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