Documentation ¶
Index ¶
- Variables
- func CalcTopicRewards(ctx sdk.Context, k keeper.Keeper, weights map[uint64]*alloraMath.Dec, ...) (map[uint64]*alloraMath.Dec, error)
- func CalculateReputerRewardFractions(stakes []alloraMath.Dec, scores []alloraMath.Dec, preward alloraMath.Dec) ([]alloraMath.Dec, error)
- func EmitRewards(ctx sdk.Context, k keeper.Keeper, blockHeight BlockHeight, ...) error
- func EnsureAllWorkersPresent(values []*types.WorkerAttributedValue, allWorkers map[string]struct{}) []*types.WorkerAttributedValue
- func EnsureAllWorkersPresentWithheld(values []*types.WithheldWorkerAttributedValue, allWorkers map[string]struct{}) []*types.WithheldWorkerAttributedValue
- func Entropy(rewardFractionsPerActor []alloraMath.Dec, numberRatio alloraMath.Dec, ...) (alloraMath.Dec, error)
- func EntropyForSingleParticipant() (alloraMath.Dec, error)
- func ExtractValues(bundle *types.ValueBundle) []alloraMath.Dec
- func ForecastingPerformanceScore(naiveNetworkInferenceLoss, networkInferenceLoss alloraMath.Dec) (alloraMath.Dec, error)
- func ForecastingUtility(forecastingTaskUtilityScore alloraMath.Dec, infererScores []types.Score, ...) (alloraMath.Dec, error)
- func GenerateForecastScores(ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, ...) ([]types.Score, error)
- func GenerateInferenceScores(ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, ...) ([]types.Score, error)
- func GenerateReputerScores(ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, ...) ([]types.Score, error)
- func GenerateRewardsDistributionByTopicParticipant(ctx sdk.Context, k keeper.Keeper, topicId uint64, topicReward *alloraMath.Dec, ...) (totalRewardsDistribution []types.TaskReward, taskReputerReward alloraMath.Dec, ...)
- func GetAdjustedStake(stake alloraMath.Dec, allStakes []alloraMath.Dec, ...) (alloraMath.Dec, error)
- func GetAllActiveEpochEndingTopics(ctx sdk.Context, k keeper.Keeper, block BlockHeight, topicPageLimit uint64, ...) []types.Topic
- func GetAllConsensusScores(allLosses [][]alloraMath.Dec, stakes []alloraMath.Dec, ...) ([]alloraMath.Dec, error)
- func GetAllReputersOutput(allLosses [][]alloraMath.Dec, stakes []alloraMath.Dec, ...) ([]alloraMath.Dec, []alloraMath.Dec, error)
- func GetAndUpdateActiveTopicWeights(ctx sdk.Context, k keeper.Keeper, block BlockHeight) (weights map[TopicId]*alloraMath.Dec, sumWeight alloraMath.Dec, ...)
- func GetChiAndGamma(...) (chi alloraMath.Dec, gamma alloraMath.Dec, err error)
- func GetConsensusScore(reputerLosses, consensusLosses, mostDistantValues []alloraMath.Dec, ...) (alloraMath.Dec, error)
- func GetFinalWorkerScoreForecastTask(scoreOneIn, scoreOneOut, fUniqueAgg alloraMath.Dec) (alloraMath.Dec, error)
- func GetForecastTaskEntropy(ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, ...) (entropy alloraMath.Dec, err error)
- func GetForecastingTaskRewardFractions(ctx sdk.Context, k keeper.Keeper, topicId uint64, blockHeight int64, ...) ([]string, []alloraMath.Dec, error)
- func GetInferenceTaskEntropy(ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, ...) (entropy alloraMath.Dec, err error)
- func GetInferenceTaskRewardFractions(ctx sdk.Context, k keeper.Keeper, topicId uint64, blockHeight int64, ...) ([]string, []alloraMath.Dec, error)
- func GetMappingFunctionValues(latestWorkerScores []alloraMath.Dec, latestTimeStepsScores []alloraMath.Dec, ...) ([]alloraMath.Dec, error)
- func GetReputerTaskEntropy(ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, ...) (entropy alloraMath.Dec, err error)
- func GetReputersRewardFractions(ctx sdk.Context, k keeper.Keeper, topicId uint64, pReward alloraMath.Dec, ...) ([]string, []alloraMath.Dec, error)
- func GetRewardForForecastingTaskInTopic(entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, ...) (alloraMath.Dec, error)
- func GetRewardForInferenceTaskInTopic(entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, ...) (alloraMath.Dec, error)
- func GetRewardForReputerFromTotalReward(ctx sdk.Context, keeper keeper.Keeper, topicId uint64, ...) ([]types.TaskReward, error)
- func GetRewardForReputerTaskInTopic(entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, ...) (alloraMath.Dec, error)
- func GetRewardPerReputer(ctx sdk.Context, keeper keeper.Keeper, topicId uint64, ...) ([]types.TaskReward, error)
- func GetRewardPerWorker(topicId uint64, taskRewardType types.TaskRewardType, ...) ([]types.TaskReward, error)
- func GetScoreFractions(latestWorkerScores []alloraMath.Dec, latestTimeStepsScores []alloraMath.Dec, ...) ([]alloraMath.Dec, error)
- func GetStakeWeightedLoss(reputersStakes, reputersReportedLosses []alloraMath.Dec) (alloraMath.Dec, error)
- func GetStakeWeightedLossMatrix(reputersAdjustedStakes []alloraMath.Dec, ...) ([]alloraMath.Dec, []alloraMath.Dec, error)
- func GetTopicReward(topicRewardFraction alloraMath.Dec, totalReward alloraMath.Dec) (alloraMath.Dec, error)
- func GetTopicRewardFraction(topicWeight *alloraMath.Dec, totalWeight alloraMath.Dec) (alloraMath.Dec, error)
- func GetWorkersRewardFractions(ctx sdk.Context, k keeper.Keeper, topicId uint64, blockHeight int64, ...) ([]string, []alloraMath.Dec, error)
- func GetfUniqueAgg(numForecasters alloraMath.Dec) (alloraMath.Dec, error)
- func Logger(ctx sdk.Context) log.Logger
- func ModifiedRewardFractions(rewardFractions []alloraMath.Dec) ([]alloraMath.Dec, error)
- func NormalizationFactor(entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, ...) (alloraMath.Dec, error)
- func NumberRatio(rewardFractions []alloraMath.Dec) (alloraMath.Dec, error)
- func PickChurnableActiveTopics(ctx sdk.Context, k keeper.Keeper, block BlockHeight, ...) error
- func Sigmoid(x alloraMath.Dec) (alloraMath.Dec, error)
- type BlockHeight
- type SortableTopicId
- type TopicId
Constants ¶
This section is empty.
Variables ¶
var TASK_FORECAST = true
var TASK_INFERENCE = false
Functions ¶
func CalcTopicRewards ¶
func CalcTopicRewards( ctx sdk.Context, k keeper.Keeper, weights map[uint64]*alloraMath.Dec, sortedTopics []uint64, sumWeight alloraMath.Dec, totalReward alloraMath.Dec, ) ( map[uint64]*alloraMath.Dec, error, )
func CalculateReputerRewardFractions ¶
func CalculateReputerRewardFractions( stakes []alloraMath.Dec, scores []alloraMath.Dec, preward alloraMath.Dec, ) ([]alloraMath.Dec, error)
CalculateReputerRewardFractions calculates the reward fractions for each reputer based on their stakes, scores, and preward parameter. W_im
func EmitRewards ¶
func EmitRewards( ctx sdk.Context, k keeper.Keeper, blockHeight BlockHeight, weights map[uint64]*alloraMath.Dec, sumWeight alloraMath.Dec, totalRevenue cosmosMath.Int, ) error
func EnsureAllWorkersPresent ¶
func EnsureAllWorkersPresent( values []*types.WorkerAttributedValue, allWorkers map[string]struct{}, ) []*types.WorkerAttributedValue
ensureAllWorkersPresent checks and adds missing workers with NaN values for a given slice of WorkerAttributedValue
func EnsureAllWorkersPresentWithheld ¶
func EnsureAllWorkersPresentWithheld( values []*types.WithheldWorkerAttributedValue, allWorkers map[string]struct{}, ) []*types.WithheldWorkerAttributedValue
ensureAllWorkersPresentWithheld checks and adds missing workers with NaN values for a given slice of WithheldWorkerAttributedValue
func Entropy ¶
func Entropy( rewardFractionsPerActor []alloraMath.Dec, numberRatio alloraMath.Dec, numParticipants alloraMath.Dec, beta alloraMath.Dec, ) (alloraMath.Dec, error)
We define a modified entropy for each class ({F_i, G_i, H_i} for the inference, forecasting, and reputer tasks, respectively Fi = - ∑_j( f_ij * ln(f_ij) * (N_{i,eff} / N_i)^β ) Gi = - ∑_k( f_ik * ln(f_ik) * (N_{f,eff} / N_f)^β ) Hi = - ∑_m( f_im * ln(f_im) * (N_{r,eff} / N_r)^β ) we use beta = 0.25 as a fiducial value
func EntropyForSingleParticipant ¶
func EntropyForSingleParticipant() (alloraMath.Dec, error)
If there's only one worker, entropy should be the default number of 0.173286795139986
func ExtractValues ¶
func ExtractValues(bundle *types.ValueBundle) []alloraMath.Dec
ExtractValues extracts all alloraMath.Dec values from a ValueBundle.
func ForecastingPerformanceScore ¶
func ForecastingPerformanceScore( naiveNetworkInferenceLoss, networkInferenceLoss alloraMath.Dec, ) (alloraMath.Dec, error)
The performance score of the entire forecasting task T_i is positive if the removal of the forecasting task would increase the network loss, and is negative if its removal would decrease the network loss We subtract the log-loss of the complete network inference (L_i) from that of the naive network (L_i^-), which is obtained by omitting all forecast-implied inferences T_i = log L_i^- - log L_i However we store the log based forms in the keeper so we do not need to take the logs again
func ForecastingUtility ¶
func ForecastingUtility( forecastingTaskUtilityScore alloraMath.Dec, infererScores []types.Score, previousForecasterScoreRatio alloraMath.Dec, alpha alloraMath.Dec, ) (alloraMath.Dec, error)
Implements the utility function for forecasting performance score with the new specification: χ = 0.1 for score < 0, χ = 0.5 for score > 1, χ = 0.4 * score + 0.1 in between
func GenerateForecastScores ¶
func GenerateForecastScores( ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, networkLosses types.ValueBundle, ) ([]types.Score, error)
GenerateForecastScores calculates and persists scores for workers based on their forecast task performance.
func GenerateInferenceScores ¶
func GenerateInferenceScores( ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, networkLosses types.ValueBundle, ) ([]types.Score, error)
GenerateInferenceScores calculates and persists scores for workers based on their inference task performance.
func GenerateReputerScores ¶
func GenerateReputerScores( ctx sdk.Context, keeper keeper.Keeper, topicId uint64, block int64, reportedLosses types.ReputerValueBundles, ) ([]types.Score, error)
GenerateReputerScores calculates and persists scores for reputers based on their reported losses.
func GenerateRewardsDistributionByTopicParticipant ¶
func GenerateRewardsDistributionByTopicParticipant( ctx sdk.Context, k keeper.Keeper, topicId uint64, topicReward *alloraMath.Dec, blockHeight int64, moduleParams types.Params, ) ( totalRewardsDistribution []types.TaskReward, taskReputerReward alloraMath.Dec, err error, )
func GetAdjustedStake ¶
func GetAdjustedStake( stake alloraMath.Dec, allStakes []alloraMath.Dec, listeningCoefficient alloraMath.Dec, allListeningCoefficients []alloraMath.Dec, numReputers alloraMath.Dec, ) (alloraMath.Dec, error)
Adjusted stake for calculating consensus S hat ^S_im = min((N_r * a_im * S_im)/(Σ_m(a_im * S_im)), 1) INPUTS: This function expects that allStakes (S_im) and allListeningCoefficients are slices of the same length (a_im) and the index to each slice corresponds to the same reputer
func GetAllActiveEpochEndingTopics ¶ added in v0.3.0
func GetAllActiveEpochEndingTopics( ctx sdk.Context, k keeper.Keeper, block BlockHeight, topicPageLimit uint64, maxTopicPages uint64, ) []types.Topic
Get all active topics that have ended their epoch
func GetAllConsensusScores ¶
func GetAllConsensusScores( allLosses [][]alloraMath.Dec, stakes []alloraMath.Dec, allListeningCoefficients []alloraMath.Dec, numReputers int64, epsilonReputer alloraMath.Dec, epsilon alloraMath.Dec, ) ([]alloraMath.Dec, error)
GetAllConsensusScores calculates the proximity to consensus score for all reputers. calculates: T_i - stake weighted total consensus returns: T_im - reputer score (proximity to consensus)
func GetAllReputersOutput ¶
func GetAllReputersOutput( allLosses [][]alloraMath.Dec, stakes []alloraMath.Dec, initialCoefficients []alloraMath.Dec, numReputers int64, learningRate alloraMath.Dec, gradientDescentMaxIters uint64, epsilonReputer alloraMath.Dec, epsilon alloraMath.Dec, minStakeFraction alloraMath.Dec, maxGradientThreshold alloraMath.Dec, ) ([]alloraMath.Dec, []alloraMath.Dec, error)
GetAllReputersOutput calculates the final scores and adjusted listening coefficients for all reputers. This function iteratively adjusts the listening coefficients based on a gradient descent method to minimize the difference between each reputer's losses and the consensus losses, taking into account each reputer's stake. returns: T_im - reputer score (proximity to consensus) a_im - listening coefficients
func GetAndUpdateActiveTopicWeights ¶
func GetAndUpdateActiveTopicWeights( ctx sdk.Context, k keeper.Keeper, block BlockHeight, ) ( weights map[TopicId]*alloraMath.Dec, sumWeight alloraMath.Dec, totalRevenue cosmosMath.Int, err error, )
Iterates through every active topic, computes its target weight, then exponential moving average to get weight. Returns the total sum of weight, topic revenue, map of all of the weights by topic. Note that the outputted weights are not normalized => not dependent on pan-topic data.
func GetChiAndGamma ¶
func GetChiAndGamma( naiveNetworkInferenceLoss, networkInferenceLoss, entropyInference, entropyForecasting alloraMath.Dec, infererScores []types.Score, previousForecasterScoreRatio alloraMath.Dec, alpha alloraMath.Dec, ) (chi alloraMath.Dec, gamma alloraMath.Dec, err error)
helper function to get chi and gamma
func GetConsensusScore ¶
func GetConsensusScore( reputerLosses, consensusLosses, mostDistantValues []alloraMath.Dec, epsilonReputer alloraMath.Dec, epsilon alloraMath.Dec, ) (alloraMath.Dec, error)
GetConsensusScore calculates the proximity to consensus score for a reputer. T_im
func GetFinalWorkerScoreForecastTask ¶ added in v0.2.6
func GetFinalWorkerScoreForecastTask( scoreOneIn, scoreOneOut, fUniqueAgg alloraMath.Dec, ) (alloraMath.Dec, error)
GetFinalWorkerScoreForecastTask calculates the worker score in forecast task. T_ik
func GetForecastTaskEntropy ¶
func GetForecastTaskEntropy( ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, betaEntropy alloraMath.Dec, workers []string, workersFractions []alloraMath.Dec, ) ( entropy alloraMath.Dec, err error, )
func GetInferenceTaskEntropy ¶
func GetInferenceTaskEntropy( ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, betaEntropy alloraMath.Dec, workers []string, workersFractions []alloraMath.Dec, ) ( entropy alloraMath.Dec, err error, )
func GetMappingFunctionValues ¶
func GetMappingFunctionValues( latestWorkerScores []alloraMath.Dec, latestTimeStepsScores []alloraMath.Dec, pReward alloraMath.Dec, cReward alloraMath.Dec, epsilon alloraMath.Dec, ) ([]alloraMath.Dec, error)
Mapping function used by score fraction calculation M(T) = φ_p (T / (σ(T) + ɛ)) phi is the phi function sigma is NOT the sigma function but rather represents standard deviation
func GetReputerTaskEntropy ¶
func GetReputerTaskEntropy( ctx sdk.Context, k keeper.Keeper, topicId uint64, emaAlpha alloraMath.Dec, betaEntropy alloraMath.Dec, reputers []string, reputerFractions []alloraMath.Dec, ) ( entropy alloraMath.Dec, err error, )
func GetRewardForForecastingTaskInTopic ¶
func GetRewardForForecastingTaskInTopic( entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, entropyReputer alloraMath.Dec, totalReward *alloraMath.Dec, chi alloraMath.Dec, gamma alloraMath.Dec, ) (alloraMath.Dec, error)
forecaster rewards calculation V_i = (χ * γ * G_i * E_i) / (F_i + G_i + H_i)
func GetRewardForInferenceTaskInTopic ¶
func GetRewardForInferenceTaskInTopic( entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, entropyReputer alloraMath.Dec, totalReward *alloraMath.Dec, chi alloraMath.Dec, gamma alloraMath.Dec, ) (alloraMath.Dec, error)
inference rewards calculation U_i = ((1 - χ) * γ * F_i * E_i ) / (F_i + G_i + H_i)
func GetRewardForReputerFromTotalReward ¶
func GetRewardForReputerFromTotalReward( ctx sdk.Context, keeper keeper.Keeper, topicId uint64, reputerDelegatorRewards []types.TaskReward, ) ([]types.TaskReward, error)
Send total reward for delegator to PENDING_ACCOUNT and return remain reward for reputer
func GetRewardForReputerTaskInTopic ¶
func GetRewardForReputerTaskInTopic( entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, entropyReputer alloraMath.Dec, topicReward *alloraMath.Dec, ) (alloraMath.Dec, error)
Get the reward allocated to the reputing task in this topic, W_i W_i = (H_i * E_i) / (F_i + G_i + H_i)
func GetRewardPerReputer ¶
func GetRewardPerReputer( ctx sdk.Context, keeper keeper.Keeper, topicId uint64, totalReputerRewards alloraMath.Dec, reputerAddresses []string, reputersFractions []alloraMath.Dec, ) ([]types.TaskReward, error)
Get reward per reputer based on total reputer rewards and reputer fractions W_im = w_ij * W_i
func GetRewardPerWorker ¶
func GetRewardPerWorker( topicId uint64, taskRewardType types.TaskRewardType, totalRewards alloraMath.Dec, workerAddresses []string, workerFractions []alloraMath.Dec, ) ([]types.TaskReward, error)
GetRewardPerWorker calculates the reward for workers for forecast and inference tasks U_ij = u_ij * Ui, V_ik = v_ik * Vi
func GetScoreFractions ¶
func GetScoreFractions( latestWorkerScores []alloraMath.Dec, latestTimeStepsScores []alloraMath.Dec, pReward alloraMath.Dec, cReward alloraMath.Dec, epsilon alloraMath.Dec, ) ([]alloraMath.Dec, error)
RewardFractions without multiplication against total rewards are used to calculate entropy note the use of lowercase u as opposed to capital u_ij = M(Tij) / ∑_j M(T_ij) v_ik = M(Tik) / ∑_k M(T_ik)
func GetStakeWeightedLoss ¶
func GetStakeWeightedLoss(reputersStakes, reputersReportedLosses []alloraMath.Dec) (alloraMath.Dec, error)
GetStakeWeightedLoss calculates the stake-weighted average loss. Consider the losses and the stake of each reputer to calculate the stake-weighted loss. The stake weighted loss is used to calculate the network-wide losses. L_i / L_ij / L_ik / L^-_i / L^-_il / L^+_ik
func GetStakeWeightedLossMatrix ¶
func GetStakeWeightedLossMatrix( reputersAdjustedStakes []alloraMath.Dec, reputersReportedLosses [][]alloraMath.Dec, ) ([]alloraMath.Dec, []alloraMath.Dec, error)
GetStakeWeightedLossMatrix calculates the stake-weighted geometric mean of the losses to generate the consensus vector. L_i - consensus loss vector
func GetTopicReward ¶
func GetTopicReward( topicRewardFraction alloraMath.Dec, totalReward alloraMath.Dec, ) (alloraMath.Dec, error)
The amount of emission rewards to be distributed to a topic E_{t,i} = f_{t,i}*E_i f_{t,i} is the reward fraction for that topic E_i is the reward emission total for that epoch
func GetTopicRewardFraction ¶
func GetTopicRewardFraction( topicWeight *alloraMath.Dec, totalWeight alloraMath.Dec, ) (alloraMath.Dec, error)
The reward fraction for a topic normalize the topic reward weight f{t,i} = (1 - f_v) * (w_{t,i}) / (∑_t w_{t,i}) where f_v is a global parameter set that controls the fraction of total reward emissions for cosmos network validators we don't use f_v here, because by the time the emissions module runs the validator rewards have already been distributed to the fee_collector account (this is done in the mint and then distribution module) w_{t,i} is the weight of topic t and the sum is naturally the total of all the weights for all topics
func GetWorkersRewardFractions ¶ added in v0.2.6
func GetfUniqueAgg ¶
func GetfUniqueAgg(numForecasters alloraMath.Dec) (alloraMath.Dec, error)
GetfUniqueAgg calculates the unique value or impact of each forecaster. ƒ^+
func ModifiedRewardFractions ¶
func ModifiedRewardFractions(rewardFractions []alloraMath.Dec) ([]alloraMath.Dec, error)
f_ij, f_ik, and f_im are all reward fractions that require computing the ratio of one participant to all participants yes this is extremely simple math yes we write a separate function for it anyway. The compiler can inline it if necessary normalizeToArray = value / sum(allValues) this covers equations f_ij = (̃U_ij) / ∑_j(̃Uij) f_ik = (̃Vik) / ∑_k(̃Vik) f_im = (̃Wim) / ∑_m(̃Wim)
func NormalizationFactor ¶
func NormalizationFactor( entropyInference alloraMath.Dec, entropyForecasting alloraMath.Dec, forecastingUtility alloraMath.Dec, ) (alloraMath.Dec, error)
renormalize with a factor γ to ensure that the total reward allocated to workers (Ui + Vi) remains constant (otherwise, this would go at the expense of reputers) γ = (F_i + G_i) / ( (1 − χ)*F_i + χ*G_i)
func NumberRatio ¶
func NumberRatio(rewardFractions []alloraMath.Dec) (alloraMath.Dec, error)
The number ratio term captures the number of participants in the network to prevent sybil attacks in the rewards distribution This function captures N_{i,eff} = 1 / ∑_j( f_ij^2 ) N_{f,eff} = 1 / ∑_k( f_ik^2 ) N_{r,eff} = 1 / ∑_m( f_im^2 )
func PickChurnableActiveTopics ¶ added in v0.3.0
func PickChurnableActiveTopics( ctx sdk.Context, k keeper.Keeper, block BlockHeight, weights map[TopicId]*alloraMath.Dec, ) error
"Churn-ready topic" is active, has an epoch that ended, and is in top N by weights, has non-zero weight. We iterate through active topics, fetch their weight, skim the top N by weight (these are the churnable topics) then finally apply a function on each of these churnable topics.
func Sigmoid ¶
func Sigmoid(x alloraMath.Dec) (alloraMath.Dec, error)
sigmoid function σ(x) = 1/(1+e^{-x}) = e^x/(1+e^x)
Types ¶
type BlockHeight ¶
type BlockHeight = int64
type SortableTopicId ¶
type SortableTopicId struct { Value TopicId Weight *alloraMath.Dec Tiebreaker uint32 }
A structure to hold the original value and a random tiebreaker
type TopicId ¶
type TopicId = uint64
func SkimTopTopicsByWeightDesc ¶
func SkimTopTopicsByWeightDesc(ctx sdk.Context, weights map[TopicId]*alloraMath.Dec, N uint64, block BlockHeight) (map[TopicId]*alloraMath.Dec, []TopicId)
Returns a map of topicId to weights of the top N topics by weight in descending order It is assumed that topicIds is of a reasonable size, throttled by perhaps MaxTopicsPerBlock global param
func SortTopicsByWeightDescWithRandomTiebreaker ¶
func SortTopicsByWeightDescWithRandomTiebreaker(topicIds []TopicId, weights map[TopicId]*alloraMath.Dec, randSeed BlockHeight) []TopicId
Sorts the given slice of topics in descending order according to their corresponding return, using pseudorandom tiebreaker e.g. ([]uint64{1, 2, 3}, map[uint64]uint64{1: 2, 2: 2, 3: 3}, 0) -> [3, 1, 2] or [3, 2, 1]