import matplotlib.pyplot as plt from decision_region import plot_decision_regions from adaline import AdalineGD from iris_array import y from iris_std import X_std ada = AdalineGD(n_iter=15, eta=0.01) ada.fit(X_std, y) plot_decision_regions(X_std, y, classifier=ada) plt.title('Adaline - Gradient Descent') plt.xlabel('sepal length [standardized]') plt.ylabel('petal length [standardized]') plt.legend(loc='upper left') plt.tight_layout() # plt.savefig('images/02_14_1.png', dpi=300) plt.show() plt.plot(range(1, len(ada.cost_) + 1), ada.cost_, marker='o') plt.xlabel('Epochs') plt.ylabel('Sum-squared-error') plt.tight_layout() # plt.savefig('images/02_14_2.png', dpi=300) plt.show()
import numpy as np from sklearn.ensemble import RandomForestClassifier import matplotlib.pyplot as plt from decision_region import plot_decision_regions from iris_split import X_train, y_train, X_test, y_test forest = RandomForestClassifier(criterion='gini', n_estimators=25, random_state=1, n_jobs=2) forest.fit(X_train, y_train) # 2019.09.01 add X_combined = np.vstack((X_train, X_test)) y_combined = np.hstack((y_train, y_test)) plot_decision_regions(X_combined, y_combined, classifier=forest, test_idx=range(105, 150)) plt.xlabel('petal length [cm]') plt.ylabel('petal width [cm]') plt.legend(loc='upper left') plt.tight_layout() #plt.savefig('images/03_22.png', dpi=300) plt.show()
from sklearn.linear_model import Perceptron ppn = Perceptron(n_iter=40, eta0=0.1, random_state=0) ppn.fit(X_train_std, y_train) y_pred = ppn.predict(X_test_std) print('Misclassified samples: %d' % (y_test != y_pred).sum()) from sklearn.metrics import accuracy_score print('Accuracy: %.2f' % accuracy_score(y_test, y_pred)) from decision_region import plot_decision_regions import matplotlib.pyplot as plt X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined = np.hstack((y_train, y_test)) plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105,150)) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.title('Perceptron') plt.legend(loc='upper left') plt.show() ### Logistic Regression from sklearn.linear_model import LogisticRegression lr = LogisticRegression(C=1000.0, random_state=0) lr.fit(X_train_std, y_train) plot_decision_regions(X_combined_std, y_combined, classifier=lr, test_idx=range(105,150)) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.title('Logistic Regression') plt.legend(loc='upper left')
import matplotlib.pyplot as plt from decision_region import plot_decision_regions from logistic_regression import LogisticRegressionGD from iris_split import X_train, y_train X_train_01_subset = X_train[(y_train == 0) | (y_train == 1)] y_train_01_subset = y_train[(y_train == 0) | (y_train == 1)] lrgd = LogisticRegressionGD(eta=0.05, n_iter=1000, random_state=1) lrgd.fit(X_train_01_subset, y_train_01_subset) plot_decision_regions(X=X_train_01_subset, y=y_train_01_subset, classifier=lrgd) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.legend(loc='upper left') plt.tight_layout() #plt.savefig('images/03_05.png', dpi=300) plt.show()
from sklearn.svm import SVC import matplotlib.pyplot as plt from decision_region import plot_decision_regions from xor import X_xor, y_xor svm = SVC(kernel='rbf', random_state=1, gamma=0.10, C=10.0) svm.fit(X_xor, y_xor) plot_decision_regions(X_xor, y_xor, classifier=svm) plt.legend(loc='upper left') plt.tight_layout() #plt.savefig('images/03_14.png', dpi=300) plt.show()
import matplotlib.pyplot as plt from iris_perceptron import ppn, X, y from decision_region import plot_decision_regions plot_decision_regions(X, y, classifier=ppn) plt.xlabel('sepal length [cm]') plt.ylabel('petal length [cm]') plt.legend(loc='upper left') # plt.savefig('images/02_08.png', dpi=300) plt.show()
import numpy as np import matplotlib.pyplot as plt from decision_region import plot_decision_regions from iris_split import y_train, y_test from iris_std import X_train_std, X_test_std from iris_perceptron import ppn X_combined_std = np.vstack((X_train_std, X_test_std)) y_combined = np.hstack((y_train, y_test)) plot_decision_regions(X=X_combined_std, y=y_combined, classifier=ppn, test_idx=range(105, 150)) plt.xlabel('petal length [standardized]') plt.ylabel('petal width [standardized]') plt.legend(loc='upper left') plt.tight_layout() #plt.savefig('images/03_01.png', dpi=300) plt.show()