def split(rng: np.random.RandomState, n: int, k: int, eps: float) -> List[int]:
probs = [1 / (n + 1) - eps for _ in range(n + 2)]
divisors: List[int] = []
for i, m in enumerate(rng.multinomial(k - 1, probs)):
for _ in range(m):
divisors.append(i)
last = 0
ks = []
for divisor in divisors:
ks.append(divisor - last)
last = divisor
ks.append(n - last)
return ks
def _sample(self, rng: np.random.RandomState, n_samples: int) -> np.ndarray:
"""
The method to sample from the parzen estimator.
This method needs to be wrapped by a child class.
Args:
rng (np.random.RandomState): The random seed
n_samples (int): The number of samples
Returns:
samples (np.ndarray):
Samples from the parzen estimator.
The shape is (n_samples, ).
"""
samples = [
self.basis[active].sample(rng)
for active in np.argmax(
rng.multinomial(n=1, pvals=self.weights, size=n_samples),
axis=-1
)
]
return np.array(samples, dtype=self.dtype)
