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Compiler for Cypher 3.3

Implementation of mockwire context management using Spring as the di container

Implementation of mockwire context management using Spring as the di container

Mockito mocker implementation for Mockwire, used to provide mocks of services not under test in an isolated context

Bootstrap support for spring that will scan the given packages and contruct a context for it

Taking the configuration out of applications, using the same principles that underly dependency injection

Taking the configuration out of applications, using the same principles that underly dependency injection

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.

Package containing a class that rescales the attributes in a classification problem based on their discriminative power. This is useful as a pre-processing step for learning algorithms such as the k-nearest-neighbour method, to replace simple normalization. Each attribute is rescaled by multiplying it with a learned weight. All attributes excluding the class are assumed to be numeric and missing values are not permitted. To achieve the rescaling, this package also contains an implementation of non-negative logistic regression, which produces a logistic regression model with non-negative weights .