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76 lines
2.3 KiB
76 lines
2.3 KiB
import numpy as np |
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import pickle |
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from scipy.signal import argrelextrema |
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def is_intersect(target_segment, segments): |
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for segment in segments: |
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start = max(segment['start'], target_segment[0]) |
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finish = min(segment['finish'], target_segment[1]) |
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if start <= finish: |
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return True |
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return False |
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def exponential_smoothing(series, alpha): |
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result = [series[0]] |
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for n in range(1, len(series)): |
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result.append(alpha * series[n] + (1 - alpha) * result[n-1]) |
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return result |
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class StepDetector: |
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def __init__(self, pattern): |
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self.pattern = pattern |
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self.segments = [] |
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self.confidence = 1.5 |
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def fit(self, dataframe, segments): |
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data = dataframe['value'] |
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confidences = [] |
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for segment in segments: |
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if segment['labeled']: |
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segment_data = data[segment['start'] : segment['finish'] + 1] |
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segment_min = min(segment_data) |
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segment_max = max(segment_data) |
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confidences.append(0.24 * (segment_max - segment_min)) |
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if len(confidences) > 0: |
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self.confidence = min(confidences) |
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else: |
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self.confidence = 1.5 |
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def predict(self, dataframe): |
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data = dataframe['value'] |
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result = self.__predict(data) |
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result.sort() |
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if len(self.segments) > 0: |
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result = [segment for segment in result if not is_intersect(segment, self.segments)] |
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return result |
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def __predict(self, data): |
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all_normal_flatten_data = data.rolling(window=10).mean() |
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all_max_flatten_data = data.rolling(window=24).mean() |
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all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0] |
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extrema_list = [] |
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for i in exponential_smoothing(data - self.confidence, 0.03): |
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extrema_list.append(i) |
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segments = [] |
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for i in all_mins: |
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if all_max_flatten_data[i] < extrema_list[i]: |
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segments.append(i - 20) |
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return [(x - 1, x + 1) for x in segments] |
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def save(self, model_filename): |
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with open(model_filename, 'wb') as file: |
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pickle.dump((self.confidence), file) |
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def load(self, model_filename): |
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try: |
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with open(model_filename, 'rb') as file: |
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self.confidence = pickle.load(file) |
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except: |
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pass
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