  The paper describes an approach to measuring convergence of an algorithm to
its result in terms of an entropy-like function of partitions of its inputs of
a given length. The goal is to look at the algorithmic data processing from the
viewpoint of information transformation, with a hope to better understand the
work of algorithm, and maybe its complexity. The entropy is a measure of
uncertainty, it does not correspond to our intuitive understanding of
information. However, it is what we have in this area. In order to realize this
approach we introduce a measure on the inputs of a given length based on the
Principle of Maximal Uncertainty: all results should be equiprobable to the
algorithm at the beginning. An algorithm is viewed as a set of events, each
event is an application of a command. The commands are very basic. To measure
the convergence we introduce a measure that is called entropic weight of events
of the algorithm. The approach is illustrated by two examples.
