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64 lines
2.2 KiB
64 lines
2.2 KiB
import numpy as np
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from typing import List, Optional, Tuple, Any
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from cereal import log
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class NPQueue:
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def __init__(self, maxlen: int, rowsize: int) -> None:
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self.maxlen = maxlen
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self.arr = np.empty((0, rowsize))
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def __len__(self) -> int:
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return len(self.arr)
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def append(self, pt: List[float]) -> None:
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if len(self.arr) < self.maxlen:
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self.arr = np.append(self.arr, [pt], axis=0)
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else:
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self.arr[:-1] = self.arr[1:]
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self.arr[-1] = pt
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class PointBuckets:
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def __init__(self, x_bounds: List[Tuple[float, float]], min_points: List[float], min_points_total: int, points_per_bucket: int, rowsize: int) -> None:
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self.x_bounds = x_bounds
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self.buckets = {bounds: NPQueue(maxlen=points_per_bucket, rowsize=rowsize) for bounds in x_bounds}
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self.buckets_min_points = dict(zip(x_bounds, min_points, strict=True))
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self.min_points_total = min_points_total
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def __len__(self) -> int:
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return sum([len(v) for v in self.buckets.values()])
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def is_valid(self) -> bool:
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individual_buckets_valid = all(len(v) >= min_pts for v, min_pts in zip(self.buckets.values(), self.buckets_min_points.values(), strict=True))
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total_points_valid = self.__len__() >= self.min_points_total
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return individual_buckets_valid and total_points_valid
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def is_calculable(self) -> bool:
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return all(len(v) > 0 for v in self.buckets.values())
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def add_point(self, x: float, y: float, bucket_val: float) -> None:
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raise NotImplementedError
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def get_points(self, num_points: Optional[int] = None) -> Any:
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points = np.vstack([x.arr for x in self.buckets.values()])
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if num_points is None:
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return points
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return points[np.random.choice(np.arange(len(points)), min(len(points), num_points), replace=False)]
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def load_points(self, points: List[List[float]]) -> None:
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for point in points:
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self.add_point(*point)
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class ParameterEstimator:
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""" Base class for parameter estimators """
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def reset(self) -> None:
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raise NotImplementedError
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def handle_log(self, t: int, which: str, msg: log.Event) -> None:
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raise NotImplementedError
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def get_msg(self, valid: bool, with_points: bool) -> log.Event:
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raise NotImplementedError
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