func Correlation(data1 interface{}, stride1 int, data2 interface{}, stride2 int, n int) (r float64) {
var ratio, delta_x, delta_y, sum_xsq, sum_ysq, sum_cross, mean_x, mean_y float64
arry1 := reflect.ValueOf(data1)
arry2 := reflect.ValueOf(data2)
/*
Compute:
sum_xsq = Sum [ (x_i - mu_x)^2 ],
sum_ysq = Sum [ (y_i - mu_y)^2 ] and
sum_cross = Sum [ (x_i - mu_x) * (y_i - mu_y) ]
using the above relation from Welford's paper
*/
mean_x = number.Float(arry1.Index(0 * stride1))
mean_y = number.Float(arry2.Index(0 * stride2))
for i := 0; i < n; i++ {
v1 := number.Float(arry1.Index(i * stride1))
v2 := number.Float(arry2.Index(i * stride2))
ratio = float64(i) / float64(i+1)
delta_x = v1 - mean_x
delta_y = v2 - mean_y
sum_xsq += delta_x * delta_x * ratio
sum_ysq += delta_y * delta_y * ratio
sum_cross += delta_x * delta_y * ratio
mean_x += delta_x / float64(i+1)
mean_y += delta_y / float64(i+1)
}
r = sum_cross / (math.Sqrt(sum_xsq) * math.Sqrt(sum_ysq))
return
}
func computeCovariance(data1 interface{}, stride1 int, data2 interface{}, stride2 int, n int, mean1, mean2 float64) (covariance float64) {
arry1 := reflect.ValueOf(data1)
arry2 := reflect.ValueOf(data2)
// find the sum of the square
for i := 0; i < n; i++ {
v1 := number.Float(arry1.Index(i * stride1))
v2 := number.Float(arry2.Index(i * stride2))
delta1 := v1 - mean1
delta2 := v2 - mean2
covariance += (delta1*delta2 - covariance) / float64(i+1)
}
return
}
func Mean(data interface{}, stride, n int) (mean float64) {
arry := reflect.ValueOf(data)
for i := 0; i < n; i++ {
v := arry.Index(i * stride)
fv := number.Float(v)
mean += (fv - mean) / float64(i+1)
}
return
}
// Take a dataset and finds the variance
func computeVariance(data interface{}, stride, n int, mean float64) (variance float64) {
arry := reflect.ValueOf(data)
// find the sum of the squares
for i := 0; i < n; i++ {
v := arry.Index(i * stride)
f := number.Float(v)
delta := f - mean
variance += (delta*delta - variance) / float64(i+1)
}
return
}
// Takes a dataset and finds the sum of squares about the mean
func computeTss(data interface{}, stride, n int, mean float64) (tss float64) {
arry := reflect.ValueOf(data)
// find the sum of the squares
for i := 0; i < n; i++ {
v := arry.Index(i * stride)
f := number.Float(v)
delta := f - mean
tss += delta * delta
}
return
}
// Takes a dataset and finds the absolute deviation with a fixed mean
func AbsdevM(data interface{}, stride, n int, mean float64) (absdev float64) {
arry := reflect.ValueOf(data)
sum := 0.0
for i := 0; i < n; i++ {
v := arry.Index(i * stride)
f := number.Float(v)
delta := math.Abs(f - mean)
sum += delta
}
absdev = sum / float64(n)
return
}
