6 results about "Composite pattern" patented technology

The present invention provides a method, apparatus, electronic device and storage medium for user interface testing of components. The method includes: obtaining a plurality of comparison test cases based on a comparison component library, and obtaining a plurality of benchmark test cases based on a benchmark component library; obtaining comparison combined components based on the comparison component library through composite pattern abstraction and permutation and combination of component elements, and obtaining benchmark combined components based on the benchmark component library; obtaining comparison combined test cases based on the comparison combined components, and obtaining benchmark combined test cases based on the benchmark combined components; performing user interface testing on the comparison component elements in the comparison component library based on the comparison test cases and the benchmark test cases, as well as the comparison combined test cases and the benchmark combined test cases, to obtain test results. By combining component elements and performing UI testing on the combined components in combination with the component elements, the present invention can quickly locate single-component errors with higher accuracy.

The present application relates to the field of artificial intelligence, and provides a method, device, equipment and storage medium for training a hidden Markov model. The method includes: obtaining the role probability of a candidate word segmentation through a hidden Markov model, the candidate word segmentation is used to indicate the word segmentation of a training text that has been annotated with a role; calculating the role weight value in the training text according to the role probability; calculating the value of the loss function for entity recognition of the training text, and using the value of the loss function as the effect value; calculating the value of the loss function for entity recognition of the training text, and using the value of the loss function as the effect value; obtaining the target role corresponding to the target role weight value; calculating the role chain with the maximum probability value based on the target role through the Viterbi algorithm, and performing entity recognition according to the role combination pattern in the role chain to obtain a target hidden Markov model. The present invention can reduce the training sample data of a hidden Markov model with a higher entity recognition effect.

The application provides an interactive material setting module design method and simulation software construction method and device, which comprises the following steps: determining a material data structure class diagram, a cross-section data structure class diagram and a cross-section assignment data structure class diagram based on a combination mode; constructing an interactive material function framework based on the material data structure class diagram; the interactive material function framework comprises a material creation dialogue window; constructing an interactive cross-section function framework based on the cross-section data structure class diagram; the interactive cross-section function framework comprises a cross-section creation dialogue window; constructing an interactive cross-section assignment function framework based on the cross-section assignment data structure class diagram; the interactive cross-section assignment function framework comprises a cross-section assignment editing dialogue window; reserving a material creation function button in the cross-section creation dialogue window and reserving a cross-section creation button in the cross-section assignment editing dialogue window. The application effectively guarantees the expandability and maintainability of the material setting module and improves the development efficiency.

To analyze when and what kind of feature quantity is calculated in a structure where a feature quantity is calculated from plural measured values.SOLUTION: An information processor 101 includes a communication unit 54 that acquires plural measured values, which are generated by plural amplifier units, and plural pieces of first time information appended to the plural measured values, a pattern designation unit 64 that designates two or more patterns of a pair of an identifier of an amplifier unit and a kind of feature quantity, a feature quantity calculation unit 63 that acquires plural object period measured values, which are measured values generated by an amplifier unit having an identifier included in each pattern and obtained during an object period, on the basis of the pieces of first time information, and calculates a feature quantity of the plural object period measured values, a second time information generation unit 65 that generates second time information associated with the object period on the basis of the pieces of first time information, and an output unit 66 that outputs the feature quantity, which is calculated by the feature quantity calculation unit, in connection with the second time information.SELECTED DRAWING: Figure 5

To provide a search program, a search method, and an information processing apparatus capable of searching for a desired pattern in a short time.SOLUTION: A computer executes: a first process for preparing a basic structure; a second process for dividing the basic structure into a plurality of local regions; a third process for searching for an improved structure with improved characteristics by searching for placement / non-placement in cells so that the characteristics of a predetermined region as a whole are improved if an initial structure, which is a combination pattern of placement / non-placement of objects included in the local region among the basic structures, is set as a combination pattern of placement / non-placement of objects different from the initial structure, for each of the plurality of local regions; and a fourth process for generating a plurality of combinations of the initial structure and the improved structure in the predetermined region as training data by specifying either the initial structure or the improved structure, for each of the plurality of local regions, and searching for the placement / non-placement of the object in the cell of the predetermined region using the plurality of training data.SELECTED DRAWING: Figure 10