diff --git a/Ataskaita lab3.docx b/Ataskaita lab3.docx
new file mode 100644
index 0000000..126beef
Binary files /dev/null and b/Ataskaita lab3.docx differ
diff --git a/lab3_1.py b/lab3_1.py
new file mode 100644
index 0000000..8785f07
--- /dev/null
+++ b/lab3_1.py
@@ -0,0 +1,212 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from sklearn.linear_model import LinearRegression
+import numpy as np
+
+# Warning, this is not great code. Just something hacked together for the report.
+
+def create_dataset(sunspots, n = 2):
+    P = []
+    T = []
+    for i in range(len(sunspots) - n):
+        P.append(np.array(sunspots["activity"][i:(i+n)]))
+        T.append(sunspots["activity"][i+n])
+    return P, T
+
+# 1. & 2.
+sunspots = pd.read_csv("sunspot.txt", delimiter='\t', header=None, names=["year", "activity"])
+
+# 4.
+if False:
+    plt.plot(sunspots['year'], sunspots["activity"])
+    plt.xlabel("Metai")
+    plt.ylabel("Aktyvumas")
+    plt.title(f"Saulės aktyvumas tarp {min(sunspots['year'])} ir {max(sunspots['year'])} metų")
+    plt.show()
+
+# 5.
+P, T = create_dataset(sunspots)
+
+# 6.
+def draw_scatterplot(Xs, Ys, marker = 'o'):
+    ax = plt.subplot(projection='3d')
+    X_coords = []
+    Y_coords = []
+    Z_coords = []
+    for i in range(len(Xs)):
+        X_coords.append(Xs[i][0])
+        Y_coords.append(Xs[i][1])
+        Z_coords.append(Ys[i])
+
+    ax.scatter(X_coords, Y_coords, Z_coords, marker=marker)
+    ax.set_xlabel('Užpraitų metų aktyvumas')
+    ax.set_ylabel('Praitų metų aktyvumas')
+    ax.set_zlabel('Šių metų aktyvumas')
+    ax.set_title('Šių metų aktyvumo priklausomybė nuo praėjusių metų')
+
+    return ax
+
+if False:
+    draw_scatterplot(P, T)
+    plt.show()
+
+# 7.
+assert len(P) > 200
+Pu = P[:200]
+Tu = T[:200]
+
+# 8.
+model = LinearRegression().fit(Pu, Tu)
+
+# 9.
+print(f"intercept: {model.intercept_}")
+print(f"coefficients: {model.coef_}")
+
+if False:
+    w1 = model.coef_[0]
+    w2 = model.coef_[1]
+    b = model.intercept_
+
+    x1 = max(p[0] for p in P)
+    x2 = max(p[1] for p in P)
+    ax = draw_scatterplot(P, T)
+    ax.plot([0, x1], [0, x2], 'r', zs=[b, x1*w1 + x2*w2 + b])
+    plt.show()
+
+# 10.
+if False:
+    draw_scatterplot(Pu, model.predict(Pu))
+    draw_scatterplot(Pu, Tu)
+
+    plt.legend(handles=[
+        mpatches.Patch(color='blue', label='Tikrasis'),
+        mpatches.Patch(color='orange', label='Numatytas')
+    ])
+    plt.show()
+
+# 11.
+if False:
+    e = np.array(Tu) - model.predict(Pu)
+    draw_scatterplot(Pu, e)
+    plt.show()
+
+# 12.
+if False:
+    e = np.array(Tu) - model.predict(Pu)
+    plt.hist(e, bins=40)
+    plt.xlabel("Klaida")
+    plt.ylabel("Dažnis")
+    plt.show()
+
+# 13.
+e = np.array(Tu) - model.predict(Pu)
+print("MSE (train)", np.sum(e**2) / len(e))
+print("MAD (train)", np.median(np.abs(e)))
+
+e = np.array(T[200:]) - model.predict(P[200:])
+print("MSE (test)", np.sum(e**2) / len(e))
+print("MAD (test)", np.median(np.abs(e)))
+
+# 14. & 15. & 16.
+class AdaptiveLinearNeuron(object):
+   def __init__(self, rate = 0.01, max_niter = 10, mse_goal = -1):
+      self.rate = rate
+      self.max_niter = max_niter
+      self.mse_goal = mse_goal
+
+   def fit(self, X, y):
+      """Fit training data
+      X : Training vectors, X.shape : [#samples, #features]
+      y : Target values, y.shape : [#samples]
+      """
+
+      # weights
+      self.weight = np.zeros(1 + X.shape[1])
+
+      # Number of misclassifications
+      self.errors = []
+
+      # Cost function
+      self.cost = []
+
+      for i in range(self.max_niter):
+         output = self.net_input(X)
+         errors = y - output
+         self.weight[1:] += self.rate * X.T.dot(errors)
+         self.weight[0] += self.rate * errors.sum()
+         cost = (errors**2).sum() / 2.0
+         self.cost.append(cost)
+
+         mse = np.average(errors**2)
+         if self.mse_goal != -1 and mse < self.mse_goal: break
+      return self
+
+   def net_input(self, X):
+      """Calculate net input"""
+      return np.dot(X, self.weight[1:]) + self.weight[0]
+
+   def activation(self, X):
+      """Compute linear activation"""
+      return self.net_input(X)
+
+   def predict(self, X):
+      """Return class label after unit step"""
+      return np.where(self.activation(X) >= 0.0, 1, -1)
+
+
+# print("---------------")
+# for lr in [5e-5, 1e-5, 5e-6, 1e-6, 5e-7, 1e-7, 5e-8, 1e-8, 5e-9, 1e-9]:
+#     aln = AdaptiveLinearNeuron(lr, 100000, 300).fit(np.array(Pu), np.array(Tu))
+
+#     print(lr, aln.cost[-1], len(aln.cost))
+
+print("---------------")
+aln = AdaptiveLinearNeuron(0.000001, 1000, 280).fit(np.array(Pu), np.array(Tu))
+if False:
+    plt.plot(aln.cost, marker='o')
+    plt.xlabel('Epochs')
+    plt.ylabel('Sum-squared-error')
+    plt.title('Adaptive Linear Neuron - Learning rate 0.000001')
+    plt.show()
+
+# 17.
+print("iterations", len(aln.cost))
+print("b", aln.weight[0])
+print("w1", aln.weight[1])
+print("w2", aln.weight[2])
+
+# 18.
+# * Taip konverguoja, nes pasiekiama užsibrėžta MSE riba
+# * b ~ 0.17, w1 ~ -0.58, w2 ~ 1.46
+
+# 19.
+# Didinti 'lr' nelabai galima, nes tada pradės nekonverguoti ir įvyks slankaus kablelio klaidos
+# Mažinant 'lr' tiesiog prailgina mokymosi ilgiai tiesiškai
+
+# 20.
+print("------ Didinimas ---------")
+for n in [2, 4, 6, 8, 10]:
+    print(f"====== n = {n}")
+    P, T = create_dataset(sunspots, n)
+    assert len(P) > 200
+    Pu = np.array(P[:200])
+    Tu = np.array(T[:200])
+
+    # aln = AdaptiveLinearNeuron(1e-7, 1000).fit(Pu, Tu)
+    # plt.plot(aln.cost, marker='o')
+    # plt.xlabel('Epochs')
+    # plt.ylabel('Sum-squared-error')
+    # plt.title('Adaptive Linear Neuron - Learning rate 0.000001')
+    # plt.show()
+    # print(f"cost (n={n}) ", aln.cost[-1])
+    # print(f"iterations (n={n}) ", len(aln.cost))
+
+    model = LinearRegression().fit(Pu, Tu)
+    e = Tu - model.predict(Pu)
+    print("MSE (train)", np.average(e**2))
+    print("MAD (train)", np.median(np.abs(e)))
+
+    e = np.array(T[200:]) - model.predict(P[200:])
+    print("MSE (test)", np.average(e**2))
+    print("MAD (test)", np.median(np.abs(e)))
\ No newline at end of file
diff --git a/lab3_2.py b/lab3_2.py
new file mode 100644
index 0000000..590f270
--- /dev/null
+++ b/lab3_2.py
@@ -0,0 +1,49 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import tensorflow as tf
+from sklearn.model_selection import cross_val_score
+
+def load_data():
+    apples = pd.read_csv("apple_quality_clean.csv")
+
+
+    return apples.drop(columns=["Quality"]), apples["Quality"]
+
+X, Y = load_data()
+# Convert values from a 0..1 range to a (-1)..1 range
+# Conclusion: makes no difference
+#X = X.map(lambda x: x*2 - 1)
+
+tf.keras.utils.set_random_seed(42)
+
+model = tf.keras.models.Sequential([
+  tf.keras.layers.Dense(1, activation='relu')
+])
+
+model.build(input_shape=[None,len(X.columns)])
+
+model.compile(
+  optimizer=tf.keras.optimizers.Adam(learning_rate=0.01),
+  loss='binary_crossentropy',
+  metrics=['accuracy'],
+)
+
+history = model.fit(
+  X,
+  Y,
+  batch_size=len(X),
+  epochs=200,
+  shuffle=True,
+  validation_split=0.2
+)
+
+# print("10-fold cross validation score:", cross_val_score(model, X, Y, cv=10))
+
+# print("Accuracy", history.history['acc'][-1])
+# print(model.score)
+
+plt.plot(history.history['accuracy'])
+plt.plot(history.history['val_accuracy'])
+plt.xlabel('Iterations')
+plt.ylabel('accuracy')
+plt.show()
diff --git a/sunspot.txt b/sunspot.txt
new file mode 100644
index 0000000..f16e2be
--- /dev/null
+++ b/sunspot.txt
@@ -0,0 +1,315 @@
+1700	5
+1701	11
+1702	16
+1703	23
+1704	36
+1705	58
+1706	29
+1707	20
+1708	10
+1709	8
+1710	3
+1711	0
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+1715	27
+1716	47
+1717	63
+1718	60
+1719	39
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+1721	26
+1722	22
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+1725	40
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+2013	65
+2014	79
\ No newline at end of file