79736546, 0.67625467], [0.39110955, 0.33281393], [0.59830875, 0.18673419], [0.67275604, 0.94180287]])
Methods
fast_forward
(n)Fast-forward the sequence by n positions.
integers
(l_bounds, *[, u_bounds, n, ...])Draw n integers from l_bounds (inclusive) to u_bounds (exclusive), or if endpoint=True, l_bounds (inclusive) to u_bounds (inclusive).
random
([n])Draw n in the half-open interval
[0, 1)
.reset
()Reset the engine to base state.