func NewStat(data []float64) Stat {
return Stat{
Min: stats.StatsMin(data),
Max: stats.StatsMax(data),
Mean: stats.StatsMean(data),
StdDev: stats.StatsPopulationStandardDeviation(data),
Total: stats.StatsSum(data),
}
}
func Stats(H []Estudiante) {
in := make([]float64, len(H))
for i := 0; i < len(H); i++ {
in[i] = H[i].Renta + H[i].Beca
}
log.Println("StdDev", stats.StatsPopulationStandardDeviation(in))
log.Println("Mean", stats.StatsMean(in))
}
func (r *Report) DistributionScore() float64 {
memoryCounts := []float64{}
for _, instances := range r.InstancesByRep {
memoryCount := 0.0
for _, instance := range instances {
memoryCount += float64(instance.MemoryMB)
}
memoryCounts = append(memoryCounts, memoryCount)
}
return stats.StatsPopulationStandardDeviation(memoryCounts) / stats.StatsMean(memoryCounts)
}
func main() {
var header bool
flag.BoolVar(&header, "t", false, "Print text header")
flag.Parse()
file := ""
if len(flag.Args()) > 0 {
file = flag.Args()[0]
}
m, vars, err := ltspice.Raw(file, header)
if err != nil {
log.Println(err)
}
if m == nil {
log.Println("null matrix")
}
col := len(m)
row := len(m[0])
if header {
for j := 1; j < col; j++ {
mean := stats.StatsMean(m[j])
sdev := stats.StatsSampleStandardDeviation(m[j])
fmt.Printf("%-20s %30g %30g %30g\n", "'"+vars[j]+"'", mean, sdev, sdev/mean)
}
}
if header {
return
}
for i := 0; i < row; i++ {
for j := 0; j < col; j++ {
fmt.Printf("%g", m[j][i])
if j < col-1 {
fmt.Print(", ")
}
}
fmt.Println("")
}
}
func calculateTotalResults(results []*RunResults, totalTime time.Duration) *TotalResults {
totals := new(TotalResults)
totals.TotalRunTime = totalTime.Seconds()
msgTimeMeans := make([]float64, len(results))
msgsPerSecs := make([]float64, len(results))
runTimes := make([]float64, len(results))
bws := make([]float64, len(results))
totals.MsgTimeMin = results[0].MsgTimeMin
for i, res := range results {
totals.Successes += res.Successes
totals.Failures += res.Failures
totals.TotalMsgsPerSec += res.MsgsPerSec
if res.MsgTimeMin < totals.MsgTimeMin {
totals.MsgTimeMin = res.MsgTimeMin
}
if res.MsgTimeMax > totals.MsgTimeMax {
totals.MsgTimeMax = res.MsgTimeMax
}
msgTimeMeans[i] = res.MsgTimeMean
msgsPerSecs[i] = res.MsgsPerSec
runTimes[i] = res.RunTime
bws[i] = res.MsgsPerSec
}
totals.Ratio = float64(totals.Successes) / float64(totals.Successes+totals.Failures)
totals.AvgMsgsPerSec = stats.StatsMean(msgsPerSecs)
totals.AvgRunTime = stats.StatsMean(runTimes)
totals.MsgTimeMeanAvg = stats.StatsMean(msgTimeMeans)
totals.MsgTimeMeanStd = stats.StatsSampleStandardDeviation(msgTimeMeans)
return totals
}
func (r *Report) InitialDistributionScore() float64 {
memoryCounts := []float64{}
for _, instances := range r.InstancesByRep {
memoryCount := 0.0
for _, instance := range instances {
if !r.IsAuctionedInstance(instance) {
memoryCount += float64(instance.MemoryMB)
}
}
memoryCounts = append(memoryCounts, memoryCount)
}
if stats.StatsSum(memoryCounts) == 0 {
return 0
}
return stats.StatsPopulationStandardDeviation(memoryCounts) / stats.StatsMean(memoryCounts)
}
func (c *Client) Run(res chan *RunResults) {
newMsgs := make(chan *Message)
pubMsgs := make(chan *Message)
doneGen := make(chan bool)
donePub := make(chan bool)
runResults := new(RunResults)
started := time.Now()
// start generator
go c.genMessages(newMsgs, doneGen)
// start publisher
go c.pubMessages(newMsgs, pubMsgs, doneGen, donePub)
runResults.ID = c.ID
times := []float64{}
for {
select {
case m := <-pubMsgs:
if m.Error {
log.Printf("CLIENT %v ERROR publishing message: %v: at %v\n", c.ID, m.Topic, m.Sent.Unix())
runResults.Failures++
} else {
// log.Printf("Message published: %v: sent: %v delivered: %v flight time: %v\n", m.Topic, m.Sent, m.Delivered, m.Delivered.Sub(m.Sent))
runResults.Successes++
times = append(times, m.Delivered.Sub(m.Sent).Seconds()*1000) // in milliseconds
}
case <-donePub:
// calculate results
duration := time.Now().Sub(started)
runResults.MsgTimeMin = stats.StatsMin(times)
runResults.MsgTimeMax = stats.StatsMax(times)
runResults.MsgTimeMean = stats.StatsMean(times)
runResults.MsgTimeStd = stats.StatsSampleStandardDeviation(times)
runResults.RunTime = duration.Seconds()
runResults.MsgsPerSec = float64(runResults.Successes) / duration.Seconds()
// report results and exit
res <- runResults
return
}
}
}
// summarizeResults Summarizes results / generates descriptive statistics so we
// don't have to send tens of thousands of trials down to the client.
// Receiver: None
// Params: detailedData -- [][]simulationTimeStep)
// Returns: []summarizedTimeStep
func summarizeResults(detailedData [][]simulationTimeStep) []summarizedTimeStep {
numberOfTrials := len(detailedData)
numberOfPeriods := len(detailedData[0])
// Prep the slice
summarizedResults := make([]summarizedTimeStep, numberOfPeriods)
for period := 0; period < numberOfPeriods; period++ {
outOfMoneyOccurences := 0.0 // initialize value
dateInt := detailedData[0][period].dateInt // same in every trial
/* Transpose the arrays so that we have a list of asset/income/expense
results by period, instead of by trial. */
periodAssetResults := make([]float64, numberOfTrials)
periodIncomeResults := make([]float64, numberOfTrials)
periodExpensesResults := make([]float64, numberOfTrials)
for trialIndex, arrayOfTrialResults := range detailedData {
periodAssetResults[trialIndex] = arrayOfTrialResults[period].assets
periodIncomeResults[trialIndex] = arrayOfTrialResults[period].income
periodExpensesResults[trialIndex] = arrayOfTrialResults[period].expenses
if arrayOfTrialResults[period].assets < 0 {
outOfMoneyOccurences++
}
}
/* */
/* Generate descriptive statistics for each period */
outOfMoneyPercentage := outOfMoneyOccurences / float64(numberOfTrials)
assetsMean := goStats.StatsMean(periodAssetResults)
assetsStdDev := goStats.StatsSampleStandardDeviation(periodAssetResults)
assetsCIFactor := 1.96 * assetsStdDev / math.Pow(float64(numberOfTrials), 0.5)
incomeMean := goStats.StatsMean(periodIncomeResults)
incomeStdDev := goStats.StatsSampleStandardDeviation(periodIncomeResults)
incomeCIFactor := 1.96 * incomeStdDev / math.Pow(float64(numberOfTrials), 0.5)
expensesMean := goStats.StatsMean(periodExpensesResults)
expensesStdDev := goStats.StatsSampleStandardDeviation(periodExpensesResults)
expensesCIFactor := 1.96 * expensesStdDev / math.Pow(float64(numberOfTrials), 0.5)
/* */
// // Remove asset results where all dead? Old code -- would need fixing
// allDead := false
// for index, entry := range meanIncome {
// if entry == 0 || meanExpenses[index] == 0 {
// allDead = true
// }
// if allDead {
// meanAssets[index] = 0
// }
// }
summarizedResults[period] = summarizedTimeStep{
AssetsMean: assetsMean,
AssetsCILow: assetsMean - assetsCIFactor,
AssetsCIHigh: assetsMean + assetsCIFactor,
IncomeMean: incomeMean,
IncomeCILow: incomeMean - incomeCIFactor,
IncomeCIHigh: incomeMean + incomeCIFactor,
ExpensesMean: expensesMean,
ExpensesCILow: expensesMean - expensesCIFactor,
ExpensesCIHigh: expensesMean + expensesCIFactor,
OutOfMoneyPercentage: outOfMoneyPercentage,
DateInt: dateInt,
}
}
return summarizedResults
}