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from models import Model
import utils
import numpy as np
import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import math
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WINDOW_SIZE = 120
class JumpModel(Model):
def __init__(self):
super()
self.state = {
'confidence': 1.5,
'convolve_max': WINDOW_SIZE
}
async def fit(self, dataframe, segments):
self.segments = segments
#self.alpha_finder()
data = dataframe['value']
confidences = []
convolve_list = []
for segment in segments:
if segment['labeled']:
segment_data = data[segment['start'] : segment['finish'] + 1]
segment_min = min(segment_data)
segment_max = max(segment_data)
confidences.append(0.20 * (segment_max - segment_min))
flat_segment = segment_data.rolling(window=4).mean() #сглаживаем сегмент
kde_segment = flat_data.dropna().plot.kde() # distribution density
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ax_list = kde_segment.get_lines()[0].get_xydata() #take coordinates of kde
mids = argrelextrema(np.array(ax_list), np.less)[0]
maxs = argrelextrema(np.array(ax_list), np.greater)[0]
min_peak = maxs[0]
max_peak = maxs[1]
min_line = ax_list[min_peak, 0]
max_line = ax_list[max_peak, 0]
sigm_heidht = max_line - min_line
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pat_sigm = utils.logistic_sigmoid(-WINDOW_SIZE, WINDOW_SIZE, 1, sigm_heidht)
for i in range(0, len(pat_sigm)):
pat_sigm[i] = pat_sigm[i] + min_line
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cen_ind = utils.intersection_segment(flat_segment, mids[0]) #finds all interseprions with median
c = [] # choose the correct one interseption by convolve
jump_center = utils.find_jump_center(cen_ind)
segment_cent_index = jump_center - 4
labeled_drop = data[segment_cent_index - WINDOW_SIZE : segment_cent_index + WINDOW_SIZE]
labeled_min = min(labeled_drop)
for value in labeled_drop: # обрезаем
value = value - labeled_min
labeled_max = max(labeled_drop)
for value in labeled_drop: # нормируем
value = value / labeled_max
convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
convolve_list.append(max(convolve)) # сворачиваем паттерн
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# TODO: add convolve with alpha sigmoid
# TODO: add size of jump rize
if len(confidences) > 0:
self.state['confidence'] = min(confidences)
else:
self.state['confidence'] = 1.5
if len(convolve_list) > 0:
self.state['convolve_max'] = max(convolve_list)
else:
self.state['convolve_max'] = WINDOW_SIZE # макс метрика свертки равна отступу(WINDOW_SIZE), вау!
async def predict(self, dataframe):
data = dataframe['value']
result = await self.__predict(data)
result.sort()
if len(self.segments) > 0:
result = [segment for segment in result if not utils.is_intersect(segment, self.segments)]
return result
async def __predict(self, data):
window_size = 24
all_max_flatten_data = data.rolling(window=window_size).mean()
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all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
possible_jumps = utils.find_all_jumps(all_max_flatten_data, 50, self.state['confidence'])
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'''
for i in utils.exponential_smoothing(data + self.state['confidence'], 0.02):
extrema_list.append(i)
segments = []
for i in all_mins:
if all_max_flatten_data[i] > extrema_list[i]:
segments.append(i - window_size)
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'''
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return [(x - 1, x + 1) for x in self.__filter_prediction(possible_jumps, all_max_flatten_data)]
def __filter_prediction(self, segments, all_max_flatten_data):
delete_list = []
variance_error = int(0.004 * len(all_max_flatten_data))
if variance_error > 200:
variance_error = 200
for i in range(1, len(segments)):
if segments[i] < segments[i - 1] + variance_error:
delete_list.append(segments[i])
for item in delete_list:
segments.remove(item)
# изменить секонд делит лист, сделать для свертки с сигмоидой
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# !!!!!!!!
# написать фильтрацию паттернов-джампов! посмотерть каждый сегмент, обрезать его
# отнормировать, сравнить с выбранным патерном.
# !!!!!!!!
delete_list = []
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pattern_data = all_max_flatten_data[segments[0] - WINDOW_SIZE : segments[0] + WINDOW_SIZE]
for segment in segments:
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convol_data = all_max_flatten_data[segment - WINDOW_SIZE : segment + WINDOW_SIZE]
conv = scipy.signal.fftconvolve(pattern_data, convol_data)
if max(conv) > self.state['convolve_max'] * 1.1 or max(conv) < self.state['convolve_max'] * 0.9:
delete_list.append(segment)
for item in delete_list:
segments.remove(item)
return segments
def alpha_finder(self, data):
"""
поиск альфы для логистической сигмоиды
"""
pass