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()
}
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()
}