the source distribution itself. To illustrate this point, consider four different ASCII text sources and check how Huffman compression works out for each of them. Here are three English sentences, or "datafiles," which have been made (ridiculously so) nontypical: Datafile 1: "Zanzibar zoo zebra Zazie has a zest for Jazz" Datafile 2: "Alex fixed the xenon tube with wax" Datafile 3: "The sass of Sierra snakes in sunny season" The above sentences are nontypical in the fact that certain letters are unusually redundant for English. Therefore, the frequency distributions associated with the symbol-characters used are very different from average English text sources. For comparison purposes, we introduce a fourth example representing an ordinary English sentence of similar character length: Datafile 4: "There is a parking lot two blocks from here" We shall now analyze how much these four sources (or datafiles) can be compressed through Huffman coding. Table 9.2 shows the corresponding symbol-character fre- quencies and distributions (overlooking spaces and upper or lower case differences), codeword assignment, mean codeword length, and compression rate, taking for ref- erence the fixed-codeword length L ref = [log2 26] = 5, corresponding to a restricted, five-bit ASCII code. It is seen from the results in Table 9.2 that the compression ratio varies from 20% (datafile 4) to about 35% (datafile 3). The difference in compression ratio cannot be attributed to the datafile length: the best and the worst results are given by datafiles having the same length of 34-35 characters. With only 28 characters, the shortest datafile (datafile 2) has a compression ratio of only 27%. The number of different characters is about the same for datafiles 1-3, but the compression ratio varies from 27% Table 9.2 Examples of file compression through Huffman coding based on the four sentences shown in the text. For each file, the tables list the characters (x), their frequency (f), their probability (p(x)), the codeword assignment (CW), and the 