()
| 7 | ) |
| 8 | |
| 9 | func main() { |
| 10 | |
| 11 | // d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"}) |
| 12 | d := stats.LoadRawData([]int{1, 2, 3, 4, 5}) |
| 13 | |
| 14 | a, _ := stats.Min(d) |
| 15 | fmt.Println(a) |
| 16 | // Output: 1.1 |
| 17 | |
| 18 | a, _ = stats.Max(d) |
| 19 | fmt.Println(a) |
| 20 | // Output: 5 |
| 21 | |
| 22 | a, _ = stats.Sum([]float64{1.1, 2.2, 3.3}) |
| 23 | fmt.Println(a) |
| 24 | // Output: 6.6 |
| 25 | |
| 26 | cs, _ := stats.CumulativeSum([]float64{1.1, 2.2, 3.3}) |
| 27 | fmt.Println(cs) // [1.1 3.3000000000000003 6.6] |
| 28 | |
| 29 | a, _ = stats.Mean([]float64{1, 2, 3, 4, 5}) |
| 30 | fmt.Println(a) |
| 31 | // Output: 3 |
| 32 | |
| 33 | a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7}) |
| 34 | fmt.Println(a) |
| 35 | // Output: 4 |
| 36 | |
| 37 | m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1}) |
| 38 | fmt.Println(m) |
| 39 | // Output: [5 3] |
| 40 | |
| 41 | a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5}) |
| 42 | fmt.Println(a) |
| 43 | // Output: 2 |
| 44 | |
| 45 | a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5}) |
| 46 | fmt.Println(a) |
| 47 | // Output: 2.5 |
| 48 | |
| 49 | a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3}) |
| 50 | fmt.Println(a) |
| 51 | // Output: 1 |
| 52 | |
| 53 | a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3}) |
| 54 | fmt.Println(a) |
| 55 | // Output: 0.816496580927726 |
| 56 | |
| 57 | a, _ = stats.StandardDeviationSample([]float64{1, 2, 3}) |
| 58 | fmt.Println(a) |
| 59 | // Output: 1 |
| 60 | |
| 61 | a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75) |
| 62 | fmt.Println(a) |
| 63 | // Output: 4 |
| 64 | |
| 65 | a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75) |
| 66 | fmt.Println(a) |
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