def check_svm_gamma(gamma):
X_train_std, X_test_std, y_train, y_test = get_data_std()
X_combined_std, y_combined = create_combined_data(X_train_std, X_test_std,
y_train, y_test)
svm = SVC(kernel='rbf', random_state=0, gamma=gamma, C=1.0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined,
classifier=svm,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
def k_neighbor_classify():
X_train_std, X_test_std, y_train, y_test = get_data_std()
X_combined_std, y_combined = create_combined_data(X_train_std, X_test_std,
y_train, y_test)
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined,
classifier=knn,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
def support_vector_machine():
X_train_std, X_test_std, y_train, y_test = get_data_std()
X_combined_std, y_combined = create_combined_data(X_train_std, X_test_std,
y_train, y_test)
svm = SVC(kernel='linear', C=10.0, random_state=0)
svm.fit(X_train_std, y_train)
plot_decision_regions(X_combined_std,
y_combined,
classifier=svm,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()
def create_random_forest_structure():
X_train, X_test, y_train, y_test = get_data()
X_combined, y_combined = create_combined_data(X_train, X_test, y_train,
y_test)
forest = RandomForestClassifier(criterion='entropy',
n_estimators=10,
random_state=1,
n_jobs=2)
forest.fit(X_train, y_train)
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.show()
def create_tree_structure(output_file):
X_train, X_test, y_train, y_test = get_data()
tree = DecisionTreeClassifier(criterion='entropy',
max_depth=3,
random_state=0)
tree.fit(X_train, y_train)
X_combined, y_combined = create_combined_data(X_train, X_test, y_train,
y_test)
plot_decision_regions(X_combined,
y_combined,
classifier=tree,
test_idx=range(105, 150))
export_graphviz(tree,
out_file=output_file,
feature_names=['petal length', 'petal width'])
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.show()