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ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram. The language independent design of ChatterBot allows it to be trained to speak any language. Additionally,...

This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you...

Flowcharts not telling you what you want to know about some C source code? Try a State Dependency map! Every C source file is an implemention of a state machine. Implicit in the definition of a state machine is a network of dependencies between the states. The Automalator * deconstructs the C code into the core state machine * collates the transactions into the dependency net * generates a diagram-format file with the states and dependencies as the nodes and edges. Pre-requisites * Perl - tested with versions 5.12.3, 5.14.2 and 5.20.2, Windows, Linux and MAC * Get the Automalator by downloading the zip, or checking out or exporting the SVN trunk * copy the source code of interest into "project.c" * windows - double-click "src2map.bat" * linux & MAC - navigate the command line to the "project" folder, then run "src2map.sh" * open "project.gml" file with diagramming software.