def DBSCAN(kalender, predicted_length, weekday):
"""
Method used to test the DBSCAN prototype
:param kalender: The calendar to use
:param predicted_length: Duration of the predicted block
:param weekday: The specific weekday to perform tests on
"""
#If the suggested times are declined by the user -> increase minPts (k) with values being [3,5,10]
dbscan = 0
predicted_times = 0
file = "scheman/" + str(kalender) + ".csv"
n_timeblocks = p.Get_n_blocks(file)
#6 månader test data
last_testBlock = p.Get_Block_By_Index(file, n_timeblocks - 30)
if (last_testBlock[0][1] > 6):
first_testBlock = p.Get_Index_By_Date(file, [
last_testBlock[0][0], last_testBlock[0][1] - 6,
last_testBlock[0][2]
])
test_data = [first_testBlock, last_testBlock]
else:
first_testBlock = p.Get_Index_By_Date(file, [
last_testBlock[0][0] - 1, last_testBlock[0][1] + 6,
last_testBlock[0][2]
])
test_data = [first_testBlock, last_testBlock]
"""
#Specific training and test interval
test_startBlock = p.Get_Block_By_Index(file,np.floor(n_timeblocks*(2/3)))
training_start = p.Get_Index_By_Date(file,[test_startBlock[0][0]-1,test_startBlock[0][1],test_startBlock[0][2]])
one_year_training = [training_start, np.floor(n_timeblocks*(2/3))-1]
"""
training_set = p.Fetch_Data(file, 0,
np.floor(n_timeblocks * (2 / 3)) - 1) #
test_set = p.Fetch_Data(file, np.floor(n_timeblocks * (2 / 3)),
n_timeblocks) #
p.InsertFreeSpace(training_set, [1, 0]) #
p.InsertFreeSpace(test_set, [1, 0]) #
#Filter by a specific weekday
filtered_training = p.GetBlocksByWeekday(training_set, weekday)
filtered_test = p.GetBlocksByWeekday(test_set, weekday)
n_of_days = p.GetNumberOfDays(filtered_test, weekday) + p.GetNumberOfDays(
filtered_training, weekday)
#Only freespace blocks
training_startTimes = p.GetStartTimes_By_Classes(filtered_training, 1)
training_lengths = p.GetLengths_By_Classes(filtered_training, 1)
training_classes = []
for i in range(0, len(training_startTimes)):
training_classes.append(1)
test_free_startTimes = p.GetStartTimes_By_Classes(filtered_test, 1)
test_free_lengths = p.GetLengths_By_Classes(filtered_test, 1)
test_free_classes = []
for i in range(0, len(test_free_startTimes)):
test_free_classes.append(1)
test_occupied_startTimes = p.GetStartTimes_By_Classes(filtered_test, 0)
test_occupied_lengths = p.GetLengths_By_Classes(filtered_test, 0)
test_occupied_classes = []
for i in range(0, len(test_occupied_startTimes)):
test_occupied_classes.append(0)
#Prepare the format of the points so they can be presented in the xy-plane
training_points = p.Prepare_Plane(training_startTimes, training_lengths)
test_free_points = p.Prepare_Plane(test_free_startTimes, test_free_lengths)
test_occupied_points = p.Prepare_Plane(test_occupied_startTimes,
test_occupied_lengths)
#print(training_set)
#Create the DBSCAN object
dbscan = uv.DB_SCAN("Kalender " + str(1) + " , Dag: " + str(1) + " , K=" +
str(3))
DB_radius = dbscan.KNNdist_plot(training_points, 10)
dbscan.learn(training_points[:dbscan.n_trainingData], DB_radius, 10,
predicted_length)
predicted_times = (dbscan.predict())
total_hours = 0
free_hours = 0
occupied_hours = 0
successful_predictions = 0
failed_predictions = 0
tid = 0
pred_counter = 0
for i in range(0, len(test_free_points)):
total_hours += test_free_points[i][1]
free_hours += test_free_points[i][1]
for i in range(0, len(test_occupied_points)):
total_hours += test_occupied_points[i][1]
occupied_hours += test_occupied_points[i][1]
for i in range(0, len(test_free_points)):
for j in range(0, len(predicted_times)):
tid = 0
#Store the start and end times in temporary variables
pred_start = predicted_times[j][0]
pred_end = pred_start + predicted_length
start_time = test_free_points[i][0]
end_time = start_time + test_free_points[i][1]
if ((pred_start >= start_time) and (pred_start < end_time)):
#true
if (pred_end <= end_time):
#true
tid = pred_end - pred_start
else:
#false
tid = end_time - pred_start
#false
elif ((pred_start < start_time) and (pred_end >= start_time)
and (pred_end <= end_time)):
#true
tid = pred_end - start_time
if (tid != 0):
successful_predictions += tid
pred_counter += predicted_length
print("--------------------------------------------")
print(str(predicted_times) + '\n')
print("Kalender: " + str(kalender) + " , Veckodag: " + str(weekday))
print("Totala timmar [h]: " + str(total_hours))
print("Lediga timmar [h]: " + str(free_hours))
print("Upptagna timmar [h]: " + str(occupied_hours) + '\n')
print("Lyckad prediktion [h]: " + str(successful_predictions))
print("Totala prediktioner [h]: " + str(pred_counter) + '\n')
return
