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8a8a0c7dec
* Use cosine distance metric for vec tables * Only apply normalization to multi modal searches * Catch possible edge case in stddev calc * Use sigmoid function for normalization for multi modal searches only * Ensure we get model state on initial page load * Only save stats for multi modal searches and only use cosine similarity for image -> image search
55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
"""Z-score normalization for search distance."""
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import math
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class ZScoreNormalization:
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def __init__(self, scale_factor: float = 1.0, bias: float = 0.0):
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"""Initialize with optional scaling and bias adjustments."""
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"""scale_factor adjusts the magnitude of each score"""
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"""bias will artificially shift the entire distribution upwards"""
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self.n = 0
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self.mean = 0
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self.m2 = 0
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self.scale_factor = scale_factor
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self.bias = bias
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@property
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def variance(self):
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return self.m2 / (self.n - 1) if self.n > 1 else 0.0
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@property
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def stddev(self):
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return math.sqrt(self.variance) if self.variance > 0 else 0.0
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def normalize(self, distances: list[float], save_stats: bool):
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if save_stats:
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self._update(distances)
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if self.stddev == 0:
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return distances
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return [
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(x - self.mean) / self.stddev * self.scale_factor + self.bias
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for x in distances
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]
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def _update(self, distances: list[float]):
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for x in distances:
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self.n += 1
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delta = x - self.mean
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self.mean += delta / self.n
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delta2 = x - self.mean
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self.m2 += delta * delta2
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def to_dict(self):
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return {
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"n": self.n,
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"mean": self.mean,
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"m2": self.m2,
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}
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def from_dict(self, data: dict):
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self.n = data["n"]
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self.mean = data["mean"]
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self.m2 = data["m2"]
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return self
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