You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 
 

97 lines
3.3 KiB

from models import Model
import scipy.signal
from scipy.fftpack import fft
from scipy.signal import argrelextrema
import utils
import numpy as np
import pickle
class StepModel(Model):
def __init__(self):
super()
self.segments = []
self.state = {
'confidence': 1.5,
'convolve_max': 570000
}
def fit(self, dataframe, segments):
self.segments = segments
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=5).mean()
segment_min_index = flat_segment.idxmin() - 5
labeled_drop = data[segment_min_index - 120 : segment_min_index + 120]
convolve = scipy.signal.fftconvolve(labeled_drop, labeled_drop)
convolve_list.append(max(convolve))
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'] = 570000
def predict(self, dataframe):
data = dataframe['value']
result = 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
def __predict(self, data):
window_size = 24
all_max_flatten_data = data.rolling(window=window_size).mean()
all_mins = argrelextrema(np.array(all_max_flatten_data), np.less)[0]
extrema_list = []
for i in utils.exponential_smoothing(data - self.state['confidence'], 0.03):
extrema_list.append(i)
segments = []
for i in all_mins:
if all_max_flatten_data[i] < extrema_list[i]:
segments.append(i - window_size)
return [(x - 1, x + 1) for x in self.__filter_prediction(segments, 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)
delete_list = []
pattern_data = all_max_flatten_data[segments[0] - 120 : segments[0] + 120]
for segment in segments:
convol_data = all_max_flatten_data[segment - 120 : segment + 120]
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