NumPy and Tensorflow implementation of the Multidimensional Contrast Limited Adaptive Histogram Equalization (MCLAHE) procedure
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Updated
Aug 2, 2022 - Python
NumPy and Tensorflow implementation of the Multidimensional Contrast Limited Adaptive Histogram Equalization (MCLAHE) procedure
Multiple Frequency Estimation Under Local Differential Privacy in Python
Multidimensional arrays storage engine
Python Image Tool for visualizing multidimensional data. Applications include analysis of data in microscopy (STM, SSM, optics), ARPES, XRD, or other multidimensional datasets on regularly gridded coordinates.
Visual Knowledge Discovery tools for interactively visualizing, exploring, and identifying complex n-D data patterns in multivariate CSV data, to visualize machine learning classifier models.
Multidimensional cluster generation in Python
Interactive management shell for Deker
Local filesystem HDF5 storage for Deker
Shared functions library for Deker components
Jarvis march algorithm to compute convex hull for 2d dataset
Server storage for Deker
class for BallTree data structure
SQLAlchemy dictionary de/serialization tool.
A k-nearest neighbours classifier that can operate on multidimensional data, no external modules needed.
Visual analysis approach presented in the paper "Exploring visual quality of multidimensional time series projections", published by the journal Visual Informatics.
Using "t-SNE trajectories" for integrated visualization of multi-dimensional longitudinal trajectory datasets.
Visualization that represents datapoints and encodes as many of the seven dimensions in a given dataset as possible for Scientific Visualization and Virtual Reality course. Grade: 9/10.
This is a dimensionality reduction project in the course DD2470 Advanced Topics in Visualization and Computer Graphics at KTH Royal Institute of Technology, Stockholm (2024), made by Linnéa Gustafsson.
A simple application that helps you classifying your data with Kmeans
a simple application that helps you classifying your multidimensional data via Support Vector Machine, Random Forest and Decision Tree
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