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An easy-to-implement library that can assist Java developers in using the GeoHash algorithm in order to create geocodes based on custom latitude and longitude values. With the help of jGeohash, Java developers will be able to quickly and easily generate a geohash code using user-defined latitude and longitude values. By using the GeoHash algorithm, the space can be divided into multiple grid shapes. This library is a basic implementation from the GeoHash algorithm written in the java and can be applied to generate from the latitude and longitude a geohash code and reverse. The library is kept simple and have no dependencies to other libraries.

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 module allows you to enable suppressions for turning off warnings about Restricted APIs. !!!WARNING!!! Classes are marked as @Restricted for a reason and this module should not be used lightly! It implies that the author does not intend for them to be used outside their defined scope and as such they may be changed/modified/removed at any stage without warning. A simple upgrade of the dependency may break your module. Use at your own risk. You should try to not use @Restricted classes in the first place, but if you _must_ use them, this is a less-brutal approach than just disabling the access-modifier-checker entirely

Dhelm is a set of java-based open source libraries which can be used to develop software to perform technical analysis in stock market. The dhelm-indicator package includes APIs to calculate few common indicators. APIs for following indicators have been packaged into the v 1.0.0- 1. Simple Moving Average(SMA). 2. Exponential Moving Average(EMA). 3. Moving Average Convergence Divergence(MACD). 4. On Balance Volume(OBV). 5. Parabolic Stop and Reverse(PSAR). 6. Relative Strength Index(RSI). 7. Average Directional Index(ADX). 8. Bollinger Bands 9. Chaikin Money flow index. 10.Rate of Change(ROC). 11.Money Flow Index(MFI).

POJava DateTime is a simple, light-weight Java-based API for parsing and manipulating dates. It parses dates from most languages and formats out of the box without having to specify which format is expected. Defaults such as time zones, and whether to interpret an internationally ambiguous date like "03/06/2014" as DMY order or MDY order are inferred by system time zone and locale and stored in a default config object that can be replaced or overridden. Multiple languages for month names are supported without any additional configuration needed. This is a forked version edited by Jianlin

This project contains a Java implementation of median relational generalized learning vector quantization as proposed by Nebel, Hammer, Frohberg, and Villmann (2015, doi:10.1016/j.neucom.2014.12.096). Given a matrix of pairwise distances D and a vector of labels Y it identifies prototypical data points (i.e. rows of D) which help to classify the data set using a simple nearest neighbor rule. In particular, the algorithm optimizes the generalized learning vector quantization cost function (Sato and Yamada, 1995) via an expectation maximization scheme where in each iteration one prototype 'jumps' to another data point in order to improve the cost function. If the cost function can not be improved anymore for any of the data points, the algorithm terminates.

Meta classifier that allows standard classification algorithms to be applied to ordinal class problems. For more information see: Eibe Frank, Mark Hall: A Simple Approach to Ordinal Classification. In: 12th European Conference on Machine Learning, 145-156, 2001. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Janos A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. In: Machine Learning, Proceedings of the Nineteenth International Conference (ICML 2002), 546-553, 2002.

A simple meta-classifier that uses a clusterer for classification. For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model. Note: at prediction time, a missing value is returned if no cluster is found for the instance. The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.