Merge branch 'IT19242996' into 'master'

It19242996

See merge request 2022-020/2022-020!21

.vscode/settings.json

+{
+    "python.analysis.extraPaths": [
+        "./TravelTimePrediction/TrainData"
+    ]
+}
\ No newline at end of file

TravelTimePrediction/TrainData/TrainData.csv

+Dipature,Arrival,Time
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min
+Dehiwala,Fort ,25min
+Dehiwala,wellawatte,5min
+Dehiwala,bambalapitiya,9min
+Dehiwala,kollupitiya,13min
+Dehiwala,kompannavidiya,16min
+wellawatte,bambalapitiya,4min
+wellawatte,kollupitiya,8min
+wellawatte,kompannavidiya,11min
+wellawatte,Fort ,13min
+bambalapitiya,kollupitiya,4min
+bambalapitiya,kompannavidiya,7min
+bambalapitiya,Fort ,9min
+kollupitiya,kompannavidiya,3min
+kollupitiya,Fort ,5min
+kompannavidiya,Fort ,3min

TravelTimePrediction/TrainData/TrainDataSet.txt

+Dehiwala	1
+wellawatte	2
+bambalapitiya	3
+kollupitiya	4
+kompannavidiya	5
+Fort	6

TravelTimePrediction/TrainData/TrainTimePrediction.py

+# %%
+import pandas as pd
+
+# %%
+data = pd.read_csv('TravelTimePrediction\TrainData\TrainData.csv')
+
+# %%
+
+
+# %%
+
+
+# %%
+
+
+# %%
+data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
+
+# %%
+data['Dipature'] = data['Dipature'].map({'Dehiwala':1,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
+
+# %%
+
+
+# %%
+data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
+
+# %%
+data['Arrival'] = data['Arrival'].map({'Fort ':6,'wellawatte':2,'bambalapitiya':3,'kollupitiya':4,'kompannavidiya':5})
+
+# %%
+
+
+# %%
+X = data.drop(['Time'],axis=1)
+
+# %%
+y = data['Time']
+
+# %%
+from sklearn.model_selection import train_test_split
+
+# %%
+X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)
+
+# %%
+from sklearn.neural_network import MLPClassifier
+
+# %%
+NN = MLPClassifier()
+NN.fit(X_train,y_train)
+
+# %%
+y_pred1 = NN.predict(X_test)
+
+# %%
+df1 = pd.DataFrame({'Actual':y_test, 'NN Results':y_pred1})
+
+# %%
+data = {'Dipature':1,
+        'Arrival':2,
+        }
+df = pd.DataFrame(data,index=[0])
+
+# %%
+predit = NN.predict(df)
+
+
+# %%
+def GetTrainPrediction(Dipature,Arrival):
+    data = {'Dipature':Dipature,
+            'Arrival':Arrival,
+        }
+    df = pd.DataFrame(data,index=[0])
+    predit = NN.predict(df)
+    print('Train Travel Time Prediction :- ',*predit,' ')
+
+# %%
+def GetDipatureCode(data):
+    if('Dehiwala' == data):
+        return 1
+    elif('wellawatte' == data):
+        return 2
+    elif('bambalapitiya' == data):
+        return 3
+    elif('kollupitiya' == data):
+        return 4
+    elif('kompannavidiya' == data):
+        return 5
+
+# %%
+def GetArrivalCode(data):
+    if('Fort' == data):
+        return 6
+    elif('wellawatte' == data):
+        return 2
+    elif('bambalapitiya' == data):
+        return 3
+    elif('kollupitiya' == data):
+        return 4
+    elif('kompannavidiya' == data):
+        return 5
+
+# %%
+
+
+def GetPrediction(Dvalue,Avalue):
+    Dipature = GetDipatureCode(Dvalue)
+    Arrival = GetArrivalCode(Avalue)
+
+    GetTrainPrediction(Dipature,Arrival)
+
+
+GetPrediction('Dehiwala','Fort')
\ No newline at end of file

TravelTimePrediction.ipynb → TravelTimePrediction/TravelTimePrediction.ipynb

@@ -1668,7 +1668,7 @@
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "26de051ba29f2982a8de78e945f0abaf191376122a1563185a90213a26c5da77"
+    "hash": "5cf8c3d3e58a91b903f819a9c98dd2220f70e367ec47969cb612bf546ce9e823"
    }
   }
  },

TravelTimePredictionNN.py → TravelTimePrediction/TravelTimePredictionNN.py

@@ -2,7 +2,7 @@
 import pandas as pd
 
 # %%
-data = pd.read_csv('BusTravelData.csv')
+data = pd.read_csv('TravelTimePrediction\BusTravelData.csv')
 
 
 # %%
@@ -180,6 +180,8 @@ def GetDateCode(date):
     
 import testingWeather
 
+
+
 city = "malabe"
 city = city+" weather"
  
@@ -187,7 +189,7 @@ time= 6  #this time come from semins data/member of route planning
 day ='Friday'  #date come from UI 
 date = GetDateCode(day)
 special = 0 # Special Day or Not 1/0
-Congestion= 8 # This is depend on the depature time---- heavy traffic =9 / No Traffic = 0 
+Congestion= 8 # This is come from TomTOm API / APK app 
 drivingspeedAVG = 40 #this is come from GPS data
 stops =14 #total Bus stops/holts between the travel --- data come from the Seminas data/member of route planning 
 weather =testingWeather.weather(city)  #Get weather in travel area
@@ -198,5 +200,17 @@ GetPrediction(time,date,special,Congestion,drivingspeedAVG,stops,weather,Distanc
 
 
 
+# Get Train Data
+
+# import sys
+ 
+# adding Folder_2/subfolder to the system path
+# sys.path.insert(0, 'TravelTimePrediction\TrainData')
+ 
+# importing the hello
+# from TrainTimePrediction import GetPrediction
+
+# GetPrediction('Dehiwala','Fort')
+