s426135/mieszkania5
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

szu

Browse Source
This commit is contained in:
SzamanFL 2020-12-16 00:12:24 +01:00
parent a9defbbc5a
commit f688e6e966
3 changed files with 452 additions and 451 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

41
src/predict.py
View File

@ -21,12 +21,10 @@ dict_column_23 = {"":9, "beton":1, "beton komórkowy":2, "cegła":3, "inne":4, "
def read_data(in_file): def read_data(in_file):
print("Reading in") print("Reading in")
all_data = pd.read_csv(in_file, sep='\t', keep_default_na=False, header = 1) all_data = pd.read_csv(in_file, sep='\t', keep_default_na=False, header = 0)
expected = all_data.iloc[:,0]
data = all_data.iloc[:,1:]
print("Data read") print("Data read")
return expected, data return all_data
def clean_df(data_): def clean_df(data_):
print("Cleaning data") print("Cleaning data")
@ -43,6 +41,7 @@ def clean_df(data_):
if col == 'parter': if col == 'parter':
data_.iloc[i,14] = 22 data_.iloc[i,14] = 22
# clear money # clear money
for i, col in enumerate(data_.iloc[:,1]): for i, col in enumerate(data_.iloc[:,1]):
try: try:
@ -50,9 +49,10 @@ def clean_df(data_):
data_.iloc[i,1] = 1.0 data_.iloc[i,1] = 1.0
else: else:
data_.iloc[i,1] = float(col.replace("zł", "").replace(" ", "")) data_.iloc[i,1] = float(col.replace("zł", "").replace(" ", ""))
except ValueError: except AttributeError:
import ipdb; ipdb.set_trace() import ipdb; ipdb.set_trace()
# deleting columns # deleting columns
deleted_columns = [4,13,18,20,22,24] deleted_columns = [4,13,18,20,22,24]
data_.drop(data_.columns[deleted_columns], axis = 1, inplace=True) data_.drop(data_.columns[deleted_columns], axis = 1, inplace=True)
@ -71,20 +71,11 @@ def clean_df(data_):
data_.iloc[i,4] = 1 data_.iloc[i,4] = 1
for i, col in enumerate(data_.iloc[:,6]): for i, col in enumerate(data_.iloc[:,6]):
data_.iloc[i,6] = col.replace(' ', '')
print("Data cleaned")
return data_
def clear(data_):
for i, col in enumerate(data_.iloc[:,2]):
try: try:
if col == "": data_.iloc[i,6] = col.replace(' ', '')
data_.iloc[i,2] = 1.0
else:
data_.iloc[i,2] = float(col.replace("zł", "").replace(" ", ""))
except AttributeError: except AttributeError:
data_.iloc[i,2] = float(data_.iloc[i,2]) pass
#import ipdb; ipdb.set_trace() print("Data cleaned")
return data_ return data_
def main(): def main():
@ -94,9 +85,8 @@ def main():
parser.add_argument("--out") parser.add_argument("--out")
args = parser.parse_args() args = parser.parse_args()
expected, data = read_data(args.in_file) data = read_data(args.in_file)
clean_data = clear(data) clean_data = clean_df(data)
clean_data = clean_data.iloc[:,2]
import ipdb; ipdb.set_trace() import ipdb; ipdb.set_trace()
#model = Network(len(clean_data.columns)) #model = Network(len(clean_data.columns))
@ -105,8 +95,15 @@ def main():
print(f"Loading model : {args.checkpoint}") print(f"Loading model : {args.checkpoint}")
model.load_state_dict(torch.load(args.checkpoint)) model.load_state_dict(torch.load(args.checkpoint))
with open(args.out, 'w+') as f: with open(args.out, 'w+') as f:
for i in clean_data: for i in range(len(clean_data.index)):
tensor = torch.tensor([i]) data_arr = clean_data.loc[i].to_numpy()
data_arr[data_arr == ""] = 0
try:
data_arr = pd.to_numeric(data_arr)
except ValueError:
ipdb.set_trace()
data_arr = np.sum(data_arr)
tensor = torch.tensor([data_arr])
y = model(tensor.float()) y = model(tensor.float())
try: try:
f.write(str(y.item()) + '\n') f.write(str(y.item()) + '\n')

28
src/train.py
View File

@ -21,7 +21,7 @@ dict_column_23 = {"":9, "beton":1, "beton komórkowy":2, "cegła":3, "inne":4, "
def read_data(in_file): def read_data(in_file):
print("Reading in") print("Reading in")
all_data = pd.read_csv(in_file, sep='\t', keep_default_na=False, header = 1) all_data = pd.read_csv(in_file, sep='\t', keep_default_na=False, header = 0)
expected = all_data.iloc[:,0] expected = all_data.iloc[:,0]
data = all_data.iloc[:,1:] data = all_data.iloc[:,1:]
@ -83,7 +83,7 @@ def main():
expected, data = read_data(args.in_file) expected, data = read_data(args.in_file)
clean_data = clean_df(data) clean_data = clean_df(data)
clean_data = clean_data.iloc[:,1] #clean_data = clean_data.iloc[:,6]
import ipdb; ipdb.set_trace() import ipdb; ipdb.set_trace()
#model = Network(len(clean_data.columns)) #model = Network(len(clean_data.columns))
@ -91,7 +91,7 @@ def main():
if args.checkpoint: if args.checkpoint:
print(f"Loading model : {args.checkpoint}") print(f"Loading model : {args.checkpoint}")
model.load_state_dict(torch.load(args.checkpoint)) model.load_state_dict(torch.load(args.checkpoint))
lr = 10 lr = 0.1
optimizer = torch.optim.Adam(model.parameters(), lr=lr) optimizer = torch.optim.Adam(model.parameters(), lr=lr)
criterion = torch.nn.MSELoss() criterion = torch.nn.MSELoss()
@ -102,21 +102,23 @@ def main():
counter = 0 counter = 0
l = [i for i in range(len(clean_data.index))] l = [i for i in range(len(clean_data.index))]
#import ipdb; ipdb.set_trace() #import ipdb; ipdb.set_trace()
for j in range(100): for j in range(500):
random.shuffle(l) random.shuffle(l)
for i in l: for i in l:
data_arr = [clean_data[i]] #data_arr = [float(clean_data[i])]
#data_arr = clean_data.loc[i].to_numpy() data_arr = clean_data.loc[i].to_numpy()
#data_arr[data_arr == ""] = 0 data_arr[data_arr == ""] = 0
#try: try:
# data_arr = pd.to_numeric(data_arr) data_arr = pd.to_numeric(data_arr)
#except ValueError: except ValueError:
# import ipdb; ipdb.set_trace() import ipdb; ipdb.set_trace()
#import ipdb; ipdb.set_trace() #import ipdb; ipdb.set_trace()
data_arr = np.sum(data_arr)
#$import ipdb; ipdb.set_trace()
expected_arr = float(expected.loc[i]) expected_arr = float(expected.loc[i])
#tensor = torch.from_numpy(data_arr) #tensor = torch.from_numpy(data_arr)
tensor = torch.tensor(data_arr) tensor = torch.tensor([data_arr])
y = torch.tensor([expected_arr]) y = torch.tensor([expected_arr])
optimizer.zero_grad() optimizer.zero_grad()
@ -134,7 +136,7 @@ def main():
counter += 1 counter += 1
print(f"Saving last model model-final-{lr}-{random_number}") print(f"Saving last model model-final-{lr}-{random_number}")
torch.save(model.state_dict(), f"model-{counter}-{lr}-{random_number}.ckpt") torch.save(model.state_dict(), f"model-{counter}-{lr}-{random_number}-final.ckpt")
main() main()

834
test-A/out.tsv
View File

@ -1,416 +1,418 @@
-495323.9375 328260.59375
-950439.375 290434.46875
-643593.0 292780.09375
-499419.0 452705.0
-515375.5625 339881.5625
-348890.5625 294927.78125
-377979.5625 318433.34375
-594170.0 247435.4375
-580049.125 256481.875
-424013.5625 323397.09375
-586827.125 319579.84375
-507609.0625 290749.3125
-588945.25 342362.5625
-690333.125 313328.3125
-865996.625 321604.28125
-130440.78125 377242.15625
-850746.125 447839.75
-943237.75 175605.453125
-456915.1875 416553.0
-761078.625 434855.46875
-813043.375 311214.34375
-839873.0 376477.53125
-755006.625 393614.1875
-615351.3125 414439.03125
-904405.375 376927.3125
-652771.5625 330591.34375
-1003675.125 423059.09375
-523848.0625 342557.3125
-224203.3125 452864.25
-415682.25 301894.875
-99657.3125 207854.734375
-1266040.75 279639.75
-682707.875 169623.359375
-614927.6875 545860.0
-457480.0 351379.78125
-509162.375 331166.625
-740179.75 288680.34375
-604619.4375 325787.25
-569034.875 369730.8125
-503514.0625 327621.4375
-861054.375 316553.25
-427120.1875 296276.6875
-844391.75 429754.0625
-474142.625 294572.46875
-320225.1875 402384.90625
-553219.5 286140.875
-755147.875 267815.0
-430085.5625 309493.03125
-615351.3125 374080.21875
-1034034.875 273927.53125
-1147849.125 330916.09375
-424296.0 473756.125
-550677.75 518154.0
-658419.9375 304233.75
-615351.3125 329205.59375
-1064536.0 344319.125
-510856.875 331068.125
-704454.0 472727.9375
-184100.0625 296890.625
-656019.375 358333.375
-690333.125 194842.5625
-804429.625 357496.78125
-581320.0 354438.28125
-882094.375 392084.9375
-572847.5 343979.53125
-594170.0 415878.34375
-671269.9375 348865.5
-754441.75 324410.0
-561409.5625 347691.5625
-480920.6875 378960.34375
-530626.125 321109.53125
-474142.625 288545.40625
-909206.5 334535.46875
-709113.875 286560.0625
-697393.5 437049.53125
-650653.4375 385360.71875
-615351.3125 356336.375
-640062.8125 341169.75
-644581.5 330366.46875
-631307.875 338291.15625
-603489.75 341802.15625
-491934.9375 336891.0
-730153.875 354904.71875
-923892.25 292480.96875
-629472.125 365907.6875
-175627.5625 448952.0625
-629330.9375 334696.5
-589368.875 205673.296875
-615351.3125 352486.25
-601230.4375 337121.71875
-506620.625 330743.375
-388852.625 324751.40625
-498148.125 315910.0625
-239877.5 257960.3125
-591204.625 293178.125
-286758.75 237045.5
-683131.5 324865.1875
-558867.8125 225967.40625
-556749.6875 352553.71875
-615351.3125 312955.0
-465952.5625 336716.90625
-392806.4375 330464.96875
-524836.5625 283372.90625
-653054.0 281213.96875
-366541.625 323102.03125
-756560.0 343063.34375
-715750.625 280764.1875
-510292.0625 376876.9375
-332934.0 364843.96875
-530626.125 297226.15625
-708831.375 257735.421875
-1035447.0 304042.59375
-583720.5625 359707.46875
-982635.0 482760.71875
-502384.375 322166.5
-604478.25 460096.25
-502384.375 295476.0625
-600524.375 345905.9375
-1014548.125 294572.46875
-631307.875 340940.34375
-792003.25 457986.8125
-700076.5 352838.84375
-550395.3125 386454.09375
-756560.0 356980.4375
-1338480.75 310644.90625
-580049.125 384384.6875
-523565.6875 560150.0
-427120.1875 319088.65625
-1105768.875 301582.75
-476119.5625 271183.875
-611679.875 509396.75
-637097.4375 286544.78125
-474566.25 328620.875
-897768.625 336402.0625
-486427.8125 286130.0625
-474142.625 419490.0625
-876022.5 288995.1875
-953687.25 305996.90625
-517352.5 413809.34375
-752606.125 478307.90625
-465670.125 300194.28125
-460163.0 373154.5625
-649382.5625 312734.59375
-843262.0 303855.9375
-418788.875 343868.4375
-397183.9375 403194.53125
-743145.125 267333.75
-522577.1875 263762.5
-784660.5 371305.0625
-700782.5 318693.75
-710384.75 383351.09375
-425708.0625 374903.3125
-676353.375 359748.40625
-468070.6875 301049.3125
-770680.75 349895.5
-742439.125 306356.71875
-545029.375 383538.65625
-608573.25 374082.03125
-383486.6875 307865.28125
-657713.875 330361.53125
-950721.875 257915.328125
-728318.25 344678.03125
-421895.4375 454784.375
-683837.5 366526.125
-580049.125 270568.5625
-597417.8125 350796.40625
-479508.5625 320226.625
-537827.75 342384.15625
-596146.9375 287331.0
-498289.3125 306716.09375
-580190.3125 335129.1875
-709537.5 324631.3125
-687932.5 319871.28125
-488969.5625 377107.21875
-615351.3125 353235.125
-991672.375 319987.78125
-976704.25 331454.46875
-693016.0 450475.46875
-607867.25 477543.28125
-493629.4375 369924.6875
-523706.875 341529.5625
-500125.0625 306176.8125
-675929.75 331454.46875
-645569.9375 308119.875
-493488.25 348750.34375
-1139800.25 340099.28125
-530061.25 292077.53125
-488122.3125 501291.6875
-526107.4375 308650.59375
-608290.875 315550.21875
-597700.1875 303163.28125
-547853.5625 328932.5625
-828858.75 325427.40625
-389135.0625 331589.40625
-546865.125 408048.53125
-756560.0 259449.078125
-707136.875 323763.21875
-427967.4375 386327.75
-728318.25 359210.46875
-701488.625 270958.96875
-584426.625 370279.5625
-489675.5625 385158.3125
-870797.75 336441.65625
-389417.4375 311407.75
-799063.75 414214.15625
-298620.3125 259488.21875
-537262.9375 388089.09375
-544746.9375 242570.15625
-474142.625 320508.625
-750770.375 317673.21875
-1625416.75 285978.5
-454514.625 391455.25
-647123.25 652008.75
-219967.0625 304260.71875
-237476.9375 339820.40625
-798922.5 206505.390625
-728318.25 211452.96875
-210364.875 387616.8125
-549124.4375 365439.9375
-756560.0 206685.296875
-766585.75 310269.8125
-707136.875 375180.8125
-1355990.625 390420.75
-527801.9375 359662.46875
-768845.125 588864.0
-356515.8125 301818.4375
-718010.0 380269.625
-798216.5 250763.8125
-635544.125 378231.6875
-613939.1875 403734.25
-692592.375 352998.96875
-937165.75 330484.3125
-493488.25 377129.71875
-645993.5625 450556.4375
-460021.75 292212.4375
-182546.8125 341207.96875
-837190.125 282172.46875
-654042.4375 213297.078125
-527378.3125 400990.59375
-383486.6875 365642.3125
-488545.9375 302893.40625
-582449.6875 258994.796875
-365976.8125 311934.0
-692451.25 320033.1875
-1745020.5 262008.328125
-589368.875 374871.8125
-615351.3125 709585.1875
-523706.875 322099.03125
-328697.75 331011.90625
-856818.125 303671.53125
-502666.8125 259116.25
-651359.5 417767.40625
-483744.8125 294442.46875
-1116077.125 361059.0625
-824340.125 289220.0625
-369365.8125 491450.5
-724223.125 398354.90625
-791862.125 263937.0
-769974.75 365631.09375
-841285.125 385387.6875
-654183.6875 379055.6875
-498571.75 402700.1875
-495182.75 343847.28125
-841285.125 312941.5
-498854.125 16417158.0
-427826.1875 401973.34375
-927563.625 293798.84375
-580049.125 283552.84375
-701771.0 432250.34375
-629472.125 319760.1875
-427543.8125 358756.1875
-500689.875 334504.875
-448442.6875 304647.5625
-746816.5 294888.65625
-758960.5 279347.375
-646558.375 372384.53125
-507185.4375 375613.9375
-950721.875 340661.5
-558867.8125 319805.15625
-820527.5 436748.15625
-394924.5625 313418.28125
-516505.25 422580.0625
-798922.5 262098.28125
-768562.625 298942.96875
-603348.5625 387841.71875
-364423.5 379760.9375
-656584.1875 327046.625
-678612.75 251523.9375
-609702.9375 344947.90625
-572847.5 374048.71875
-463693.1875 328937.5
-401279.0 315614.09375
-475837.125 281888.21875
-645146.3125 280888.3125
-404385.5625 288109.09375
-663644.625 339343.625
-682990.25 265021.875
-714197.375 362174.5
-473295.375 373733.875
-500407.4375 361927.125
-1003675.125 307769.03125
-815867.625 307004.40625
-785931.375 453070.71875
-923751.0 413141.875
-781412.625 391198.84375
-868256.0 442505.34375
-756560.0 385788.0
-646982.0 411106.1875
-554631.5625 375663.4375
-931799.875 358126.46875
-441523.4375 312175.0625
-615351.3125 431710.625
-580049.125 276041.5
-458327.25 330350.71875
-1180185.875 320290.90625
-286617.5625 281139.3125
-580049.125 513075.96875
-6459.59375 227483.15625
-755147.875 318900.1875
-601230.4375 137194.171875
-672964.4375 374488.15625
-540651.9375 325707.625
-728318.25 349265.8125
-678895.125 322998.59375
-1358673.625 366007.09375
-364564.75 349900.0
-421613.0 566824.75
-1029657.5 252248.09375
-672540.8125 270432.71875
-577224.9375 465381.1875
-378685.625 370607.90625
-684402.375 317736.15625
-577931.0 256179.171875
-833236.25 354525.09375
-697675.875 320007.5625
-601230.4375 399551.28125
-656725.4375 371179.125
-911889.5 325746.78125
-685955.625 365570.34375
-492358.5625 424458.78125
-717021.5 353677.6875
-562680.4375 303248.28125
-544746.9375 363015.59375
-289865.375 314392.5
-498854.125 309851.53125
-493488.25 229255.28125
-661385.3125 293753.84375
-1250790.25 292392.375
-628766.125 345505.1875
-424296.0 535908.1875
-784801.625 334153.15625
-403538.3125 304233.75
-1102521.125 401305.4375
-467082.1875 264437.15625
-302291.6875 484245.0
-722669.875 299754.84375
-567199.125 250828.578125
-473154.1875 364333.46875
-551807.375 327136.59375
-615351.3125 289534.90625
-558867.8125 309634.28125
-516505.25 329670.65625
-678612.75 312779.125
-510856.875 299207.0
-613656.8125 373643.90625
-243972.5625 297068.3125
-332934.0 357919.59375
-587109.5625 215006.234375
-800758.25 240958.59375
-251597.8125 322157.5
-735519.875 407107.59375
-963289.375 219079.0
-700076.5 368633.375
-788755.5 450425.96875
-428955.875 357406.40625
-734672.625 405623.34375
-589933.75 271993.46875
-502384.375 368542.0625
-930529.0 341306.9375
-792144.5 294221.1875
-481909.125 434104.8125
-1391998.875 386309.71875
-565928.25 290960.71875
-401843.8125 578506.9375
-445900.9375 314542.28125
-561692.0 264093.96875
-594170.0 276502.53125
-594170.0 312560.09375
-608290.875 324631.3125
-551525.0 324586.3125
-608290.875 330631.40625
-580049.125 311647.9375
-629472.125 327977.6875
-544746.9375 319125.09375
-563527.6875 335225.90625
-332086.75 308579.09375
-629472.125 314317.84375
-940131.125 240164.265625
-629472.125 334759.9375
-601230.4375 433824.59375
-587109.5625 335073.40625
-587109.5625 327641.6875
-161365.5 321804.0
-636532.5625 321961.40625
-728318.25 187614.59375
-724929.25 336919.3125
-388005.375 367617.75
-615351.3125 369609.375
-629472.125 258921.9375
-516646.4375 330701.09375
-664774.3125 334808.0625
-636532.5625 298486.90625
-544605.75 345846.5625
-961453.75 336715.59375
-943237.75 307528.84375
-544746.9375 441907.59375
436236.75
308643.40625

1 -495323.9375 328260.59375
2 -950439.375 290434.46875
3 -643593.0 292780.09375
4 -499419.0 452705.0
5 -515375.5625 339881.5625
6 -348890.5625 294927.78125
7 -377979.5625 318433.34375
8 -594170.0 247435.4375
9 -580049.125 256481.875
10 -424013.5625 323397.09375
11 -586827.125 319579.84375
12 -507609.0625 290749.3125
13 -588945.25 342362.5625
14 -690333.125 313328.3125
15 -865996.625 321604.28125
16 -130440.78125 377242.15625
17 -850746.125 447839.75
18 -943237.75 175605.453125
19 -456915.1875 416553.0
20 -761078.625 434855.46875
21 -813043.375 311214.34375
22 -839873.0 376477.53125
23 -755006.625 393614.1875
24 -615351.3125 414439.03125
25 -904405.375 376927.3125
26 -652771.5625 330591.34375
27 -1003675.125 423059.09375
28 -523848.0625 342557.3125
29 -224203.3125 452864.25
30 -415682.25 301894.875
31 -99657.3125 207854.734375
32 -1266040.75 279639.75
33 -682707.875 169623.359375
34 -614927.6875 545860.0
35 -457480.0 351379.78125
36 -509162.375 331166.625
37 -740179.75 288680.34375
38 -604619.4375 325787.25
39 -569034.875 369730.8125
40 -503514.0625 327621.4375
41 -861054.375 316553.25
42 -427120.1875 296276.6875
43 -844391.75 429754.0625
44 -474142.625 294572.46875
45 -320225.1875 402384.90625
46 -553219.5 286140.875
47 -755147.875 267815.0
48 -430085.5625 309493.03125
49 -615351.3125 374080.21875
50 -1034034.875 273927.53125
51 -1147849.125 330916.09375
52 -424296.0 473756.125
53 -550677.75 518154.0
54 -658419.9375 304233.75
55 -615351.3125 329205.59375
56 -1064536.0 344319.125
57 -510856.875 331068.125
58 -704454.0 472727.9375
59 -184100.0625 296890.625
60 -656019.375 358333.375
61 -690333.125 194842.5625
62 -804429.625 357496.78125
63 -581320.0 354438.28125
64 -882094.375 392084.9375
65 -572847.5 343979.53125
66 -594170.0 415878.34375
67 -671269.9375 348865.5
68 -754441.75 324410.0
69 -561409.5625 347691.5625
70 -480920.6875 378960.34375
71 -530626.125 321109.53125
72 -474142.625 288545.40625
73 -909206.5 334535.46875
74 -709113.875 286560.0625
75 -697393.5 437049.53125
76 -650653.4375 385360.71875
77 -615351.3125 356336.375
78 -640062.8125 341169.75
79 -644581.5 330366.46875
80 -631307.875 338291.15625
81 -603489.75 341802.15625
82 -491934.9375 336891.0
83 -730153.875 354904.71875
84 -923892.25 292480.96875
85 -629472.125 365907.6875
86 -175627.5625 448952.0625
87 -629330.9375 334696.5
88 -589368.875 205673.296875
89 -615351.3125 352486.25
90 -601230.4375 337121.71875
91 -506620.625 330743.375
92 -388852.625 324751.40625
93 -498148.125 315910.0625
94 -239877.5 257960.3125
95 -591204.625 293178.125
96 -286758.75 237045.5
97 -683131.5 324865.1875
98 -558867.8125 225967.40625
99 -556749.6875 352553.71875
100 -615351.3125 312955.0
101 -465952.5625 336716.90625
102 -392806.4375 330464.96875
103 -524836.5625 283372.90625
104 -653054.0 281213.96875
105 -366541.625 323102.03125
106 -756560.0 343063.34375
107 -715750.625 280764.1875
108 -510292.0625 376876.9375
109 -332934.0 364843.96875
110 -530626.125 297226.15625
111 -708831.375 257735.421875
112 -1035447.0 304042.59375
113 -583720.5625 359707.46875
114 -982635.0 482760.71875
115 -502384.375 322166.5
116 -604478.25 460096.25
117 -502384.375 295476.0625
118 -600524.375 345905.9375
119 -1014548.125 294572.46875
120 -631307.875 340940.34375
121 -792003.25 457986.8125
122 -700076.5 352838.84375
123 -550395.3125 386454.09375
124 -756560.0 356980.4375
125 -1338480.75 310644.90625
126 -580049.125 384384.6875
127 -523565.6875 560150.0
128 -427120.1875 319088.65625
129 -1105768.875 301582.75
130 -476119.5625 271183.875
131 -611679.875 509396.75
132 -637097.4375 286544.78125
133 -474566.25 328620.875
134 -897768.625 336402.0625
135 -486427.8125 286130.0625
136 -474142.625 419490.0625
137 -876022.5 288995.1875
138 -953687.25 305996.90625
139 -517352.5 413809.34375
140 -752606.125 478307.90625
141 -465670.125 300194.28125
142 -460163.0 373154.5625
143 -649382.5625 312734.59375
144 -843262.0 303855.9375
145 -418788.875 343868.4375
146 -397183.9375 403194.53125
147 -743145.125 267333.75
148 -522577.1875 263762.5
149 -784660.5 371305.0625
150 -700782.5 318693.75
151 -710384.75 383351.09375
152 -425708.0625 374903.3125
153 -676353.375 359748.40625
154 -468070.6875 301049.3125
155 -770680.75 349895.5
156 -742439.125 306356.71875
157 -545029.375 383538.65625
158 -608573.25 374082.03125
159 -383486.6875 307865.28125
160 -657713.875 330361.53125
161 -950721.875 257915.328125
162 -728318.25 344678.03125
163 -421895.4375 454784.375
164 -683837.5 366526.125
165 -580049.125 270568.5625
166 -597417.8125 350796.40625
167 -479508.5625 320226.625
168 -537827.75 342384.15625
169 -596146.9375 287331.0
170 -498289.3125 306716.09375
171 -580190.3125 335129.1875
172 -709537.5 324631.3125
173 -687932.5 319871.28125
174 -488969.5625 377107.21875
175 -615351.3125 353235.125
176 -991672.375 319987.78125
177 -976704.25 331454.46875
178 -693016.0 450475.46875
179 -607867.25 477543.28125
180 -493629.4375 369924.6875
181 -523706.875 341529.5625
182 -500125.0625 306176.8125
183 -675929.75 331454.46875
184 -645569.9375 308119.875
185 -493488.25 348750.34375
186 -1139800.25 340099.28125
187 -530061.25 292077.53125
188 -488122.3125 501291.6875
189 -526107.4375 308650.59375
190 -608290.875 315550.21875
191 -597700.1875 303163.28125
192 -547853.5625 328932.5625
193 -828858.75 325427.40625
194 -389135.0625 331589.40625
195 -546865.125 408048.53125
196 -756560.0 259449.078125
197 -707136.875 323763.21875
198 -427967.4375 386327.75
199 -728318.25 359210.46875
200 -701488.625 270958.96875
201 -584426.625 370279.5625
202 -489675.5625 385158.3125
203 -870797.75 336441.65625
204 -389417.4375 311407.75
205 -799063.75 414214.15625
206 -298620.3125 259488.21875
207 -537262.9375 388089.09375
208 -544746.9375 242570.15625
209 -474142.625 320508.625
210 -750770.375 317673.21875
211 -1625416.75 285978.5
212 -454514.625 391455.25
213 -647123.25 652008.75
214 -219967.0625 304260.71875
215 -237476.9375 339820.40625
216 -798922.5 206505.390625
217 -728318.25 211452.96875
218 -210364.875 387616.8125
219 -549124.4375 365439.9375
220 -756560.0 206685.296875
221 -766585.75 310269.8125
222 -707136.875 375180.8125
223 -1355990.625 390420.75
224 -527801.9375 359662.46875
225 -768845.125 588864.0
226 -356515.8125 301818.4375
227 -718010.0 380269.625
228 -798216.5 250763.8125
229 -635544.125 378231.6875
230 -613939.1875 403734.25
231 -692592.375 352998.96875
232 -937165.75 330484.3125
233 -493488.25 377129.71875
234 -645993.5625 450556.4375
235 -460021.75 292212.4375
236 -182546.8125 341207.96875
237 -837190.125 282172.46875
238 -654042.4375 213297.078125
239 -527378.3125 400990.59375
240 -383486.6875 365642.3125
241 -488545.9375 302893.40625
242 -582449.6875 258994.796875
243 -365976.8125 311934.0
244 -692451.25 320033.1875
245 -1745020.5 262008.328125
246 -589368.875 374871.8125
247 -615351.3125 709585.1875
248 -523706.875 322099.03125
249 -328697.75 331011.90625
250 -856818.125 303671.53125
251 -502666.8125 259116.25
252 -651359.5 417767.40625
253 -483744.8125 294442.46875
254 -1116077.125 361059.0625
255 -824340.125 289220.0625
256 -369365.8125 491450.5
257 -724223.125 398354.90625
258 -791862.125 263937.0
259 -769974.75 365631.09375
260 -841285.125 385387.6875
261 -654183.6875 379055.6875
262 -498571.75 402700.1875
263 -495182.75 343847.28125
264 -841285.125 312941.5
265 -498854.125 16417158.0
266 -427826.1875 401973.34375
267 -927563.625 293798.84375
268 -580049.125 283552.84375
269 -701771.0 432250.34375
270 -629472.125 319760.1875
271 -427543.8125 358756.1875
272 -500689.875 334504.875
273 -448442.6875 304647.5625
274 -746816.5 294888.65625
275 -758960.5 279347.375
276 -646558.375 372384.53125
277 -507185.4375 375613.9375
278 -950721.875 340661.5
279 -558867.8125 319805.15625
280 -820527.5 436748.15625
281 -394924.5625 313418.28125
282 -516505.25 422580.0625
283 -798922.5 262098.28125
284 -768562.625 298942.96875
285 -603348.5625 387841.71875
286 -364423.5 379760.9375
287 -656584.1875 327046.625
288 -678612.75 251523.9375
289 -609702.9375 344947.90625
290 -572847.5 374048.71875
291 -463693.1875 328937.5
292 -401279.0 315614.09375
293 -475837.125 281888.21875
294 -645146.3125 280888.3125
295 -404385.5625 288109.09375
296 -663644.625 339343.625
297 -682990.25 265021.875
298 -714197.375 362174.5
299 -473295.375 373733.875
300 -500407.4375 361927.125
301 -1003675.125 307769.03125
302 -815867.625 307004.40625
303 -785931.375 453070.71875
304 -923751.0 413141.875
305 -781412.625 391198.84375
306 -868256.0 442505.34375
307 -756560.0 385788.0
308 -646982.0 411106.1875
309 -554631.5625 375663.4375
310 -931799.875 358126.46875
311 -441523.4375 312175.0625
312 -615351.3125 431710.625
313 -580049.125 276041.5
314 -458327.25 330350.71875
315 -1180185.875 320290.90625
316 -286617.5625 281139.3125
317 -580049.125 513075.96875
318 -6459.59375 227483.15625
319 -755147.875 318900.1875
320 -601230.4375 137194.171875
321 -672964.4375 374488.15625
322 -540651.9375 325707.625
323 -728318.25 349265.8125
324 -678895.125 322998.59375
325 -1358673.625 366007.09375
326 -364564.75 349900.0
327 -421613.0 566824.75
328 -1029657.5 252248.09375
329 -672540.8125 270432.71875
330 -577224.9375 465381.1875
331 -378685.625 370607.90625
332 -684402.375 317736.15625
333 -577931.0 256179.171875
334 -833236.25 354525.09375
335 -697675.875 320007.5625
336 -601230.4375 399551.28125
337 -656725.4375 371179.125
338 -911889.5 325746.78125
339 -685955.625 365570.34375
340 -492358.5625 424458.78125
341 -717021.5 353677.6875
342 -562680.4375 303248.28125
343 -544746.9375 363015.59375
344 -289865.375 314392.5
345 -498854.125 309851.53125
346 -493488.25 229255.28125
347 -661385.3125 293753.84375
348 -1250790.25 292392.375
349 -628766.125 345505.1875
350 -424296.0 535908.1875
351 -784801.625 334153.15625
352 -403538.3125 304233.75
353 -1102521.125 401305.4375
354 -467082.1875 264437.15625
355 -302291.6875 484245.0
356 -722669.875 299754.84375
357 -567199.125 250828.578125
358 -473154.1875 364333.46875
359 -551807.375 327136.59375
360 -615351.3125 289534.90625
361 -558867.8125 309634.28125
362 -516505.25 329670.65625
363 -678612.75 312779.125
364 -510856.875 299207.0
365 -613656.8125 373643.90625
366 -243972.5625 297068.3125
367 -332934.0 357919.59375
368 -587109.5625 215006.234375
369 -800758.25 240958.59375
370 -251597.8125 322157.5
371 -735519.875 407107.59375
372 -963289.375 219079.0
373 -700076.5 368633.375
374 -788755.5 450425.96875
375 -428955.875 357406.40625
376 -734672.625 405623.34375
377 -589933.75 271993.46875
378 -502384.375 368542.0625
379 -930529.0 341306.9375
380 -792144.5 294221.1875
381 -481909.125 434104.8125
382 -1391998.875 386309.71875
383 -565928.25 290960.71875
384 -401843.8125 578506.9375
385 -445900.9375 314542.28125
386 -561692.0 264093.96875
387 -594170.0 276502.53125
388 -594170.0 312560.09375
389 -608290.875 324631.3125
390 -551525.0 324586.3125
391 -608290.875 330631.40625
392 -580049.125 311647.9375
393 -629472.125 327977.6875
394 -544746.9375 319125.09375
395 -563527.6875 335225.90625
396 -332086.75 308579.09375
397 -629472.125 314317.84375
398 -940131.125 240164.265625
399 -629472.125 334759.9375
400 -601230.4375 433824.59375
401 -587109.5625 335073.40625
402 -587109.5625 327641.6875
403 -161365.5 321804.0
404 -636532.5625 321961.40625
405 -728318.25 187614.59375
406 -724929.25 336919.3125
407 -388005.375 367617.75
408 -615351.3125 369609.375
409 -629472.125 258921.9375
410 -516646.4375 330701.09375
411 -664774.3125 334808.0625
412 -636532.5625 298486.90625
413 -544605.75 345846.5625
414 -961453.75 336715.59375
415 -943237.75 307528.84375
416 -544746.9375 441907.59375
417 436236.75
418 308643.40625