Parameter estimation device, method and program
The present invention relates to a parameter estimation system, a parameter estimation method, and a program, and more particularly to a parameter estimation system, a parameter estimation method, and a program that efficiently estimate parameters of machine learning and simulation, etc. An objective of the present invention is to provide a parameter estimation system and a parameter estimation method that may rapidly determine the optimum input parameter.
1. A parameter estimation system comprising:
a searching range determiner configured to determine, according to an input data dimension number that is a dimension number of input data, a reduced dimension number that is lower than the input data dimension number, and a parallel number, as many transformation matrices as the parallel number, each transformation matrix being for transforming a space defined by the input data dimension number to a space defined by the reduced dimension number, and thus determines as many searching ranges as the parallel number;
an optimization performer configured to repeat a predetermined number of times, for the as many searching ranges as the parallel number, in the searching range, inputting a parameter selected from the searching range and input data obtained from the transformation matrix to a predetermined device that outputs an objective function value about a previously provided observation, and acquiring the objective function value, and repeats in parallel, wherein a parallel processing is iteratively performed on each of a plurality of subspaces in the space, a predetermined number of times, determining the objective function value obtained from the parameter wherein the parameter represents a search range with reduced dimension, and the transformation matrix that provide the optimum objective function value; and
an optimum value determiner configured to, on the basis of the objective function values determined for the respective searching ranges, determine an optimum input parameter obtained from the parameter and the transformation matrix that provide the optimum objective function value.
2. The parameter estimation system according to claim 1 , wherein the optimization performer, for the as many searching ranges as the parallel number, after acquiring the objective function value, repeats in parallel, a predetermined number of times, approximating a function representing a relationship between the objective function value and input data using a probabilistic model, determining a next input parameter using the approximated function and an acquisition function that uses the parameter providing the optimum objective function value, inputting the determined next input parameter and input data obtained from the transformation matrix to the predetermined device, and determining the objective function value.
3. The parameter estimation system according to claim 2 , wherein the optimization performer, for the as many searching ranges as the parallel number, repeats a predetermined number of times, in the searching range, inputting to a simulator a parameter selected from the searching range and input data obtained from the transformation matrix and acquiring output data and the objective function value, and repeats in parallel, a predetermined number of times, determining a next input parameter using the acquisition function, inputting to the simulator the determined next input parameter and input data obtained from the transformation matrix, and determining the objective function value.
4. The parameter estimation system according to claim 1 , further comprising a determiner, the determiner repeating as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
5. The parameter estimation system according to claim 4 , wherein in the repeating, the searching range determiner prioritizes the searching range that comprises the optimum input parameter determined in a previous cycle in determining as many searching ranges as the parallel number.
6. A parameter estimation method, comprising:
determining, by a searching range determiner, according to an input data dimension number that is a dimension number of input data, a reduced dimension number that is lower than the input data dimension number, and a parallel number, as many transformation matrices as the parallel number, each transformation matrix being for transforming a space defined by the input data dimension number to a space defined by the reduced dimension number and, thus determining as many searching ranges as the parallel number;
repeating a predetermined number of times, by an optimization performer, for the as many searching ranges as the parallel number, in the searching range, inputting a parameter selected from the searching range and input data obtained from the transformation matrix to a predetermined device that outputs an objective function value about a previously provided observation, and acquiring the objective function value, and repeating in parallel, wherein a parallel processing is iteratively performed on each of a plurality of subspaces in the space, a predetermined number of times, determining the objective function value obtained from the parameter wherein the parameter represents a search range with reduced dimension, and the transformation matrix that provide the optimum objective function value; and
determining, by an optimum value determiner, on the basis of the objective function values determined for the respective searching ranges, an optimum input parameter obtained from the parameter and the transformation matrix that provide the optimum objective function value.
7. A computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor cause a computer system to execute:
determining, by a search range determiner according to an input data dimension number that is a dimension number of input data, a reduced dimension number that is lower than the input data dimension number, and a parallel number, as many transformation matrices as the parallel number, each transformation matrix being for transforming a space defined by the input data dimension number to a space defined by the reduced dimension number, and thus determine as many searching ranges as the parallel number;
repeating, by an optimization performer, a predetermined number of times, for the as many searching ranges as the parallel number, in the searching range, inputting a parameter selected from the searching range and input data obtained from the transformation matrix to a predetermined device that outputs an objective function value about a previously provided observation, and acquiring the objective function value, and repeating in parallel, wherein a parallel processing is iteratively performed on each of a plurality of subspaces in the space, a predetermined number of times, determining the objective function value obtained from the parameter wherein the parameter represents a search range with reduced dimension, and the transformation matrix that provide the optimum objective function value; and
determining, by an optimum value determiner, on the basis of the objective function values determined for the respective searching ranges, an optimum input parameter obtained from the parameter and the transformation matrix that provide the optimum objective function value.
8. The parameter estimation system according to claim 2 , further comprising a determiner, the determiner repeating as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
9. The parameter estimation system according to claim 3 , further comprising a determiner, the determiner repeating as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
10. The parameter estimation method according to claim 6 , wherein the optimization performer, for the as many searching ranges as the parallel number, after acquiring the objective function value, repeats in parallel, a predetermined number of times, approximating a function representing a relationship between the objective function value and input data using a probabilistic model, determining a next input parameter using the approximated function and an acquisition function that uses the parameter providing the optimum objective function value, inputting the determined next input parameter and input data obtained from the transformation matrix to the predetermined device, and determining the objective function value.
11. The parameter estimation method according to claim 6 , further comprising:
repeating, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
12. The parameter estimation method according to claim 10 , further comprising:
repeating, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
13. The parameter estimation method according to claim 10 , wherein the optimization performer, for the as many searching ranges as the parallel number, repeats a predetermined number of times, in the searching range, inputting to a simulator a parameter selected from the searching range and input data obtained from the transformation matrix and acquiring output data and the objective function value, and repeats in parallel, a predetermined number of times, determining a next input parameter using the acquisition function, inputting to the simulator the determined next input parameter and input data obtained from the transformation matrix, and determining the objective function value.
14. The parameter estimation method according to claim 13 , further comprising:
repeating, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
15. The parameter estimation method according to claim 14 , wherein in the repeating, the searching range determiner prioritizes the searching range that comprises the optimum input parameter determined in a previous cycle in determining as many searching ranges as the parallel number.
16. The computer-readable non-transitory recording medium of claim 7 , wherein the optimization performer, for the as many searching ranges as the parallel number, after acquiring the objective function value, repeats in parallel, a predetermined number of times, approximating a function representing a relationship between the objective function value and input data using a probabilistic model, determining a next input parameter using the approximated function and an acquisition function that uses the parameter providing the optimum objective function value, inputting the determined next input parameter and input data obtained from the transformation matrix to the predetermined device, and determining the objective function value.
17. The computer-readable non-transitory recording medium of claim 7 , the computer-executable instructions when executed further causing the computer system to:
repeat, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
18. The computer-readable non-transitory recording medium of claim 16 ,
the computer-executable instructions when executed further causing the computer system to:
repeat, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner.
19. The computer-readable non-transitory recording medium of claim 16 , wherein the optimization performer, for the as many searching ranges as the parallel number, repeats a predetermined number of times, in the searching range, inputting to a simulator a parameter selected from the searching range and input data obtained from the transformation matrix and acquiring output data and the objective function value, and repeats in parallel, a predetermined number of times, determining a next input parameter using the acquisition function, inputting to the simulator the determined next input parameter and input data obtained from the transformation matrix, and determining the objective function value.
20. The computer-readable non-transitory recording medium of claim 19 , the computer-executable instructions when executed further causing the computer system to:
repeat, by a determiner, as one cycle the processes by the searching range determiner, the optimization performer, and the optimum value determiner, wherein in the repeating, the searching range determiner prioritizes the searching range that comprises the optimum input parameter determined in a previous cycle in determining as many searching ranges as the parallel number.