// Returns a random vector using the mean and sd vectors.
func RandomVector(mean, sd []float64, r *rand.Rand) []float64 {
nrows := len(mean)
if !floats.EqualLengths(mean, sd) {
panic(floatx.ErrLength)
}
vec := make([]float64, nrows)
for i := 0; i < nrows; i++ {
v := r.NormFloat64()*sd[i] + mean[i]
vec[i] = v
}
return vec
}
// RandomModel generates a random Gaussian mixture model using mean and variance vectors as seed.
// Use this function to initialize the GMM before training. The mean and sd
// vector can be estimated from the data set using a Gaussian model.
func RandomModel(mean, sd []float64, numComponents int,
name string, seed int64) *Model {
n := len(mean)
if !floats.EqualLengths(mean, sd) {
panic(floatx.ErrLength)
}
cs := make([]*gaussian.Model, n, n)
r := rand.New(rand.NewSource(seed))
for i := 0; i < n; i++ {
rv := RandomVector(mean, sd, r)
cs[i] = gaussian.NewModel(n, gaussian.Mean(rv), gaussian.StdDev(sd))
}
gmm := NewModel(n, numComponents, Name(name), Components(cs))
return gmm
}
