commit 3d28b2d7edb4b90f77a47973d2fce8444cbd2423
Author: Waneksio <waneksiomiszczu@gmail.com>
Date:   Tue Nov 29 23:36:06 2022 +0100

    Add data converter and datasets

diff --git a/MasterThesis/AirPassengers.csv b/MasterThesis/AirPassengers.csv
new file mode 100644
index 0000000..7014d86
--- /dev/null
+++ b/MasterThesis/AirPassengers.csv
@@ -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
diff --git a/MasterThesis/TimeSeries.py b/MasterThesis/TimeSeries.py
new file mode 100644
index 0000000..9ed4510
--- /dev/null
+++ b/MasterThesis/TimeSeries.py
@@ -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)
\ No newline at end of file
diff --git a/MasterThesis/TimeSeriesForecasting.py b/MasterThesis/TimeSeriesForecasting.py
new file mode 100644
index 0000000..ea6d7c3
--- /dev/null
+++ b/MasterThesis/TimeSeriesForecasting.py
@@ -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)
\ No newline at end of file
diff --git a/MasterThesis/__init__.py b/MasterThesis/__init__.py
new file mode 100644
index 0000000..81384a5
--- /dev/null
+++ b/MasterThesis/__init__.py
@@ -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()
\ No newline at end of file
diff --git a/MasterThesis/configs/.azureml/myconfig.json b/MasterThesis/configs/.azureml/myconfig.json
new file mode 100644
index 0000000..ec74f03
--- /dev/null
+++ b/MasterThesis/configs/.azureml/myconfig.json
@@ -0,0 +1 @@
+{"Id": null, "Scope": "/subscriptions/616d95e9-f2f8-4e95-8535-e2449b5bd652/resourceGroups/myresourcegroup/providers/Microsoft.MachineLearningServices/workspaces/newworkspace"}
\ No newline at end of file
diff --git a/MasterThesis/converter.py b/MasterThesis/converter.py
new file mode 100644
index 0000000..23fc08a
--- /dev/null
+++ b/MasterThesis/converter.py
@@ -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")
\ No newline at end of file
diff --git a/MasterThesis/keywords for presentation.txt b/MasterThesis/keywords for presentation.txt
new file mode 100644
index 0000000..37abce0
--- /dev/null
+++ b/MasterThesis/keywords for presentation.txt	
@@ -0,0 +1,7 @@
+stationarity
+autoregressive moving average ARMA
+Dickey Fuller test
+rolling mean
+Autocorrelation
+Decomposition
+Forecasting (ARIMA)
\ No newline at end of file
diff --git a/MasterThesis/predykcje.py b/MasterThesis/predykcje.py
new file mode 100644
index 0000000..bc67033
--- /dev/null
+++ b/MasterThesis/predykcje.py
@@ -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()
\ No newline at end of file
diff --git a/MasterThesis/resources/air-jordan-1-retro-high-pollen.txt b/MasterThesis/resources/air-jordan-1-retro-high-pollen.txt
new file mode 100644
index 0000000..e69de29
diff --git a/MasterThesis/resources/air-jordan-1-retro-high-shattered-backboard-3.txt b/MasterThesis/resources/air-jordan-1-retro-high-shattered-backboard-3.txt
new file mode 100644
index 0000000..9dc3e2e
--- /dev/null
+++ b/MasterThesis/resources/air-jordan-1-retro-high-shattered-backboard-3.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/air-jordan-4-retro-white-midnight-navy.txt b/MasterThesis/resources/air-jordan-4-retro-white-midnight-navy.txt
new file mode 100644
index 0000000..88c990e
--- /dev/null
+++ b/MasterThesis/resources/air-jordan-4-retro-white-midnight-navy.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/blastoise-holo-1999-pokemon-base-set-2-102.txt b/MasterThesis/resources/blastoise-holo-1999-pokemon-base-set-2-102.txt
new file mode 100644
index 0000000..8f37403
--- /dev/null
+++ b/MasterThesis/resources/blastoise-holo-1999-pokemon-base-set-2-102.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/charizard-holo-1999-pokemon-base-set-4-102.txt b/MasterThesis/resources/charizard-holo-1999-pokemon-base-set-4-102.txt
new file mode 100644
index 0000000..5c7de8b
--- /dev/null
+++ b/MasterThesis/resources/charizard-holo-1999-pokemon-base-set-4-102.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/jordan-1-retro-fragment.txt b/MasterThesis/resources/jordan-1-retro-fragment.txt
new file mode 100644
index 0000000..e0a9c7a
--- /dev/null
+++ b/MasterThesis/resources/jordan-1-retro-fragment.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/lego-imperial-star-destroyer-ultimate-collector-series-set-75252.txt b/MasterThesis/resources/lego-imperial-star-destroyer-ultimate-collector-series-set-75252.txt
new file mode 100644
index 0000000..9843109
--- /dev/null
+++ b/MasterThesis/resources/lego-imperial-star-destroyer-ultimate-collector-series-set-75252.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/lego-millennium-falcon-ultimate-collector-series-set-75192.txt b/MasterThesis/resources/lego-millennium-falcon-ultimate-collector-series-set-75192.txt
new file mode 100644
index 0000000..4b54a4e
--- /dev/null
+++ b/MasterThesis/resources/lego-millennium-falcon-ultimate-collector-series-set-75192.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/resources/lego-star-wars-death-star-set-75159.txt b/MasterThesis/resources/lego-star-wars-death-star-set-75159.txt
new file mode 100644
index 0000000..4f025aa
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\ No newline at end of file
diff --git a/MasterThesis/resources/lego-star-wars-ultimate-collector-series-at-at-set-75313-gray.txt b/MasterThesis/resources/lego-star-wars-ultimate-collector-series-at-at-set-75313-gray.txt
new file mode 100644
index 0000000..788a337
--- /dev/null
+++ b/MasterThesis/resources/lego-star-wars-ultimate-collector-series-at-at-set-75313-gray.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/MasterThesis/zadanie10.py b/MasterThesis/zadanie10.py
new file mode 100644
index 0000000..a1c0ef0
--- /dev/null
+++ b/MasterThesis/zadanie10.py
@@ -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()
\ No newline at end of file
diff --git a/MasterThesis/zadanie3.py b/MasterThesis/zadanie3.py
new file mode 100644
index 0000000..08b7140
--- /dev/null
+++ b/MasterThesis/zadanie3.py
@@ -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))
+