Binary tree random number generation method with interval weights applied to simulation data

Abstract

The invention discloses a binary tree random number generation method with interval weights applied to simulation data, belonging to the technical field of random number generation with weighted values, comprising the following steps: setting the value range of the random number and the proportion of certain numerical values in the result; dividing into two large intervals: set proportion and unset proportion, and selecting the interval to which it belongs; if the interval is a weighted interval, the interval is subdivided into sub-intervals and spliced into consecutive intervals; the numerical value in the interval is obtained randomly and the interval belongs to is judged, obtaining a random number in the obtained interval to obtain a final result; if the interval to which the result belongs is an interval in which no weight value is set, obtaining the final result through the above steps. The invention increases controllability, is closer to actual data, uses binary tree algorithm to accelerate the generation speed, improves the overall operation efficiency, and obviously improves the time complexity and the space complexity.

Description

technical field [0001] The invention relates to a calculation and storage of weight intervals and a generation method of search index random numbers, in particular to a method for generating random numbers of binary trees with interval weights applied in simulated data, which belongs to the random number generation technology with weight values field. Background technique [0002] In the process of generating simulated data, the random numbers generated by the random number generation method sometimes cannot meet the requirements, and the generated results may not meet the expected simulation purpose. In order to simulate the real data, there may be some generated random data The results are very different from the real situation, such as data in some extreme or individual cases, or some impossible data, and most of the data results are not well displayed by graphs, or because the data differ too much or there is almost no difference. At this time, if the results of the sim...

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Problems solved by technology

[0002] In the process of generating simulated data, the random numbers generated by the random number generation method sometimes cannot meet the requirements, and the generated results may not meet the expected simulation purpose. In order to simulate the real data, there may be some generated random data The results are very different from the real situation, such as data in some extreme or individual cases, or some impossible data, and most of the data results are not well displayed by graphs, or because the data differ too much or there is almost no difference. At this time, if the results of the simulated data can be artificially controlled, it will qualitatively improve the real effect of the simulation and the final display effect

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