利用嵌套循环打印九九乘法表（nest_for.py）（三种方法）

以下是三种利用嵌套循环打印九九乘法表的 Python 代码：

方法一：

for i in range(1, 10):
for j in range(1, i + 1):
print('{}*{}={}\t'.format(i, j, i*j), end='')
print()

方法二：

i = 1
while i <= 9:
j = 1
while j <= i:
print('{}*{}={}\t'.format(i, j, i*j), end='')
j += 1
print()
i += 1

方法三：

i = 1
while i <= 9:
for j in range(i, 10):
print('{}*{}={}\t'.format(i, j, i*j), end='')
print()
i += 1

请注意，以上代码中的任何一个都可以实现相同的功能，只是实现方式略有不同。

相关问题

Traceback (most recent call last): File "E:\Duzhuan\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3460, in run_code exec(code_obj, self.user_global_ns, self.user_ns) File "<ipython-input-2-c833f4fdcae2>", line 1, in <module> runfile('C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration\\apc_1.py', wdir='C:\\Users\\zhangxiao\\Desktop\\Algorithm Integration') File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile pydev_imports.execfile(filename, global_vars, local_vars) # execute the script File "E:\Duzhuan\JetBrainsPyCharm 2023.1\PyCharm 2023.1\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile exec(compile(contents+"\n", file, 'exec'), glob, loc) File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 184, in <module> integrated_optimization() File "C:\Users\zhangxiao\Desktop\Algorithm Integration\apc_1.py", line 160, in integrated_optimization global_best_nest = cuckoo_search(objective_function, num_nests, num_generations) NameError: name 'objective_function' is not defined

这个错误是因为在调用`cuckoo_search`函数时，`objective_function`未被定义。请确保您已经定义了目标函数`objective_function`，并将其作为参数传递给`cuckoo_search`函数。以下是一个示例的目标函数，您可以根据实际情况修改或替换它：

# 示例目标函数，这里使用一个简单的二维函数作为示例
def objective_function(x):
    return np.sum(np.square(x))

将此目标函数定义添加到代码中，并确保它在调用`cuckoo_search`函数之前可用。然后再次运行代码，看看是否解决了问题。如果还有其他问题，请随时提问。

>>> model.fit(x_train, y_train, epochs=10, callbacks=[tensorboard_callback]) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 728, in fit use_multiprocessing=use_multiprocessing) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 224, in fit distribution_strategy=strategy) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 547, in _process_training_inputs use_multiprocessing=use_multiprocessing) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/training_v2.py", line 606, in _process_inputs use_multiprocessing=use_multiprocessing) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 479, in __init__ batch_size=batch_size, shuffle=shuffle, **kwargs) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 238, in __init__ num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs)) File "/usr/local/lib/python3.7/dist-packages/tensorflow_core/python/keras/engine/data_adapter.py", line 238, in <genexpr> num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs)) IndexError: tuple index out of range

这个错误通常是由输入数据的形状不正确引起的。具体来说，这个错误可能是由于输入数据的维度不符合模型的要求所导致的。请检查你的数据集是否有任何维度上的问题，并确保它们与你的模型的输入尺寸匹配。你还可以尝试将数据转换为numpy数组，并检查它们的形状是否正确。如果问题仍然存在，请提供更多的代码和数据集信息以便我更好地帮助你解决问题。