07/06/2020

WalkingPatternRecognition/TrainingAndTestingData/Dictionary.py

+
+class Dictionary:    
+     data= ''
+         
+     def __init__(self,data):
+        self.data = data
+        
+        Dictionary = {}
+        Dictionary[data[0]]= data[1]
+        print(Dictionary)
+        x = Dictionary["1/1/2020 0:01"]
+        print("Dict activity",x)
+
+        
+        
+        
+        
+      
\ No newline at end of file

WalkingPatternRecognition/TrainingAndTestingData/DogData.csv

 StartTime,EndTime,Date,Activity
+1/1/2020 0:00,1/1/2020 0:01,1/1/2020 0:01,0
+1/1/2020 0:00,1/1/2020 0:01,1/1/2020 0:01,0
+1/1/2020 0:00,1/1/2020 0:01,1/1/2020 0:01,0
+1/1/2020 0:00,1/1/2020 0:01,1/1/2020 0:01,0
+1/1/2020 0:00,1/1/2020 0:01,1/1/2020 0:01,0

WalkingPatternRecognition/TrainingAndTestingData/FeatureExtraction.py

 import numpy as np
 from sympy import fft
-from TrainingAndTestingData import GetData as gd
-
+#from TrainingAndTestingData import GetData as gd
+import GetData as gd
 
 class FeatureExtraction:    
     list=[]

WalkingPatternRecognition/TrainingAndTestingData/FeatureVectorTest.csv

+Xmin,Ymin,Zmin,Xmax,Ymax,Zmax,Xmean,Ymean,Zmean,StartTime,EndTime
+-1.426944,3.888184,7.445967,0.464475,5.7269309999999995,9.097966,-0.15897496666666663,4.837724166666667,8.376513633333333,1/1/2020 0:00,1/1/2020 0:01
+-1.426944,3.888184,7.445967,0.464475,5.7269309999999995,9.097966,-0.15897496666666663,4.837724166666667,8.376513633333333,1/1/2020 0:00,1/1/2020 0:01
+-1.426944,3.888184,7.445967,0.464475,5.7269309999999995,9.097966,-0.15897496666666663,4.837724166666667,8.376513633333333,1/1/2020 0:00,1/1/2020 0:01
+-1.426944,3.888184,7.445967,0.464475,5.7269309999999995,9.097966,-0.15897496666666663,4.837724166666667,8.376513633333333,1/1/2020 0:00,1/1/2020 0:01
+-1.426944,3.888184,7.445967,0.464475,5.7269309999999995,9.097966,-0.15897496666666663,4.837724166666667,8.376513633333333,1/1/2020 0:00,1/1/2020 0:01

WalkingPatternRecognition/TrainingAndTestingData/GetData.py

@@ -44,4 +44,4 @@ class GetData:
         Times.append(EndTime)
         return Times
 
-   
\ No newline at end of file
+    
\ No newline at end of file

WalkingPatternRecognition/TrainingAndTestingData/Main.py

 # In this file , the data is obtained from a csv to create the feature vector, send that to a trained model,
 # predict the activity and then transfer the dog details to a csv to be sent to the database
 
-from TrainingAndTestingData import Data as Data
-from TrainingAndTestingData import GetData as getdata
-from TrainingAndTestingData import FeatureExtraction as fe
-from TrainingAndTestingData import Test as Test
-from TrainingAndTestingData import ReadData as ReadData
-
+#from TrainingAndTestingData import Data as Data
+#from TrainingAndTestingData import GetData as getdata
+#from TrainingAndTestingData import FeatureExtraction as fe
+#from TrainingAndTestingData import Test as Test
+#from TrainingAndTestingData import ReadData as ReadData
+
+import Data as Data
+import GetData as getdata
+import FeatureExtraction as fe
+import Test as Test
+import ReadData as ReadData
 
 class Main:
     def __init__(self):
-        d = Data.Data('TrainingAndTestingData/Example.csv')
+        d = Data.Data('Readings.csv')
         data = d.ReadData()
         i, j = 0, 0
         length = len(data)
@@ -31,11 +36,12 @@ class Main:
                 if 'activity' in data.columns:
                     print("For Training ")
                     FeatureVector = f.getSmallFeatureSingle(dataset)
-                    d.append_list_as_row('FeatureVector.csv', FeatureVector)
+                    d.append_list_as_row('FeatureVectorTrain.csv', FeatureVector)
                 else:
+                    print("In")
                     FeatureVector = f.getSmallTestFeatureSingle(dataset)
-                    d.append_list_as_row('Test.csv', FeatureVector)
-                    r = Data.Data('Test.csv')
+                    d.append_list_as_row('FeatureVectorTest.csv', FeatureVector)
+                    r = Data.Data('FeatureVectorTest.csv')
                     predictingActivity = r.ReadData()
                     predictedActivity = Test.Test(predictingActivity)
 
@@ -48,5 +54,7 @@ class Main:
         activityCountData = ReadData.ReadData(data1)
 
 # Once the feature vector is created data from the Example.csv should be cleared.
-# Once the activity is recognized adn inserted to DogData,csv , the Feature vector should be deleted from the Test.csv.
+# Once the activity is recognized and inserted to DogData,csv , the Feature vector should be deleted from the Test.csv.
 # Once the activity count is taken and the DB is updated the DogData.csv should be cleared.
+
+Main()
\ No newline at end of file

WalkingPatternRecognition/TrainingAndTestingData/ReadData.py

@@ -11,15 +11,16 @@ class ReadData:
         i, Rest, Walk, Run = 0,0,0,0
         
         while i<len(data):
-            print(i)
-            if(data.xs(i)['Activity'] == 'rest'):
+            if(data.xs(i)['Activity'] == 0):
                 Rest += 1
-            if(data.xs(i)['Activity'] == 'walk'):
+            if(data.xs(i)['Activity'] == 1):
                 Walk += 1
-            if(data.xs(i)['Activity'] == 'run'):
+            if(data.xs(i)['Activity'] == 2):
                 Run += 1
             i += 1
 
         print("Resting Minutes: ", Rest)
         print("Walking Minutes: ", Walk)
         print("Running Minutes: ", Run)
+
+        
\ No newline at end of file

WalkingPatternRecognition/TrainingAndTestingData/Readings.csv

+timestamp,accelerometer_X,accelerometer_Y,accelerometer_Z
+1/1/2020 0:00,0.296881,5.621586,8.001422
+1/2/2020 0:00,0.464475,5.262456,7.627927
+1/3/2020 0:00,0.105345,5.324705,7.915231
+1/4/2020 0:00,-0.019154,5.726931,8.279149
+1/5/2020 0:00,-0.253785,5.616797,8.063671
+1/6/2020 0:00,-0.248997,5.48751,7.886501
+1/7/2020 0:00,-0.368707,5.396531,8.063671
+1/8/2020 0:00,-0.167594,5.425261,8.288726
+1/9/2020 0:00,-0.430956,5.621586,8.226477
+1/10/2020 0:00,-0.272939,5.118803,8.408437
+1/11/2020 0:00,0.263362,4.668694,8.317456
+1/12/2020 0:00,-0.311246,4.989517,7.513005
+1/13/2020 0:00,0.105345,4.730943,9.002198
+1/14/2020 0:00,0.095768,4.797981,8.848969
+1/15/2020 0:00,-0.153229,4.453215,9.097966
+1/16/2020 0:00,0.09098,4.644751,8.585608
+1/17/2020 0:00,0.038307,4.644751,8.719683
+1/18/2020 0:00,0.205901,4.999093,8.643068
+1/19/2020 0:00,-0.301669,4.367024,8.207323
+1/20/2020 0:00,-0.129287,4.673482,8.06846
+1/21/2020 0:00,-0.181959,4.458004,8.705317
+1/22/2020 0:00,-0.019154,4.381389,8.853758
+1/23/2020 0:00,0.100556,4.443639,8.705317
+1/24/2020 0:00,-0.004788,4.390966,8.777143
+1/25/2020 0:00,-0.172383,4.472369,8.858546
+1/26/2020 0:00,-1.043872,3.979163,7.445967
+1/27/2020 0:00,-0.679953,4.194642,8.446744
+1/28/2020 0:00,-0.215478,3.888184,9.045294
+1/29/2020 0:00,-0.134075,4.242526,8.743625
+1/1/2020 0:01,-1.426944,5.109226,7.94875

WalkingPatternRecognition/TrainingAndTestingData/Test.py

 #This file reads the feature vector predicts the Activity and writes it to a file DogData.csv
 
 import joblib
-from TrainingAndTestingData import Data as Data
-
+#from TrainingAndTestingData import Data as Data
+import Data as Data
+import Dictionary as Dictionary
 
 class Test:    
      data = ''
@@ -19,6 +20,7 @@ class Test:
         Date=Times.xs(0)['EndTime']
 
         ActivityArray=[]
+        dictionary=[]
 
         # load the Gaussian Bayes Model from disk
         filename= 'SimplifiedFinalSVMModel.sav'
@@ -39,7 +41,13 @@ class Test:
         ActivityArray.append(StartTime)
         ActivityArray.append(EndTime)
         ActivityArray.append(Date)
-        ActivityArray.append(activity)
+        ActivityArray.append(activityPrediction)
+        
+        dictionary.append(Date)
+        dictionary.append(activityPrediction)        
 
         d= Data.Data(ActivityArray)
-        d.append_list_as_row('DogData.csv', ActivityArray)
+        d.append_list_as_row('DogData.csv', ActivityArray)        
+        
+        Dictionary.Dictionary(dictionary)
+