Response to CR
Value: [0.005 to 0.43]
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@ -16,7 +16,7 @@ type error =
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| OperationError(Operation.Error.t)
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| PointSetConversionError(SampleSetDist.pointsetConversionError)
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| SparklineError(PointSetTypes.sparklineError) // This type of error is for when we find a sparkline of a discrete distribution. This should probably at some point be actually implemented
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| RequestedStrategyInvalidError
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| RequestedStrategyInvalidError(string)
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| LogarithmOfDistributionError(string)
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| OtherError(string)
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@ -38,7 +38,7 @@ module Error = {
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| OperationError(err) => Operation.Error.toString(err)
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| PointSetConversionError(err) => SampleSetDist.pointsetConversionErrorToString(err)
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| SparklineError(err) => PointSetTypes.sparklineErrorToString(err)
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| RequestedStrategyInvalidError => `Requested strategy invalid`
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| RequestedStrategyInvalidError(err) => `Requested strategy invalid: ${err}`
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| OtherError(s) => s
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}
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@ -225,55 +225,22 @@ module AlgebraicCombination = {
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| _ => 1000
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}
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type calculationMethod = MonteCarlo | Convolution(Operation.convolutionOperation)
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type calculationStrategy = MonteCarloStrat | ConvolutionStrat(Operation.convolutionOperation)
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let chooseConvolutionOrMonteCarloDefault = (
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op: Operation.algebraicOperation,
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t2: t,
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t1: t,
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): calculationMethod =>
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): calculationStrategy =>
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switch op {
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| #Divide
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| #Power
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| #Logarithm =>
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MonteCarlo
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MonteCarloStrat
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| (#Add | #Subtract | #Multiply) as convOp =>
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expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > 10000
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? MonteCarlo
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: Convolution(convOp)
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}
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let chooseConvolutionOrMonteCarlo = (
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~strat: DistributionTypes.asAlgebraicCombinationStrategy,
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op: Operation.algebraicOperation,
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t2: t,
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t1: t,
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): result<calculationMethod, error> => {
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switch strat {
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| AsDefault => Ok(chooseConvolutionOrMonteCarloDefault(op, t2, t1))
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| AsConvolution =>
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switch op {
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| #Divide | #Power | #Logarithm => Error(RequestedStrategyInvalidError)
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| (#Add | #Subtract | #Multiply) as convOp => Ok(Convolution(convOp))
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}
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| AsMonteCarlo => Ok(MonteCarlo)
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| AsSymbolic => Error(RequestedStrategyInvalidError)
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}
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}
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let tryAnalyticalSimplificationDefault = (
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arithmeticOperation: Operation.algebraicOperation,
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t1: t,
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t2: t,
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): option<result<SymbolicDistTypes.symbolicDist, Operation.Error.t>> =>
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switch (t1, t2) {
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| (Symbolic(d1), Symbolic(d2)) =>
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switch SymbolicDist.T.tryAnalyticalSimplification(d1, d2, arithmeticOperation) {
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| #AnalyticalSolution(symbolicDist) => Some(Ok(symbolicDist))
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| #Error(er) => Some(Error(er))
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| #NoSolution => None
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}
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| _ => None
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? MonteCarloStrat
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: ConvolutionStrat(convOp)
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}
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let tryAnalyticalSimplification = (
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@ -295,16 +262,17 @@ module AlgebraicCombination = {
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~arithmeticOperation,
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~t2: t,
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): result<t, error> => {
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switch tryAnalyticalSimplificationDefault(arithmeticOperation, t1, t2) {
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| Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(Error(e)) => Error(OperationError(e))
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switch tryAnalyticalSimplification(arithmeticOperation, t1, t2) {
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| Some(#AnalyticalSolution(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(#Error(e)) => Error(OperationError(e))
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| Some(#NoSolution)
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| None =>
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switch getInvalidOperationError(t1, t2, ~toPointSetFn, ~arithmeticOperation) {
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| Some(e) => Error(e)
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| None =>
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switch chooseConvolutionOrMonteCarloDefault(arithmeticOperation, t1, t2) {
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| MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| Convolution(convOp) =>
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| MonteCarloStrat => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| ConvolutionStrat(convOp) =>
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(
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r,
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))
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@ -318,7 +286,7 @@ module AlgebraicCombination = {
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t1: t,
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~toPointSetFn: toPointSetFn,
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~toSampleSetFn: toSampleSetFn,
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~arithmeticOperation,
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~arithmeticOperation: Operation.algebraicOperation,
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~t2: t,
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): result<t, error> => {
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switch strategy {
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@ -326,20 +294,23 @@ module AlgebraicCombination = {
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| AsSymbolic =>
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switch tryAnalyticalSimplification(arithmeticOperation, t1, t2) {
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| Some(#AnalyticalSolution(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(#NoSolution)
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| None =>
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Error(RequestedStrategyInvalidError)
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| Some(#NoSolution) => Error(RequestedStrategyInvalidError(`No analytical solution`))
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| None => Error(RequestedStrategyInvalidError("Inputs were not even symbolic"))
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| Some(#Error(err)) => Error(OperationError(err))
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}
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| AsConvolution
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| AsMonteCarlo =>
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switch chooseConvolutionOrMonteCarlo(~strat=strategy, arithmeticOperation, t1, t2) {
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| Ok(MonteCarlo) => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| Ok(Convolution(convOp)) =>
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(r))
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| Error(RequestedStrategyInvalidError) => Error(RequestedStrategyInvalidError)
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| Error(err) => Error(err)
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| AsConvolution => {
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let errString = opString => `Can't convolve on ${opString}`
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switch arithmeticOperation {
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| (#Add | #Subtract | #Multiply) as convOp =>
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(
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r,
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))
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| #Divide => "divide"->errString->RequestedStrategyInvalidError->Error
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| #Power => "power"->errString->RequestedStrategyInvalidError->Error
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| #Logarithm => "logarithm"->errString->RequestedStrategyInvalidError->Error
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}
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}
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| AsMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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}
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}
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}
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