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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 .

This library provides the necessary implementations to screen the following patterns: Bullish Belt Hold Bearish Belt Hold Hammer Hanging Man Southern Doji Northern Doji Gapping Up Doji Gapping Down Doji Takuri Line One Candle Shooting Star Bullish Strong Line Bearish Strong Line Bullish Harami Bearish Harami Bullish Engulfing Bearish Engulfing Bullish Kicking Bearish Kicking Matching Low Matching High Bullish Harami Cross Bearish Harami Cross Bullish Doji Star Bearish Doji Star Dark Cloud Cover Piercing Line Descending Hawk Homing Pigeon Bullish Abandoned Baby Bearish Abandoned Baby Three Inside Up Three Inside Down Three Outside Up Three Outside Down Three White Soldiers Three Black Crows Morning Doji Star Evening Doji Star Morning Star Evening Star Upside Tasuki Gap Downside Tasuki Gap Bullish Side by Side White Lines Bearish Side by Side White Lines Rising Three Method

zlib is designed to be a free, general-purpose, legally unencumbered -- that is, not covered by any patents -- lossless data-compression library for use on virtually any computer hardware and operating system. The zlib data format is itself portable across platforms. Unlike the LZW compression method used in Unix compress(1) and in the GIF image format, the compression method currently used in zlib essentially never expands the data. (LZW can double or triple the file size in extreme cases.) zlib's memory footprint is also independent of the input data and can be reduced, if necessary, at some cost in compression.

Skeletal implementation of interface javax.lang.model.util.Types, plus concrete realization backed by core Java Reflection API, akin to JEP 119. This module implements those methods in interface javax.lang.model.util.Types that pertain to the Java type system, plus it provides a method for resolving formal type parameters to actual type arguments. These methods have no equivalent in the Java Reflection API. The abstract skeletal implementation in this module is meant to be specialized for a particular javax.lang.model implementation. The abstraction makes this module well-suited to be used in projects that contain javax.lang.model as part of their domain model (for instance, implementations of alternative languages on top of the JVM).

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

Provides basic support of servlet filter. Boilerplate code etc. It provides a boilerplate implementation of the Filter interface. The GenericFilter provides a getter and setter for the FilterConfig object and ensures that this is set when init is called. The init method also calls the configureFilter method after the FilterConfig object is set and can be used by subclasses to process the filter config during initiation. It also provides an empty implementation of the destory method. The only method that requires implementation is the doFilter method. The GenericHTTPFilter extends the GenericFilter class, it provides a doFilter method that attempts to cast the ServletRequest and ServletResponse objects to HttpServletRequest and HttpServletResponse objects. If these objects can be cast successfuly then it calls the doHTTPFilter method and passes the cast objects references. If the objects cannot be cast a ServletException is thrown. The doHTTPFilter method is abstract and must be implemented by subclasses.

Class for building a best-first decision tree classifier. This class uses binary split for both nominal and numeric attributes. For missing values, the method of 'fractional' instances is used. For more information, see: Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407.

The goal of quality-check is to provide a small Java library for basic runtime code quality checks. It provides similar features to org.springframework.util.Assert or com.google.common.base.Preconditions without the need to include big libraries or frameworks such as Spring or Guava. The package quality-check tries to replace these libraries and provide all the basic code quality checks you need. The checks provided here are typically used to validate method parameters and detect errors during runtime. To detect errors before runtime we use JSR-305 Annotations. With these annotations you are able to detect possible bugs earlier. For more informations look at FindBugs™ JSR-305 support.