add detector file

analytics/.gitignore

@@ -2,3 +2,4 @@ build/
 dist/
 *.spec
 __pycache__/
+test/

analytics/jump_detector.py

@@ -0,0 +1,138 @@
+import numpy as np
+import pickle
+import scipy.signal
+from scipy.fftpack import fft
+from scipy.signal import argrelextrema
+import math
+
+def is_intersect(target_segment, segments):
+    for segment in segments:
+        start = max(segment['start'], target_segment[0])
+        finish = min(segment['finish'], target_segment[1])
+        if start <= finish:
+            return True
+    return False
+
+def exponential_smoothing(series, alpha):
+    result = [series[0]]
+    for n in range(1, len(series)):
+        result.append(alpha * series[n] + (1 - alpha) * result[n-1])
+    return result
+
+class Jumpdetector:
+
+    def __init__(self, pattern):
+        self.pattern = pattern
+        self.segments = []
+        self.confidence = 1.5
+        self.convolve_max = 120
+    
+    def fit(self, dataframe, 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() #сглаживаем сегмент
+                    # в идеале нужно посмотреть гистограмму сегмента и выбрать среднее значение,
+                    # далее от него брать + -120 
+                    segment_summ = 0
+                    for val in flat_segment:
+                        segment_summ += val
+                    segment_mid = segment_summ /  len(flat_segment) #посчитать нормально среднее значение/медиану
+                    for ind in range(1, len(flat_segment) - 1):
+                        if flat_segment[ind + 1] > segment_mid and flat_segment[ind - 1] < segment_mid:
+                            flat_mid_index = ind   # найти пересечение средней и графика, получить его индекс
+                    segment_mid_index = flat_mid_index - 5
+                    labeled_drop = data[segment_mid_index - 120 : segment_mid_index + 120]
+                    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
+                    # плюс надо впихнуть сюда логистическую сигмоиду и поиск альфы
+
+            if len(confidences) > 0:
+                self.confidence = min(confidences)
+            else:
+                self.confidence = 1.5
+
+            if len(convolve_list) > 0:
+                self.convolve_max = max(convolve_list)
+            else:
+                self.convolve_max = 120 # макс метрика свертки равна отступу(120), вау!
+        
+    def logistic_sigmoid(x1, x2, alpha, height):
+        distribution = []
+        for i in range(x, y):
+            F = 1 * height / (1 + math.exp(-i * alpha))
+            distribution.append(F)
+        return distribution
+    
+    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 is_intersect(segment, self.segments)]
+        return result
+
+    def __predict(self, data):
+            window_size = 24
+            all_max_flatten_data = data.rolling(window=window_size).mean()
+            extrema_list = []
+            # добавить все пересечения экспоненты со сглаженным графиком
+            # 
+            for i in exponential_smoothing(data + self.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)
+
+            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)
+
+        # изменить секонд делит лист, сделать для свертки с сигмоидой
+        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.convolve_max * 1.1 or max(conv) < self.convolve_max * 0.9:
+                delete_list.append(segment)
+        for item in delete_list:
+            segments.remove(item)
+
+        return segments
+
+    def save(self, model_filename):
+        with open(model_filename, 'wb') as file:
+            pickle.dump((self.confidence, self.convolve_max), file)
+
+    def load(self, model_filename):
+        try:
+            with open(model_filename, 'rb') as file:
+                (self.confidence, self.convolve_max) = pickle.load(file)
+        except:
+            pass