non-parametric-density-estimation | Topic

Topic: "non-parametric-density-estimation"

mariaderrico/DPA

The DPA package is the scikit-learn compatible implementation of the Density Peaks Advanced clustering algorithm. The algorithm provides robust and visual information about the clusters, their statistical reliability and their hierarchical organization.

Language: Jupyter Notebook - Size: 36.5 MB - Last synced at: about 2 months ago - Pushed at: over 3 years ago - Stars: 29 - Forks: 9

XuegongLab/neoguider

NeoGuider, neoepitope detection using advanced feature engineering

Language: Python - Size: 17.9 MB - Last synced at: 3 days ago - Pushed at: 3 days ago - Stars: 3 - Forks: 1

eyabesbes/Statistique-Non-parametrique

Non-parametric estimation of density using kernels and histograms with Python and R.

Language: Jupyter Notebook - Size: 1020 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 1 - Forks: 0

knowthebird/KernelDensityEstimator

Non parametric Kernel Density Estimator / Classifier. Allows user to input bandwidth but does not find it. Can classify for N dimensions, but can only plot class / decision boundaries for 2.

Language: Python - Size: 116 KB - Last synced at: over 1 year ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0

apeterson91/bendr

Nested Dirichlet Process Mixture Models to characterize Built Environment Data

Language: C++ - Size: 3.92 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 1

fardinabbasi/ParzenWindow

Performing non-parametric Parzen Window density estimation with a Gaussian kernel, implementing the method both from scratch and using built-in functions, and compares the results across various window widths and sample sizes.

Language: Jupyter Notebook - Size: 2.82 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0