Search Results for "q learning algorithm" - Page 4
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Showing 244 open source projects for "q learning algorithm"
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Lori's Help
An Android app to help people with Down Syndrome in their literacy
Lori Help is an Android application that provides support for the literacy of people with Down syndrome. The application has 4 activities to aid in learning, 3 of them with emphasis on the literacy process and 1 focused on sensory stimuli. Application activities are monitored by a biofeedback algorithm (known as Attention Meter). The algorithm observes the variations of the user's micro facial expressions with the intention of measuring the level of attention during the accomplishment... -
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Data Algorithm/leetcode/lintcode
Data Structure and Algorithm notes
This work is some notes of learning and practicing data structures and algorithms. Part I is a brief introduction of basic data structures and algorithms, such as, linked lists, stack, queues, trees, sorting and etc. This book notes about learning data structure and algorithms. It was written in Simplified Chinese but other languages such as English and Traditional Chinese are also working in progress. -
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Objective Function Analysis
An alternative to neural nets for machine learning.
Objective Function Analysis models knowledge as a multi-dimensional probability density function (MD-PDF) of the perceptions and responses (which are themselves perceptions) of an entity and an objective function (OF). The learning algorithm is the action of choosing a response, given the perceptions, which maximizes the objective function. The MD-PDF is initially seeded by a uniform random number generator. The response is used to evaluate the OF and the OF is either reinforced or diminished... -
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OpenDino
Open Source Java platform for Optimization, DoE, and Learning.
OpenDino is an open source Java platform for optimization, design of experiment and learning. It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing... -
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Deep Reinforcement Learning for Keras
Deep Reinforcement Learning for Keras.
keras-rl implements some state-of-the-art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even... -
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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
... platform Easy Machine Learning presents a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real-world tasks. In the system, a learning task is formulated as a directed acyclic graph (DAG) in which each node represents an operation (e.g. a machine learning algorithm), and each edge represents the flow of the data from one node to its descendants. -
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AI learning
AiLearning, data analysis plus machine learning practice
We actively respond to the Research Open Source Initiative (DOCX) . Open source today is not just open source, but datasets, models, tutorials, and experimental records. We are also exploring other categories of open source solutions and protocols. I hope you will understand this initiative, combine this initiative with your own interests, and do what you can. Everyone's tiny contributions, together, are the entire open source ecosystem. We are iBooker, a large open-source community,... -
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Tsf_Mdnnhn
Time Series Forecasting.
Implementation of algorithm allowing Time Series Forecasting. -
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spark-msna
Algorithm on Spark for aligning multiple similar DNA/RNA sequences
The algorithm uses suffix tree for identifying common substrings and uses a modified Needleman-Wunsch algorithm for pairwise alignments. In order to improve the efficiency of pairwise alignments, an unsupervised learning based on clustering technique is used to create a knowledge base to guide them. -
We design our technology to make workforce management easier. APS offers core HR, payroll, benefits administration, attendance, recruiting, employee onboarding, and more.
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Clustering by Shared Subspaces
Grouping Points by Shared Subspaces for Effective Subspace Clustering
These functions implement a subspace clustering algorithm, proposed by Ye Zhu, Kai Ming Ting, and Mark J. Carman: "Grouping Points by Shared Subspaces for Effective Subspace Clustering", Published in Pattern Recognition Journal at https://doi.org/10.1016/j.patcog.2018.05.027 -
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The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners. Obtaining the teachingbox: FOR USERS: If you want to download the latest releases, please visit: http://search.maven.org...
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DGRLVQ
Dynamic Generalized Relevance Learning Vector Quantization
Some of the usual problems for Learning vector quantization (LVQ) based methods are that one cannot optimally guess about the number of prototypes required for initialization for multimodal data structures i.e.these algorithms are very sensitive to initialization of prototypes and one has to pre define the optimal number of prototypes before running the algorithm. If a prototype, for some reasons, is ‘outside’ the cluster which it should represent and if there are points of a different... -
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UAV Drone
Airborne avionics platform for drone system identification and control
.... This UAV drone hardware senses critical orientation parameters through a MPU-9250 9 Degree of Freedom sensor. This sensor measures 3-axis Acceleration Nx, Ny, & Nz, Compass Mx, My, Mz, and Gyroscopic rate P, Q, & R. A sensor fusion and numeric integration algorithm then combines these 9 parameters to 4 Euler parameters/Quaternions. An Euler body 1-2-3 sequence relates these to Roll/Pitch/Yaw. Air data is measured through altimeter and differential pressures. GPS measures position and track. -
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OWL Machine Learning
Machine learning algorithm using OWL
Feature construction and selection are two key factors in the field of Machine Learning (ML). Usually, these are very time-consuming and complex tasks because the features have to be manually crafted. The features are aggregated, combined or split to create features from raw data. This project makes use of ontologies to automatically generate features for the ML algorithms. The features are generated by combining the concepts and relationships that are already in the knowledge base, expressed... -
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Asedio
RTS game for learning Algorithm Design
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Machine Learning:Perceptron
Minimal version of the perceptron algorithm.Coded the simpler way.
A minimal version of the perceptron algorithm is implemented in C#. Coded for ease of understanding the referred to algorithm. Enter your info-press the learn button-then type in new info which the program will try and recognnise. There are some typos in the text displayed-but the code is correct. -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage... -
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Genetic Oversampling Weka Plugin
A Weka Plugin that uses a Genetic Algorithm for Data Oversampling
Weka genetic algorithm filter plugin to generate synthetic instances. This Weka Plugin implementation uses a Genetic Algorithm to create new synthetic instances to solve the imbalanced dataset problem. See my master thesis available for download, for further details. -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data... -
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... the calculations involved in each step. The output of PAVT has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. For instructions to use, see readme.txt.
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nunn
This is an implementation of a machine learning library in C++17
nunn is a collection of ML algorithms and related examples written in modern C++17. -
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ingap-cdg
codon-based de Bruijn graph algorithm for gene construction
Currently, most gene prediction methods detect coding sequences (CDSs) from transcriptome assembly when lacking of closely related reference genomes. However, these methods are of limited application due to highly fragmented transcripts and extensive assembly errors, which may lead to redundant or false CDS predictions. Here we present a novel algorithm, inGAP-CDG, for effective construction of full-length and non-redundant CDSs from unassembled transcriptomes. inGAP-CDG achieves... -
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NiceShaper - Dynamic Traffic Shaper
NiceShaper provides dynamic traffic shaping for Linux router
NiceShaper is the program developed for Linux router environment. It works in user space on top of standard Linux QOS implementation and iptables. By default, a proven HTB algorithm is used for the root, inner, and leaf classes, SFQ packets scheduling algorithm is the default queuing discipline (qdisc) contained within each of leaf classes, U32 and FW are used as the packets classifiers. NiceShaper provides dynamic traffic shaping approach which is more effective than traditional shaping... -
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Density-ratio based clustering
Discovering clusters with varying densities
This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying...