ML-GPR API reference#
This section documents the public classes, functions, and modules that make up ML-GPR. For an explanation of how to assemble and run an analysis, see the ML-GPR user guide.
- High-level fitting and results
- Configuration
- Kernels and time covariance
make_kernel()UVScaledKernelWhiteHeteroscedasticKernelVAEKernTorchTimeCovarianceIndependentTimeCovarianceCoherentTimeCovarianceSharedFractionTimeCovarianceExponentialTimeCovarianceTimeKronCombinedAdditiveKernCombinedProductKerncombine_kernels()UniformPriorLog10UniformPriorGaussianPriormake_prior()
- Data representation and regression
- Inference and posterior products
- Covariance and parameter utilities
- VAE training and 21-cm kernels