def get_labels(indices, y_train):
    labels = [y_train[i] for i in indices]
    return labels

def accuracy_by_rank(y_pred, y_true, rank=1):
    return sum([y in prediction[:rank] for prediction, y in zip(y_pred, y_true)]) / len(y_true)

ranks = 36
predictions = []
for i in range(len(y_train)):
    knn = KNeighborsClassifier(n_neighbors=1, p=1)
    x = np.vstack((x_train[:i], x_train[i+1:]))
    y = np.concatenate((y_train[:i], y_train[i+1:]))
    knn.fit(x, y)
    neighbours = knn.kneighbors(x_train[None, i], ranks)
    predictions.append(get_labels(neighbours[1][0], y_train))

for i in range(1, ranks):
    print(f'Rank {i} Accuracy {accuracy_by_rank(predictions, y_train, i)}')