Error too many arguments for method map scala

#ERROR TOO MANY ARGUMENTS FOR METHOD MAP SCALA ZIP#

In the case of communication of information over a noisy channel, this abstract concept was formalized in 1948 by Claude Shannon in a paper entitled A Mathematical Theory of Communication, in which information is thought of as a set of possible messages, and the goal is to send these messages over a noisy channel, and to have the receiver reconstruct the message with low probability of error, in spite of the channel noise. Abstractly, information can be thought of as the resolution of uncertainty. Information theory studies the transmission, processing, extraction, and utilization of information. 5.1 Intelligence uses and secrecy applications.4.2.2 Channels with memory and directed information.4.2.1 Capacity of particular channel models.3.5 KullbackвЂ“Leibler divergence (information gain).3.4 Mutual information (transinformation).The theory has also found applications in other areas, including statistical inference, cryptography, neurobiology, perception, linguistics, the evolution and function of molecular codes ( bioinformatics), thermal physics, molecular dynamics, quantum computing, black holes, information retrieval, intelligence gathering, plagiarism detection, pattern recognition, anomaly detection and even art creation. Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones and the development of the Internet.

#ERROR TOO MANY ARGUMENTS FOR METHOD MAP SCALA ZIP#

for ZIP files), and channel coding/ error detection and correction (e.g. Important sub-fields of information theory include source coding, algorithmic complexity theory, algorithmic information theory and information-theoretic security.ДЄpplications of fundamental topics of information theory include source coding/ data compression (e.g. Some other important measures in information theory are mutual information, channel capacity, error exponents, and relative entropy. For example, identifying the outcome of a fair coin flip (with two equally likely outcomes) provides less information (lower entropy) than specifying the outcome from a roll of a die (with six equally likely outcomes). Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. :вЂЉvii The field is at the intersection of probability theory, statistics, computer science, statistical mechanics, information engineering, and electrical engineering.ДЄ key measure in information theory is entropy. The field was fundamentally established by the works of Harry Nyquist and Ralph Hartley, in the 1920s, and Claude Shannon in the 1940s. Information theory is the scientific study of the quantification, storage, and communication of digital information.

Error too many arguments for method map scala