ximeg · April 20, 2017 07:20 · Apr 20, 2017
diff --git a/ThresholdingAlgo.py b/ThresholdingAlgo.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python
+# Implementation of algorithm from http://stackoverflow.com/a/22640362/6029703
+import numpy as np
+import pylab
+
+def thresholding_algo(y, lag, threshold, influence):
+    signals = np.zeros(len(y))
+    filteredY = np.array(y)
+    avgFilter = [0]*len(y)
+    stdFilter = [0]*len(y)
+    avgFilter[lag - 1] = np.mean(y[0:lag])
+    stdFilter[lag - 1] = np.std(y[0:lag])
+    for i in range(lag, len(y) - 1):
+        if abs(y[i] - avgFilter[i-1]) > threshold * stdFilter [i-1]:
+            if y[i] > avgFilter[i-1]:
+                signals[i] = 1
+            else:
+                signals[i] = -1
+
+            filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i-1]
+            avgFilter[i] = np.mean(filteredY[(i-lag):i])
+            stdFilter[i] = np.std(filteredY[(i-lag):i])
+        else:
+            signals[i] = 0
+            filteredY[i] = y[i]
+            avgFilter[i] = np.mean(filteredY[(i-lag):i])
+            stdFilter[i] = np.std(filteredY[(i-lag):i])
+
+    return dict(signals = np.asarray(signals),
+                avgFilter = np.asarray(avgFilter),
+                stdFilter = np.asarray(stdFilter))
+
+
+
+# Data
+y = np.array([1,1,1.1,1,0.9,1,1,1.1,1,0.9,1,1.1,1,1,0.9,1,1,1.1,1,1,1,1,1.1,0.9,1,1.1,1,1,0.9,
+       1,1.1,1,1,1.1,1,0.8,0.9,1,1.2,0.9,1,1,1.1,1.2,1,1.5,1,3,2,5,3,2,1,1,1,0.9,1,1,3,
+       2.6,4,3,3.2,2,1,1,0.8,4,4,2,2.5,1,1,1])
+
+# Settings: lag = 30, threshold = 5, influence = 0
+lag = 30
+threshold = 5
+influence = 0
+
+# Run algo with settings from above
+result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence)
+
+# Plot result
+pylab.subplot(211)
+pylab.plot(np.arange(1, len(y)+1), y)
+
+pylab.plot(np.arange(1, len(y)+1),
+           result["avgFilter"], color="cyan", lw=2)
+
+pylab.plot(np.arange(1, len(y)+1),
+           result["avgFilter"] + threshold * result["stdFilter"], color="green", lw=2)
+
+pylab.plot(np.arange(1, len(y)+1),
+           result["avgFilter"] - threshold * result["stdFilter"], color="green", lw=2)
+
+pylab.subplot(212)
+pylab.step(np.arange(1, len(y)+1), result["signals"], color="red", lw=2)
+pylab.ylim(-1.5, 1.5)
+
+
+
+