38 results about "Class variable" patented technology

Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.

A system and method for detecting the mutability of fields and classes in an arbitrary program component written in an object oriented programming language is disclosed. A variable is considered to be mutable if a new value is stored into it, as well as if any of its reachable variables are mutable. The system and method uses a static analysis algorithm which can be applied to any software component rather than whole programs. The analysis classifies fields and classes as either mutable or immutable. In order to facilitate open-world analysis, the algorithm identifies situations that expose variables to potential modification by code outside the component, as well as situations where variables are modified by the analyzed code. An implementation of the analysis is presented which focuses on detecting mutability of class variables, so as to avoid isolation problems. The implementation incorporates intra- and inter-procedural data-flow analyses and is shown to be highly scalable. Experimental results demonstrate the effectiveness of the algorithms.

Methods and apparatus are provided for generating a decision trees using linear discriminant analysis and implementing such a decision tree in the classification (also referred to as categorization) of data. The data is preferably in the form of multidimensional objects, e.g., data records including feature variables and class variables in a decision tree generation mode, and data records including only feature variables in a decision tree traversal mode. Such an inventive approach, for example, creates more effective supervised classification systems. In general, the present invention comprises splitting a decision tree, recursively, such that the greatest amount of separation among the class values of the training data is achieved. This is accomplished by finding effective combinations of variables in order to recursively split the training data and create the decision tree. The decision tree is then used to classify input testing data.

One embodiment of the present invention provides a system for efficient class initialization barrier and access to class information private to a task that enables sharing between multiple tasks/virtual machines of both interpreted platform-independent code and the equivalent native code produced at runtime, wherein a class initialization barrier guarantees that a task initializes a class before the class is first used by the program executed by the task. The system operates by associating the shared runtime representation of classes with tables of pointers to task class mirror objects that hold the task private representation of a class. Entries of task class mirror tables are used both as a mean to encode the initialization status of the associated class for a plurality of tasks, and to provide access to the task private representation of a class loaded by a task irrespectively of the initialization state of the class for this task.

The embodiment of the invention provides a mobile terminal which comprises a variable judgment module, a user class recognition module, an interface style management module and a display module, wherein the variable judgment module is used for judging whether a user class variable is valid; the user class recognition module is used for providing a user class setting interface through which a usercan input user class information, and judging the class of the user in accordance with the user class information; the interface style management module is used for providing interface parameters corresponding to the class of the user in accordance with the class of the user; and the display module is used for displaying a user interface in accordance with the interface parameters provided by theinterface style management module. The embodiment of the invention also provides a mobile terminal user interface management method. By using the mobile terminal and the user interface management method thereof, the user can be automatically switched to the style and settings matched with the user group in accordance with the user class information, thereby achieving better user experience.

The invention discloses a network failure diagnosis method based on a selective hidden Naive Bayesian classifier, comprising: (1), obtaining history data from a network history database, wherein the history data comprise a symptom variable set and a failure class variable set; (2), constructing a selective hidden Naive Bayesian classifier prediction model, determining corresponding most related symptom variable set according to every symptom variable in the symptom variable set; (3), automatically learning classifier parameters by the selective hidden Naive Bayesian classifier through training the history data; (4), in failure diagnosis, estimating the test data by using the selective hidden Naive Bayesian classifier so as to obtain corresponding final failure diagnosis result. Through executing the network failure diagnosis method of the invention, the problems in the existing network failure diagnosis that the operation complexity is high and the network diagnosis result is great in deviation are effectively solved; the network diagnosis accuracy is greatly improved; the operation complexity is further reduced, and better learning capability and fault-tolerant character are kept at the same time.

The present invention provides a mutual information based parallel feature selection method for document classification, which comprises: a, selecting samples and performing classification; b, solving TF-ID values of words; c, generating an initialized data set D = { x1, x2, ..., xN }; d, carrying out distributed calculating and evenly distributing all sub data sets to m calculation nodes; e, establishing sets, wherein S = phi and V = { X1, X2,..., XM }; f, calculating joint probability distribution and conditional probability distribution; g, calculating mutual information; h, selecting a feature variable; i, determining if the number is enough; and i, performing document classification. According to the parallel feature selection method for document classification, which is provided by the present invention, Rayleigh entropy based mutual information is used for measuring correlation between the feature variable and a class variable, so that the finally selected feature variable can further represent a document classification feature, a classification effect is more accurate, and a classification result is better than a result obtained by using a common feature selection method. The selection method has advantageous effects, and is suitable for promotion and application.

The invention discloses a parallel feature selection method based on a MapReduce. The method includes a, dividing data into parts; b, distributing the data parts to a Map computing node; c, establishing a selected feature set and a feature set not selected; d, figuring out combined mutual information between vectors and class variables; e, allowing a maximum feature variable of the combined mutual information to serve as a feature vector, and transmitting a feature variable serial number and corresponding combined mutual information values to a Reduce computing node; f, judging whether a feature vector which is equal to the count exists or not; g, comparing the combined mutual information values; h, adding and deleting selected vectors; i, judging the numbers of the selected features. According to the parallel feature selection method based on the MapReduce, a feature vector combination with the largest amount of information can be extracted rapidly and effectively from massive data sets; the method is adaptable to feature extraction of massive data.

The invention discloses a feature database-based industrial wastewater pollutant tracing analysis method and belongs to the technical field of industrial wastewater pollutant supervision. The method is characterized by comprising the steps of establishing a feature weight database M of parameters of all types of pollutants of each factory; acquiring pollutant data of wastewater discharged by each factory in a target area; establishing a feature data sample library N of pollution discharge of the factory in the target area; establishing a class variable of a KD-tree by utilizing the feature data sample library N; taking the input pollutant measurement values of mixed industrial wastewater as a to-be-identified vector z; and performing matching identification by adopting a kNN classifier and data in the feature data sample library N, thereby finishing pollutant tracing. According to the method, the pollutant tracing of the mixed industrial wastewater in various areas can be finished, an order of target discharge factories is quickly and effectively given, applicability and universality are good, technical support is provided for related functional departments of government to check pollution source factories in order, the check efficiency is greatly improved, and the check success rate is greatly increased.

Inheritance contributions of programming class functions and class variables are diagrammed. A functional diagram illustrates individual class contributions of functions. A variable composition diagram illustrates individual class contributions of variables. A diagrammatic depiction of functions overridden and functions contributed in the inheritance hierarchy is provided. Functions which are unique, overridden and/or have contributions in different classes of the hierarchy are visually distinguished (e.g., by distinguishing marks). Classes in the hierarchy are graphically depicted with relative sizes based on percent contribution.

The present invention provides a feature weighting filter method based on correlation and a Naive Bayes classification method. The weight value of each feature variable is directly defined as the difference of the correlation of the feature variable and the class variable and the average redundancy of the feature variable and other feature variables. The present invention also provides a feature weighting Naive Bayes classification method depending on the method mentioned above. One group of obtained weighting values is substituted into the Naive Bayes classification formula for final classification of the test example. The feature weighting filter method based on correlation and the Naive Bayes classification method consider the correlation of the feature variable and the class variable and the redundancy between feature variables and maintain the model calculation complexity and the simplicity so as to verify the validity and the accuracy of the feature weighting method and the classification method provided by the invention through lots of experiment researches.

A method for saving data locally on a data processing system running a first instance of a browser. A program is received from a server with the first instance of the browser. The data is formatted in a selected format and placed into a class variable responsive to another user input. A second instance is initiated of the browser. The data is retrieved into the second instance of the browser to form retrieved data. The retrieved data is saved locally on the data processing system.

The present invention discloses a method for rapid cooperation expression of face classification. The method comprises: obtaining a plurality of face images of all the measured personnel, and combining a training sample set and a test sample set; projecting the training sampling set into a PCA subspace, and obtaining the feature face set of the sample set; performing common linear expression of the test samples through adoption of the feature faces and an intra-class variable dictionary; initializing dictionary coefficients through the 12 paradigm iterative computations in a dictionary space, and subsequently performing second introduction of 11 paradigm to complete the accurate optimization of the coefficients; projecting reconstructed test samples PCA coefficients and dimensionality reduction samples to an LDA subspace, and respectively obtaining LDA coefficients of the reconstructed test samples and the training samples; and selecting the category having the minimum reconstruction errors with the test samples as the final candidate category of face test samples. The method for rapid cooperation expression of face classification effectively rejects the information redundancy between the training samples and the test samples, improves the face identification precision, greatly reduces the time expenditure of a traditional expression optimization method and has good universality and the robustness.

The invention discloses a method for extracting characteristics in pattern recognition which aims at effectively avoiding subjectivity of pre-selecting the number of characteristics manually in previous characteristic extracting and a device thereof. The characteristic extracting method comprises the steps of: determining and preprocessing discrete characteristic variables and class variables according to original information of a sample pattern, setting a joint contribution rate threshold value, determining a joint contribution rate of a combination of the characteristic variables and the class variables and obtaining the combinations of the characteristic variables with the joint contribution rate more than or equal to the set joint contribution rate threshold value. The characteristic extracting device comprises a numerical value preprocessing module, a threshold value setting module, a joint contribution rate determining module and a characteristic extracting module. The method and device for extracting characteristics in pattern recognition of the invention can be widely used for extracting the characteristics of discrete digital image information, fingerprint information, face print information, voice message or handwritten/printed character information, etc.

The invention discloses a default user probability prediction method based on sparse feature embedding. The method comprises the following steps: firstly, converting original data of a user into variable features, and then mapping multi-class variables in the variable features into a sparse matrix (similar to one-hot processing); And on the basis, mapping the sparse matrix to a probability througha basic decision tree model, and adding the probability as a feature to the model to predict a default user. Compared with the prior art, the default user probability prediction method based on sparse feature embedding has the advantages that the processing capacity of category coding is effectively improved, meanwhile, the dimension of feature space is effectively reduced in the subsequent machine learning process, and learning and processing of a machine learning model are facilitated.

The invention relates to a method and a device for selecting variable processing logic. The method comprises the following steps: acquiring a pedestrian credit investigation report; extracting variables according to the pedestrian credit report, wherein the variables comprise configuration class variables and non-configuration class variables; and calling variable processing logic for processing according to the variable. Reflection dynamic calling of the to-be-processed variable and the execution method and dynamic expansion of multiple threads during variable processing can be realized, andthe variable processing efficiency is improved.

The invention discloses an unsupervised pile collection method in a complex system, which comprises the steps that: discrete characteristic variables and class variables are determined according to the original information of a complex system sample; the relevancy between two characteristic variables is calculated; 'the crowd of friends and relatives' of each characteristic variable is determined; the unsupervised pile collection is carried out to the characteristic variables according to the self-organization of the pile collection to obtain the combination of the characteristic variables; each pile is back substituted into original data to obtain the sensitivity; the degree of the sensitivity is judged; verification is carried out to the unsupervised pile collection method by utilizing the class variables of the system to obtain the optimum combination of the characteristic variables. The method solves the problem that the traditional relevancy can not distinguish positive correlation and negative correlation, has self-organization, needs no human intervention, has high running speed, and is suitable for a large amount of data, even mass data. Furthermore, the method can realize clustering and the appearance of certain variables in certain different classes, can carry out the verification to the unsupervised pile collection so as to find out the optimal pile, and has wide application value in the fields such as ecological differentiation and clinical medical data analysis, etc.

The invention discloses a rapid identification method for spiral seaweeds having different adsorption capacities of copper ions. The method comprises the following steps of S1, collecting infrared spectrums, namely collecting infrared spectrums of different kinds of spiral seaweeds; S2, carrying out infrared spectrum processing: preprocessing the infrared spectrums obtained in the step S1, and solving a second-order derivative of the preprocessed infrared spectrums to obtain a second-order derivative infrared spectrum, and selecting characteristic spectrum data in the second-order derivative infrared spectrum to carry out principal component analysis; and S3, carrying out clustering analysis: selecting main component characteristic values obtained by the principal component analysis in thestep S2 to be used as fixed-class variables of different kinds of spiral seaweeds, ad clustering the spiral seaweeds into different types according to the different copper ion adsorption capacities based on the fixed-class variables. The method is based on the infrared spectrums and is combined with second-order derivative and principal component analysis processing, so that the spiral seaweeds with different copper ion adsorption capacities can be identified simply and rapidly, thereby providing important theoretical support and practical significance for finding spiral seaweeds capable of efficiently removing heavy metals in sewage.