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{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 +1712 0 +1713 2 +1714 11 +1715 27 +1716 47 +1717 63 +1718 60 +1719 39 +1720 28 +1721 26 +1722 22 +1723 11 +1724 21 +1725 40 +1726 78 +1727 122 +1728 103 +1729 73 +1730 47 +1731 35 +1732 11 +1733 5 +1734 16 +1735 34 +1736 70 +1737 81 +1738 111 +1739 101 +1740 73 +1741 40 +1742 20 +1743 16 +1744 5 +1745 11 +1746 22 +1747 40 +1748 60 +1749 81 +1750 83 +1751 48 +1752 48 +1753 31 +1754 12 +1755 10 +1756 10 +1757 32 +1758 48 +1759 54 +1760 63 +1761 86 +1762 61 +1763 45 +1764 36 +1765 21 +1766 11 +1767 38 +1768 70 +1769 106 +1770 101 +1771 82 +1772 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