LVQ學習矢量化算法源程序
This directory contains code implementing the Learning vector quantization
network. Source code may be found in LVQ.CPP. Sample training data is found
in LVQ1.PAT. Sample test data is found in LVQTEST1.TST and LVQTEST2.TST. The
LVQ program accepts input consisting of vectors and calculates the LVQ
network weights. If a test set is specified, the winning neuron (class) for
each neuron is identified and the Euclidean distance between the pattern and
each neuron is reported. Output is directed to the screen.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
For the incomplete methods, we kept the representation of the queens by a table and the method of calculation to determine if two queens are in conflict, which is much faster for this kind of problems than the representation by a matrix.
heuristics: descent.
Tests: 100 queens in less than 1 second and 67 iterations. 500 queens in 1 second and 257 iterations. 1000 queens in 11 seconds and 492 iterations.
heuristics: Simulated annealing.
Tests: 100 queens in less than 1 second and 47 iterations. 500 queens in 5 seconds and 243 iterations. 1000 queens in 13 seconds and 497 iterations.
heuristics: based on Simulated Annealing.
Tests: 100 queens in less than 1 second and 60 iterations. 500 queens in 1 second and 224 iterations. 1000 queens in 5 seconds and 459 iterations. 10 000 queens in 20 minutes 30 seconds and 4885 iterations.
