EstimateCC
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package infcalcs
Calculates the mutual information for a set of data with no weights and without the linear regression estimator
Estimates the channel capacity for a DRData given a CalcConfig
Estimates the channel capacity for a DRData given a CalcConfig
This function generates a list of Weight instances to apply to the data and iterates over signal and response bin numbers as specified in by the configuration parameters to estimate the maximum mutual information.
Alternate to estimateCCVerbose for bootstrapped data
An imperative equivalent to estimateCC for pretty-printing of calculation status to stdout
An imperative equivalent to estimateCC for pretty-printing of calculation status to stdout
Channel capacity estimate for given list of weights
Generates list of bimodal weights.
Generates a set of piece-wise functions to create uniform weighting from bin i to bin j with all outer bins (j) weighted to 0
Constructs a joint uniform distribution Weight for all dimensions in signal space
Generates list of unimodal (Gaussian) weights.
Generates a list of weights for n-dim input data
Generates a list of weights for n-dim input data
Weights are generated for a n-dim input distribution by calculating the marginal distributions for the n independent random variables representing the signal. These are used to produce a joint distribution (see Weight.makeJoint) in order to construct a list of weights corresponding to the structure of the data set.
list of ordered tuples (signal values)
function determining calculation of weight distribution
list of weights for a signal set of ordered tuples
Similar to genWeights1 but with a different weight function signature
Contains functions for generating signal weights and estimating the channel capacity.
Most importantly are the EstimateCC.estimateCC and EstimateCC.estimateCCVerbose functions which are used in the EstCC main object to estimate the channel capacity.
The weighting functions EstimateCC.genUnimodalWeights and EstimateCC.genBimodalWeights generate weights for unimodal and bimodal Gaussian-based probability density functions and EstimateCC.genPieceWiseUniformWeights produces discrete piecewise probability mass functions by iteratively selecting signal values to omit from the mutual information estimation.