60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
# Copyright 2019 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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from typing import Tuple
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from precise_lite.functions import asigmoid, sigmoid, pdf
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class ThresholdDecoder:
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"""
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Decode raw network output into a relatively linear threshold using
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This works by estimating the logit normal distribution of network
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activations using a series of averages and standard deviations to
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calculate a cumulative probability distribution
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"""
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def __init__(self, mu_stds: Tuple[Tuple[float, float]], center=0.5, resolution=200, min_z=-4, max_z=4):
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self.min_out = int(min(mu + min_z * std for mu, std in mu_stds))
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self.max_out = int(max(mu + max_z * std for mu, std in mu_stds))
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self.out_range = self.max_out - self.min_out
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self.cd = np.cumsum(self._calc_pd(mu_stds, resolution))
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self.center = center
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def decode(self, raw_output: float) -> float:
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if raw_output == 1.0 or raw_output == 0.0:
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return raw_output
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if self.out_range == 0:
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cp = int(raw_output > self.min_out)
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else:
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ratio = (asigmoid(raw_output) - self.min_out) / self.out_range
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ratio = min(max(ratio, 0.0), 1.0)
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cp = self.cd[int(ratio * (len(self.cd) - 1) + 0.5)]
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if cp < self.center:
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return 0.5 * cp / self.center
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else:
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return 0.5 + 0.5 * (cp - self.center) / (1 - self.center)
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def encode(self, threshold: float) -> float:
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threshold = 0.5 * threshold / self.center
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if threshold < 0.5:
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cp = threshold * self.center * 2
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else:
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cp = (threshold - 0.5) * 2 * (1 - self.center) + self.center
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ratio = np.searchsorted(self.cd, cp) / len(self.cd)
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return sigmoid(self.min_out + self.out_range * ratio)
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def _calc_pd(self, mu_stds, resolution):
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points = np.linspace(self.min_out, self.max_out, resolution * self.out_range)
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return np.sum([pdf(points, mu, std) for mu, std in mu_stds], axis=0) / (resolution * len(mu_stds))
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