import numpy as np

def knn_predict(X_train, y_train, X_test, k=9):
    predictions = []
    for x in X_test:
        distances = [np.linalg.norm(x - x_train) for x_train in X_train]
        print("Distances: ", distances)
        sorted_points = np.argsort(distances)[:k]
        print("Sorted Distance Points: ", sorted_points)
        k_nearest_labels = [y_train[i] for i in sorted_points]
        print("Nearest Labels: ", k_nearest_labels)
        common_labels = np.bincount(k_nearest_labels).argmax()
        print("Common Label: ", common_labels)
        predictions.append(common_labels)
        print("Predictions: ", predictions)
    return np.array(predictions)

X_train = np.array([[1, 2], [1, 3], [2, 3], [1, 4], [2, 4], [2, 1], [3, 1], [3, 1.5], [4, 2], [5, 2]])
y_train = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
X_test = np.array([[2.5, 1]])

predictions = knn_predict(X_train, y_train, X_test, k=9)

print("Class for new data point is:", predictions[0])