Search Results for "fitting"
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Showing 38 open source projects for "fitting"
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1
jlens
Companion code for the global workspace interpretability paper
...The transformed vectors are decoded through the model’s own unembedding into ranked vocabulary predictions. The package can fit new lenses, load saved ones, apply them to prompts, and merge results from parallel fitting jobs. Interactive layer-by-position views reveal how token rankings evolve across the network and compare them with the model’s final output. It supports open-weight Hugging Face decoder models, with Qwen used in the included examples. The repository also provides synthetic evaluation data and an end-to-end notebook, but it is not maintained. -
2
Implicit
Fast Python collaborative filtering for implicit feedback datasets
...All models have multi-threaded training routines, using Cython and OpenMP to fit the models in parallel among all available CPU cores. In addition, the ALS and BPR models both have custom CUDA kernels - enabling fitting on compatible GPU’s. This library also supports using approximate nearest neighbour libraries such as Annoy, NMSLIB and Faiss for speeding up making recommendations. -
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MNE-Python
Magnetoencephalography (MEG) and Electroencephalography EEG in Python
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data. MNE-Python is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, statistics, and more. -
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TabFM
scikit-learn compatible tabular foundation model
TabFM is a tabular foundation model from Google Research for zero-shot classification and regression on structured datasets. It is designed to work with mixed numerical and categorical columns without requiring a custom training run for every new table. Instead of fitting model weights to the user’s dataset, TabFM uses in-context learning by reading training examples and test rows together at inference time. The library provides scikit-learn-compatible classifier and regressor interfaces, which makes it familiar for data scientists already using Python ML workflows. It supports both JAX and PyTorch backends and can automatically download pretrained TabFM v1.0.0 weights. ... -
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5
LuxTTS
A high-quality rapid TTS voice cloning model
...Intended for developers, hobbyists, and creators, the repository includes installation instructions, usage examples, and Python APIs that make it feasible to integrate the model in local workflows, web demos, or production systems. Its design emphasizes efficiency and practicality, fitting within modest GPU memory footprints. -
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GT New Horizons Mod Pack
New Modpack with Gregtech, Thaumcraft and Witchery
...The main intentions of the pack are a long-lasting experience and tying mods together in a progressive fashion, making it feel more like a single game than a compilation of mods thrown together. To reach this goal, GT New Horizons is using the tiers (basically ages of technology) from GregTech and allocates the content of other mods to a fitting point within the progression. Starting in the Stone Age you will barely be able to survive until you get your first steam machines and, eventually, reach electricity. -
7
PySINDy
A package for the sparse identification of nonlinear dynamical systems
...The framework focuses on identifying governing equations that describe the behavior of complex physical systems by selecting sparse combinations of candidate functions. Instead of fitting a purely predictive machine learning model, PySINDy attempts to recover interpretable differential equations that explain how a system evolves over time. This approach is particularly valuable in scientific fields such as physics, engineering, and biology where researchers seek both predictive accuracy and theoretical insight. The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. ... -
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PyMC3
Probabilistic programming in Python
PyMC3 allows you to write down models using an intuitive syntax to describe a data generating process. Fit your model using gradient-based MCMC algorithms like NUTS, using ADVI for fast approximate inference — including minibatch-ADVI for scaling to large datasets, or using Gaussian processes to build Bayesian nonparametric models. PyMC3 includes a comprehensive set of pre-defined statistical distributions that can be used as model building blocks. Sometimes an unknown parameter or variable... -
9
KherveSheet
Excel for scientists: high-quality plots, Python in cells, AI side bar
...Plotting Charts are generated using matplotlib and embedded directly in the sheet as movable, resizable objects. You have full control over axes, scales, ticks, titles and grids. Curve fitting and peak fitting are supported, with goodness-of-fit reporting overlaid on the plot. Every cell can also run a full Python program, not just a formula. An integrated AI assistant can generate the cell code from a plain-language description if needed. Learn by doing with 300 ready-made scientific examples covering plotting, data analysis, fitting and simulation. ... -
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10
GenX
X-Ray and Neutron Reflectivity Modeling
GenX is a scientific program to refine x-ray refelcetivity, neutron reflectivity and surface x-ray diffraction data using the differential evolution algorithm. GenX is very modular and highly extensible and can be used as a general fitting program. -
11
relax
Molecular dynamics by NMR data analysis
The software package 'relax' is designed for the study of molecular dynamics through the analysis of experimental NMR data. Organic molecules, proteins, RNA, DNA, sugars, and other biomolecules are all supported. It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, the Lipari and Szabo model-free analysis, study of domain motions via the N-state model and frame order dynamics theories using anisotropic NMR parameters such as RDCs and PCSs, the investigation of stereochemistry in dynamic ensembles, and the analysis of relaxation dispersion data. -
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Toloka-Kit
Toloka-Kit is a Python library for working with Toloka API
...For example, you can pass data between two related projects: one for data labeling, and another for its validation. AutoQuality feature which automatically finds the best fitting quality control rules for your project. -
13
Tempico software
Software to control and analyze Tausand Tempico timing data
...The software generates histograms from captured data, supports lifetime measurements via single-photon correlation (SPC), and displays real-time graphs with adjustable fitting options. It includes a 'Counts Estimation' panel to estimate count rates per channel over time, and a 'Time Stamping' feature to record timing of events. Users can view, configure, and control the device directly from the application, as well as save both raw data and graphical results for later analysis. Tempico Software is ideal for researchers working with photon timing and lifetime studies. -
14
auto-sklearn
Automated machine learning with scikit-learn
...It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Auto-sklearn 2.0 includes latest research on automatically configuring the AutoML system itself and contains a multitude of improvements which speed up the fitting the AutoML system. auto-sklearn 2.0 works the same way as regular auto-sklearn. auto-sklearn is licensed the same way as scikit-learn, namely the 3-clause BSD license. -
15
Twinify
Privacy-preserving generation of a synthetic twin to a data set
...Depending on the nature of your data, twinify implements either the NAPSU-MQ approach described by Räisä et al. or finds an approximate parameter posterior for any probabilistic model you formulated using differentially private variational inference (DPVI). For the latter, twinify also offers automatic modeling for easy building of models fitting the data. If you have existing experience with NumPyro you can also implement your own model directly. Often data that would be very useful for the scientific community is subject to privacy regulations and concerns and cannot be shared. Differentially private data sharing allows generating of synthetic data that is statistically similar to the original data. -
16
PyNanoLab
data analysis and Visualization with matplotlib
PyNanoLab contains a variety of tools to complete the data analysis, statistics, curve fitting, and basic machine learning application. Visualization in pynanolab is based on matplotlib. The setup tools is desinged to control and set-up all the details of the figure with a GUI. -
17
Hasklig
A code font with monospaced ligatures
...Academic articles featuring Haskell code often use lhs2tex to achieve an appealing rendering, but it is of no use when programming. Hasklig solves the problem the way typographers have always solved ill-fitting characters which co-occur often, ligatures. The underlying code stays the same, only the representation changes. Not only can multi-character glyphs be rendered more vividly, other problematic things in monospaced fonts, such as spacing can be corrected. -
18
QtiPlot is a user-friendly, platform independent data analysis and visualization application similar to the non-free Windows program Origin.
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19
Scikit-learn Tutorial
An introductory tutorial for scikit-learn
...It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. The repository specifies a clear list of dependencies so that participants can reproduce the environment used in the tutorial, and many downstream forks keep the content updated for newer versions of scikit-learn. ... -
20
TF Quant Finance
High-performance TensorFlow library for quantitative finance
TF Quant Finance is a high-performance library of quantitative finance components built on TensorFlow, aimed at research and production workloads. It implements pricing engines, risk measures, stochastic models, optimizers, and random number generators that are differentiable and vectorized for accelerators. Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The... -
21
abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
...The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
22
ParamIT
a Toolset for Molecular Mechanical Force Field Parameterization
...The developed tools include: 1) generator of molecule-water complexes with graphical user interface (GUI), 2) semi-automatic frequency analysis using symbolic potential energy distribution matrix and comparison of optimized internal coordinates, 3) GUI for charge fitting with three modes: manual, Monte-Carlo sampling or brute force, and 4) GUI for dihedral terms fitting. The usage of these tools decreases the labor effort, lowers manual input errors and reduces the time needed for accurate MM parameterization efforts. -
23
plot.py
direct data plotting and evaluation
...Many raw experimental data types (mostly of x-ray and neutron scattering experiments) are supported with more to be added on user request. The data treatment includes non-linear fitting, integration and differentiation, peak-finder and more. User python code can be executed in the integrated IPython console. -
24
YFitter
Fitting Y chromosome haplogroups by maximum likelihood
Yfitter is a program for assigning Y chromosome haplogroups to individuals sequenced at low coverage. It is designed to be used in a samtools/bcftools pipeline. Yfitter also supports haplogrouping using chip genotype data. -
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SloppyCell is a complete environment for building models of biochemical systems, fitting them to data, and extracting falsifiable predictions via a parameter ensemble. SBML l2v1 compatible.
