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This repository provides a guidance on integrating an Android app with a myPOS Smart device. Once integrated, the app will be able to communicate with the core device components in order to accept card payments (including but not limited to VISA, Mastercard, UnionPay International, JCB, Bancontact). myPOS-Smart-SDK complеments a Smart POS terminal in order to initiate transaction, complete all steps for processing payment, make refund to the customer card account and print a custom bill slip using the device printer. The built-in functionalities of myPOS-Smart-SDK allow you to send Payment Requests, manage operations for the first and second SAM component the myPOS Smart device has and print bitmap image integrated to the receipt (e.g. QR Code).

This repository provides a guidance on integrating an Android app with a myPOS Smart device. Once integrated, the app will be able to communicate with the core device components in order to accept card payments (including but not limited to VISA, Mastercard, UnionPay International, JCB, Bancontact). myPOS-Smart-SDK complеments a Smart POS terminal in order to initiate transaction, complete all steps for processing payment, make refund to the customer card account and print a custom bill slip using the device printer. The built-in functionalities of myPOS-Smart-SDK allow you to send Payment Requests, manage operations for the first and second SAM component the myPOS Smart device has and print bitmap image integrated to the receipt (e.g. QR Code).

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.

This module provides algorithms to compare sets, that is, order-invariant lists. These algorithms are implementations of the AlignmentAlgorithm interface defined in the algorithms module. In particular, this module contains the StrictSetAlignmentScoreAlgorithm for computing the cost of the optimal unordered alignment of two sets, the StrictSetAlignmentFullAlgorithm which provides the Alignment itself as well, and the GreedySetAlignmentScoreAlgorithm as well as the GreedySetAlignmentFullAlgorithm for computing a potentially sub-optimal but faster alignment of two sets. The optimal alignments rely on the HungarianAlgorithm for solving the assignment problem in bipartite graphs. Here, we rely on the implementation provided by Kevin L. Stern which is provided within this distribution.

This module contains the sequence datastructure of the TCS Alignment Toolbox. It defines the possible value sets in the ValueType enum as well as the different KeywordSpecification classes, namely: 1.) StringKeywordSpecification for string type values. 2.) SymbolicKeywordSpecification for values from a discrete alphabet (also refer to the Alphabet class) 3.) VectorialKeywordSpecification for vectors of some length (or for scalars) A NodeSpecification is a vector of such KeywordSpecifications and defines the order of value sets. A node, then, is defined as a vector of values from these value sets (also refer to the Value interface as well as the StringValue, SymbolicValue and VectorialValue classes). Finally a sequence is defined as a list of such nodes.

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.

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.