Sometimes your dataset is just too large, and you need a way to shrink it down to a reasonable size. I am suffering through this right now as I work on different machine learning techniques for checkers. I could work for over 18 years and buy over 10 petabytes of data to solve it, but I would rather just give up on some of the quality of the solution, get a program that can play checkers well, and use fewer resources in the process.

有時您的數據集過大，您需要一種將其縮小到合理大小的方法。 當我為跳棋員開發不同的機器學習技術時，我現在正遭受這種痛苦。 我可以工作18年以上，并購買超過10 PB的數據來解決它 ，但是我寧愿放棄一些解決方案的質量，獲得一個可以很好地運行檢查程序并在此過程中使用更少資源的程序。

One technique you can use is called bit folding. This technique is similar to hashing in that the function is one way and it can produce the same result for multiple inputs. This latter phenomenon is known as collision. While collision has a bad connotation when hashing, we need collision in order for this to work for data shrinking.

您可以使用的一種技術稱為位折疊。 該技術類似于哈希，因為函數是一種方式，并且可以為多個輸入產生相同的結果。 后一種現象稱為碰撞。 盡管在散列時沖突具有不好的含義，但我們需要沖突才能使其在數據收縮時起作用。

When folding bits, you start with your data in binary form. Then, you start folding the bits into one another, losing half of the information that you had in the two bits. How do you combine the bits? When you start with bits A and B, you may choose one of the seven operations shown in Table 1 to condense the information.

折疊位時，您將從二進制格式的數據開始。 然后，您開始將位相互折疊，從而丟失了兩位中一半的信息。 您如何組合位？ 當您從位A和B開始時，可??以選擇表1所示的七個操作之一來壓縮信息。

Upon first observation, one may notice several combinations missing. First, outputs of all zeros and all ones are not present. This result would zeroize the information, not condense it. We do not want to remove all of the information in the input bits; we just want to shrink the information. Second, we omit all of the inverses of these outputs because inverses convey the same information as one another.

首次觀察時，可能會注意到缺少幾種組合。 首先，不存在全零和全零的輸出。 該結果將使信息歸零，而不是壓縮信息。 我們不想刪除輸入位中的所有信息。 我們只想縮小信息范圍。 其次，我們忽略了這些輸出的所有反函數，因為反函數相互傳遞相同的信息。

I wrote a short Python script that you can use to fold your bits. The first function is gen_param(size). This function generates random parameters for bit folding given the size of the input data that you want to fold. It returns two lists. The first list maps which bits to fold into which other bits, and the second gives the operations that you will use for each fold. We generate random parameters because the data is already too large, so you just need random fold parameters to help you shrink the data. Once you shrink the data and test it, you can compare randomly generated parameters to one another for improved performance. The second function, fold(value, new_size, mapping, ops), takes your parameters and returns your value folded into the size new_size.

我寫了一個簡短的Python腳本，您可以用它折疊位。 第一個函數是gen_param(size)。 給定您要折疊的輸入數據的大小，此函數將為位折疊生成隨機參數。 它返回兩個列表。 第一個列表將要折疊的位映射為其他位，第二個列表給出了每次折疊將使用的操作。 我們生成隨機參數是因為數據已經太大，因此您只需要隨機折疊參數即可幫助您縮小數據。 收縮數據并對其進行測試后，可以將隨機生成的參數相互比較以提高性能。 第二個函數fold(value，new_size，mapping，ops)，獲取您的參數并返回折疊為new_size大小的值。

If your dataset is too large and you are looking for ways to shrink the data, try out my program. I use comparison testing between two sets of parameters to find the better one and improve my algorithms. With data sets that are too large, bit folding gives you speed and wieldy data sizes in exchange for precision.

如果您的數據集太大，并且您正在尋找縮小數據的方法，請嘗試我的程序。 我使用兩組參數之間的比較測試來找到更好的參數并改進算法。 對于過大的數據集，位折疊可為您提供速度快而復雜的數據大小，以換取精度。