Finished presentation

src/activity_recognition_copy.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 25 07:38:10 2020
+
+@author: frida
+"""
+
+import numpy as np
+import sys
+from get_data import get_data
+import matplotlib.pyplot as plt
+
+#%%
+
+def main(person):
+    freq = 100 # Assume data was collected at 100 Hz
+    still = 2
+    walk = 20
+
+    # Get mean of accelerometer x,y,z data
+    acc, ground_truth = get_data(person, 'ACC')
+    confMat = np.zeros((3,3))
+    # Check minimum and maximum value over time interval. Determine activity during time interval based on difference between these
+    time_interval = 2 # s
+    for i in np.arange(0, len(acc)//freq - time_interval, time_interval):
+        acc_one_sec = acc[i * freq:(i + time_interval) * freq]
+        xMin = min(acc_one_sec)
+        xMax = max(acc_one_sec)
+        
+        classes = ['Still','Walking','Running']
+        true_idx = classes.index(ground_truth[int((2 * i + time_interval) * freq / 2.0)])
+
+        if xMax - xMin < still: # Some limit for still
+            print("At time: {}-{}, the person is - Still ({}, Diff: {:0.2f})".format(i, i + time_interval, ground_truth[int((2 * i + time_interval) * freq / 2.0)], xMax - xMin))
+            pred_idx = 0
+        elif xMax - xMin < walk: # Some limit for walk
+            print("At time: {}-{}, the person is - Walking ({}, Diff: {:0.2f})".format(i, i + time_interval, ground_truth[int((2 * i + time_interval) * freq / 2.0)], xMax - xMin))
+            pred_idx = 1
+        else:
+            print("At time: {}-{}, the person is - Running ({}, Diff: {:0.2f})".format(i, i + time_interval, ground_truth[int((2 * i + time_interval) * freq / 2.0)], xMax - xMin))
+            pred_idx = 2
+        confMat[true_idx,pred_idx] += 1
+    
+    fig, ax = plt.subplots()
+    im = ax.imshow(confMat,cmap='rainbow')
+    
+    # We want to show all ticks...
+    #ax.set_xticks(np.arange(len(classes)))
+    #ax.set_yticks(np.arange(len(classes)))
+    # ... and label them with the respective list entries
+    classes_tick = [' ','Still',' ','Walking',' ','Running',' ']
+    ax.set_xticklabels(classes_tick)
+    ax.set_yticklabels(classes_tick)
+    
+    # Rotate the tick labels and set their alignment.
+    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
+             rotation_mode="anchor")
+    
+    # Loop over data dimensions and create text annotations.
+    for i in range(len(classes)):
+        for j in range(len(classes)):
+            text = ax.text(j, i, confMat[i, j],
+                           ha="center", va="center", color="k")
+    
+    ax.set_title("Confusion matrix {}, \nrows - true activity, cols - predicted activity".format(person))
+    #fig.tight_layout()
+    fig.colorbar(im)
+    plt.show()
+    
+if __name__ == "__main__":
+    main()
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