Multiples+of+Ten:+Ask+the+user+for+a+number,+and+then+report+wheth er+the+number+is+a+multiple+of+10

时间: 2023-12-03 08:04:13 浏览: 27
以下是Python实现判断一个数是否为10的倍数的代码: ```python num = int(input("请输入一个数字:")) if num % 10 == 0: print(num, "是10的倍数") else: print(num, "不是10的倍数") ``` 运行代码后,程序会要求用户输入一个数字,然后判断这个数字是否为10的倍数,并输出相应的结果。 --相关问题--: 1. Python中如何判断一个数是否为偶数? 2. 如何在Python中实现计算器功能? 3.
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public class MultiplesOf3 { public static void main(String[] args) { for (int i = 3; i <= 100; i += 3) { System.out.println(i); } } }

### 回答1: 看起来很有趣!程序输出的是3到100之间的所有3的倍数:3、6、9、12、15、18、21、24、27、30、33、36、39、42、45、48、51、54、57、60、63、66、69、72、75、78、81、84、87、90、93、96、99。 ### 回答2: 这段代码是一个简单的Java程序,实现了求解3的倍数的功能。下面是代码的解释。 代码中的public class MultiplesOf3是定义了一个名为MultiplesOf3的公开的类。一个Java程序必须有一个公开的类,并且类名必须与文件名相同。 代码中的main方法用于程序的入口,它是程序执行的起点。在Java程序中,main方法是必须的。 代码中的for循环用于计算、打印出3的倍数。循环的初始条件是i=3,循环执行的条件是i<=100,循环结束后i增加3。循环体中的System.out.println(i)语句用于打印i的值。 代码的执行流程如下: - 程序从main方法开始执行。 - i被初始化为3。 - 执行循环条件判断,i小于等于100,符合条件。 - 打印i的值,即输出3。 - i增加3,i的值变为6。 - 执行循环条件判断,i小于等于100,符合条件。 - 打印i的值,即输出6。 - i增加3,i的值变为9。 - 重复上述步骤,直到i的值大于100,循环结束。 整个程序的输出结果是3、6、9、12、15、18、21、24、...、99。 这段程序实现了打印出3的倍数的功能,它是一个简单的循环示例。 ### 回答3: 这段代码是一个Java程序,名为MultiplesOf3。它的功能是输出从3到100之间所有3的倍数。 程序中的main方法是程序的入口,也是程序开始执行的地方。它使用一个for循环来遍历从3到100之间的数字。循环初始值为i=3,并且每次循环i的值都增加3,直到i大于100为止。 循环体中使用System.out.println(i)语句来输出当前的i值。因此,程序运行时会输出从3到100之间所有3的倍数,每个数字占一行。 总结起来,这段代码是一个简单的Java程序,用于输出从3到100之间所有3的倍数。

Write a for loop that will print out all the multiples of 3 from 3 to 36, that is: 3 6 9 12 15 18 21 24 27 30 33 36

Sure, here's the code: for i = 3, 36, 3 do print(i) end This will print out all the multiples of 3 from 3 to 36.

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## Problem 5: Remainder Generator Like functions, generators can also be higher-order. For this problem, we will be writing remainders_generator, which yields a series of generator objects. remainders_generator takes in an integer m, and yields m different generators. The first generator is a generator of multiples of m, i.e. numbers where the remainder is 0. The second is a generator of natural numbers with remainder 1 when divided by m. The last generator yields natural numbers with remainder m - 1 when divided by m. Note that different generators should not influence each other. > Hint: Consider defining an inner generator function. Each yielded generator varies only in that the elements of each generator have a particular remainder when divided by m. What does that tell you about the argument(s) that the inner function should take in? python def remainders_generator(m): """ Yields m generators. The ith yielded generator yields natural numbers whose remainder is i when divided by m. >>> import types >>> [isinstance(gen, types.GeneratorType) for gen in remainders_generator(5)] [True, True, True, True, True] >>> remainders_four = remainders_generator(4) >>> for i in range(4): ... print("First 3 natural numbers with remainder {0} when divided by 4:".format(i)) ... gen = next(remainders_four) ... for _ in range(3): ... print(next(gen)) First 3 natural numbers with remainder 0 when divided by 4: 4 8 12 First 3 natural numbers with remainder 1 when divided by 4: 1 5 9 First 3 natural numbers with remainder 2 when divided by 4: 2 6 10 First 3 natural numbers with remainder 3 when divided by 4: 3 7 11 """ "*** YOUR CODE HERE ***" Note that if you have implemented this correctly, each of the generators yielded by remainder_generator will be infinite - you can keep calling next on them forever without running into a StopIteration exception.

Given the grid below for the game of ACSL Patolli, utilize the following rules to play the game. All rules must be applied in the sequential order listed. 1 . There are 2 players. Each player has 3 markers. 2. The markers move according to the roll of a die (1 – 6). 3. Markers move in numerical order around the grid. 4. If, on a die roll, a marker lands on an occupied location, then that marker loses its turn and remains at its previous location. 5. A marker can jump over another marker on its way to finish its move. 6. A marker finishes its way around the grid when it lands on location 52. It is then removed from the board. A move can’t take a marker beyond location 52. If it does, the marker remains at its previous location. 7. If, on a die roll, a marker lands on an unoccupied location that is a prime number, the marker then moves six locations forward. However, it stops immediately before any occupied location. 8. If, on a die roll, a marker lands on an unoccupied location that is a perfect square greater than 4, the marker then moves 6 locations backwards. However, it stops immediately before any occupied location. 9. If, on a die roll, a marker lands on an unoccupied location that is neither a prime number nor a perfect square, then determine if the marker made at least one horizontal move followed by at least one vertical move (such as going from 6 to 8, 11 to 13, 26 to 28 … but not 2 to 4 or 30 to 32). In that case, the marker can only land on a location on its path that is a multiple of the die roll value even if it moves a smaller distance than the die roll value. However, if all the locations in its path that are multiples are occupied, then the marker does not move from its current location. The rules listed in #7 and #8 do not apply when using #9.

class HorNet(nn.Module): # HorNet # hornet by iscyy/yoloair def __init__(self, index, in_chans, depths, dim_base, drop_path_rate=0.,layer_scale_init_value=1e-6, gnconv=[ partial(gnconv, order=2, s=1.0/3.0), partial(gnconv, order=3, s=1.0/3.0), partial(gnconv, order=4, s=1.0/3.0), partial(gnconv, order=5, s=1.0/3.0), # GlobalLocalFilter ], ): super().__init__() dims = [dim_base, dim_base * 2, dim_base * 4, dim_base * 8] self.index = index self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers hornet by iscyy/air stem = nn.Sequential( nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4), HorLayerNorm(dims[0], eps=1e-6, data_format="channels_first") ) self.downsample_layers.append(stem) for i in range(3): downsample_layer = nn.Sequential( HorLayerNorm(dims[i], eps=1e-6, data_format="channels_first"), nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2), ) self.downsample_layers.append(downsample_layer) self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiples bind residual blocks dummy dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] if not isinstance(gnconv, list): gnconv = [gnconv, gnconv, gnconv, gnconv] else: gnconv = gnconv assert len(gnconv) == 4 cur = 0 for i in range(4): stage = nn.Sequential( *[HorBlock(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value, gnconv=gnconv[i]) for j in range(depths[i])]# hornet by iscyy/air ) self.stages.append(stage) cur += depths[i] self.apply(self._init_weights)

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