func convertAndUpdateStat(value string, nodeStat *stats.Stats) {
runValue, err := strconv.ParseFloat(value, 64)
if err != nil {
fmt.Println(err)
return
}
nodeStat.Update(runValue)
}
func (c ping_collector) CollectOnce() newcore.CollectResult {
var (
md newcore.MultiDataPoint
d stats.Stats
p = fastping.NewPinger()
rtt_chan = make(chan float64)
)
ip, err := net.ResolveIPAddr("ip4:icmp", c.config.Target)
if err != nil {
logging.Errorf("ping_collector: DNS resolve error: %v", err)
return newcore.CollectResult{
Collected: nil,
Next: time.Now().Add(c.interval),
Err: fmt.Errorf("ping_collector: DNS resolve error: %v", err),
}
}
p.MaxRTT = c.timeout
p.AddIPAddr(ip)
p.OnRecv = func(addr *net.IPAddr, rtt time.Duration) {
rtt_chan <- float64(rtt.Nanoseconds() / 1000 / 1000)
}
go func() {
for i := 0; i < c.config.Packets; i++ {
err = p.Run()
if err != nil {
logging.Errorf("ping_collector run err: ", err)
}
}
close(rtt_chan)
}()
for rtt := range rtt_chan {
d.Update(rtt)
}
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "time_min"), d.Min(), c.tags, "", "", ""))
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "time_max"), d.Max(), c.tags, "", "", ""))
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "time_avg"), d.Mean(), c.tags, "", "", ""))
std := d.SampleStandardDeviation()
if math.IsNaN(std) {
std = 0
}
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "time_mdev"), std, c.tags, "", "", ""))
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "ip"), ip.IP.String(), c.tags, "", "", ""))
lost_pct := float64((c.config.Packets-d.Count())/c.config.Packets) * 100
md = append(md, newcore.NewDP(c.prefix, fmt.Sprintf("%s.%s", c.config.Metric, "lost_pct"), lost_pct, c.tags, "", "", ""))
return newcore.CollectResult{
Collected: md,
Next: time.Now().Add(c.interval),
Err: nil,
}
}
func testUnpred(r io.Reader) (float64, float64) {
dict := make(map[int16]bool)
buff := make([]byte, 2)
count := 0
var prob stats.Stats
for i := 0; i < TestingRounds; i++ {
r.Read(buff)
val := int16(buff[0]) + int16(buff[1])*256
if _, ok := dict[val]; ok {
count++
} else {
dict[val] = true
}
prob.Update(float64(buff[0]))
prob.Update(float64(buff[1]))
}
return float64(count) / float64(TestingRounds),
prob.PopulationStandardDeviation()
}
func handleExecuteQuery(rw http.ResponseWriter, req *http.Request) {
startTime := time.Now()
db, err := sql.Open("sqlite3", *dataSrc)
if err != nil {
http.Error(rw, err.Error(), http.StatusInternalServerError)
return
}
defer db.Close()
var (
request struct {
Features map[string]float64 `json:"features"`
Geo *geoData `json:"geo"`
MaxResults int `json:"maxResults"`
MinScore float64 `json:"minScore"`
Modes map[string]string `json:"modes"`
Profile map[string]float64 `json:"profile"`
Resolution int `json:"resolution"`
SortAsc bool `json:"sortAsc"`
SortKey string `json:"sortKey"`
WalkingDist float64 `json:"walkingDist"`
}
response struct {
Columns map[string]*column `json:"columns"`
Count int `json:"count"`
MinScore float64 `json:"minScore"`
Records []record `json:"records"`
ElapsedTime int64 `json:"elapsedTime"`
}
)
if err := json.NewDecoder(req.Body).Decode(&request); err != nil {
http.Error(rw, err.Error(), http.StatusInternalServerError)
return
}
var geo *geoData
if request.Geo != nil {
geo = &geoData{request.Geo.Latitude, request.Geo.Longitude}
}
allEntries, err := fetchRecords(db, queryContext{geo, request.Profile, request.WalkingDist})
if err != nil {
http.Error(rw, err.Error(), http.StatusInternalServerError)
return
}
features := fixFeatures(request.Features)
modes := fixModes(request.Modes)
matchedEntries := findRecords(allEntries, features, modes, request.MinScore)
sorter := recordSorter{entries: matchedEntries, key: request.SortKey, ascending: request.SortAsc}
sorter.sort()
var wg sync.WaitGroup
wg.Add(len(features))
response.Columns = make(map[string]*column)
for name := range features {
response.Columns[name] = new(column)
go func(name string) {
defer wg.Done()
col := response.Columns[name]
col.Bracket = bracket{Max: -1.0, Min: 1.0}
col.Hints = project(allEntries, features, modes, name, request.MinScore, request.Resolution)
col.Mode = modes[name].String()
col.Steps = request.Resolution
col.Value = features[name]
var d stats.Stats
for _, record := range matchedEntries {
if feature, ok := record.features[name]; ok {
d.Update(feature)
}
}
if d.Count() > 0 {
var dev float64
if d.Count() > 1 {
dev = d.SampleStandardDeviation() * 3
}
mean := d.Mean()
col.Bracket.Max = math.Min(mean+dev, d.Max())
col.Bracket.Min = math.Max(mean-dev, d.Min())
}
}(name)
}
wg.Wait()
response.Count = len(matchedEntries)
response.MinScore = request.MinScore
response.ElapsedTime = time.Since(startTime).Nanoseconds()
if len(matchedEntries) > request.MaxResults {
response.Records = matchedEntries[:request.MaxResults]
} else {
response.Records = matchedEntries
}
js, err := json.Marshal(response)
if err != nil {
http.Error(rw, err.Error(), http.StatusInternalServerError)
return
}
rw.Header().Set("Content-Type", "application/json")
rw.Write(js)
}
func randmatstat(t int) (float64, float64) {
n := 5
var v stats.Stats
var w stats.Stats
for i := 0; i < t; i++ {
a := matrix.Zeros(n, n)
b := matrix.Zeros(n, n)
c := matrix.Zeros(n, n)
d := matrix.Zeros(n, n)
for j := 0; j < n; j++ {
for k := 0; k < n; k++ {
a.Set(j, k, rand.NormFloat64())
b.Set(j, k, rand.NormFloat64())
c.Set(j, k, rand.NormFloat64())
d.Set(j, k, rand.NormFloat64())
}
}
P := matrix.Zeros(n, 4*n)
for j := 0; j < n; j++ {
for k := 0; k < n; k++ {
P.Set(j, k, a.Get(j, k))
P.Set(j, n+k, b.Get(j, k))
P.Set(j, 2*n+k, c.Get(j, k))
P.Set(j, 3*n+k, d.Get(j, k))
}
}
Q := matrix.Zeros(2*n, 2*n)
for j := 0; j < n; j++ {
for k := 0; k < n; k++ {
Q.Set(j, k, a.Get(j, k))
Q.Set(j, n+k, b.Get(j, k))
Q.Set(n+j, k, c.Get(j, k))
Q.Set(n+j, n+k, d.Get(j, k))
}
}
P = matrix.Product(matrix.Transpose(P), P)
P = matrix.Product(P, P)
P = matrix.Product(P, P)
Q = matrix.Product(matrix.Transpose(Q), Q)
Q = matrix.Product(Q, Q)
Q = matrix.Product(Q, Q)
v.Update(P.Trace())
w.Update(Q.Trace())
}
return v.PopulationStandardDeviation() / float64(v.Count()) / v.Mean(), w.PopulationStandardDeviation() / float64(w.Count()) / w.Mean()
}