s470659/CICP
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Add data converter and datasets

Browse Source
This commit is contained in:
Waneksio 2022-11-29 23:36:06 +01:00
commit 3d28b2d7ed
20 changed files with 718 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

145
MasterThesis/AirPassengers.csv Normal file
View File

@ -0,0 +1,145 @@
Month,#Passengers
1949-01,112
1949-02,118
1949-03,132
1949-04,129
1949-05,121
1949-06,135
1949-07,148
1949-08,148
1949-09,136
1949-10,119
1949-11,104
1949-12,118
1950-01,115
1950-02,126
1950-03,141
1950-04,135
1950-05,125
1950-06,149
1950-07,170
1950-08,170
1950-09,158
1950-10,133
1950-11,114
1950-12,140
1951-01,145
1951-02,150
1951-03,178
1951-04,163
1951-05,172
1951-06,178
1951-07,199
1951-08,199
1951-09,184
1951-10,162
1951-11,146
1951-12,166
1952-01,171
1952-02,180
1952-03,193
1952-04,181
1952-05,183
1952-06,218
1952-07,230
1952-08,242
1952-09,209
1952-10,191
1952-11,172
1952-12,194
1953-01,196
1953-02,196
1953-03,236
1953-04,235
1953-05,229
1953-06,243
1953-07,264
1953-08,272
1953-09,237
1953-10,211
1953-11,180
1953-12,201
1954-01,204
1954-02,188
1954-03,235
1954-04,227
1954-05,234
1954-06,264
1954-07,302
1954-08,293
1954-09,259
1954-10,229
1954-11,203
1954-12,229
1955-01,242
1955-02,233
1955-03,267
1955-04,269
1955-05,270
1955-06,315
1955-07,364
1955-08,347
1955-09,312
1955-10,274
1955-11,237
1955-12,278
1956-01,284
1956-02,277
1956-03,317
1956-04,313
1956-05,318
1956-06,374
1956-07,413
1956-08,405
1956-09,355
1956-10,306
1956-11,271
1956-12,306
1957-01,315
1957-02,301
1957-03,356
1957-04,348
1957-05,355
1957-06,422
1957-07,465
1957-08,467
1957-09,404
1957-10,347
1957-11,305
1957-12,336
1958-01,340
1958-02,318
1958-03,362
1958-04,348
1958-05,363
1958-06,435
1958-07,491
1958-08,505
1958-09,404
1958-10,359
1958-11,310
1958-12,337
1959-01,360
1959-02,342
1959-03,406
1959-04,396
1959-05,420
1959-06,472
1959-07,548
1959-08,559
1959-09,463
1959-10,407
1959-11,362
1959-12,405
1960-01,417
1960-02,391
1960-03,419
1960-04,461
1960-05,472
1960-06,535
1960-07,622
1960-08,606
1960-09,508
1960-10,461
1960-11,390
1960-12,432
1 Month #Passengers
2 1949-01 112
3 1949-02 118
4 1949-03 132
5 1949-04 129
6 1949-05 121
7 1949-06 135
8 1949-07 148
9 1949-08 148
10 1949-09 136
11 1949-10 119
12 1949-11 104
13 1949-12 118
14 1950-01 115
15 1950-02 126
16 1950-03 141
17 1950-04 135
18 1950-05 125
19 1950-06 149
20 1950-07 170
21 1950-08 170
22 1950-09 158
23 1950-10 133
24 1950-11 114
25 1950-12 140
26 1951-01 145
27 1951-02 150
28 1951-03 178
29 1951-04 163
30 1951-05 172
31 1951-06 178
32 1951-07 199
33 1951-08 199
34 1951-09 184
35 1951-10 162
36 1951-11 146
37 1951-12 166
38 1952-01 171
39 1952-02 180
40 1952-03 193
41 1952-04 181
42 1952-05 183
43 1952-06 218
44 1952-07 230
45 1952-08 242
46 1952-09 209
47 1952-10 191
48 1952-11 172
49 1952-12 194
50 1953-01 196
51 1953-02 196
52 1953-03 236
53 1953-04 235
54 1953-05 229
55 1953-06 243
56 1953-07 264
57 1953-08 272
58 1953-09 237
59 1953-10 211
60 1953-11 180
61 1953-12 201
62 1954-01 204
63 1954-02 188
64 1954-03 235
65 1954-04 227
66 1954-05 234
67 1954-06 264
68 1954-07 302
69 1954-08 293
70 1954-09 259
71 1954-10 229
72 1954-11 203
73 1954-12 229
74 1955-01 242
75 1955-02 233
76 1955-03 267
77 1955-04 269
78 1955-05 270
79 1955-06 315
80 1955-07 364
81 1955-08 347
82 1955-09 312
83 1955-10 274
84 1955-11 237
85 1955-12 278
86 1956-01 284
87 1956-02 277
88 1956-03 317
89 1956-04 313
90 1956-05 318
91 1956-06 374
92 1956-07 413
93 1956-08 405
94 1956-09 355
95 1956-10 306
96 1956-11 271
97 1956-12 306
98 1957-01 315
99 1957-02 301
100 1957-03 356
101 1957-04 348
102 1957-05 355
103 1957-06 422
104 1957-07 465
105 1957-08 467
106 1957-09 404
107 1957-10 347
108 1957-11 305
109 1957-12 336
110 1958-01 340
111 1958-02 318
112 1958-03 362
113 1958-04 348
114 1958-05 363
115 1958-06 435
116 1958-07 491
117 1958-08 505
118 1958-09 404
119 1958-10 359
120 1958-11 310
121 1958-12 337
122 1959-01 360
123 1959-02 342
124 1959-03 406
125 1959-04 396
126 1959-05 420
127 1959-06 472
128 1959-07 548
129 1959-08 559
130 1959-09 463
131 1959-10 407
132 1959-11 362
133 1959-12 405
134 1960-01 417
135 1960-02 391
136 1960-03 419
137 1960-04 461
138 1960-05 472
139 1960-06 535
140 1960-07 622
141 1960-08 606
142 1960-09 508
143 1960-10 461
144 1960-11 390
145 1960-12 432

99
MasterThesis/TimeSeries.py Normal file
View File

@ -0,0 +1,99 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 14 13:37:58 2021
@author: sadrachpierre
"""
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
df = pd.read_csv("AirPassengers.csv")
print(df.head())
print(df.tail())
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df.index = df['Month']
del df['Month']
print(df.head())
# sns.lineplot(data=df)
# plt.ylabel("Number of Passengers")
# plt.show()
rolling_mean = df.rolling(7).mean()
rolling_std = df.rolling(7).std()
plt.plot(df, color="blue", label="Original Passenger Data")
plt.plot(rolling_mean, color="red", label="Rolling Mean #Passenger")
plt.plot(rolling_std, color="black", label="Rolling Standard Deviation in #Passenger")
plt.title("Passenger Time Series, Rolling Mean, Standard Deviation")
plt.legend(loc="best")
plt.show()
adft = adfuller(df, autolag="AIC")
output_df = pd.DataFrame({"Values": [adft[0], adft[1], adft[2], adft[3], adft[4]['1%'], adft[4]['5%'], adft[4]['10%']],
"Metric": ["Test Statistics", "p-value", "No. of lags used", "Number of observations used",
"critical value (1%)", "critical value (5%)", "critical value (10%)"]})
print(output_df)
autocorrelation_lag1 = df['#Passengers'].autocorr(lag=1)
print("One Month Lag: ", autocorrelation_lag1)
autocorrelation_lag3 = df['#Passengers'].autocorr(lag=3)
print("Three Month Lag: ", autocorrelation_lag3)
autocorrelation_lag6 = df['#Passengers'].autocorr(lag=6)
print("Six Month Lag: ", autocorrelation_lag6)
autocorrelation_lag9 = df['#Passengers'].autocorr(lag=9)
print("Nine Month Lag: ", autocorrelation_lag9)
decompose = seasonal_decompose(df['#Passengers'], model='additive', period=7)
decompose.plot()
plt.show()
df['Date'] = df.index
train = df[df['Date'] < pd.to_datetime("1960-08", format='%Y-%m')]
train['train'] = train['#Passengers']
del train['Date']
del train['#Passengers']
test = df[df['Date'] >= pd.to_datetime("1960-08", format='%Y-%m')]
del test['Date']
test['test'] = test['#Passengers']
del test['#Passengers']
plt.plot(train, color="black")
plt.plot(test, color="red")
plt.title("Train/Test split for Passenger Data")
plt.ylabel("Passenger Number")
plt.xlabel('Year-Month')
sns.set()
plt.show()
from pmdarima.arima import auto_arima
model = auto_arima(train, trace=True, error_action='ignore', suppress_warnings=True)
model.fit(train)
forecast = model.predict(n_periods=len(test))
forecast = pd.DataFrame(forecast, index=test.index, columns=['Prediction'])
plt.plot(train, label='Train')
plt.plot(test, label='Test')
plt.plot(forecast, label='Prediction')
plt.title('#Passenger Prediction')
plt.xlabel('Date')
plt.ylabel('Actual #Passenger')
plt.legend(loc='upper left', fontsize=8)
plt.show()
from math import sqrt
from sklearn.metrics import mean_squared_error
print("RMSE: ", rms)

111
MasterThesis/TimeSeriesForecasting.py Normal file
View File

@ -0,0 +1,111 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 28 11:11:06 2021
@author: sadrachpierre
"""
import pandas as pd
import pandas_datareader as web
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.arima.model import ARIMA
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
# btc = web.get_data_yahoo(['BTC-USD'], start=datetime.datetime(2018, 1, 1), end=datetime.datetime(2020, 12, 2))
# btc = btc['Close']
# btc.to_csv("btc.csv")
btc = pd.read_csv("btc.csv")
btc.index = pd.to_datetime(btc['Date'], format='%Y-%m-%d')
del btc['Date']
print(btc.head())
sns.set()
plt.ylabel('BTC Price')
plt.xlabel('Date')
plt.xticks(rotation=45)
plt.plot(btc.index, btc['BTC-USD'], )
plt.show()
train = btc[btc.index < pd.to_datetime("2020-11-01", format='%Y-%m-%d')]
test = btc[btc.index >= pd.to_datetime("2020-11-01", format='%Y-%m-%d')]
print(test)
plt.plot(train, color = "black", label = 'Training')
plt.plot(test, color = "red", label = 'Testing')
plt.ylabel('BTC Price')
plt.xlabel('Date')
plt.xticks(rotation=45)
plt.title("Train/Test split for BTC Data")
y = train['BTC-USD']
ARMAmodel = SARIMAX(y, order = (1, 0, 1))
ARMAmodel = ARMAmodel.fit()
y_pred = ARMAmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = ARMAmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='green', label = 'ARMA Predictions')
plt.legend()
import numpy as np
from sklearn.metrics import mean_squared_error
arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("ARMA RMSE: ",arma_rmse)
ARIMAmodel = ARIMA(y, order = (5, 4, 2))
ARIMAmodel = ARIMAmodel.fit()
y_pred = ARIMAmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = ARIMAmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='Yellow', label = 'ARIMA Predictions')
plt.legend()
import numpy as np
from sklearn.metrics import mean_squared_error
arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("ARIMA RMSE: ",arma_rmse)
SARIMAXmodel = SARIMAX(y, order = (5, 4, 2), seasonal_order=(2,2,2,12))
SARIMAXmodel = SARIMAXmodel.fit()
y_pred = SARIMAXmodel.get_forecast(len(test.index))
y_pred_df = y_pred.conf_int(alpha = 0.05)
y_pred_df["Predictions"] = SARIMAXmodel.predict(start = y_pred_df.index[0], end = y_pred_df.index[-1])
y_pred_df.index = test.index
y_pred_out = y_pred_df["Predictions"]
plt.plot(y_pred_out, color='Blue', label = 'SARIMA Predictions')
plt.legend()
import numpy as np
from sklearn.metrics import mean_squared_error
arma_rmse = np.sqrt(mean_squared_error(test["BTC-USD"].values, y_pred_df["Predictions"]))
print("SARIMA RMSE: ",arma_rmse)

12
MasterThesis/__init__.py Normal file
View File

@ -0,0 +1,12 @@
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
if __name__ == '__main__':
driver = webdriver.Chrome()
driver.get("https://stockx.com/jordan-1-retro-black-royal-blue-2001")
button = driver.find_element_by_xpath('//*[@id="main-content"]/div/section[4]/div/div/div/div/div[1]/div[2]/button')
button.click()
elem = driver.find_element_by_xpath('//*[@id="chakra-modal--body-38"]/div/div/table/tbody/tr[100]/td[4]/p/p')
price = elem.text
print(price)
driver.close()

1
MasterThesis/configs/.azureml/myconfig.json Normal file
View File

@ -0,0 +1 @@
{"Id": null, "Scope": "/subscriptions/616d95e9-f2f8-4e95-8535-e2449b5bd652/resourceGroups/myresourcegroup/providers/Microsoft.MachineLearningServices/workspaces/newworkspace"}

25
MasterThesis/converter.py Normal file
View File

@ -0,0 +1,25 @@
import matplotlib.pyplot as plt
def convert(text):
points = text.split("L")
result = []
max_y = 0
min_y = 1000000
for point in points:
x, y = point.split(",")
x = int(float(x))
y = int(float(y))
if y > max_y:
max_y = y
if y < min_y:
min_y = y
result.append([x, y])
print(result)
# print([row[1] for row in result])
plt.plot([row[0] for row in result], [max_y - row[1] + min_y for row in result])
plt.show()
if __name__ == '__main__':
convert("25,35.67999999999999L35.07070707070707,60.08L45.141414141414145,60.08L55.21212121212121,60.08L65.28282828282829,21.999999999999993L75.35353535353535,21.999999999999993L85.42424242424242,21.999999999999993L95.49494949494948,21.999999999999993L105.56565656565657,50L115.63636363636363,50L125.7070707070707,27.200000000000003L135.77777777777777,27.200000000000003L145.84848484848484,76L155.91919191919192,50.23999999999999L165.98989898989896,50.23999999999999L176.06060606060606,50.23999999999999L186.13131313131314,50.23999999999999L196.20202020202018,58.16000000000001L206.27272727272725,54.00000000000001L216.34343434343432,41.99999999999999L226.4141414141414,41.99999999999999L236.48484848484847,66.72000000000001L246.55555555555554,55.44000000000001L256.6262626262626,64.48L266.6969696969697,80.08L276.7676767676768,66.08000000000001L286.83838383838383,76L296.9090909090909,30L306.9797979797979,70L317.050505050505,70L327.1212121212121,47.599999999999994L337.19191919191917,68.64L347.26262626262627,68.64L357.3333333333333,68.64L367.40404040404036,72.32L377.47474747474746,70L387.54545454545456,70L397.6161616161616,66.48L407.68686868686865,71.92L417.7575757575757,80.8L427.8282828282828,80.8L437.8989898989899,78.32000000000001L447.96969696969694,52.400000000000006L458.04040404040404,58.32000000000001L468.1111111111111,58.32000000000001L478.18181818181813,58.96000000000001L488.25252525252523,79.67999999999999L498.3232323232323,78.08L508.3939393939394,78.08L518.4646464646464,66L528.5353535353536,74.08L538.6060606060606,74.08L548.6767676767677,74.08L558.7474747474748,74.08L568.8181818181818,70L578.8888888888889,58.88L588.959595959596,74.88L599.0303030303031,81.92L609.10101010101,53.52L619.1717171717171,58.96000000000001L629.2424242424242,67.36000000000001L639.3131313131313,71.12L649.3838383838383,67.68L659.4545454545455,73.91999999999999L669.5252525252525,80.08L679.5959595959596,77.60000000000001L689.6666666666666,87.2L699.7373737373738,70.48L709.8080808080807,80.88L719.878787878788,88.96000000000001L729.9494949494949,78.88L740.020202020202,78.88L750.0909090909091,68.08L760.1616161616162,98.00000000000001L770.2323232323232,94L780.3030303030303,94L790.3737373737373,80.55999999999999L800.4444444444445,86.32000000000001L810.5151515151514,81.52L820.5858585858587,85.92L830.6565656565656,109.28000000000002L840.7272727272727,123.76000000000002L850.7979797979798,125.76L860.8686868686868,125.36L870.9393939393939,124.80000000000001L881.010101010101,125.03999999999999L891.0808080808081,124.72000000000001L901.1515151515151,126.4L911.2222222222222,126.72L921.2929292929293,124.80000000000001L931.3636363636363,127.12L941.4343434343434,126.72L951.5050505050505,126.47999999999999L961.5757575757576,127.52L971.6464646464646,127.68L981.7171717171717,128.56L991.7878787878788,131.6L1001.8585858585858,134.88L1011.929292929293,137.27999999999997L1022,137.35999999999999")

7
MasterThesis/keywords for presentation.txt Normal file
View File

@ -0,0 +1,7 @@
stationarity
autoregressive moving average ARMA
Dickey Fuller test
rolling mean
Autocorrelation
Decomposition
Forecasting (ARIMA)

17
MasterThesis/predykcje.py Normal file
View File

@ -0,0 +1,17 @@
from azureml.core import Workspace
from azureml.core.experiment import Experiment
from azureml.train
def main():
ws = Workspace.get(name='newworkspace', subscription_id='616d95e9-f2f8-4e95-8535-e2449b5bd652', resource_group='myresourcegroup')
# ws = Workspace.create(name='newworkspace',
# subscription_id='616d95e9-f2f8-4e95-8535-e2449b5bd652',
# resource_group='myresourcegroup',
# create_resource_group=True,
# location='eastus2'
# )
if __name__ == '__main__':
main()

0
MasterThesis/resources/air-jordan-1-retro-high-pollen.txt Normal file
View File

1
MasterThesis/resources/air-jordan-1-retro-high-shattered-backboard-3.txt Normal file
View File

@ -0,0 +1 @@
M25,35.60000000000001L35.07070707070707,47.527272727272724L45.141414141414145,88.25454545454546L55.21212121212121,94.65454545454544L65.28282828282829,99.89090909090908L75.35353535353535,105.12727272727273L85.42424242424242,104.83636363636364L95.49494949494948,103.38181818181818L105.56565656565657,101.63636363636364L115.63636363636363,100.18181818181819L125.7070707070707,100.18181818181819L135.77777777777777,99.01818181818183L145.84848484848484,97.27272727272727L155.91919191919192,96.4L165.98989898989896,95.23636363636363L176.06060606060606,100.18181818181819L186.13131313131314,95.52727272727273L196.20202020202018,91.74545454545456L206.27272727272725,85.92727272727271L216.34343434343432,82.14545454545454L226.4141414141414,78.94545454545454L236.48484848484847,77.2L246.55555555555554,75.16363636363636L256.6262626262626,73.41818181818181L266.6969696969697,73.41818181818181L276.7676767676768,69.34545454545454L286.83838383838383,66.43636363636365L296.9090909090909,61.78181818181818L306.9797979797979,63.23636363636364L317.050505050505,64.39999999999999L327.1212121212121,58.00000000000001L337.19191919191917,60.90909090909092L347.26262626262627,54.8L357.3333333333333,53.63636363636363L367.40404040404036,54.509090909090915L377.47474747474746,58.87272727272727L387.54545454545456,60.32727272727273L397.6161616161616,58.58181818181817L407.68686868686865,57.70909090909091L417.7575757575757,58.58181818181817L427.8282828282828,57.12727272727273L437.8989898989899,58.87272727272727L447.96969696969694,59.163636363636364L458.04040404040404,57.12727272727273L468.1111111111111,55.09090909090909L478.18181818181813,53.05454545454545L488.25252525252523,55.38181818181819L498.3232323232323,57.12727272727273L508.3939393939394,58.58181818181817L518.4646464646464,56.25454545454545L528.5353535353536,57.12727272727273L538.6060606060606,53.63636363636363L548.6767676767677,52.18181818181819L558.7474747474748,48.981818181818184L568.8181818181818,51.8909090909091L578.8888888888889,49.27272727272728L588.959595959596,54.21818181818182L599.0303030303031,54.8L609.10101010101,52.472727272727276L619.1717171717171,50.43636363636362L629.2424242424242,51.30909090909091L639.3131313131313,50.43636363636362L649.3838383838383,49.85454545454546L659.4545454545455,47.81818181818181L669.5252525252525,54.21818181818182L679.5959595959596,48.4L689.6666666666666,52.472727272727276L699.7373737373738,52.472727272727276L709.8080808080807,54.509090909090915L719.878787878788,53.92727272727273L729.9494949494949,51.30909090909091L740.020202020202,50.72727272727272L750.0909090909091,46.94545454545455L760.1616161616162,49.27272727272728L770.2323232323232,48.981818181818184L780.3030303030303,46.36363636363637L790.3737373737373,49.85454545454546L800.4444444444445,51.018181818181816L810.5151515151514,44.327272727272735L820.5858585858587,51.8909090909091L830.6565656565656,48.1090909090909L840.7272727272727,49.85454545454546L850.7979797979798,51.6L860.8686868686868,55.38181818181819L870.9393939393939,55.09090909090909L881.010101010101,54.509090909090915L891.0808080808081,59.74545454545454L901.1515151515151,63.81818181818181L911.2222222222222,63.52727272727274L921.2929292929293,63.23636363636364L931.3636363636363,64.10909090909091L941.4343434343434,61.19999999999999L951.5050505050505,58.29090909090908L961.5757575757576,61.78181818181818L971.6464646464646,61.19999999999999L981.7171717171717,61.78181818181818L991.7878787878788,58.58181818181817L1001.8585858585858,61.78181818181818L1011.929292929293,63.81818181818181L1022,63.52727272727274

1
MasterThesis/resources/air-jordan-4-retro-white-midnight-navy.txt Normal file
View File

@ -0,0 +1 @@
M25,54.8L35.07070707070707,54.8L45.141414141414145,72.66666666666667L55.21212121212121,82.26666666666667L65.28282828282829,91.33333333333333L75.35353535353535,94.53333333333333L85.42424242424242,92.13333333333333L95.49494949494948,81.73333333333333L105.56565656565657,86.26666666666667L115.63636363636363,63.86666666666667L125.7070707070707,65.73333333333335L135.77777777777777,68.93333333333332L145.84848484848484,74.53333333333333L155.91919191919192,73.73333333333333L165.98989898989896,73.2L176.06060606060606,70.8L186.13131313131314,68.39999999999999L196.20202020202018,68.66666666666667L206.27272727272725,58.266666666666666L216.34343434343432,63.599999999999994L226.4141414141414,62.266666666666666L236.48484848484847,66L246.55555555555554,63.333333333333336L256.6262626262626,69.46666666666667L266.6969696969697,68.66666666666667L276.7676767676768,68.66666666666667L286.83838383838383,68.66666666666667L296.9090909090909,67.86666666666667L306.9797979797979,54.00000000000001L317.050505050505,54.00000000000001L327.1212121212121,71.06666666666668L337.19191919191917,72.66666666666667L347.26262626262627,70.26666666666668L357.3333333333333,72.93333333333332L367.40404040404036,70.26666666666668L377.47474747474746,64.13333333333334L387.54545454545456,57.2L397.6161616161616,58.266666666666666L407.68686868686865,55.86666666666666L417.7575757575757,61.19999999999999L427.8282828282828,36.666666666666664L437.8989898989899,65.73333333333335L447.96969696969694,65.73333333333335L458.04040404040404,63.06666666666666L468.1111111111111,63.86666666666667L478.18181818181813,56.93333333333334L488.25252525252523,56.666666666666664L498.3232323232323,57.2L508.3939393939394,59.333333333333336L518.4646464646464,60.93333333333334L528.5353535353536,63.333333333333336L538.6060606060606,45.73333333333334L548.6767676767677,60.666666666666664L558.7474747474748,65.73333333333335L568.8181818181818,65.73333333333335L578.8888888888889,42.53333333333334L588.959595959596,50.800000000000004L599.0303030303031,50.800000000000004L609.10101010101,51.33333333333333L619.1717171717171,51.866666666666674L629.2424242424242,56.93333333333334L639.3131313131313,63.599999999999994L649.3838383838383,32.66666666666667L659.4545454545455,49.199999999999996L669.5252525252525,67.33333333333333L679.5959595959596,56.13333333333333L689.6666666666666,61.99999999999999L699.7373737373738,66.53333333333335L709.8080808080807,59.60000000000001L719.878787878788,67.33333333333333L729.9494949494949,70L740.020202020202,69.2L750.0909090909091,76.66666666666666L760.1616161616162,79.60000000000001L770.2323232323232,76.93333333333332L780.3030303030303,81.19999999999999L790.3737373737373,76.13333333333333L800.4444444444445,80.39999999999999L810.5151515151514,80.93333333333334L820.5858585858587,77.46666666666665L830.6565656565656,84.13333333333334L840.7272727272727,85.19999999999999L850.7979797979798,85.73333333333333L860.8686868686868,86L870.9393939393939,85.46666666666667L881.010101010101,90L891.0808080808081,93.46666666666667L901.1515151515151,86.8L911.2222222222222,84.39999999999999L921.2929292929293,85.46666666666667L931.3636363636363,85.73333333333333L941.4343434343434,87.06666666666668L951.5050505050505,88.13333333333333L961.5757575757576,88.93333333333332L971.6464646464646,91.60000000000001L981.7171717171717,91.60000000000001L991.7878787878788,92.13333333333333L1001.8585858585858,92.93333333333332L1011.929292929293,91.06666666666666L1022,92.39999999999999

1
MasterThesis/resources/blastoise-holo-1999-pokemon-base-set-2-102.txt Normal file
View File

@ -0,0 +1 @@
M25,148.03636363636366L35.07070707070707,155.45454545454544L45.141414141414145,155.45454545454544L55.21212121212121,142.5090909090909L65.28282828282829,142.5090909090909L75.35353535353535,142.5090909090909L85.42424242424242,145.27272727272725L95.49494949494948,145.27272727272725L105.56565656565657,145.27272727272725L115.63636363636363,162.72727272727275L125.7070707070707,162.72727272727275L135.77777777777777,162.72727272727275L145.84848484848484,147.45454545454547L155.91919191919192,147.45454545454547L165.98989898989896,147.45454545454547L176.06060606060606,147.45454545454547L186.13131313131314,147.45454545454547L196.20202020202018,147.45454545454547L206.27272727272725,147.45454545454547L216.34343434343432,147.45454545454547L226.4141414141414,147.45454545454547L236.48484848484847,147.45454545454547L246.55555555555554,107.30909090909091L256.6262626262626,107.30909090909091L266.6969696969697,107.30909090909091L276.7676767676768,126.36363636363637L286.83838383838383,137.27272727272725L296.9090909090909,137.27272727272725L306.9797979797979,137.27272727272725L317.050505050505,137.27272727272725L327.1212121212121,31.818181818181817L337.19191919191917,32.981818181818184L347.26262626262627,104.25454545454546L357.3333333333333,123.16363636363636L367.40404040404036,110.8L377.47474747474746,111.0909090909091L387.54545454545456,100.76363636363637L397.6161616161616,124.03636363636363L407.68686868686865,133.05454545454546L417.7575757575757,98.00000000000001L427.8282828282828,82.87272727272727L437.8989898989899,82.58181818181818L447.96969696969694,106L458.04040404040404,60.90909090909092L468.1111111111111,85.05454545454546L478.18181818181813,113.7090909090909L488.25252525252523,137.27272727272725L498.3232323232323,92.47272727272725L508.3939393939394,30.36363636363637L518.4646464646464,98.87272727272727L528.5353535353536,109.78181818181818L538.6060606060606,120.10909090909092L548.6767676767677,120.10909090909092L558.7474747474748,120.10909090909092L568.8181818181818,119.09090909090911L578.8888888888889,106.58181818181819L588.959595959596,106.58181818181819L599.0303030303031,132.1818181818182L609.10101010101,112.25454545454546L619.1717171717171,101.49090909090908L629.2424242424242,101.49090909090908L639.3131313131313,115.45454545454547L649.3838383838383,115.45454545454547L659.4545454545455,136.4L669.5252525252525,117.4909090909091L679.5959595959596,117.4909090909091L689.6666666666666,117.4909090909091L699.7373737373738,145.27272727272725L709.8080808080807,144.98181818181817L719.878787878788,117.92727272727272L729.9494949494949,131.16363636363639L740.020202020202,128.54545454545456L750.0909090909091,142.36363636363635L760.1616161616162,147.45454545454547L770.2323232323232,142.36363636363635L780.3030303030303,125.78181818181818L790.3737373737373,142.36363636363635L800.4444444444445,142.36363636363635L810.5151515151514,126.65454545454544L820.5858585858587,136.9818181818182L830.6565656565656,136.9818181818182L840.7272727272727,145.27272727272725L850.7979797979798,139.16363636363636L860.8686868686868,138L870.9393939393939,148.1818181818182L881.010101010101,126.94545454545455L891.0808080808081,102.36363636363637L901.1515151515151,101.92727272727271L911.2222222222222,119.09090909090911L921.2929292929293,119.09090909090911L931.3636363636363,139.16363636363636L941.4343434343434,139.16363636363636L951.5050505050505,139.16363636363636L961.5757575757576,138.72727272727275L971.6464646464646,102.07272727272728L981.7171717171717,115.6L991.7878787878788,115.6L1001.8585858585858,115.6L1011.929292929293,115.6L1022,145.70909090909092

1
MasterThesis/resources/charizard-holo-1999-pokemon-base-set-4-102.txt Normal file
View File

@ -0,0 +1 @@
M25,155.49333333333334L35.07070707070707,155.49333333333334L45.141414141414145,162.05333333333334L55.21212121212121,155.65333333333334L65.28282828282829,155.65333333333334L75.35353535353535,155.92L85.42424242424242,157.78666666666666L95.49494949494948,157.78666666666666L105.56565656565657,148.72L115.63636363636363,157.62666666666667L125.7070707070707,150L135.77777777777777,150L145.84848484848484,150L155.91919191919192,150L165.98989898989896,145.73333333333335L176.06060606060606,145.73333333333335L186.13131313131314,157.73333333333335L196.20202020202018,157.73333333333335L206.27272727272725,157.73333333333335L216.34343434343432,142.05333333333334L226.4141414141414,142.05333333333334L236.48484848484847,142.05333333333334L246.55555555555554,130L256.6262626262626,130L266.6969696969697,130L276.7676767676768,134.69333333333336L286.83838383838383,140.66666666666669L296.9090909090909,140.66666666666669L306.9797979797979,127.33333333333334L317.050505050505,19.60000000000001L327.1212121212121,58.64000000000001L337.19191919191917,73.94666666666666L347.26262626262627,97.36000000000001L357.3333333333333,120.13333333333333L367.40404040404036,134L377.47474747474746,129.62666666666667L387.54545454545456,135.28L397.6161616161616,124.02666666666667L407.68686868686865,113.14666666666668L417.7575757575757,104.13333333333335L427.8282828282828,124.61333333333333L437.8989898989899,93.52000000000001L447.96969696969694,107.54666666666667L458.04040404040404,133.20000000000002L468.1111111111111,128.18666666666667L478.18181818181813,115.76L488.25252525252523,123.38666666666666L498.3232323232323,124.93333333333332L508.3939393939394,109.04L518.4646464646464,130.26666666666668L528.5353535353536,137.14666666666668L538.6060606060606,144.72L548.6767676767677,136.13333333333335L558.7474747474748,140.18666666666667L568.8181818181818,135.60000000000002L578.8888888888889,143.38666666666666L588.959595959596,122.16000000000001L599.0303030303031,134.64000000000001L609.10101010101,127.81333333333333L619.1717171717171,115.12L629.2424242424242,115.12L639.3131313131313,134.05333333333334L649.3838383838383,151.65333333333334L659.4545454545455,140.02666666666667L669.5252525252525,134.21333333333334L679.5959595959596,147.12L689.6666666666666,133.35999999999999L699.7373737373738,133.35999999999999L709.8080808080807,113.36000000000001L719.878787878788,138.26666666666665L729.9494949494949,141.52L740.020202020202,142.48000000000002L750.0909090909091,133.84L760.1616161616162,141.68L770.2323232323232,90L780.3030303030303,132.88L790.3737373737373,148.24L800.4444444444445,135.54666666666665L810.5151515151514,143.44L820.5858585858587,143.54666666666668L830.6565656565656,139.86666666666665L840.7272727272727,141.94666666666666L850.7979797979798,144.08L860.8686868686868,151.86666666666667L870.9393939393939,142.53333333333333L881.010101010101,142.53333333333333L891.0808080808081,136.45333333333332L901.1515151515151,144.82666666666665L911.2222222222222,121.36L921.2929292929293,121.36L931.3636363636363,124.66666666666666L941.4343434343434,151.17333333333335L951.5050505050505,150.79999999999998L961.5757575757576,144.82666666666665L971.6464646464646,147.4933333333333L981.7171717171717,151.65333333333334L991.7878787878788,131.76L1001.8585858585858,131.76L1011.929292929293,143.33333333333334L1022,147.8133333333333

1
MasterThesis/resources/jordan-1-retro-fragment.txt Normal file
View File

@ -0,0 +1 @@
M25,152.57454545454544L35.07070707070707,155.3090909090909L45.141414141414145,145.6509090909091L55.21212121212121,155.16363636363636L65.28282828282829,127.87636363636364L75.35353535353535,128.60363636363635L85.42424242424242,131.2218181818182L95.49494949494948,126.2181818181818L105.56565656565657,127.38181818181818L115.63636363636363,124.90909090909092L125.7070707070707,121.76727272727274L135.77777777777777,122.52363636363637L145.84848484848484,125.89818181818181L155.91919191919192,117.25818181818182L165.98989898989896,121.85454545454546L176.06060606060606,120.92363636363636L186.13131313131314,120.19636363636364L196.20202020202018,123.83272727272728L206.27272727272725,124.26909090909092L216.34343434343432,121.44727272727273L226.4141414141414,122.72727272727273L236.48484848484847,121.27272727272727L246.55555555555554,122.72727272727273L256.6262626262626,121.94181818181818L266.6969696969697,120.13818181818179L276.7676767676768,123.16363636363636L286.83838383838383,121.30181818181818L296.9090909090909,116.35636363636364L306.9797979797979,121.53454545454545L317.050505050505,119.75999999999999L327.1212121212121,117.08363636363636L337.19191919191917,117.92727272727272L347.26262626262627,119.00363636363636L357.3333333333333,119.23636363636363L367.40404040404036,119.32363636363637L377.47474747474746,111.44L387.54545454545456,116.18181818181817L397.6161616161616,114.75636363636363L407.68686868686865,116.64727272727272L417.7575757575757,118.33454545454546L427.8282828282828,118.27636363636364L437.8989898989899,114.49454545454547L447.96969696969694,117.54909090909092L458.04040404040404,116.38545454545454L468.1111111111111,120.34181818181818L478.18181818181813,115.54181818181817L488.25252525252523,114.9309090909091L498.3232323232323,116.15272727272728L508.3939393939394,114.11636363636364L518.4646464646464,117.11272727272728L528.5353535353536,112.54545454545455L538.6060606060606,109.08363636363636L548.6767676767677,104.69090909090909L558.7474747474748,103.29454545454546L568.8181818181818,93.63636363636363L578.8888888888889,98.17454545454545L588.959595959596,100.90909090909089L599.0303030303031,93.86909090909091L609.10101010101,70.53818181818181L619.1717171717171,79.52727272727273L629.2424242424242,73.97090909090909L639.3131313131313,86.07272727272728L649.3838383838383,81.33090909090909L659.4545454545455,87.0909090909091L669.5252525252525,72.95272727272727L679.5959595959596,67.86181818181818L689.6666666666666,84.32727272727274L699.7373737373738,66.55272727272727L709.8080808080807,71.29454545454544L719.878787878788,84.35636363636365L729.9494949494949,63.585454545454546L740.020202020202,74.78545454545454L750.0909090909091,69.31636363636363L760.1616161616162,67.5709090909091L770.2323232323232,69.34545454545454L780.3030303030303,60.35636363636364L790.3737373737373,54.21818181818182L800.4444444444445,47.14909090909091L810.5151515151514,46.39272727272728L820.5858585858587,42.029090909090904L830.6565656565656,45.81090909090909L840.7272727272727,39.20727272727273L850.7979797979798,47.99272727272727L860.8686868686868,33.010909090909095L870.9393939393939,17.09818181818182L881.010101010101,39.09090909090908L891.0808080808081,44.18181818181817L901.1515151515151,44.18181818181817L911.2222222222222,45.63636363636365L921.2929292929293,46.88727272727272L931.3636363636363,35.10545454545454L941.4343434343434,35.68727272727273L951.5050505050505,35.68727272727273L961.5757575757576,46.974545454545456L971.6464646464646,57.41818181818182L981.7171717171717,56.80727272727273L991.7878787878788,68.44363636363637L1001.8585858585858,68.44363636363637L1011.929292929293,72.63272727272728L1022,82.72727272727273

1
MasterThesis/resources/lego-imperial-star-destroyer-ultimate-collector-series-set-75252.txt Normal file
View File

@ -0,0 +1 @@
M25,54.977777777777774L35.07070707070707,61.55555555555555L45.141414141414145,66.88888888888889L55.21212121212121,70.26666666666668L65.28282828282829,72.93333333333332L75.35353535353535,73.11111111111111L85.42424242424242,72.22222222222221L95.49494949494948,70.97777777777776L105.56565656565657,61.911111111111104L115.63636363636363,77.37777777777778L125.7070707070707,68.66666666666667L135.77777777777777,68.66666666666667L145.84848484848484,68.66666666666667L155.91919191919192,74L165.98989898989896,67.42222222222223L176.06060606060606,68.66666666666667L186.13131313131314,67.77777777777779L196.20202020202018,69.02222222222223L206.27272727272725,69.02222222222223L216.34343434343432,69.02222222222223L226.4141414141414,87.33333333333333L236.48484848484847,87.33333333333333L246.55555555555554,84.66666666666666L256.6262626262626,92.66666666666666L266.6969696969697,93.02222222222224L276.7676767676768,91.77777777777777L286.83838383838383,88.57777777777778L296.9090909090909,89.28888888888889L306.9797979797979,87.33333333333333L317.050505050505,88.57777777777778L327.1212121212121,97.28888888888889L337.19191919191917,81.11111111111111L347.26262626262627,81.11111111111111L357.3333333333333,79.33333333333334L367.40404040404036,44.48888888888888L377.47474747474746,38.44444444444445L387.54545454545456,80.93333333333334L397.6161616161616,80.93333333333334L407.68686868686865,58.53333333333334L417.7575757575757,75.77777777777777L427.8282828282828,68.13333333333333L437.8989898989899,80.57777777777778L447.96969696969694,80.22222222222223L458.04040404040404,80.93333333333334L468.1111111111111,80.93333333333334L478.18181818181813,74.71111111111111L488.25252525252523,66.88888888888889L498.3232323232323,71.86666666666667L508.3939393939394,77.73333333333333L518.4646464646464,68.66666666666667L528.5353535353536,60.13333333333334L538.6060606060606,46.08888888888888L548.6767676767677,69.91111111111111L558.7474747474748,72.57777777777777L568.8181818181818,74.17777777777778L578.8888888888889,83.06666666666668L588.959595959596,74.17777777777778L599.0303030303031,76.4888888888889L609.10101010101,82.71111111111111L619.1717171717171,85.73333333333333L629.2424242424242,81.99999999999999L639.3131313131313,83.6L649.3838383838383,87.33333333333333L659.4545454545455,86.26666666666667L669.5252525252525,80.93333333333334L679.5959595959596,71.33333333333333L689.6666666666666,51.42222222222221L699.7373737373738,46.8L709.8080808080807,43.06666666666666L719.878787878788,39.86666666666666L729.9494949494949,57.644444444444446L740.020202020202,69.55555555555556L750.0909090909091,72.57777777777777L760.1616161616162,72.57777777777777L770.2323232323232,58.7111111111111L780.3030303030303,72.22222222222221L790.3737373737373,72.75555555555556L800.4444444444445,71.33333333333333L810.5151515151514,60.3111111111111L820.5858585858587,68.31111111111112L830.6565656565656,63.333333333333336L840.7272727272727,64.75555555555556L850.7979797979798,67.24444444444444L860.8686868686868,67.77777777777779L870.9393939393939,71.15555555555555L881.010101010101,69.02222222222223L891.0808080808081,68.84444444444443L901.1515151515151,69.02222222222223L911.2222222222222,67.42222222222223L921.2929292929293,61.19999999999999L931.3636363636363,50.53333333333333L941.4343434343434,61.55555555555555L951.5050505050505,54.44444444444444L961.5757575757576,54.8L971.6464646464646,53.73333333333333L981.7171717171717,59.244444444444454L991.7878787878788,64.04444444444444L1001.8585858585858,62.62222222222222L1011.929292929293,61.911111111111104L1022,62.08888888888889

1
MasterThesis/resources/lego-millennium-falcon-ultimate-collector-series-set-75192.txt Normal file
View File

@ -0,0 +1 @@
M25,34L35.07070707070707,34L45.141414141414145,35.760000000000005L55.21212121212121,41.99999999999999L65.28282828282829,23.439999999999994L75.35353535353535,26.479999999999997L85.42424242424242,25.199999999999996L95.49494949494948,30.480000000000004L105.56565656565657,27.92L115.63636363636363,26.959999999999994L125.7070707070707,26.319999999999997L135.77777777777777,26.479999999999997L145.84848484848484,35.28000000000001L155.91919191919192,32.08L165.98989898989896,29.2L176.06060606060606,29.519999999999996L186.13131313131314,25.36L196.20202020202018,24.399999999999995L206.27272727272725,30.64L216.34343434343432,30.96L226.4141414141414,47.28L236.48484848484847,42.16L246.55555555555554,44.71999999999999L256.6262626262626,49.839999999999996L266.6969696969697,43.919999999999995L276.7676767676768,59.60000000000001L286.83838383838383,44.879999999999995L296.9090909090909,52.400000000000006L306.9797979797979,47.92L317.050505050505,55.6L327.1212121212121,49.199999999999996L337.19191919191917,46.8L347.26262626262627,40.87999999999999L357.3333333333333,41.19999999999999L367.40404040404036,41.19999999999999L377.47474747474746,40.23999999999999L387.54545454545456,43.599999999999994L397.6161616161616,37.84000000000001L407.68686868686865,31.76L417.7575757575757,38.80000000000001L427.8282828282828,49.67999999999999L437.8989898989899,35.440000000000005L447.96969696969694,40.87999999999999L458.04040404040404,44.239999999999995L468.1111111111111,34.480000000000004L478.18181818181813,31.6L488.25252525252523,31.6L498.3232323232323,32.24L508.3939393939394,32.56L518.4646464646464,38.00000000000001L528.5353535353536,37.2L538.6060606060606,38.32000000000001L548.6767676767677,40.39999999999999L558.7474747474748,41.519999999999996L568.8181818181818,45.519999999999996L578.8888888888889,40.71999999999999L588.959595959596,45.04L599.0303030303031,46.48L609.10101010101,57.040000000000006L619.1717171717171,49.52L629.2424242424242,58.80000000000001L639.3131313131313,65.84L649.3838383838383,70.16L659.4545454545455,71.44L669.5252525252525,69.03999999999999L679.5959595959596,61.67999999999999L689.6666666666666,61.519999999999996L699.7373737373738,69.03999999999999L709.8080808080807,69.2L719.878787878788,63.11999999999999L729.9494949494949,62.959999999999994L740.020202020202,69.67999999999999L750.0909090909091,70.32000000000001L760.1616161616162,65.52L770.2323232323232,73.2L780.3030303030303,70.48L790.3737373737373,66.16L800.4444444444445,66.96L810.5151515151514,62.63999999999999L820.5858585858587,59.92L830.6565656565656,65.52L840.7272727272727,65.52L850.7979797979798,67.28L860.8686868686868,72.08000000000001L870.9393939393939,72.39999999999999L881.010101010101,64.24L891.0808080808081,59.92L901.1515151515151,61.519999999999996L911.2222222222222,63.919999999999995L921.2929292929293,61.519999999999996L931.3636363636363,63.76L941.4343434343434,61.99999999999999L951.5050505050505,62.959999999999994L961.5757575757576,66.16L971.6464646464646,67.6L981.7171717171717,68.56L991.7878787878788,67.12L1001.8585858585858,67.6L1011.929292929293,66.96L1022,65.52

1
MasterThesis/resources/lego-star-wars-death-star-set-75159.txt Normal file
View File

@ -0,0 +1 @@
M25,71.73333333333333L35.07070707070707,64L45.141414141414145,72.93333333333332L55.21212121212121,69.86666666666666L65.28282828282829,83.33333333333334L75.35353535353535,83.33333333333334L85.42424242424242,85.33333333333334L95.49494949494948,82.93333333333332L105.56565656565657,82.93333333333332L115.63636363636363,82.26666666666667L125.7070707070707,80.53333333333333L135.77777777777777,76L145.84848484848484,76L155.91919191919192,76L165.98989898989896,76L176.06060606060606,76L186.13131313131314,76L196.20202020202018,77.60000000000001L206.27272727272725,77.60000000000001L216.34343434343432,83.33333333333334L226.4141414141414,83.33333333333334L236.48484848484847,83.33333333333334L246.55555555555554,83.33333333333334L256.6262626262626,83.33333333333334L266.6969696969697,83.33333333333334L276.7676767676768,83.33333333333334L286.83838383838383,83.33333333333334L296.9090909090909,83.33333333333334L306.9797979797979,83.33333333333334L317.050505050505,83.33333333333334L327.1212121212121,83.33333333333334L337.19191919191917,83.33333333333334L347.26262626262627,70L357.3333333333333,70L367.40404040404036,70L377.47474747474746,78.53333333333333L387.54545454545456,63.466666666666654L397.6161616161616,59.333333333333336L407.68686868686865,59.333333333333336L417.7575757575757,58.53333333333334L427.8282828282828,69.33333333333334L437.8989898989899,67.33333333333333L447.96969696969694,67.33333333333333L458.04040404040404,67.33333333333333L468.1111111111111,67.33333333333333L478.18181818181813,56.666666666666664L488.25252525252523,48.00000000000001L498.3232323232323,54.4L508.3939393939394,40L518.4646464646464,54.53333333333333L528.5353535353536,36.8L538.6060606060606,36.8L548.6767676767677,50.800000000000004L558.7474747474748,50.800000000000004L568.8181818181818,50.800000000000004L578.8888888888889,50L588.959595959596,50L599.0303030303031,33.733333333333334L609.10101010101,33.733333333333334L619.1717171717171,19.466666666666672L629.2424242424242,19.466666666666672L639.3131313131313,19.466666666666672L649.3838383838383,19.466666666666672L659.4545454545455,43.06666666666666L669.5252525252525,30.666666666666664L679.5959595959596,59.06666666666666L689.6666666666666,59.06666666666666L699.7373737373738,51.33333333333333L709.8080808080807,51.33333333333333L719.878787878788,34.66666666666667L729.9494949494949,34.66666666666667L740.020202020202,34.66666666666667L750.0909090909091,43.333333333333336L760.1616161616162,43.333333333333336L770.2323232323232,43.333333333333336L780.3030303030303,43.333333333333336L790.3737373737373,43.333333333333336L800.4444444444445,43.333333333333336L810.5151515151514,43.333333333333336L820.5858585858587,43.333333333333336L830.6565656565656,63.333333333333336L840.7272727272727,55.73333333333334L850.7979797979798,55.73333333333334L860.8686868686868,38.266666666666666L870.9393939393939,38.266666666666666L881.010101010101,60L891.0808080808081,45.99999999999999L901.1515151515151,33.2L911.2222222222222,33.2L921.2929292929293,28.933333333333326L931.3636363636363,28.933333333333326L941.4343434343434,30L951.5050505050505,60.26666666666667L961.5757575757576,60.26666666666667L971.6464646464646,60.13333333333334L981.7171717171717,58.66666666666667L991.7878787878788,58.66666666666667L1001.8585858585858,58.66666666666667L1011.929292929293,59.60000000000001L1022,24.53333333333333

1
MasterThesis/resources/lego-star-wars-ultimate-collector-series-at-at-set-75313-gray.txt Normal file
View File

@ -0,0 +1 @@
M25,33.85454545454545L35.07070707070707,40.25454545454546L45.141414141414145,39.96363636363636L55.21212121212121,42.14545454545455L65.28282828282829,47.38181818181818L75.35353535353535,49.12727272727272L85.42424242424242,62.8L95.49494949494948,65.7090909090909L105.56565656565657,59.60000000000001L115.63636363636363,52.76363636363636L125.7070707070707,49.709090909090904L135.77777777777777,48.83636363636363L145.84848484848484,51.6L155.91919191919192,44.61818181818181L165.98989898989896,39.236363636363635L176.06060606060606,56.98181818181817L186.13131313131314,49.12727272727272L196.20202020202018,44.763636363636365L206.27272727272725,46.94545454545455L216.34343434343432,50.14545454545454L226.4141414141414,49.12727272727272L236.48484848484847,52.909090909090914L246.55555555555554,67.3090909090909L256.6262626262626,66.72727272727272L266.6969696969697,66.72727272727272L276.7676767676768,70.94545454545454L286.83838383838383,72.25454545454545L296.9090909090909,72.83636363636364L306.9797979797979,76.47272727272727L317.050505050505,74.43636363636362L327.1212121212121,78.36363636363636L337.19191919191917,77.34545454545454L347.26262626262627,86.94545454545455L357.3333333333333,90.2909090909091L367.40404040404036,91.60000000000001L377.47474747474746,88.69090909090909L387.54545454545456,88.83636363636363L397.6161616161616,87.0909090909091L407.68686868686865,81.56363636363636L417.7575757575757,84.32727272727274L427.8282828282828,81.99999999999999L437.8989898989899,79.81818181818183L447.96969696969694,82.29090909090908L458.04040404040404,77.2L468.1111111111111,80.39999999999999L478.18181818181813,82.87272727272727L488.25252525252523,78.36363636363636L498.3232323232323,77.78181818181818L508.3939393939394,76.76363636363637L518.4646464646464,83.16363636363636L528.5353535353536,83.74545454545455L538.6060606060606,84.03636363636365L548.6767676767677,79.81818181818183L558.7474747474748,73.27272727272728L568.8181818181818,79.52727272727273L578.8888888888889,79.81818181818183L588.959595959596,77.63636363636363L599.0303030303031,75.16363636363636L609.10101010101,80.83636363636364L619.1717171717171,78.80000000000001L629.2424242424242,84.90909090909089L639.3131313131313,86.8L649.3838383838383,85.78181818181818L659.4545454545455,85.49090909090908L669.5252525252525,86.36363636363637L679.5959595959596,86.94545454545455L689.6666666666666,80.83636363636364L699.7373737373738,82.87272727272727L709.8080808080807,88.98181818181818L719.878787878788,84.90909090909089L729.9494949494949,82.87272727272727L740.020202020202,80.25454545454545L750.0909090909091,83.45454545454545L760.1616161616162,83.89090909090908L770.2323232323232,79.67272727272726L780.3030303030303,83.16363636363636L790.3737373737373,84.61818181818181L800.4444444444445,86.2181818181818L810.5151515151514,88.10909090909091L820.5858585858587,88.98181818181818L830.6565656565656,88.54545454545456L840.7272727272727,86.8L850.7979797979798,87.81818181818181L860.8686868686868,87.81818181818181L870.9393939393939,87.67272727272727L881.010101010101,87.81818181818181L891.0808080808081,89.56363636363638L901.1515151515151,92.61818181818182L911.2222222222222,90.43636363636362L921.2929292929293,88.98181818181818L931.3636363636363,91.60000000000001L941.4343434343434,86.2181818181818L951.5050505050505,85.92727272727271L961.5757575757576,89.56363636363638L971.6464646464646,89.27272727272728L981.7171717171717,89.27272727272728L991.7878787878788,89.12727272727273L1001.8585858585858,90.72727272727272L1011.929292929293,86.5090909090909L1022,90.43636363636362

135
MasterThesis/zadanie10.py Normal file
View File

@ -0,0 +1,135 @@
class Node:
def __init__(self, value):
self.value = value
self.parent = None
self.right = None
self.left = None
def min_tree(self):
return self.min_value(self)
def successor(self):
node = self
if self.right is not None:
return self.min_value(self.right)
parent = self.parent
while parent is not None and node == parent.right:
node = parent
parent = parent.parent
return parent
def min_value(self, node):
current = node
while current.left is not None:
current = current.left
return current
def free(self):
self.right = None
self.left = None
self.parent = None
def __str__(self):
return str(self.value)
class Bstree:
def search_recursive(self, key):
return self.recursive_search(key, self.root)
def recursive_search(self, key, node):
if node is None or node.value == key:
return node
if key < node.value:
return self.recursive_search(key, node.left)
return self.recursive_search(key, node.right)
def search_iterative(self, key):
node = self.root
while node is not None and node.value != key:
if key < node.value:
node = node.left
else:
node = node.right
return node
def __init__(self, root):
self.toPrint = []
self.root = root
def insert_after(self, node, parent):
if node.value < parent.value:
if parent.left is not None:
self.insert_after(node, parent.left)
else:
parent.left = node
node.parent = parent
if node.value > parent.value:
if parent.right is not None:
self.insert_after(node, parent.right)
else:
parent.right = node
node.parent = parent
def insert(self, node):
self.insert_after(node, self.root)
def write(self):
self.print_node(self.root, 0)
def print_node(self, node, level):
if node.right is not None:
self.print_node(node.right, level + 1)
print("\t" * level + str(node.value))
if node.left is not None:
self.print_node(node.left, level + 1)
def transplant(self, node1, node2):
node1.parent = node2.parent
if node2.parent.left == node2:
node2.parent.left = node1
else:
node2.parent.right = node1
node2.free()
def delete(self, node):
if node.left is None and node.right is None:
if node.parent.left == node:
node.parent.left = None
else:
node.parent.right = None
if node.left is None and node.right is not None:
self.transplant(node.right, node)
if node.left is not None and node.right is None:
self.transplant(node.left, node)
if node.left is not None and node.right is not None:
temp = node.min_value(node.right)
node.value = temp.value
if temp.left:
node = temp.left
else:
node = temp.right
if node:
node.parent = temp.parent
if temp.parent is None:
self.root = node
if temp == temp.parent.left:
temp.parent.left = node
else:
temp.parent.right = node
def main():
for i in range(1, 20):
print(i % 2)
return 0
if __name__ == '__main__':
main()

157
MasterThesis/zadanie3.py Normal file
View File

@ -0,0 +1,157 @@
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
data = load_iris()
data.target[[10, 25, 50]]
list(data.target_names)
def generate_data():
# Keep results deterministic
np.random.seed(1234)
X, y = datasets.make_moons(200, noise=0.25)
# X, y = datasets.make_classification(200, 2, 2, 0)
return X, y
def visualize(X, y, model=None):
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = 0.01
xx, yy = np.meshgrid(
np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
if model:
Z = predict(model, np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.contourf(xx, yy, Z, cmap=plt.cm.viridis)
plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.viridis)
plt.show()
def initialize_model(dim_in=2, dim_hid=3, dim_out=2):
# Keep results deterministic
np.random.seed(1234)
W1 = np.random.randn(dim_in, dim_hid) / np.sqrt(dim_in)
b1 = np.zeros((1, dim_hid))
W2 = np.random.randn(dim_hid, dim_out) / np.sqrt(dim_hid)
b2 = np.zeros((1, dim_out))
return W1, b1, W2, b2
def softmax(X):
e = np.exp(X)
return e / np.sum(e, axis=1, keepdims=True)
def predict(model, X):
W1, b1, W2, b2 = model
z1 = X.dot(W1) + b1
a1 = np.tanh(z1)
z2 = a1.dot(W2) + b2
probs = softmax(z2)
return np.argmax(probs, axis=1)
def calculate_cost(model, X, y):
W1, b1, W2, b2 = model
z1 = X.dot(W1) + b1
a1 = np.tanh(z1)
z2 = a1.dot(W2) + b2
probs = softmax(z2)
preds = probs[:, 1]
return -1. / len(y) * np.sum(
np.multiply(y, np.log(preds)) + np.multiply(1 - y, np.log(1 - preds)),
axis=0)
# def accuracy(model, X, y):
# predicted = predict(model, X)
# return len([1 for x, y in predict(model, X) if x==y])/len(y)
def accuracy(model, X, y):
y_pred = predict(model, X)
return accuracy_score(y, y_pred)
# def accuracy1(x, y, model):
# y_pred = (model.predict(x)).type(torch.FloatTensor)
# y = y.unsqueeze(1)
# correct = (y_pred == y).type(torch.FloatTensor)
# return correct.mean()
def train(model, X, y, alpha=0.01, epochs=10000, debug=False):
W1, b1, W2, b2 = model
m = len(X)
for i in range(epochs):
# Forward propagation
z1 = X.dot(W1) + b1
a1 = np.tanh(z1)
z2 = a1.dot(W2) + b2
probs = softmax(z2)
# Backpropagation
delta3 = probs
delta3[range(m), y] -= 1
dW2 = (a1.T).dot(delta3)
db2 = np.sum(delta3, axis=0, keepdims=True)
delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))
dW1 = np.dot(X.T, delta2)
db1 = np.sum(delta2, axis=0)
# Parameter update
W1 -= alpha * dW1
b1 -= alpha * db1
W2 -= alpha * dW2
b2 -= alpha * db2
# Print loss
if debug and i % 1000 == 0:
model = (W1, b1, W2, b2)
print("Cost after iteration {}: {:.4f}".format(i, calculate_cost(
model, X, y)))
print("Accuracy iteration {}: {:.4f}".format(i, accuracy(model, X, y)))
return W1, b1, W2, b2
if __name__ == '__main__':
X, y = generate_data()
visualize(X, y)
model = train(initialize_model(dim_hid=5), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora:", accuracy(X, y, model))
model = train(initialize_model(dim_hid=1), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora dla wielkości warstwy równej 1:", accuracy(X, y, model))
model = train(initialize_model(dim_hid=2), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora dla wielkości warstwy równej 2:", accuracy(X, y, model))
model = train(initialize_model(dim_hid=5), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora dla wielkości warstwy równej 3:", accuracy(X, y, model))
model = train(initialize_model(dim_hid=10), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora dla wielkości warstwy równej 10:", accuracy(X, y, model))
model = train(initialize_model(dim_hid=15), X, y, debug=True)
visualize(X, y, model)
print("Skuteczność klasyfikatora dla wielkości warstwy równej 15:", accuracy(X, y, model))