def updateCSVFile(self):
fileDescription = StepData.get_fileDescription(self)
filename = StepData.get_filename(self)
manifoldSolidBrepCount = StepData.get_manifoldSolidBrepCount(self)
totalPlaneSurfaces = StepData.get_totalPlaneSurfaces(self)
radius = StepData.get_radius(self)
category = StepData.get_category(self)
gdriveFileId = StepData.get_gdriveFileId(self)
serialNumber = 1
StepData.set_serialNumber(self, serialNumber)
try:
with open('PartDetails.csv', 'r') as readFile:
reader = csv.reader(readFile)
serialNumber= len(list(reader))
StepData.set_serialNumber(self, serialNumber)
with open('PartDetails.csv', 'a', newline='') as csvFile:
fieldnames = ["SERIAL_NUMBER","FILE_DESCRIPTION", "FILENAME","MANIFOLD_SOLID_BREP","PLANE","RADIUS","CATEGORY","GDRIVE_FILE_ID"]
writer = csv.DictWriter(csvFile, fieldnames=fieldnames)
writer.writerow({'SERIAL_NUMBER': serialNumber,"FILE_DESCRIPTION": fileDescription, "FILENAME": filename,
"MANIFOLD_SOLID_BREP": manifoldSolidBrepCount, "PLANE": totalPlaneSurfaces, "RADIUS":radius,"CATEGORY":category,
"GDRIVE_FILE_ID": gdriveFileId})
except FileNotFoundError:
with open('PartDetails.csv', 'w', newline='') as csvFile:
fieldnames = ["SERIAL_NUMBER","FILE_DESCRIPTION", "FILENAME","MANIFOLD_SOLID_BREP","PLANE","RADIUS","CATEGORY","GDRIVE_FILE_ID"]
writer = csv.DictWriter(csvFile, fieldnames=fieldnames)
writer.writeheader()
writer.writerow({'SERIAL_NUMBER': serialNumber,"FILE_DESCRIPTION": fileDescription, "FILENAME": filename,
"MANIFOLD_SOLID_BREP": manifoldSolidBrepCount, "PLANE": totalPlaneSurfaces, "RADIUS":radius,"CATEGORY":category,
"GDRIVE_FILE_ID": gdriveFileId})
def aiTrainer(self, isCompare):
fileDescription = StepData.get_fileDescription(self)
filename = StepData.get_filename(self)
manifoldSolidBrepCount = StepData.get_manifoldSolidBrepCount(self)
totalPlaneSurfaces = StepData.get_totalPlaneSurfaces(self)
radius = StepData.get_radius(self)
category = StepData.get_category(self)
gdriveFileId = StepData.get_gdriveFileId(self)
serialNumber = StepData.get_serialNumber(self)
#NEO4J DATA, data of the form [radius,id,class]
partClass = 1
resultTrainSet = dbConnection.getradius(serialNumber, partClass, radius)
# TODO : Megha
testInstance = []
if isCompare:
# Compare
arrayOutside = []
for i in range(len(resultTrainSet)):
arrayInside = []
list1 = itemgetter(i)(resultTrainSet)
listprep1 = (list1['n.radius'])
arrayInside.append(listprep1)
listprep11 = (list1['n.id'])
arrayInside.append(listprep11)
listprep12 = (list1['n.partClass'])
arrayInside.append(listprep12)
arrayOutside.append(arrayInside)
trainSet = arrayOutside
else:
# Import
trainSet = [[4,11,'1'], [1,12,'1'],[2,13,'1'],[3,14,'1'],[5,16,'2'],[7,10,'2'],[10,19,'3']]
#getting radius from step file algorithm
testInstance.append(radius)
# checking if id already exists , generate non repeating random number
for i in range(len(trainSet)):
list1 = itemgetter(i)(trainSet)
list1_element1 = itemgetter(1)(list1)
step_data_id = random.randint(20,200)
if not step_data_id == list1_element1:
testInstance.append(step_data_id)
a = testInstance[0:1]
#extracting first element of list to find class
a = itemgetter(0)(testInstance)
# 1-4 is class 1, 5-8 is class 2 , 9-12 is class 3
if a > 1 and a <= 4 :
partClass = '1'
elif a > 4 and a <= 8:
partClass = '2'
elif a > 8 and a <= 12:
partClass = '3'
else :
partClass = '4'
importResult = dbConnection.savePartDetails(serialNumber, fileDescription, filename, manifoldSolidBrepCount, totalPlaneSurfaces, radius, category, partClass, gdriveFileId)
importData = []
for item in importResult:
importData.append(item['n'])
# if compare:
if isCompare:
# TODO : change the value of kNearestNeighbours for more accuracy
kNearestNeighbours = 3
neighbors = GetNeighbors.getNeighors(self, trainSet , testInstance , kNearestNeighbours)
#getting predicted class of data
response = GetResponse.getResponse(self, neighbors)
predictions= []
predictions.append(response)
#getting accuracy based on true class and predicted class
# TODO : return to UI
accuracy = GetAccuracy.getAccuracy(self, predictions,partClass)
getClassResult = dbConnection.getclass(partClass)
testInstance.append(partClass)
trainSet.append(testInstance)
# to Neo4j
compareResult = dbConnection.getclass(partClass)
compareData = []
for item in compareResult:
compareData.append(item['n'])
originalPart = []
similarParts = []
for item in compareData:
if item['reference'] == StepData.get_filename(self):
originalPart.append(item)
else:
similarParts.append(item)
return originalPart, similarParts, accuracy
return importData