func main() {
m1 := matrix.MakeDenseMatrix([]float64{1, 2, 3, 4, 5, 6}, 3, 2)
fmt.Println(m1)
fmt.Println(matrix.Transpose(m1))
res, _ := http.Get("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv")
defer res.Body.Close()
reader := csv.NewReader(res.Body)
reader.Comma = ';'
//contents, _ := ioutil.ReadAll(res.Body)
testData := make([][]float64, 0)
for {
col, err := reader.Read()
if err == io.EOF {
break
}
//strings.Split(col, ";")
x := make([]float64, len(col))
for i := range col {
x[i], _ = strconv.ParseFloat(col[i], 64)
}
testData = append(testData, x)
}
ae := NewAutoEncoder()
ae.Train(testData[1])
}
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()
}
