Mixture Mapping

Layers

• Distribution
  • Distribution
    • Distribution.build()
    • Distribution.call()
    • Distribution.compute_output_shape()
    • Distribution.get_config()
  • DistributionKLLoss
    • DistributionKLLoss.build()
    • DistributionKLLoss.call()
  • DistributionMean
    • DistributionMean.call()
  • DistributionSampleLoss
    • DistributionSampleLoss.call()
  • DistributionSamples
    • DistributionSamples.call()
    • DistributionSamples.get_config()
  • TrainableCovMatrix
    • TrainableCovMatrix.build()
    • TrainableCovMatrix.call()
    • TrainableCovMatrix.compute_output_shape()
    • TrainableCovMatrix.get_config()
  • VarCovMatrix
    • VarCovMatrix.build()
    • VarCovMatrix.call()
    • VarCovMatrix.compute_output_shape()
    • VarCovMatrix.get_config()
• Mixture Mapping
  • LinearMapping
    • LinearMapping.build()
    • LinearMapping.call()
  • GeneralMapping
    • GeneralMapping.build()
    • GeneralMapping.call()
    • GeneralMapping.get_config()
• Gaussian Mixture Regression
  • DynamicGMR
    • DynamicGMR.build()
    • DynamicGMR.call()
    • DynamicGMR.fit_gmms()
    • DynamicGMR.get_config()
    • DynamicGMR.set_gmm_values()
• Binning
  • BinYield
    • BinYield.build()
    • BinYield.call()
  • BinYieldStatic
    • BinYieldStatic.build()
    • BinYieldStatic.call()
    • BinYieldStatic.compute_output_shape()
• Normalization
  • FixedBackward
    • FixedBackward.call()
  • FixedForward
    • FixedForward.call()
• Distribution Trafos
  • AppendCatAsFloat
    • AppendCatAsFloat.build()
    • AppendCatAsFloat.call()
    • AppendCatAsFloat.compute_output_shape()
    • AppendCatAsFloat.get_config()
  • CatTrainableOffset
    • CatTrainableOffset.build()
    • CatTrainableOffset.call()
    • CatTrainableOffset.compute_output_shape()
    • CatTrainableOffset.get_config()
  • CatTrainableScale
    • CatTrainableScale.build()
    • CatTrainableScale.call()
    • CatTrainableScale.compute_output_shape()
    • CatTrainableScale.get_config()
  • DistMerger
    • DistMerger.build()
    • DistMerger.call()
    • DistMerger.compute_output_shape()

Distributions

createCovMatrix(stdDevTensor, correlationTensor, size, name='CorrelationMatrix')

createMixDistBYmeanCovWeight(meanTensor, covTensor, weightTensor, mixSize, matrixSize, name='GaussianMixture')

createMixDistribution(meanTensor, stdDevTensor, correlationTensor, weightTensor, mixSize, matrixSize, name='GaussianMixture')

regularizeCovMatrix(covMatrix, epsilon=0.999)

Utils

getSkleanGM(weights, means, stdOrCov)

lastEntryJacobian(ys, xs, use_pfor=True, parallel_iterations=None, name='Jacobian', stop_gradients=None)

Binning

class BinningScheme

Bases: object

append(input)

property covariances

extend(input)

property means

to_dict(batch_tiles=1)

property weights

class SingleBin(means: 0, weights: 0, covariances: 0, area: 0, name: 0)

Bases: object

area: 0

property as_gmm

covariances: 0

means: 0

name: 0

plot(dims=None, **C)

sample(n)

weights: 0

combine(bins)

getPolygonBin(points, name=None, n=250, n_start=None, n_oversample=100.0, tol=0.1, max_iter=5000)

getRangeBin(min, max, name=None, n=30, n_start=None, n_oversample=200.0, tol=0.01, max_iter=5000)