Search Results for "artificial intelligence database"
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Showing 12 open source projects for "artificial intelligence database"
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GoogleTest
Google Testing and Mocking Framework
GoogleTest is Google's C++ mocking and test framework. It's used by many internal projects at Google, as well as a number of notable projects such as The Chromium projects, the OpenCV computer vision library, and the LLVM compiler. This GoogleTest project is actually a union of what used to be two separate projects: the old GoogleTest and GoogleMock, an extension of GoogleTest for writing and using C++ mock classes. Since they were so closely related, they were merged to create an even... -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate... -
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Taylorplot_Neptune
Creation of a Taylorplot for several machine learning models
Here we present the lines of code for creating a taylor plot with python to display several machine learning models. We show the solution for displaying 10 models, but the list and number can be changed simply by modifying the sample list. -
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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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TDD MAS Toolkit
A set of powerful tools to perform TDD on MAS based on JADE
The TDD MAS Toolkit provides a set of tools to perform Test Driven Development of MAS based on Jade. The toolkit allows developers to set up simulation based and test case scenario based testing, providing powerful insights of the MAS under construction and assuring that the interation protocols and agent interaction in general are been enacted as expected. -
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The purpose of the project is summarising effort from a number of analytic libraries, adding interactive web-based user interface and making a free open source solution for risk analytics and stress testing. Feb 8, 2012 Paul Glasserman's Importance Sampling and Tail Approximations as well as plain Monte Carlo have been implemented for for the widely used normal copula model of portfolio credit risk. The package includes source code, examples, spreadsheet with results and references to the papers.
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Siafu simulates individual agents and their context, from home to city-wide scenarios. As a developer, you use the API to write your simulation for the purposes of data-set generation, test or visualization, optionally hooking it to your own application.
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The aim of the project is to provide a testing tool that would reduce the cost associated with the regression testing process. The tool provides test case subset selection so that the user need not run the redundant test cases.
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Artifical World simulator, it simulate virtual 3D world and execute IA programs. It have API for programming IAs in C++. it use Irrlicht as 3D engine.
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Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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A test suite and benchmark for exact Euclidean distance transform algorithms used in Image Processing and computational geometry. It evaluates the exactness and speed of algorithms for a large number of test cases. Results can be visualized in Scilab.
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D-Cog (Declarative-Cognition) is a Java based framework for training software components (reusable, object-oriented, interface-driven components). Instead of programmed, software components are trained by example to get the expected results.
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3A-TRE
3A-TRE evaluates traditionally logical expressions under 3 assumptions
3A-TRE evaluates traditionally logical expressions under the three assumptions. The open world assumption (OWA) states that the truth value of a statement that is not included in or inferred from the knowledge explicitly recorded in the system shall be considered unknown. The closed world assumption (CWA) is the assumption that any statement that is not known to be true is false. We distinguish yet another case in software testing. Frequently, there are test stubs which simulate the...
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