class KNNKernelDensity:
SQRT2PI = np.sqrt(2 * np.pi)
def __init__(self, X: np.ndarray[Any, Any], online: Optional[bool] = False):
self.X = X
self.index = Index(X)
if not online:
self.index.add_points(len(X))
def run(self, ops: Any) -> Any:
return self.index.run(ops)
def run_ids(self, ids: Iterable[int]) -> Any:
return self.index.run_ids(ids)
def score_samples(
self, X: np.ndarray[Any, Any], k: int = 10, bandwidth: float = 0.2
) -> float:
_, dists = self.index.knn_search_points(X, k=k)
scores = self._gaussian_score(dists, bandwidth) / k
return scores
def _gaussian_score(self, dists: float, bandwidth: float) -> float:
logg = -0.5 * (dists / bandwidth) ** 2
g = np.exp(logg) / bandwidth / self.SQRT2PI
return g.sum(axis=1) # type: ignore
class KNNKernelDensity():
SQRT2PI = np.sqrt(2 * np.pi)
def __init__(self, X, online=False):
self.X = X
self.index = Index(X)
if not online: # if offline
self.index.add_points(len(X))
def run(self, ops):
return self.index.run(ops)
def run_ids(self, ids):
return self.index.run_ids(ids)
def score_samples(self, X, k=10, bandwidth=0.2):
_, dists = self.index.knn_search_points(X, k=k)
scores = self._gaussian_score(dists, bandwidth) / k
return scores
def _gaussian_score(self, dists, bandwidth):
logg = -0.5 * (dists / bandwidth) ** 2
g = np.exp(logg) / bandwidth / self.SQRT2PI
return g.sum(axis=1)
