Bit, Shannon informational entropy and Hamming code. How to measure any information and transfer it without loss

Space of random events

In 1946, American statistical scientist John Tukey proposed the name BIT

(BIT, BInary digiT - “binary number” - “High-tech”)- One of the main concepts of the XX century. Tukey chose a bit to denote one binary digit capable of taking the value 0 or 1. In his program article “Mathematical Communication Theory”, Claude Shannon proposed measuring the amount of information in bits. But this is not the only concept introduced and explored by Shannon in his article.

Imagine a space of random eventswhich consists of throwing one fake coin, on both sides of which is an eagle. When does an eagle fall? It is clear that always. We know this in advance, because our space is so arranged. The falling of an eagle is a reliable event, that is, its probability is equal to 1. Will we give much information if we say about a dropped eagle? Not. The amount of information in such a message, we will be considered equal to 0.

Now let's throw the correct coin: on the one hand she has an eagle, and on the other tails, as it should be. The fall of an eagle or a tail will be two different events that make up our space of random events. If we report the outcome of a single throw, it will really be new information. When the eagle falls, we will report 0, and when it is solved, 1. In order to communicate this information, 1 bit is enough for us.

What changed? Uncertainty appeared in our space of events. We have something to tell about him to someone who does not throw a coin and does not see the outcome of the throwing. But in order to properly understand our message, he must know exactly what we are doing, what 0 and 1 mean. Our event space must match, and the decoding process is uniquely restore the result of the throw. If the transmitting and receiving events have no coinciding or no possibility of unambiguous decoding of the message, the information will remain only noise in the communication channel.

If independently and simultaneously throwing twocoins, there will be four different equally probable results: eagle-eagle, tails, tails-heads and tails. In order to transmit information, we will need 2 bits already, and our messages will be like this: 00, 01, 10 and 11. The information has become twice as much. This happened because uncertainty increased. If we try to guess the outcome of such a pair throw, then we have two times more chances to make a mistake.

The greater the uncertainty of the event space, the more information contains a message about its state.

Let's slightly complicate our event space. So far, all the events that happened were equally probable. But in real spaces, not all events are equally likely. For example, the probability that the crow we see will be black is close to 1. The probability that the first passer-by on the street would be a man is about 0.5. But it’s almost unbelievable to meet a crocodile on Moscow street. Intuitively, we understand that reporting a meeting with a crocodile has much greater informational value than about the black crow. The lower the probability of an event, the more information in the message about such an event.

Let the event space not be so exotic. We just stand at the window and look at the passing cars. Cars passing by in four colors, which we need to tell. To do this, we encode the colors: black - 00, white - 01, red - 10, blue - 11. To report exactly which car drove, we just need to transfer 2 bits of information.

But for quite a long time watching the cars,We note that the color of cars is uneven: black - 50% (every second), white - 25% (every fourth), red and blue - 12.5% ​​(every eighth). Then you can optimize the transmitted information.

Most black cars, solet black - 0 - the shortest code, and let the rest of the code begin at 1. Of the remaining half, white is 10, and the remaining colors start at 11. In conclusion, we denote red - 110, and blue - 111.

Now, passing information about the color of the car, we can encode it more closely.

Shannon Entropy

Let our event space consist of ndifferent events. When throwing a coin with two eagles, such an event is exactly one, when throwing one correct coin - 2, when throwing two coins or watching cars - 4. Each event corresponds to the probability of its occurrence. When a coin with two eagles is thrown, there is one event (loss of the eagle) and its probability is p1 = 1. When a correct coin is thrown, two events are equally probable and the probability of each is 0.5: p1 = 0.5, p2 = 0.5. When two correct coins are thrown, there are four events, they are all equally probable and the probability of each is 0.25: p1 = 0.25, p2 = 0.25, p3 = 0.25, p4 = 0.25. When observing cars, there are four events and they have different probabilities: black — 0.5, white — 0.25, red — 0.125, blue — 0.125: p1 = 0.5, p2 = 0.25, p3 = 0.125, p4 = 0.125.

This is not a coincidence. Shannon chose entropy (a measure of uncertainty in event space) so that three conditions were met:

  • 1Entropy of a reliable event, the probability of which is 1, is 0.
  • The entropy of two independent events is equal to the sum of the entropies of these events.
  • Entropy is maximal if all events are equally probable.

All these requirements are fully consistent with ourideas about the uncertainty of the event space. If the event is one (the first example) - there is no uncertainty. If the events are independent - the uncertainty of the amount is equal to the sum of the uncertainties - they simply add up (an example of throwing two coins). And finally, if all events are equally probable, then the degree of uncertainty of the system is maximum. As in the case of throwing two coins, all four events are equally probable and the entropy is 2, it is more than in the case with cars, when there are four events too, but they have different probability - in this case the entropy is 1.75.

The value of H plays a central role in information theory as a measure of information quantity, choice and uncertainty.

Claude Shannon

Claude Elwood Shannon - American engineer, cryptanalyst andmathematician. It is considered the "father of the information age." The founder of the theory of information, which has found application in modern high-tech communication systems. Provided the fundamental concepts, ideas and their mathematical formulations that currently form the basis for modern communication technologies.

In 1948, proposed to use the word "bit"to indicate the smallest unit of information. He also demonstrated that the entropy entered by him is equivalent to the uncertainty of the information in the transmitted message. Shannon's articles "Mathematical Theory of Communication" and "Theory of Communication in Secret Systems" are considered fundamental to information theory and cryptography.

During World War II, Shannon at Bell Laboratories was developing cryptographic systems, which later helped him discover error-correction coding methods.

Shannon made a key contribution to the theory of probabilistic schemes, game theory, automaton theory and control system theory — fields of science that are part of the concept of cybernetics.


And thrown coins, and passing cars are notare similar to the numbers 0 and 1. To report events occurring in spaces, you need to think of a way to describe these events. This description is called coding.

You can encode messages in an infinite number of different ways. But Shannon showed that the shortest code cannot be less in bits than entropy.

That is why the entropy of the message is the measureinformation in the message. Since in all the considered cases the number of bits in encoding is equal to entropy, it means that the encoding has passed optimally. In short, it is no longer possible to encode messages about events in our spaces.

With optimal coding, you cannot lose ordistort a single transmitted bit in the message. If at least one bit is lost, the information will be distorted. But all real communication channels do not give a 100 percent certainty that all bits of the message will reach the recipient undistorted.

To fix this problem you need to dothe code is not optimal, but redundant. For example, to transmit along with a message its checksum — a specially calculated value obtained when converting the message code, and which can be checked by recalculating upon receipt of a message. If the transferred checksum coincides with the calculated one, the probability that the transmission passed without errors will be quite high. And if the checksum does not match, then you need to request a retransmission. This is how most of the communication channels work today, for example, when transmitting information packets over the Internet.

Natural language messages

Consider the event space that isfrom posts in natural language. This is a special case, but one of the most important. Events here will be the transmitted characters (letters of the fixed alphabet). These characters are found in the language with different probabilities.

The most frequency symbol (i.e. one thatis most often found in all texts written in Russian) is a space: of a thousand characters, an average space is found 175 times. The second in frequency is the symbol "o" - 90, followed by other vowels: "e" (or "e" - we will not distinguish them) - 72, "a" - 62, "and" - 62, and only further meets the first consonant "t" - 53. And the rarest "f" - this symbol is found only twice per thousand characters.

We will use the 31-letter alphabet of Russianlanguage (it does not differ "e" and "e", as well as "ъ" and "ь"). If all the letters were encountered in the language with the same probability, then the entropy per symbol would be H = 5 bits, but if we take into account the real frequencies of the symbols, the entropy will be less: H = 4.35 bits. (This is almost two times less than with traditional coding, when a character is transmitted as a byte - 8 bits).

But the entropy of the character in the language is even lower. The probability of occurrence of the next character is not completely predetermined by the average frequency of the character in all texts. Which character will follow depends on the characters already transferred. For example, in modern Russian after the symbol "ъ" can not follow the symbol of a consonant sound. After two consecutive vowels "e", the third vowel "e" follows extremely rarely, except in the word "long-necked". That is, the next character is to some extent predetermined. If we take into account such predetermination of the next symbol, the uncertainty (that is, information) of the next symbol will be even less than 4.35. According to some estimates, the next character in Russian is predetermined by the structure of the language by more than 50%, that is, with optimal coding, all information can be transmitted by deleting half of the letters from the message.

Another thing is that not every letter can be crossed out painlessly. High-frequency "o" (and generally vowels), for example, is easy to cross out, but rare "f" or "e" is quite problematic.

The natural language in which we communicate with each other is highly redundant, and therefore reliable, if we do not hear something - nothing bad, the information will still be transmitted.

But until Shannon introduced the measure of information, we could not understand that the language was redundant, and to what extent we could compress the messages (and why text files are so well compressed by the archiver).

Natural language redundancy

In the article "About how we vorpsimanie tektkt"(the name sounds exactly like this!) a fragment of Ivan Turgenev's Noble Nest novel was taken and subjected to some transformation: 34% of letters were deleted from the fragment, but not random ones. The first and last letters in words were left, only vowels were crossed out, and not all. The goal was not only to get the opportunity to recover all the information on the converted text, but also to ensure that the person reading this text did not experience any particular difficulties due to missing letters.

Why is it relatively easy to read this spoiledtext? It really contains the necessary information to recover whole words. A Russian speaker has a specific set of events (words and whole sentences) that he uses in recognition. In addition, the carrier also has standard language constructs that help it recover information. For example, "She is blae blee" - can be read with high probability as "She was more sensitive.". But a separate phrase "She's more blah"rather, it will be restored as “She was whiter”. Since we are dealing in everyday communicationwith channels in which there is noise and interference, we are fairly well able to recover information, but only the one that we already know in advance. For example, the phrase "Her features are not in the least pleasant, htya nanny rspkhli and zlls" readable except for the last word "Splash" - "rallied". This word is not in the modern lexicon. With quick reading the word "Splash" reads more like "stuck together", while slow - just baffles.

Signal Digitization

Sound, or acoustic oscillations - this is a sine wave. This is seen, for example, on the sound editor screen. To accurately transmit the sound, you will need an infinite number of values ​​- the entire sine wave. This is possible with an analog connection. He sings - you listen, the contact is not interrupted while the song lasts.

With digital communication over the channel, we can transmit only a finite number of values. Does this mean that the sound can not be accurately transmitted? It turns out no.

Different sounds are differently modulated sinusoids. We transmit only discrete values ​​(frequencies and amplitudes), but we don’t need to transmit the sinusoid itself - the receiving device can generate it. It generates a sinusoid and is superimposed on it.modulation created by the values ​​transmitted over the communication channel. There are exact principles of what discrete values ​​should be transmitted so that the sound at the input to the communication channel coincides with the sound at the output, where these values ​​are superimposed on some standard sinusoid (this is just the Kotelnikov theorem).

Kotelnikov's theorem (in the English-language literature - the Nyquist-Shannon theorem, the reading theorem) - fundamental statement in the field of digitalsignal processing, connecting continuous and discrete signals and stating that “any function F (t), consisting of frequencies from 0 to f1, can be continuously transmitted with any accuracy using numbers following each other through 1 / (2 * f1) seconds

Anti-interference coding. Hamming codes

If on an unreliable channel to transmitIvan Turgenev's coded text, albeit with some errors, will result in a quite meaningful text. But if we need to transfer everything up to a bit, the task will be unresolved: we do not know which bits are wrong, because the error is random. Even the checksum does not always save.

That is why today when transmitting data onnetworks tend not so much to optimal coding, in which the maximum amount of information can be pushed into the channel, but rather to such coding (obviously redundant) in which errors can be recovered - just like when we read the words in Ivan Turgenev's fragment.

There are special error-correcting codes that allow you to recover information after a failure. One of them is Hamming code. Suppose our entire language consists of three words: 111000, 001110, 100011. These words know both the source of the message and the receiver. And we know that errors occur in the communication channel, but no more than one bit of information is distorted when transmitting one word.

Suppose we first pass the word 111000. As a result, no more than one error (we have identified the error) it can turn into one of the words:

1) 111,000 011,000, 101000, 110000, 111100, 111010, 111001.

When you send the word 001110, you can get any of the words:

2) 001110, 101110, 011110, 000110, 001010, 001100, 001111.

Finally, for 100011 we can get at the reception:

3) 100011, 000011, 110011, 101011, 100111, 100001, 100010.

Note that all three lists are not pairwise.intersect. In other words, if at the other end of the communication channel any word from list 1 appears, the recipient knows for sure that the word 111000 was transmitted to him, and if any word from list 2 appears, the word 001110, and from list 3, the word 100011 appears. In this case They say that our code fixed one error.

The correction occurred due to two factors. First, the recipient knows the whole “dictionary”, i.e. the event space of the message recipient coincides with the space of the one who transmitted the message. When the code was transmitted with only one error, a word came out, which was not in the dictionary.

Secondly, the words in the dictionary were chosen in a special way. Even when an error occurred, the recipient could notconfuse one word with another. For example, if the dictionary consists of the words “daughter”, “point”, “bump”, and the transfer turned out to be “little”, the recipient, knowing that there is no such word, could not correct the error - any of the three words may turn out to be correct. If the dictionary includes “point”, “jackdaw”, “branch” and we know that no more than one mistake is allowed, then “little bit” is obviously a “point” and not “daw”. In error correction codes, words are chosen in such a way that they are “recognizable” even after an error. The only difference is that in the code "alphabet" only two letters - zero and one.

The redundancy of such coding is very large, and the number of words that we can thus convey is relatively small. We need to exclude any word from the dictionary,which, on error, may coincide with the whole list corresponding to the transmitted words (for example, the words “daughter” and “dot” cannot be in the dictionary). But the exact transmission of the message is so important that large forces are spent on the study of noise-resistant codes.


Concepts of entropy (or uncertainty andunpredictability) of the message and redundancy (or predetermination and predictability) very naturally correspond to our intuitive ideas about the measure of information. The more unpredictable the message (the greater its entropy, because there is less likelihood), the more information it carries. A sensation (for example, a meeting with a crocodile on Tverskaya) is a rare event, its predictability is very low, and therefore the information value is high. Often information is called news — reports of events that have just occurred, about which we still know nothing. But if they tell us about the second and third times about the same words, the redundancy of the message will be great, its unpredictability will drop to zero, and we simply will not listen, dismissing the speaker with the words "I know, I know." Therefore, the media are trying so hard to be the first. This correspondence to the intuitive feeling of novelty, which gives rise to really unexpected news, played a major role in the fact that Shannon’s article, which was not intended for the general reader, became a sensation, which the press picked up as a universal key to the knowledge of nature. - from linguists and literary critics to biologists.

But Shannon information concept - rigorous mathematical theory, and its application outside of communication theory is very unreliable. But in the theory of communication itself, it plays a central role.

Semantic information

Shannon, introducing the concept of entropy as a measureinformation, got the opportunity to work with information - first of all, to measure it and evaluate such characteristics as channel capacity or coding optimality. But the main assumption that allowed Shannon to successfully operate with information was the assumption that the generation of information is a random process that can be successfully described in terms of probability theory. If the process is non-random, that is, it obeys the laws (moreover, it is not always clear, as it happens in natural language), then Shannon's reasoning is not applicable to it. Everything Shannon says is in no way connected with the meaningfulness of the information.

While we are talking about characters (or letters of the alphabet),we can well argue in terms of random events, but as soon as we get to the words of the language, the situation will change dramatically. Speech is a process that is specially organized, and here the structure of the message is no less important than the characters by which it is transmitted.

More recently, it seemed that we can notto do something to get closer to measuring the meaning of the text, but in recent years the situation has begun to change. And this is primarily due to the use of artificial neural networks to the tasks of machine translation, automatic text summarization, extraction of information from texts, generation of reports in natural language. In all these tasks, the transformation, encoding and decoding of meaningful information contained in natural language takes place. And gradually, the idea of ​​information losses in such transformations, and therefore - the extent of meaningful information, is taking shape. But today the clarity and accuracy that the Shannon theory of information has in these difficult tasks is not yet.