faster "bit" there is a library from "ice"
https://github.com/iceland2k14/secp256k1 there it is necessary to throw its libraries into the folder with the script.
***
and read (with a translator)
https://istina.msu.ru/profile/FilatovOV/ ,
https://vk.com/@fil_post-eta-statya-rvet-vse-predstavleniya-o-veroyatnostyah ,
https://disk.yandex.ru/i/LsEWmhs3ArM7TwThis article breaks all ideas about probabilities.
Everyone knows that it is impossible to guess the sides of a coin, but this article will show a mechanism that allows you to predict the sides of a coin, that is, you can actually guess. First, I will briefly describe the traditional way of working with probabilities, working in this way does not allow you to predict the sides of the coin. And then, in the same terse way, I will describe the way I discovered to work with probability, which allows you to predict the loss of the sides of the coin.
And so, the coin was tossed many times N and the result of its loss formed a sequence of ones and zeros. Let's determine the average length of a drop-down series of repeating identical events, for example: "00000.." or "11111.." in our large series of N flips.
It is described here: how to look at a random sequence so that the probabilities of guessing and not guessing are equal.
The traditional way to determine the average length of outliers from repeating identical events is to sequentially look through all the recorded values and accurately count the number of runs of detected lengths.
The total number of series of unit lengths: "0" and "1" will be N/4. The total number of series of length two: "00" and "11" will be N/8. The total number of series of length three: "000" and "111" will be N/16. The total number of series of length four: "0000" and "1111" will be N/32. Etc. Of course, the detected numbers of series are unlikely to be exactly equal to the calculated values, since, despite the frequency stability, there are still random probabilistic fluctuations in the actual number of events around the theoretically obtained mats.expectations. Taking into account all elementary events N of our sequence, we find that the total number of all our series ("0" + "1" + "00" + "11" + "000" + "111" + ...) is equal to N / 2 (again up to random fluctuations). Now let's solve the problem: to determine the average length of the drop-down series, for this we need to divide the number of members of the sequence N by the sum of all series N / 2. Divide N / (N/2) = 2. That is, we found that the average length of a series with the traditional way of looking at and guessing the sides of a coin is two. That is, with an average length of a consecutive series of two events, it is impossible to guess the fallout of the sides. Obviously, if the average length of a consecutive series ("0"; "1"; "00"; "11"; "000"; "111"; ...) were three events, then we would begin to guess the fallout of the sides of the coin much more often than not guessing. Let's now look at my way of getting the average event length, which is three.
It describes how to look at a random sequence so that the probabilities of guessing and not guessing become different.
In order to influence the probability, it is necessary to change the average length of a series of events falling out in a row. This is achieved by applying a well-known geometric probability to the guessing process.
The principle of geometric probability states that objects with a larger size are hit more often than objects with a smaller size. With regard to our random sequence N, this means that if we count, for example, every hundredth member of the sequence and determine the length of the series ("0"; "1"; "00"; "11"; "000"; "111"; ... ) to which it belongs, it turns out that the frequency of hits of every hundredth event in long series increased, and decreased in short series.
That is, the average length of the detected series, in the case of a geometric set of statistics, will become equal to three. And it is precisely this increase in the average length of a series from two events (with sequential counting of each event) to three events (with gaps of sufficient length between guesses) that makes it possible to guess the side of the dropped coin more often than in half of the predictions. Here, now, I have described the fundamental principle of geometric probability, in relation to changing the average length of the found series in a random binary sequence.
he has studies of random events, sequences, there are formulas, I still don’t understand everything, he launches a probe there into a file with 20000000 random bits and...
pz64 ripmd160 hex to bin 1111101110010000010011001111011001100100011111010100101111110111110110101000100
1011100100110000011010011100000011101000101101011000111010000100100100010100100
11111 0 111 00 1 00000 1 00 11 00 1111 0 11 00 11 00 1 000 11111 0 1 0 1 00 1 0 111111 0 11111 0 11 0 1 0 1 000 1 00 1 0 111 00 1 00 11 00000 11 0 1 00 111 000000 111 0 1 000 1 0 11 0 1 0 11 000 111 0 1 0000 1 00 1 00 1 000 1 0 1 00 1 00
1 22
11 8
111 5
1111 1
11111 3
111111 1
1111111
11111111
0 17
00 14
000 5
0000 1
00000 2
000000 1
0000000
00000000
0, 1 - elementary events, they make waves 111 000 1111 0000... when nearby the same half-wavelength 111000 11110000...
In order to influence the probability, it is necessary to change the average length of a series of events falling out in a row. This is achieved by applying a well-known geometric probability to the guessing process.
The principle of geometric probability states that objects with a larger size are hit more often than objects with a smaller size. With regard to our random sequence N, this means that if we count, for example, every hundredth member of the sequence and determine the length of the series ("0"; "1"; "00"; "11"; "000"; "111"; ... ) to which it belongs, it turns out that the frequency of hits of every hundredth event in long series increased, and decreased in short series.
what does this mean? we can run over the 2^256 range and generate these bit sets over several hashes...
Its better to be safe than sorry.