Sas Institute Inc.

Other

Raymond Eugene Wright

Ilknur Kaynar Kabul

Susan Edwards Haller

Joshua David Griffin

Yingjian Wang

Ricky Dee Tharrington, Jr

Yan Gao

Yu-Min Lin

Scott M Wright

Pei-Yi Tan

Bengt Wisen Pederson

A computing device selects new test configurations for testing software. Software under test is executed with first test configurations to generate a test result for each test configuration. Each test configuration includes a value for each test parameter where each test parameter is an input to the software under test. A predictive model is trained using each test configuration of the first test configurations in association with the test result generated for each test configuration based on an objective function value. The predictive model is executed with second test configurations to predict the test result for each test configuration of the second test configurations. Test configurations are selected from the second test configurations based on the predicted test results to define third test configurations. The software under test is executed with the defined third test configurations to generate the test result for each test configuration of the third test configurations.

Automated machine learning test system

A computing system trains a clustering model. (A) Beta distribution parameter values are computed for each cluster using a mass parameter value and a responsibility parameter vector of each observation vector. (B) Parameter values are computed for a normal-Wishart distribution for each observation vector included in a batch of a plurality of observation vectors. (C) Each responsibility parameter vector defined for each observation vector of the batch is updated using the beta distribution parameter values, the parameter values for the normal-Wishart distribution, and a respective observation vector of the selected batch of plurality of observation vectors. (D) A convergence parameter value is computed. (E) (A) to (D) are repeated until the convergence parameter value indicates the responsibility parameter vector defined for each observation vector is converged. A cluster membership is determined for each observation vector using the responsibility parameter vector. The determined cluster membership is output for each observation vector.

Distributable clustering model training system

One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Computer system to generate scalable plots using clustering

An optical attenuation measurement system is provided for obtaining signals proportional to movement of a system to which it is attached. The measurement system includes a light source positioned within a length of conduit in which the light emitted will produce multiple reflections. A fiber optic cable is slidably disposed within the conduit to transmit the radiation emitted from the light source gathered thereby. The relative displacement of the fiber optic cable to the light source produces a signal as a function of this displacement which is converted into electrical signals by a light detecting element at the opposite end of the cable. The system is particularly useful to form an interactive device for control of a remote intelligent machine, robot or the like. For example, the system may be used on an operator's hand to detect movement of the hand and fingers for controlling a robotic hand which will mimic the operator's hand movements.

Optical attenuator movement detection system

Growing community of inventors

Cary, NC, United States of America

Raymond Eugene Wright