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--- tanszek:oktatas:techcomm:information [2024/10/08 07:33] – [Redundancy] knehez
+++ tanszek:oktatas:techcomm:information [2025/10/14 06:25] (current) – [Entropy] knehez
@@ Line 11: / Line 11: @@
 $$ I_E = \log_2 \frac{1}{p_E} = -\log_2( p_E ) [bit] $$
-The properties of a logarithm function play an important role in the modeling procedure of the quantitative properties of a given information.
+Shannon used the logarithm to measure information because only the logarithmic function makes the information of independent events additive. If two independent events 𝐴 𝐵 occur, their joint probability is: \( p(A,B) = p(A) \cdot p(B) \).
+We expect that the total information should add up:
+$$ I(A,B) = I(A) + I(B) $$
+Only the logarithm satisfies this property:
+$$ I(p) = -\log p \quad \Rightarrow \quad I(A,B) = -\log(p(A)p(B)) = I(A) + I(B) $$
+If we used \( I(p) = 1/p \), the values would multiply, not add.
+The properties of a logarithm function play an important role in modeling the quantitative properties of a given information.
 If an event space consist of two equal-probability event \(p(E_1) = p(E_2) = 0.5 \) then,
-$$ I_{E_1} = I_{E_2} = \log_2 \frac{1}{0.5} = - \log_2 2 = 1 [bit] $$
+$$ I_{E_1} = I_{E_2} = \log_2 \frac{1}{0.5} = - \log_2 0.5 = 1 [bit] $$
 So the unit of the information means the news value which is connected to the simple, less likely, same probability choice.
@@ Line 33: / Line 45: @@
 The average information content of the set of messages is called the //entropy// of the message set.
-$$ H_E = \sum_{i=1}^n p_i \cdot I_{E_i} = \sum_{i=1}^n p_i \cdot \log_2 \frac{1}{p_i} = - \sum_{i=1}^n p_i \cdot \log_2 p_i$$
+$$ H_E = \sum_{i=1}^n p_i \cdot I_{E_i} = \sum_{i=1}^n p_i \cdot \log_2 \frac{1}{p_i} = - \sum_{i=1}^n p_i \cdot \log_2 p_i  [bit]$$
 **Example**: Given an event space consisting of two events: \( E = \{E_1, E_2\} \), and further \( p = \{p_1, p_2\} \) with \( p_2 = 1 - p_1 \), then the average information content is:
@@ Line 46: / Line 58: @@
 Entropy can also be viewed as a measure of the information "richness" of a message. In communication systems, higher entropy implies a greater potential for the message to carry a variety of content, whereas lower entropy suggests that the message is more predictable or redundant.
+Example:
+  * If a source always sends the same letter (“AAAAA…”), the entropy = 0 bits → there is no new information.
+  * If every letter occurs with equal probability (e.g., random characters), the entropy is maximal → the source is rich in information.
 This concept is crucial in various fields, including //data compression//, //cryptography//, and //machine learning//, where understanding and managing entropy can lead to more efficient algorithms and systems. For example, in data compression, reducing redundancy (and thus reducing entropy) can lead to more compact data representations. Similarly, in cryptography, managing entropy ensures that keys and encrypted messages are less predictable and more secure.
@@ Line 99: / Line 116: @@
 which means the redundancy of the event system is approximately 16%.
+==== Example of Entropy calculation ====
+Why do not store raw password strings in compiled code?
+<sxh c>
+#include <stdio.h>
+#include <math.h>
+#include <string.h>
+float calculateEntropy(unsigned char counts[], int length);
+int main(void) {
+    const char sample[] =
+        "Some poetry types are unique to particular cultures and genres "
+        "and respond to yQ%v?FY}ZT=/5cJ.m~A{9^8L the characteristics of the language in which the "
+        "poet writes. Readers accustomed to identifying poetry with Dante, "
+        "Goethe, Mickiewicz, or Rumi may think of it as written in lines "
+        "based on rhyme and regular meter. There are, however, traditions, "
+        "such as Biblical poetry and alliterative verse, that use other "
+        "means to create rhythm and euphony. Much modern poetry reflects "
+        "a critique of poetic tradition, testing the principle of euphony "
+        "itself or altogether forgoing rhyme or set rhythm.";
+    const int windowWidth = 20;
+    unsigned char counts[256];
+    int sampleLength = strlen(sample);
+    for (int start = 0; start <= sampleLength - windowWidth; start++) {
+        memset(counts, 0, sizeof(counts));
+        // Count characters in current window
+        for (int j = 0; j < windowWidth; j++) {
+            unsigned char c = (unsigned char)sample[start + j];
+            counts[c]++;
+        }
+        float entropy = calculateEntropy(counts, windowWidth);
+        printf("%d - %.3f\n", start, entropy);
+    }
+    return 0;
+}
+float calculateEntropy(unsigned char counts[], int length) {
+    float entropy = 0.0f;
+    for (int i = 0; i < 256; i++) {
+        if (counts[i] > 0) {
+            float freq = (float)counts[i] / length;
+            entropy -= freq * log2f(freq);
+        }
+    }
+    return entropy;
+}
+</sxh>