Adding dep files and dependencies.

vendor/github.com/montanaflynn/stats/variance.go

@@ -0,0 +1,105 @@
+package stats
+
+import "math"
+
+// _variance finds the variance for both population and sample data
+func _variance(input Float64Data, sample int) (variance float64, err error) {
+
+	if input.Len() == 0 {
+		return math.NaN(), EmptyInput
+	}
+
+	// Sum the square of the mean subtracted from each number
+	m, _ := Mean(input)
+
+	for _, n := range input {
+		variance += (float64(n) - m) * (float64(n) - m)
+	}
+
+	// When getting the mean of the squared differences
+	// "sample" will allow us to know if it's a sample
+	// or population and wether to subtract by one or not
+	return variance / float64((input.Len() - (1 * sample))), nil
+}
+
+// Variance the amount of variation in the dataset
+func Variance(input Float64Data) (sdev float64, err error) {
+	return PopulationVariance(input)
+}
+
+// PopulationVariance finds the amount of variance within a population
+func PopulationVariance(input Float64Data) (pvar float64, err error) {
+
+	v, err := _variance(input, 0)
+	if err != nil {
+		return math.NaN(), err
+	}
+
+	return v, nil
+}
+
+// SampleVariance finds the amount of variance within a sample
+func SampleVariance(input Float64Data) (svar float64, err error) {
+
+	v, err := _variance(input, 1)
+	if err != nil {
+		return math.NaN(), err
+	}
+
+	return v, nil
+}
+
+// Covariance is a measure of how much two sets of data change
+func Covariance(data1, data2 Float64Data) (float64, error) {
+
+	l1 := data1.Len()
+	l2 := data2.Len()
+
+	if l1 == 0 || l2 == 0 {
+		return math.NaN(), EmptyInput
+	}
+
+	if l1 != l2 {
+		return math.NaN(), SizeErr
+	}
+
+	m1, _ := Mean(data1)
+	m2, _ := Mean(data2)
+
+	// Calculate sum of squares
+	var ss float64
+	for i := 0; i < l1; i++ {
+		delta1 := (data1.Get(i) - m1)
+		delta2 := (data2.Get(i) - m2)
+		ss += (delta1*delta2 - ss) / float64(i+1)
+	}
+
+	return ss * float64(l1) / float64(l1-1), nil
+}
+
+// CovariancePopulation computes covariance for entire population between two variables.
+func CovariancePopulation(data1, data2 Float64Data) (float64, error) {
+
+	l1 := data1.Len()
+	l2 := data2.Len()
+
+	if l1 == 0 || l2 == 0 {
+		return math.NaN(), EmptyInput
+	}
+
+	if l1 != l2 {
+		return math.NaN(), SizeErr
+	}
+
+	m1, _ := Mean(data1)
+	m2, _ := Mean(data2)
+
+	var s float64
+	for i := 0; i < l1; i++ {
+		delta1 := (data1.Get(i) - m1)
+		delta2 := (data2.Get(i) - m2)
+		s += delta1 * delta2
+	}
+
+	return s / float64(l1), nil
+}