CN111275347A - Probability threshold calculation method, device, equipment and storage medium for game rough set - Google Patents
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Abstract
The invention is suitable for the technical field of computers, and provides a probability threshold value calculation method, a probability threshold value calculation device, probability threshold value calculation equipment and a storage medium for a game rough set, wherein the calculation method comprises the following steps: acquiring probability information data; calculating a rough set evaluation index value based on the probability data information; determining an optimal rough set evaluation index value; updating the probability threshold value according to the optimal rough set evaluation index value; updating the step value; and circulating the steps of calculating the rough set evaluation index value, determining the optimal rough set evaluation index value and updating the probability threshold value and the step value until the conditions are met, and outputting the probability threshold value at the moment. The probability threshold value calculation method provided by the invention can reduce the adjustment amplitude of the probability threshold value when the probability threshold value is close to the global optimum point by adjusting the step value used for updating the threshold value in the process of updating the threshold value, thereby effectively avoiding the risk that the global optimum point is skipped and ensuring the effect of the solved probability threshold value.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a probability threshold value calculation method, a probability threshold value calculation device, probability threshold value calculation equipment and a storage medium for a game rough set.
Background
The document "Zhang Y, Yao J t, multi-criterion based third-way classification [ C ]// International Symposium on method for an Intelligent system, spring, Cham,2017: 550-. The model constructs a game, the mutually conflicting or cooperative rough set indexes are used as game players, the action strategy adopted by the players is that the probability threshold value is increased or decreased by a fixed step value, the Nash equilibrium point is found out by calculating the return function of the search strategy combination, and the probability threshold value is updated.
However, the game rough set model for calculating the probability threshold disclosed in this document is prone to fall into local optima, that is, global optima are prone to be skipped in the process of updating the probability threshold, so that the solved probability threshold is not the global optima, thereby affecting the effect of the finally determined probability threshold.
Therefore, the existing method for calculating the probability threshold in the game rough set model also has the technical problem that the solved probability threshold is not ideal enough due to the fact that the probability threshold is easy to fall into local optimization.
Disclosure of Invention
The embodiment of the invention aims to provide a probability threshold value calculation method for a game rough set, and aims to solve the technical problem that the solved probability threshold value is not ideal enough in effect because the probability threshold value calculation method in the existing game rough set model is easy to fall into local optimization.
The embodiment of the invention is realized in such a way that a probability threshold value calculation method of a game rough set comprises the following steps:
another object of an embodiment of the present invention is to provide a device for calculating a probability threshold of a game rough set, including:
the probability information data acquisition module is used for acquiring a plurality of probability information data of known target concepts;
an evaluation index value calculation module, configured to calculate according to the multiple probability information data, the current first probability threshold, the current second probability threshold, and multiple probability threshold update combination policies, and obtain multiple rough set evaluation index values corresponding to the multiple probability threshold update combination policies, respectively; the probability threshold updating combination strategy is determined by a preset first probability threshold updating strategy and a preset second probability threshold updating strategy, and the current step value in the probability threshold updating combination strategy is used for adjusting the probability threshold;
an optimal probability threshold adjustment strategy determination module, configured to determine an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determine an optimal probability threshold update combination strategy according to the optimal rough set evaluation index value;
a probability threshold updating module for updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy;
the step value adjusting module is used for updating the current step value according to a preset step value updating rule;
the termination judging module is used for judging whether the updating times of the probability threshold value meet the preset termination condition or not;
the circulation module is used for returning to the evaluation index value calculation module when the updating times of the probability threshold are judged not to meet the preset termination condition;
and the probability threshold output module is used for outputting the current first probability threshold and the current second probability threshold when the updating times of the probability threshold are judged to meet the preset termination condition.
It is a further object of an embodiment of the present invention to provide a computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of the method for probability threshold calculation of a game rough set as described above.
It is a further object of an embodiment of the present invention to provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the steps of the method for calculating a probability threshold for a game roughness set as described above.
According to the probability threshold value calculation method for the game rough set, provided by the embodiment of the invention, after a plurality of probability information data of a known target concept are obtained, the values of a plurality of rough set evaluation indexes obtained under a plurality of probability threshold value adjustment combination strategies are further calculated, the values of the plurality of rough set evaluation indexes can reflect the advantages and disadvantages of each probability threshold value adjustment combination strategy, the optimal probability threshold value adjustment combination strategy is determined, and then the probability threshold value is adjusted based on the optimal probability threshold value adjustment combination strategy. In addition, in the process of adjusting the probability threshold, the step value used for updating the probability threshold at each time is adjusted by comparing the value of the rough set evaluation index obtained under the optimal probability threshold adjustment combination strategy with the value of the current rough set evaluation index, so that when the global optimum point is approached, the amplitude of updating the probability threshold at each time can be reduced by adjusting the step value, the risk of skipping the global optimum point in the process of updating the probability threshold is reduced, the finally solved probability threshold can approach the global optimum point, and the effect of the final probability threshold is ensured.
Drawings
Fig. 1 is a method for calculating a probability threshold of a game rough set according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another method for calculating a probability threshold of a game rough set according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for calculating a probability threshold of a coarse set of games according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a method for calculating a probability threshold of a game rough set according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating a step of calculating values of multiple rough set evaluation indicators according to an embodiment of the present invention;
FIG. 6 is a flowchart of the steps provided in an embodiment of the present invention for calculating accuracy and coverage;
fig. 7 is a schematic structural diagram of a probability threshold calculation device for a game rough set according to an embodiment of the present invention;
fig. 8 is a block diagram of a terminal computer device for executing the probability threshold calculation method for the game rough set provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention aims to solve the technical problem that the existing probability threshold value calculation method of the game rough set is easy to fall into local optimum, improves the specific process of the probability threshold value calculation method, and can be understood as providing an improved probability threshold value calculation method of the game rough set. Compared with the conventional probability threshold value calculation method, the improved probability threshold value calculation method for the game rough set provided by the invention further adjusts the step value required by updating the probability threshold value in the process of updating the probability threshold value, namely, adjusts the fixed step value into the variable step value, and can reduce the adjustment amplitude of the probability threshold value in updating when the probability threshold value is close to the global optimum point, thereby effectively avoiding the risk of skipping the global optimum point and ensuring the effect of the solved probability threshold value.
As shown in fig. 1, in an embodiment, a method for calculating a probability threshold of a game rough set is provided, which specifically includes the following steps:
step S102, a plurality of probability information data of known target concepts are obtained.
In an embodiment of the present invention, the known target concepts are associated with a plurality of known classes.
In an embodiment of the present invention, the plurality of probability information data of the known target concept includes a distribution probability of the plurality of known classes and a conditional probability of the plurality of known classes, where the conditional probability of the known class is a probability of belonging to the known target concept simultaneously on the premise of belonging to the known class.
In the embodiment of the present invention, since the game rough set and the probability threshold are technical terms understood by those skilled in the art, the present invention does not specifically explain the specific game rough set and the probability threshold, the probability threshold of the game rough set is generally represented by (α), and the first probability threshold and the second probability threshold are respectively expressed in the technical solution of the present invention, so as to facilitate understanding of the technical solution provided by the present invention, the present invention explains the technical solution provided by the present invention in specific cases, which is specifically described as follows.
In a specific embodiment, there are several blocks of different shapes, including triangle, circle, rectangle, trapezoid, pentagon, etc., and the stability of each block is known, including both stable and unstable cases, in order to determine the relationship between the block shape and the block stability, we can divide the block shape into three types, positive domain shape, negative domain shape and boundary domain shape, for the block of positive domain shape, it indicates that it has a greater likelihood of being stable, for the block of negative domain shape, it indicates that it has a lesser likelihood of being stable, in the process, the greater likelihood and the lesser likelihood are the probability thresholds that need to be determined by the present invention, and after determining the probability thresholds, it can determine the positive domain, negative domain and boundary domain of the shape for stability, the known target concept can be equally understood as the stability of the building blocks, the related known classes can be understood as the shape of the building blocks, and the probability information data of the known target concept comprises the distribution probability of each shape building block and the conditional probability that the building block in each shape building block is stable, and the sum of the distribution probabilities of all the shape building blocks is 1.
And step S104, calculating according to the plurality of probability information data, the current first probability threshold, the current second probability threshold and a plurality of probability threshold updating combination strategies to obtain a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold updating combination strategies.
In the embodiment of the present invention, the probability threshold updating combination strategy is determined by a combination of a preset first probability threshold updating strategy and a preset second probability threshold updating strategy.
In the embodiment of the present invention, the probability threshold updating combination policy includes a current step value, which is used to adjust the probability threshold.
In the embodiment of the present invention, the probability threshold updating policy includes keeping the current probability threshold unchanged, and adding the current probability threshold to the current step value, or adding the current probability threshold to any multiple of the current step value, and the present invention does not limit the specific probability threshold updating policy, and as an optimization, generally consists of three policies: keeping the current probability threshold constant, adding the current probability threshold to the current step value, and adding the current probability threshold to twice the current step value, it is understood that the probability threshold may be a first probability threshold or a second probability threshold, and the first probability threshold updating strategy may be different from the second probability threshold updating strategy, so that the probability threshold updating combination strategy has 9 combinations of 3 × 3 in total.
In the embodiment of the present invention, the rough set evaluation index value is generally selected from accuracy and coverage, wherein the specific calculation process is described with reference to fig. 6 and the explanation thereof.
And step S106, determining an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determining an optimal probability threshold value updating combination strategy according to the optimal rough set evaluation index value.
In the embodiment of the present invention, it is preferable that the maximum value of the rough set evaluation index value or the maximum value of the sum is determined as the optimal rough set evaluation index value, and the optimal rough set evaluation index value is determined by using a nash equilibrium point.
In the embodiment of the invention, after the optimal rough set evaluation index value is determined, the optimal probability threshold combination strategy can be determined by simply utilizing the corresponding relation between the probability threshold updating combination strategy and the rough set evaluation index value.
And step S108, updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy.
In the embodiment of the present invention, since the optimal probability threshold combination policy is determined by combining a preset first probability threshold updating policy and a preset second probability threshold updating policy, the current first probability threshold is updated by using the first probability threshold updating policy, for example, the current first probability threshold is added with a step value, and the current second probability threshold is updated by using the second probability threshold updating policy, for example, the current second probability threshold is added with a double step value.
Step S110, updating the current step value according to a preset step value updating rule.
In the embodiment of the invention, compared with the existing probability threshold value calculation method, the step value for updating the probability threshold value is further adjusted, so that the probability threshold value can be adjusted by a smaller step value in the subsequent probability threshold value updating process, namely, the adjustment amplitude of the probability threshold value is reduced, the risk of skipping the optimal point is reduced, and the effect of the determined probability threshold value is improved.
Step S112, determining whether the update times of the probability threshold satisfy a preset termination condition. And returning to the step S104 when the updating times of the probability threshold value are judged not to meet the preset termination condition. When it is determined that the number of updates of the probability threshold satisfies the preset termination condition, step S114 is performed.
And step S114, outputting the current first probability threshold and the current second probability threshold.
In the embodiment of the present invention, after a certain number of cycles, it may be considered that the current probability threshold is already close to the optimal probability threshold, and at this time, the output is only required, and if the number of iterations of the cycle is not enough, the step 104 needs to be returned to perform the cycle.
According to the probability threshold value calculation method for the game rough set, provided by the embodiment of the invention, after a plurality of probability information data of a known target concept are obtained, the values of a plurality of rough set evaluation indexes obtained under a plurality of probability threshold value adjustment combination strategies are further calculated, the values of the plurality of rough set evaluation indexes can reflect the advantages and disadvantages of each probability threshold value adjustment combination strategy, the optimal probability threshold value adjustment combination strategy is determined, and then the probability threshold value is adjusted based on the optimal probability threshold value adjustment combination strategy. In addition, in the process of adjusting the probability threshold, the step value used for updating the probability threshold at each time is adjusted by comparing the value of the rough set evaluation index obtained under the optimal probability threshold adjustment combination strategy with the value of the current rough set evaluation index, so that when the global optimum point is approached, the amplitude of updating the probability threshold at each time can be reduced by adjusting the step value, the risk of skipping the global optimum point in the process of updating the probability threshold is reduced, the finally solved probability threshold can approach the global optimum point, and the effect of the final probability threshold is ensured.
As shown in fig. 2, another method for calculating a probability threshold of a game rough set is provided for the embodiment of the present invention, wherein the method is different from the method for calculating a probability threshold of another game rough set shown in fig. 1 in that: before step S104, the method further includes:
in step S202, the initialized first probability threshold, second probability threshold and step size value are determined.
In the embodiment of the present invention, in a calculation process, the initialized first probability threshold, the initialized second probability threshold, and the initialized step value correspond to the current first probability threshold, the current second probability threshold, and the current step value, respectively.
In the embodiment of the present invention, the initialized first probability threshold, the initialized second probability threshold, and the step value do not significantly affect the first probability threshold and the second probability threshold of the final output, and preferably, the first probability threshold is 0.5, the second probability threshold is 1, and the step value is-0.05.
As shown in fig. 3, a further method for calculating a probability threshold of a game rough set is provided for the embodiment of the present invention, where the difference from the method for calculating a probability threshold of another game rough set shown in fig. 1 is that the step S110 specifically includes:
in step S302, when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is determined to be a negative value, a product operation is performed on the current step value by using a preset first adjustment coefficient.
In an embodiment of the present invention, the first adjustment coefficient is a negative number greater than-1.
In the embodiment of the invention, when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is judged to be a negative value, which indicates that the optimal probability threshold value is positioned before and after adjustment, and then the product operation is performed on the current step value by using a negative number larger than-1, on one hand, the negative number is used as a step length adjustment coefficient to enable the next probability threshold value to be reversely adjusted, namely, the adjustment is performed on the probability threshold values before and after adjustment to approach the optimal probability threshold value, on the other hand, the absolute value of the step length value is reduced, namely, the adjustment range of the probability threshold value is reduced, the more accurate determination range can be realized, the search of the optimal probability threshold value is ensured, and as the preference, the dichotomy is considered, and the first adjustment coefficient is selected to be-0.5.
As shown in fig. 4, a further method for calculating a probability threshold of a game rough set according to an embodiment of the present invention is different from the another method for calculating a probability threshold of a game rough set shown in fig. 3, in that the method further includes:
and step S402, when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is judged to be a positive value, multiplying the current step value by a preset second adjustment coefficient.
In the embodiment of the present invention, the second adjustment coefficient is a positive number greater than 1.
In the embodiment of the present invention, when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is a positive value, it indicates that the current probability threshold still needs to be updated in the same direction, and at this time, by appropriately increasing the absolute value of the step value, the retrieval rate can be increased, and preferably, the second adjustment coefficient is selected to be 1.2.
In the embodiment of the present invention, for convenience of understanding, the update rule of the threshold and the step is described with an initial first threshold of 0.5, an initial second threshold of 1, and an initial step value of-0.05. The method comprises the following specific steps:
if the first determined optimal probability threshold updates the combination policy to: adding the current first probability threshold value to the current step value, adding the current second probability threshold value to twice the current step value, at the moment, adding-0.45 of the updated current first probability threshold value, adding-0.9 of the updated current second probability threshold value, at the moment, if the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is a positive value, indicating that the optimal value of the first probability threshold value and the optimal value of the second probability threshold value are still smaller than the current first probability threshold value and the current second probability threshold value, and at the moment, determining the step value as-0.06; adding a second time determination optimal probability threshold value updating combination strategy which is the same as the first time, wherein the updated current first probability threshold value is-0.39, the updated current second probability threshold value is-0.78, and at the moment, if the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is a positive value, the optimal value of the first probability threshold value and the optimal value of the second probability threshold value are smaller than the current first probability threshold value and the current second probability threshold value, and the step value is determined to be-0.072; further, when the optimal probability threshold updating combination strategy is determined to be that the current first probability threshold is kept unchanged and the current second probability threshold is added to one time of the current step value, at this time, the updated current first probability threshold is-0.39, the updated current second probability threshold is-0.708, at this time, if the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is a negative value, it indicates that the optimal point of the second probability threshold falls within the range before and after updating, at this time, the step value is determined to be-0.036, it is seen that the step size at this time is smaller than the initial step size, and the value of the step size is smaller and smaller, that is, the updating amplitude of the probability threshold is smaller and smaller, and finally, the optimal point is gradually approached.
As shown in fig. 5, a flowchart of steps for calculating and obtaining values of multiple rough set evaluation indexes provided in an embodiment of the present invention specifically includes the following steps:
step S502, according to the current first probability threshold and the current second probability threshold, determining a plurality of first updating probability thresholds and second updating probability thresholds respectively corresponding to a plurality of probability threshold updating combination strategies.
In the embodiment of the present invention, the probability threshold updating combination policy describes an updating policy for the probability threshold, so that for each probability threshold updating combination policy, the first probability threshold and the second probability threshold after updating can be correspondingly determined.
Step S504, calculating according to the plurality of probability information data, the plurality of first update probability thresholds, and the plurality of second update probability thresholds, and obtaining a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold update combination policies.
In the embodiment of the present invention, the rough set evaluation index value can be determined based on the probability information data and the first probability threshold and the second probability threshold, wherein when the rough set evaluation index value is accuracy and coverage, the specific formula of the calculation refers to fig. 6 and the explanation thereof.
As shown in fig. 6, a flowchart of the steps for calculating accuracy and coverage provided in the embodiment of the present invention specifically includes the following steps:
step S602, determining a known class corresponding to the conditional probability not greater than the current first probability threshold as a negative domain known class.
In the embodiment of the present invention, it can be understood that the first probability threshold and the second probability threshold may respectively set the conditional probabilities of the classes to be smaller than the first probability threshold and larger than the second probability threshold, and to be located between the first probability threshold and the second probability threshold, where the three cases correspond to a negative domain, a positive domain, and a boundary domain.
Step S604, determining the known class corresponding to the conditional probability not less than the current second probability threshold as the positive domain known class.
In step S606, the accuracy is calculated based on the distribution probability and the conditional probability of the known class determined as the positive domain known class or the known class determined as the negative domain known class.
In an embodiment of the present invention, accuracy is defined as the sum of the known class of negative domains and the known class of positive domains that does not belong to the known target concept in the known class of negative domains determined to belong to the known target concept.
Step S608, coverage is calculated according to the distribution probability of the known class determined as the positive domain known class or the negative domain known class.
In an embodiment of the invention, the positioning of the coverage is a ratio of the sum of the distributed probabilities of the classes determined to be positive domain known and the classes determined to be negative domain known to the whole.
In the embodiment of the invention, the specific calculation formula of the accuracy and the coverage rate is the same as the calculation formula disclosed in the prior document pointed out in the background of the invention.
In the embodiment of the present invention, for convenience of explanation, a building block is also explained, and if the building block has five shapes a, B, C, D, E, the distribution probability of the building block is sequentially a: 0.05, B: 0.2, C: 0.25, D: 0.3, E; 0.2 (each block must belong to one of these five shapes, so the sum of the distribution probabilities is 1), while the conditional probabilities for the five shapes are a: 0.9, B: 0.8, C: 0.5, D: 0.4, E: 0.2 (conditional probability refers to the ratio of the number of stabilities for a certain shape), provided that the current first probability threshold is 0.45 and the second probability threshold is 0.7, at which time it is clear that a, B belong to the positive domain, C belong to the boundary domain, D, E belong to the negative domain, the coverage is 0.05+0.2+0.3+ 0.2-0.75-75%, and the accuracy is (0.05+ 0.9+ 0.2-0.8 + 0.3- (1-0.4) + 0.2- (1-0.2)) (0.05+0.2+0.3+0.2) -126.7%, the above calculation formula is merely illustrative and not intended for the limitation of alignment accuracy and coverage.
Fig. 7 is a schematic structural diagram of a probability threshold calculation device for a game rough set according to an embodiment of the present invention, which is described in detail below.
In an embodiment of the present invention, the probability threshold calculation device for the coarse set of games includes:
the probability information data obtaining module 710 obtains a plurality of probability information numbers of known target concepts.
In an embodiment of the present invention, the known target concepts are associated with a plurality of known classes.
In an embodiment of the present invention, the plurality of probability information data of the known target concept includes a distribution probability of the plurality of known classes and a conditional probability of the plurality of known classes, where the conditional probability of the known class is a probability of belonging to the known target concept simultaneously on the premise of belonging to the known class.
In the embodiment of the present invention, since the game rough set and the probability threshold are technical terms understood by those skilled in the art, the present invention does not specifically explain the specific game rough set and the probability threshold, the probability threshold of the game rough set is generally represented by (α), and the first probability threshold and the second probability threshold are respectively expressed in the technical solution of the present invention, so as to facilitate understanding of the technical solution provided by the present invention, the present invention explains the technical solution provided by the present invention in specific cases, which is specifically described as follows.
In a specific embodiment, there are several blocks of different shapes, including triangle, circle, rectangle, trapezoid, pentagon, etc., and the stability of each block is known, including both stable and unstable cases, in order to determine the relationship between the block shape and the block stability, we can divide the block shape into three types, positive domain shape, negative domain shape and boundary domain shape, for the block of positive domain shape, it indicates that it has a greater likelihood of being stable, for the block of negative domain shape, it indicates that it has a lesser likelihood of being stable, in the process, the greater likelihood and the lesser likelihood are the probability thresholds that need to be determined by the present invention, and after determining the probability thresholds, it can determine the positive domain, negative domain and boundary domain of the shape for stability, the known target concept can be equally understood as the stability of the building blocks, the related known classes can be understood as the shape of the building blocks, and the probability information data of the known target concept comprises the distribution probability of each shape building block and the conditional probability that the building block in each shape building block is stable, and the sum of the distribution probabilities of all the shape building blocks is 1.
And an evaluation index calculation module 720, configured to perform calculation according to the multiple probability information data, the current first probability threshold, the current second probability threshold, and multiple probability threshold update combination strategies, and obtain multiple rough set evaluation index values corresponding to the multiple probability threshold update combination strategies, respectively.
In the embodiment of the present invention, the probability threshold updating combination strategy is determined by a combination of a preset first probability threshold updating strategy and a preset second probability threshold updating strategy.
In the embodiment of the present invention, the probability threshold updating combination policy includes a current step value, which is used to adjust the probability threshold.
In the embodiment of the present invention, the probability threshold updating policy includes keeping the current probability threshold unchanged, and adding the current probability threshold to the current step value, or adding the current probability threshold to any multiple of the current step value, and the present invention does not limit the specific probability threshold updating policy, and as an optimization, generally consists of three policies: keeping the current probability threshold constant, adding the current probability threshold to the current step value, and adding the current probability threshold to twice the current step value, it is understood that the probability threshold may be a first probability threshold or a second probability threshold, and the first probability threshold updating strategy may be different from the second probability threshold updating strategy, so that the probability threshold updating combination strategy has 9 combinations of 3 × 3 in total.
An optimal probability threshold adjustment strategy determination module 730, configured to determine an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determine an optimal probability threshold update combination strategy according to the optimal rough set evaluation index value.
In the embodiment of the present invention, it is preferable that the maximum value of the rough set evaluation index value or the maximum value of the sum is determined as the optimal rough set evaluation index value, and the optimal rough set evaluation index value is determined by using a nash equilibrium point.
In the embodiment of the invention, after the optimal rough set evaluation index value is determined, the optimal probability threshold combination strategy can be determined by simply utilizing the corresponding relation between the probability threshold updating combination strategy and the rough set evaluation index value.
A probability threshold updating module 740, configured to update the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination policy.
In the embodiment of the present invention, since the optimal probability threshold combination policy is determined by combining a preset first probability threshold updating policy and a preset second probability threshold updating policy, the current first probability threshold is updated by using the first probability threshold updating policy, for example, the current first probability threshold is added with a step value, and the current second probability threshold is updated by using the second probability threshold updating policy, for example, the current second probability threshold is added with a double step value.
The step value adjusting module 750 is configured to update the current step value according to a preset step value updating rule.
In the embodiment of the invention, compared with the existing probability threshold value calculation method, the step value for updating the probability threshold value is further adjusted, so that the probability threshold value can be adjusted by a smaller step value in the subsequent probability threshold value updating process, namely, the adjustment amplitude of the probability threshold value is reduced, the risk of skipping the optimal point is reduced, and the effect of the determined probability threshold value is improved.
The termination determining module 760 is configured to determine whether the update times of the probability threshold satisfy a preset termination condition.
And the circulation module 770 is configured to return to the evaluation index value calculation module when it is determined that the update frequency of the probability threshold does not meet the preset termination condition.
And a probability threshold output module 780, configured to output the current first probability threshold and the current second probability threshold when it is determined that the update times of the probability threshold satisfy the preset termination condition.
In the embodiment of the present invention, after a certain number of cycles, it may be considered that the current probability threshold is already close to the optimal probability threshold, and at this time, the output is only required, and if the number of iterations of the cycle is not enough, the step 104 needs to be returned to perform the cycle.
According to the probability threshold value calculation device for the game rough set, provided by the embodiment of the invention, after a plurality of probability information data of known target concepts are obtained, the values of a plurality of rough set evaluation indexes obtained under a plurality of probability threshold value adjustment combination strategies are further calculated, the values of the plurality of rough set evaluation indexes can reflect the advantages and disadvantages of each probability threshold value adjustment combination strategy, the optimal probability threshold value adjustment combination strategy is determined, and then the probability threshold value is adjusted based on the optimal probability threshold value adjustment combination strategy. In addition, in the process of adjusting the probability threshold, the step value used for updating the probability threshold at each time is adjusted by comparing the value of the rough set evaluation index obtained under the optimal probability threshold adjustment combination strategy with the value of the current rough set evaluation index, so that when the global optimum point is approached, the amplitude of updating the probability threshold at each time can be reduced by adjusting the step value, the risk of skipping the global optimum point in the process of updating the probability threshold is reduced, the finally solved probability threshold can approach the global optimum point, and the effect of the final probability threshold is ensured.
FIG. 8 is a diagram illustrating an internal structure of a computer device in one embodiment. On which a probability threshold calculation method of the game rough set according to claims 1-7 can be run. As shown in fig. 8, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program that, when executed by the processor, causes the processor to implement a method of probability threshold calculation for a game of asperities. The internal memory may also have stored therein a computer program that, when executed by the processor, causes the processor to perform a method of probability threshold calculation for a game of coarseness set. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the probability threshold calculation means for the game rough set provided in the present application can be implemented in the form of a computer program, which can be run on a computer device as shown in fig. 8. The memory of the computer device may store various program modules of the probability threshold value calculation means constituting the game rough set, such as the probability information data acquisition module, the evaluation index calculation module, and the like shown in fig. 7. The program modules constitute computer programs to make the processor execute the steps of the probability threshold calculation method for the game rough set of the embodiments of the present application described in the present specification.
For example, the computer device shown in fig. 8 can execute step S102 through the probability information data obtaining module 710 in the probability threshold value calculating device of the game rough set shown in fig. 7, and obtain a plurality of probability information data of known target concepts; the computer device may execute step S104 through the evaluation index calculation module 720, for performing calculation according to the plurality of probability information data, the current first probability threshold, the current second probability threshold, and the plurality of probability threshold update combination policies, obtaining a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold update combination policies, and so on.
In one embodiment, a computer device is proposed, the computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a plurality of probability information data of known target concepts;
calculating according to the plurality of probability information data, the current first probability threshold, the current second probability threshold and a plurality of probability threshold updating combination strategies to obtain a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold updating combination strategies; the probability threshold updating combination strategy is determined by a preset first probability threshold updating strategy and a preset second probability threshold updating strategy, and the current step value in the probability threshold updating combination strategy is used for adjusting the probability threshold;
determining an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determining an optimal probability threshold value updating combination strategy according to the optimal rough set evaluation index value;
updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy;
updating the current step value according to a preset step value updating rule;
judging whether the updating times of the probability threshold value meet a preset termination condition or not;
when the updating times of the probability threshold value are judged not to meet the preset termination condition, returning to the step of calculating according to the plurality of probability information data, the current first probability threshold value, the current second probability threshold value and the plurality of probability threshold value updating combination strategies;
and when the updating times of the probability threshold value are judged to meet the preset termination condition, outputting the current first probability threshold value and the current second probability threshold value.
In one embodiment, a computer readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, causes the processor to perform the steps of:
acquiring a plurality of probability information data of known target concepts;
calculating according to the plurality of probability information data, the current first probability threshold, the current second probability threshold and a plurality of probability threshold updating combination strategies to obtain a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold updating combination strategies; the probability threshold updating combination strategy is determined by a preset first probability threshold updating strategy and a preset second probability threshold updating strategy, and the current step value in the probability threshold updating combination strategy is used for adjusting the probability threshold;
determining an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determining an optimal probability threshold value updating combination strategy according to the optimal rough set evaluation index value;
updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy;
updating the current step value according to a preset step value updating rule;
judging whether the updating times of the probability threshold value meet a preset termination condition or not;
when the updating times of the probability threshold value are judged not to meet the preset termination condition, returning to the step of calculating according to the plurality of probability information data, the current first probability threshold value, the current second probability threshold value and the plurality of probability threshold value updating combination strategies;
and when the updating times of the probability threshold value are judged to meet the preset termination condition, outputting the current first probability threshold value and the current second probability threshold value.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A probability threshold calculation method for a game rough set is characterized by comprising the following steps:
acquiring a plurality of probability information data of known target concepts;
calculating according to the plurality of probability information data, the current first probability threshold, the current second probability threshold and a plurality of probability threshold updating combination strategies to obtain a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold updating combination strategies; the probability threshold updating combination strategy is determined by a preset first probability threshold updating strategy and a preset second probability threshold updating strategy, and the current step value in the probability threshold updating combination strategy is used for adjusting the probability threshold;
determining an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determining an optimal probability threshold value updating combination strategy according to the optimal rough set evaluation index value;
updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy;
updating the current step value according to a preset step value updating rule;
judging whether the updating times of the probability threshold value meet a preset termination condition or not;
when the updating times of the probability threshold value are judged not to meet the preset termination condition, returning to the step of calculating according to the plurality of probability information data, the current first probability threshold value, the current second probability threshold value and the plurality of probability threshold value updating combination strategies;
and when the updating times of the probability threshold value are judged to meet the preset termination condition, outputting the current first probability threshold value and the current second probability threshold value.
2. The method for calculating probability thresholds for a coarser set of games according to claim 1, further comprising, before said step of updating the combined strategy based on said plurality of probability information data, the current first probability threshold, the current second probability threshold and the plurality of probability thresholds:
an initialized first probability threshold, a second probability threshold, and a step size value are determined.
3. The method for calculating the probability threshold of the game rough set according to claim 1, wherein the step of updating the current step value according to the preset step value updating rule specifically comprises:
when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is judged to be a negative value, performing product operation on the current step value by using a preset first adjustment coefficient; the first adjustment coefficient is a negative number greater than-1.
4. The method for calculating the probability threshold of the roughage set in a game as recited in claim 3, wherein the step of updating the current step value according to a preset step value updating rule further comprises:
when the difference between the optimal rough set evaluation index value and the current rough set evaluation index value is judged to be a positive value, performing product operation on the current step value by using a preset second adjustment coefficient; the second adjustment factor is a positive number greater than 1.
5. The method for calculating the probability threshold of the game rough set according to claim 1, wherein the step of obtaining the plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold updating combination strategies by calculating according to the plurality of probability information data, the current first probability threshold, the current second probability threshold and the plurality of probability threshold updating combination strategies specifically comprises:
determining a plurality of first updating probability threshold values and second updating probability threshold values respectively corresponding to a plurality of probability threshold value updating combination strategies according to the current first probability threshold value and the current second probability threshold value;
and calculating according to the plurality of probability information data, the plurality of first updating probability threshold values and the plurality of second updating probability threshold values to obtain a plurality of rough set evaluation index values respectively corresponding to the plurality of probability threshold value updating combination strategies.
6. The method for probability threshold computation of a coarser set of games as recited in claim 1, wherein said known target concepts are associated with a plurality of known classes; the plurality of probability information data of the known target concept includes distribution probabilities of the plurality of known classes and conditional probabilities of the plurality of known classes; the conditional probability of the known class is the probability of simultaneously belonging to the known target concept on the premise of belonging to the known class; the rough set evaluation index includes accuracy and coverage, and the step of calculating the accuracy and coverage according to the plurality of probability information data and the current first probability threshold and the current second probability threshold specifically includes:
determining a known class corresponding to the conditional probability not greater than the current first probability threshold as a negative domain known class;
determining the known class corresponding to the conditional probability not less than the current second probability threshold as a positive domain known class;
calculating accuracy from the distribution probability and the conditional probability of the known class determined to be a positive domain known class or a negative domain known class;
coverage is calculated from the distributed probabilities of known classes determined to be either positive domain known classes or negative domain known classes.
7. The method for calculating the probability threshold of the game rough set according to claim 1, wherein the step of determining the optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule specifically comprises:
and determining an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a Nash equilibrium point determination rule.
8. A device for computing a probability threshold for a game of chance in a rough set, comprising:
the probability information data acquisition module is used for acquiring a plurality of probability information data of known target concepts;
an evaluation index value calculation module, configured to calculate according to the multiple probability information data, the current first probability threshold, the current second probability threshold, and multiple probability threshold update combination policies, and obtain multiple rough set evaluation index values corresponding to the multiple probability threshold update combination policies, respectively; the probability threshold updating combination strategy is determined by a preset first probability threshold updating strategy and a preset second probability threshold updating strategy, and the current step value in the probability threshold updating combination strategy is used for adjusting the probability threshold;
an optimal probability threshold adjustment strategy determination module, configured to determine an optimal rough set evaluation index value from the plurality of rough set evaluation index values according to a preset optimal rough set evaluation index value determination rule, and determine an optimal probability threshold update combination strategy according to the optimal rough set evaluation index value;
a probability threshold updating module for updating the current first probability threshold and the current second probability threshold according to the optimal probability threshold combination strategy;
the step value adjusting module is used for updating the current step value according to a preset step value updating rule;
the termination judging module is used for judging whether the updating times of the probability threshold value meet the preset termination condition or not;
the circulation module is used for returning to the evaluation index value calculation module when the updating times of the probability threshold are judged not to meet the preset termination condition;
and the probability threshold output module is used for outputting the current first probability threshold and the current second probability threshold when the updating times of the probability threshold are judged to meet the preset termination condition.
9. A computer device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to carry out the steps of the method of probability threshold calculation for a coarser set of games as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, causes the processor to carry out the steps of the method of probability threshold computation of a coarser set of games as claimed in any one of claims 1 to 7.
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