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Accord.NET Framework

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  Analyzed about 14 hours ago

The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications. A comprehensive set ... [More] of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details. [Less]

2.2M lines of code

15 current contributors

over 3 years since last commit

20 users on Open Hub

Inactive
5.0
 
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bard-python

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  Analyzed about 3 hours ago

A module which generates pseudorandom text. It utilizes Markov chains to produce new text based on some input text. You could use it, for example, to write a ten million word science fiction epic using the science fiction category of the Brown corpus (yes, I have done it). This module requires NLTK.

474 lines of code

0 current contributors

over 11 years since last commit

1 users on Open Hub

Inactive
0.0
 
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triplie

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  Analyzed about 12 hours ago

Triplie is an AI bot based on 2nd up to 6th order Markov model. It uses an SQLite database for storage, and can be distributed to work on multiple machines on a LAN. Triplie creates directed graphs which are made of nodes, which represent the words read from the user A graph representing ... [More] Markov chains of 6th order links that represent the associations between words from conversations in a network based on the Hebbian rule To respond to a user, triplie extracts keywords from the user's text, finds their most appropriate associated keywords in the Hebbian association network, and generates replies that contain the associated keywords using multiple breadth-first-search Markov chains algorithm. For more information on installing and configuring the bot see the README page. You can join the project's IRC channel too: irc://irc.freenode.net/#triplie [Less]

7.41K lines of code

0 current contributors

almost 11 years since last commit

1 users on Open Hub

Inactive
0.0
 
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markov_brain

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  Analyzed about 22 hours ago

markov_brain is a generic markov chain text generation module in python. It was written as an easy-to-use black box for other applications. Example usage can be found in test_markov_brain.py. Simply create a new Brain() using the "past_memory" configuration to load any previous knowledge. ... [More] Adding more 'memory' is done by calling remember(words_list). Generating text is done by calling speak_about(subject, max_chars=140). [Less]

155 lines of code

0 current contributors

over 13 years since last commit

0 users on Open Hub

Inactive
0.0
 
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dadacore

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  Analyzed about 9 hours ago

Markov chain text generator (stupid and unusable for the time being)

643 lines of code

0 current contributors

over 11 years since last commit

0 users on Open Hub

Inactive
0.0
 
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Licenses: No declared licenses

Mulm

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  Analyzed about 24 hours ago

Mulm is a state-of-the-art Hidden Markov Model toolkit.

12.8K lines of code

0 current contributors

over 6 years since last commit

0 users on Open Hub

Inactive
0.0
 
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WinASSIST

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Claimed by NASA Analyzed 1 day ago

The WinASSIST program uses a rule-oriented language to automatically generate input files for the SURE/WinSURE program. The user describes the failure behavior and recovery behavior of a fault-tolerant computer system in an abstract language. The WinASSIST program then automatically generates a ... [More] corresponding semi-Markov model. The abstract language allows efficient description of large, complex systems. A one-page WinASSIST-language description may result in a semi-Markov model with thousands of states and transitions. The WinASSIST program also provides model-reduction techniques to facilitate efficient modeling of large systems. [Less]

541K lines of code

0 current contributors

about 8 years since last commit

0 users on Open Hub

Inactive
0.0
 
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Licenses: No declared licenses

WinSURE

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Claimed by NASA Analyzed 1 day ago

WinSURE is a reliability analysis program used for calculating upper and lower bounds on for the operational and death state probabilities for a large class of semi-Markov models. The program is especially suited for the analysis of fault-tolerant reconfigurable systems. The calculated bounds are ... [More] close enough (usually within 5 percent of each other) for use in reliability studies of ultra-reliable computer systems. The SURE bounding theorems have algebraic solutions and are consequently computationally efficient even for large and complex systems. SURE can optionally regard a specified parameter as a variable over a range of values, enabling an automatic sensitivity analysis. The WinSURE program is written in Java and has been tested on Linux, Mac, and Windows operating systems. [Less]

330K lines of code

0 current contributors

about 8 years since last commit

0 users on Open Hub

Inactive
0.0
 
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citar

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  Analyzed about 14 hours ago

Citar is a C++ free software part of speech tagger using a trigram Hidden Markov Model (HMM), with linear interpolation smoothing of trigrams and suffix-based unknown word handling. Features Citar has the following major features: * High accuracy tagging through a trigram Hidden Markov ... [More] Model with Viterbi decoding. * Handling of unknown words through suffix analysis. * Licensed under the GNU Lesser General Public License version 2.1 (LGPLv2.1), which only imposes restrictions on redistribution of Citar itself. * Written in C++ for performance. [Less]

1.56K lines of code

0 current contributors

over 5 years since last commit

0 users on Open Hub

Inactive
0.0
 
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QSMM

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  Analyzed about 20 hours ago

A framework for learning finite automatons that perform goal-directed interaction with entities which exhibit deterministic or stochastic behavior. The learning process can be carried out in real time together with the interaction process. A basic building block for supporting state models of finite ... [More] automatons is adaptive probabilistic mapping, which for an argument from its domain returns more often results that maximize or minimize values of one or more objective functions. Finite automatons can be represented by assembler programs with user-defined instructions that perform effective work. To assist in the learning of a finite automaton, a template for its state model can be provided as an assembler program with probabilistic jump instructions. [Less]

121K lines of code

1 current contributors

22 days since last commit

0 users on Open Hub

Very Low Activity
0.0
 
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Licenses: GFDL-1-3, gpl3_or_l...