SloppyCell.Vandermonde.OptimizeSumDets

Module OptimizeSumDets

Functions

C(p, origMatrix, weightsRDP=0., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0., *args, **kwargs)
p is list {a_{ij}, i: 1..n, j: i+1..n}...

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calcPriors(paramsList, pOpt=0., pSigma=10.)

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sumRowDotProdsOLD(origMatrix)
Makes more sense to use on Jacobian than on Hessian.

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sumRowDotProdsNEW(origMatrix)
Makes more sense to use on Jacobian than on Hessian.

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sumAllDotProds(origMatrix)

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sum2Determinants(matrixToSum)

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sumLogSpacings(matrixToSum)

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ParticipationRatio(origMatrix)

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ProcessFullMatrix(p)
this is used if parameters define all elements of a matrix

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ProcessHalfMatrix(p)
this is used if parameters define upper right triangular portion of skew-symmetric matrix.

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ProcessQuarterMatrix(p)
this is used if parameters define just upper right and lower left quadrants of antisymmetric matrix.

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BestMatrix(origMatrix, p=None, weightsRDP=1., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0., seed=None, *args, **kwargs)

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ManyBest(origMatrix, numberTries=10, p=None, numOpts=10, weightsRDP=1., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0., seed=None, *args, **kwargs)

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OptimizeManyTimes(origMatrix, p=None, numOpts=10, weightsRDP=1., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0., seed=None, *args, **kwargs)

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getGammas(numExps, distWidth=1.)

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getAmounts(numExps, distWidth=1.)

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netRadiation(gammas, amounts, time)

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getHessFull(gammas=None, amounts=None, numExps=3)

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getHessFullLogTime(gammas=None, amounts=None, numExps=3)
use this routine for new paper

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getHessGG(gammas=None, numExps=3)

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getHessGGLogTime(gammas=None, amounts=None, numExps=3)
use this routine for the new paper

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getHessAA(amounts=None, gammas=None, numExps=3)

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getHessAALogTime(amounts=None, gammas=None, numExps=3)
use this routine for the new paper

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getJacobian(times, amounts=None, gammas=None)

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getJacobianLogTime(times, amounts=None, gammas=None)

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getJacobianLog(times, gammas, amounts=None)
This is the routine to use for new paper (with times exponentially distributed.

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getJacobianLogG(times, gammas, amounts=None)
This is the routine to use for new paper (with times exponentially distributed.

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getJacobianLogA(times, gammas, amounts)
This is the routine to use for new paper (with times exponentially distributed.

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getJacobianG(times, gammas, amounts=None)

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getJacobianGLogTime(times, gammas, amounts=None)

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getJacobianA(times, amounts, gammas=None)

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getJacobianALogTime(times, amounts, gammas=None)

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normRows(matrixToPlot)
Useful for plotting and otherwise comparing alignment of rows of matrices.

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transformMatrix(origMatrix, transformation)

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findPermutation(permMat)
In so far as permMat is a permutation matrix, returns the permutation.

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makePermutationMatrix(permList)
Takes a list defining the permutation and makes the appropriate matrix.

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timeCostEval(mat, p, numEvals=100)

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timeGammas(listNumGammas, numEvals=1000)

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plotLevels(levels, offset=0)

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eVec(k, n)

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sLSAlongP(origMatrix, params, k, deltaP)

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PRAlongP(params, k, deltaP)

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CostAlongP(origMatrix, params, k, deltaP, weightsRDP=0., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0.)

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Imports: scipy, time, Plotting

Function Details

[hide private]

C(p, origMatrix, weightsRDP=0., weights2D=0., weightsLS=0., weightPR=0., weightPriors=0., *args, **kwargs)

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p is list {a_{ij}, i: 1..n, j: i+1..n}
n is size of total matrix

ProcessHalfMatrix(p)

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this is used if parameters define upper right triangular portion of
skew-symmetric matrix.
Then performs a Cayley transformation of this skew-symmetric matrix.

normRows(matrixToPlot)

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Useful for plotting and otherwise comparing alignment of rows of matrices.
Be careful that if vec[1] is zero, entire row gets zerod.