func incrementalDemo() {
var d stats.Stats
fmt.Printf("\n**** Descriptive Statistics, Incremental Updates **\n")
fmt.Printf("** create a new descriptive stats struct:\n")
fmt.Printf("var d stats.Stats\n")
fmt.Printf("** update it with new values:\n")
for i := 0; i < NUM_SAMPLES; i++ {
x := rand.Float64()*100.0 - 25.0 // uniform samples in {-25, 75}
fmt.Printf("d.Update(%v)\n", x)
d.Update(x)
}
fmt.Printf("then request stats, for example: d.Count()\n")
fmt.Printf("\n** Descriptive Statistics:\n")
fmt.Printf("count = %v\n", d.Count())
fmt.Printf("min = %v\n", d.Min())
fmt.Printf("max = %v\n", d.Max())
fmt.Printf("sum = %v\n", d.Sum())
fmt.Printf("mean = %v\n", d.Mean())
fmt.Printf("standard deviation = %v\n", d.SampleStandardDeviation())
fmt.Printf("variance = %v\n", d.SampleVariance())
skew := d.SampleSkew()
fmt.Printf("skew = %v\n", skew)
//
// The rand functions return uniformly distributed values. Therefore, with
// enough samples, the skew should be 0. Try increasing NUM_SAMPLES
if skew < 0.0 {
fmt.Printf(" The sample is skews left (longer tail to left).\n")
} else if skew > 0.0 {
fmt.Printf(" The sample is skews right (longer tail to right).\n")
}
kurtosis := d.SampleKurtosis()
fmt.Printf("kurtosis = %v\n", kurtosis)
//
// The rand functions return uniformly distributed values. Therefore, with
// enough of them, our sample should be flat, or platykurtic.
if kurtosis < 0.0 {
fmt.Printf(" The sample is platykurtic.\n")
} else if kurtosis > 0.0 {
fmt.Printf(" The sample is leptokurtic.\n")
}
}
func normalDistributionDemo() {
maxTrials := 100
printEvery := 10
fmt.Printf("\nGenerating %v normal samples. The descriptive statistics ", maxTrials)
fmt.Printf("converge to the appropriate values: mean→0, variance→1, skew→0, kurtosis→0. \n")
var d stats.Stats
for i := 0; i <= maxTrials; i++ {
y := rand.NormFloat64()
d.Update(y)
if i != 0 && i%printEvery == 0 {
mean := d.Mean()
variance := d.PopulationVariance()
skew := d.PopulationSkew()
kurtosis := d.PopulationKurtosis()
fmt.Printf("itr %v: mean (→0.0) = %0.5f, variance (→1.0) = %0.5f, skew (→0.0) = %0.5f, kurtosis (→0.0) = %0.5f\n",
i, mean, variance, skew, kurtosis)
}
}
}