def main():
# create random training samples for 3 categories in range 0-3, 2-5, 4-7
np.random.seed(5)
X1 = [
3 * np.random.random_sample(75) + 0,
3 * np.random.random_sample(75) + 0
]
X2 = [
3 * np.random.random_sample(75) + 2,
3 * np.random.random_sample(75) + 2
]
X3 = [
3 * np.random.random_sample(75) + 4,
3 * np.random.random_sample(75) + 4
]
X_train = np.hstack((X1, X2, X3)).T
# create testing random samples in range 0-7
X_test = np.array(
[7 * np.random.random_sample(30), 7 * np.random.random_sample(30)])
# create labels for training data
y1 = [1 for _ in range(75)]
y2 = [2 for _ in range(75)]
y3 = [3 for _ in range(75)]
y_train = np.hstack((y1, y2, y3))
# plot data in different colors
plt.scatter(X1[0], X1[1], c='r', marker='s', label='X1')
plt.scatter(X2[0], X2[1], c='b', marker='x', label='X2')
plt.scatter(X3[0], X3[1], c='lightgreen', marker='o', label='X3')
plt.scatter(X_test[0], X_test[1], c='black', marker='^', label='test set')
plt.legend(loc='upper left')
plt.show()
# create classifier
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(X_train, y_train)
# prepare test set and predict labels
X_test = X_test.T
y_test = knn.predict(X_test)
# combine train and test data
X_combined = np.vstack((X_train, X_test))
y_combined = np.hstack((y_train, y_test))
Visualizer.plot_decision_regions(X_combined,
y_combined,
classifier=knn,
test_idx=range(225, 255))
plt.legend(loc='upper left')
plt.show()
