GitHub - kenflat2/Nonlinear-Regression: Collection of Python code written during my time at Stephan Munch's Lab. Included is my implementation of a Gaussian Process called KenGP, which is a nonlinear, (mostly) nonparametric prediction tool. I also have implementations for Empirical Dynamical Modelling(like GP but much simpler), Convergent Cross Mapping(detects causal relationships between time series), Diffusion Maps(nonlinear dimensionality reduction), Slow Feature Analysis(extraction of slowly changing of dataset), and False Nearest Neighbors(for finding an adequate time delay embedding dimension for a dataset), all of which are methods introduced used in and around the nonlinear dynamics literature.

Branches Tags

Name		Name	Last commit message	Last commit date
Latest commit History 69 Commits
.ipynb_checkpoints		.ipynb_checkpoints
.vs		.vs
simulation_results_final		simulation_results_final
.gitignore		.gitignore
Baltic Sea Mesocosm NSMap Analysis.ipynb		Baltic Sea Mesocosm NSMap Analysis.ipynb
BasicNormalDistribution.py		BasicNormalDistribution.py
Blasius Analysis.ipynb		Blasius Analysis.ipynb
Conditional Mutual Information.ipynb		Conditional Mutual Information.ipynb
Convergent Cross Mapping Testing.ipynb		Convergent Cross Mapping Testing.ipynb
CovEigenVal Test.py		CovEigenVal Test.py
Critical Slowing Down Experiments.ipynb		Critical Slowing Down Experiments.ipynb
DLM Fitting.ipynb		DLM Fitting.ipynb
Diffusion Mapped Delay Coordinates.ipynb		Diffusion Mapped Delay Coordinates.ipynb
DriverPeriodvsEigenIndex.py		DriverPeriodvsEigenIndex.py
EDM.py		EDM.py
EDMtoolkit.py		EDMtoolkit.py
EDMtoolkit.py.lprof		EDMtoolkit.py.lprof
EDMtoolkit.py.prof		EDMtoolkit.py.prof
E_vs_likelihood_NS_S.npy		E_vs_likelihood_NS_S.npy
Empirical Results Figure.ipynb		Empirical Results Figure.ipynb
Estimating continuous distributions.ipynb		Estimating continuous distributions.ipynb
Figure Playground.ipynb		Figure Playground.ipynb
Fluctuation of Similarity.ipynb		Fluctuation of Similarity.ipynb
GP Experimenter.ipynb		GP Experimenter.ipynb
GP Nonstationary Driver Inference.ipynb		GP Nonstationary Driver Inference.ipynb
GPBasic.py		GPBasic.py
GPDD NSMap.ipynb		GPDD NSMap.ipynb
GPTester.py		GPTester.py
GPandRegressionProblem10.py		GPandRegressionProblem10.py
GPandRegressionProblem2.py		GPandRegressionProblem2.py
GPandRegressionProblem8.py		GPandRegressionProblem8.py
Huge Plankton Dataset NSMap Analysis.ipynb		Huge Plankton Dataset NSMap Analysis.ipynb
IDPO.txt		IDPO.txt
KenGP Unit Testing.ipynb		KenGP Unit Testing.ipynb
KenGP.py		KenGP.py
Linear Systems.ipynb		Linear Systems.ipynb
LogisticFvO		LogisticFvO
Manifold Playground.py		Manifold Playground.py
NSMap_demonstration.pkl		NSMap_demonstration.pkl
Newport Data Analysis.ipynb		Newport Data Analysis.ipynb
Nonlinear SFA.ipynb		Nonlinear SFA.ipynb
Nonstationary Rate vs Our Estimation of Nonstationarity.ipynb		Nonstationary Rate vs Our Estimation of Nonstationarity.ipynb
Nonstationary SMap.ipynb		Nonstationary SMap.ipynb
Nonstationary Tests.ipynb		Nonstationary Tests.ipynb
Paramecium Didinium Analysis.ipynb		Paramecium Didinium Analysis.ipynb
SFA3dPlotting.py		SFA3dPlotting.py
Screenshot 2021-12-22 195641.jpg		Screenshot 2021-12-22 195641.jpg
Simulation Results Round 1.ipynb		Simulation Results Round 1.ipynb
Simulation Results Round 2.ipynb		Simulation Results Round 2.ipynb
Slow Feature Analysis Analysis.ipynb		Slow Feature Analysis Analysis.ipynb
Tanya Plankton Data Analysis.ipynb		Tanya Plankton Data Analysis.ipynb
TimeDelayEmbedding.py		TimeDelayEmbedding.py
Untitled.ipynb		Untitled.ipynb
calanoids_time_window		calanoids_time_window
driverVdelta_1.npy		driverVdelta_1.npy
driverVdelta_2.npy		driverVdelta_2.npy
driverVdelta_3.npy		driverVdelta_3.npy
lake_temp_vs_w_s		lake_temp_vs_w_s
lynxhare.xls		lynxhare.xls
lynxhareEDM.py		lynxhareEDM.py
lynxharedata.txt		lynxharedata.txt
modelSystems.py		modelSystems.py
newport_species_fg.txt		newport_species_fg.txt
newport_w_s_r2.txt		newport_w_s_r2.txt
nonstationarity_rate_vs_delta.py		nonstationarity_rate_vs_delta.py
nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_07-05-2022.npy		nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_07-05-2022.npy
nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_07-06-2022.npy		nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_07-06-2022.npy
nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_6_30_2022.npy		nonstationarity_rate_vs_delta_and_log_likelihood_NS_S_6_30_2022.npy
nonstattests.py		nonstattests.py
parallel_161917.out		parallel_161917.out
parameciumDidiniumEDM.py		parameciumDidiniumEDM.py
paramecium_didinium.xlsx		paramecium_didinium.xlsx
plane_draft.txt		plane_draft.txt
plankton_species_NSMap_results_raw.txt		plankton_species_NSMap_results_raw.txt
species_nonstationarity_versus_latitude_7_29_2022.npy		species_nonstationarity_versus_latitude_7_29_2022.npy
species_nonstationarity_versus_latitude_data.npy		species_nonstationarity_versus_latitude_data.npy
species_nonstationarity_versus_latitude_data_EandTau.npy		species_nonstationarity_versus_latitude_data_EandTau.npy
species_nonstationarity_versus_latitude_data_EandTau_6_5_2022.npy		species_nonstationarity_versus_latitude_data_EandTau_6_5_2022.npy
species_nonstationarity_versus_latitude_titles.npy		species_nonstationarity_versus_latitude_titles.npy
species_nonstationarity_versus_latitude_titles_EandTau.npy		species_nonstationarity_versus_latitude_titles_EandTau.npy
species_nonstationarity_versus_latitude_titles_EandTau_6_5_2022.npy		species_nonstationarity_versus_latitude_titles_EandTau_6_5_2022.npy
species_results_nonstationarity_versus_latitude_8_4_2022.npy		species_results_nonstationarity_versus_latitude_8_4_2022.npy
species_titles_nonstationarity_versus_latitude_8_4_2022.npy		species_titles_nonstationarity_versus_latitude_8_4_2022.npy
temperature_results		temperature_results
temperature_results.npy		temperature_results.npy
temperature_titles		temperature_titles
temperature_titles.npy		temperature_titles.npy
tickTargetsEDM.py		tickTargetsEDM.py
untitled.txt		untitled.txt
y		y
y.pub		y.pub

About

Collection of Python code written during my time at Stephan Munch's Lab. Included is my implementation of a Gaussian Process called KenGP, which is a nonlinear, (mostly) nonparametric prediction tool. I also have implementations for Empirical Dynamical Modelling(like GP but much simpler), Convergent Cross Mapping(detects causal relationships betwe…

data-science gaussianprocess jupyter-notebook python