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Loops, which can confound naive maze solvers, may be introduced by adding random edges to the result during the course of the algorithm. The animation shows the maze generation steps for a graph that is not on a rectangular grid. First, the computer creates a random planar graph G shown in blue, and its dual F shown in yellow. Second, the ...
Nelson began using random event generator (REG) technology in the field to study effects of special states of group consciousness. [ 7 ] In an extension of the laboratory research utilizing hardware Random Event Generators (REG) [ 8 ] called FieldREG, investigators examined the outputs of REGs in the field before, during and after highly ...
The square step: For each diamond in the array, set the midpoint of that diamond to be the average of the four corner points plus a random value. Each random value is multiplied by a scale constant, which decreases with each iteration by a factor of 2 −h, where h is a value between 0.0 and 1.0 (lower values produce rougher terrain). [2]
Sobol’ sequences (also called LP τ sequences or (t, s) sequences in base 2) are a type of quasi-random low-discrepancy sequence.They were first introduced by the Russian mathematician Ilya M. Sobol’ (Илья Меерович Соболь) in 1967.
Robot in a wooden maze. A maze-solving algorithm is an automated method for solving a maze.The random mouse, wall follower, Pledge, and Trémaux's algorithms are designed to be used inside the maze by a traveler with no prior knowledge of the maze, whereas the dead-end filling and shortest path algorithms are designed to be used by a person or computer program that can see the whole maze at once.
In two dimensions the difference between random sampling, Latin hypercube sampling, and orthogonal sampling can be explained as follows: In random sampling new sample points are generated without taking into account the previously generated sample points. One does not necessarily need to know beforehand how many sample points are needed.
Default generator in R and the Python language starting from version 2.3. Xorshift: 2003 G. Marsaglia [26] It is a very fast sub-type of LFSR generators. Marsaglia also suggested as an improvement the xorwow generator, in which the output of a xorshift generator is added with a Weyl sequence.
Watts–Strogatz small-world model generated by igraph and visualized by Cytoscape 2.5. 100 nodes. The Watts–Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.