Linear Regression Library for Go

Question

I am looking for a Go library that implements linear regression using MLE or LSE. Has anyone seen him?

There is this statistics library, but it doesn't seem to have what I need: https://github.com/grd/statistics

Thanks!

Answer 1

I applied the following using gradient descent, it gives only coefficients, but takes any number of explanatory variables and is fairly accurate:

package main import "fmt" func calc_ols_params(y []float64, x[][]float64, n_iterations int, alpha float64) []float64 { thetas := make([]float64, len(x)) for i := 0; i < n_iterations; i++ { my_diffs := calc_diff(thetas, y, x) my_grad := calc_gradient(my_diffs, x) for j := 0; j < len(my_grad); j++ { thetas[j] += alpha * my_grad[j] } } return thetas } func calc_diff (thetas []float64, y []float64, x[][]float64) []float64 { diffs := make([]float64, len(y)) for i := 0; i < len(y); i++ { prediction := 0.0 for j := 0; j < len(thetas); j++ { prediction += thetas[j] * x[j][i] } diffs[i] = y[i] - prediction } return diffs } func calc_gradient(diffs[] float64, x[][]float64) []float64 { gradient := make([]float64, len(x)) for i := 0; i < len(diffs); i++ { for j := 0; j < len(x); j++ { gradient[j] += diffs[i] * x[j][i] } } for i := 0; i < len(x); i++ { gradient[i] = gradient[i] / float64(len(diffs)) } return gradient } func main(){ y := []float64 {3,4,5,6,7} x := [][]float64 {{1,1,1,1,1}, {4,3,2,1,3}} thetas := calc_ols_params(y, x, 100000, 0.001) fmt.Println("Thetas : ", thetas) y_2 := []float64 {1,2,3,4,3,4,5,4,5,5,4,5,4,5,4,5,6,5,4,5,4,3,4} x_2 := [][]float64 {{1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1}, {4,2,3,4,5,4,5,6,7,4,8,9,8,8,6,6,5,5,5,5,5,5,5}, {4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,7,7,7,7,7,6,5}, {4,1,2,5,6,7,8,9,7,8,7,8,7,7,7,7,7,7,6,6,4,4,4},} thetas_2 := calc_ols_params(y_2, x_2, 100000, 0.001) fmt.Println("Thetas_2 : ", thetas_2) } 

Result:

 Thetas : [6.999959251448524 -0.769216974483968] Thetas_2 : [1.5694174539341945 -0.06169183063112409 0.2359981255871977 0.2424327101610395] 

go playground

I checked the results with python.pandas and they were very close:

 In [24]: from pandas.stats.api import ols In [25]: df = pd.DataFrame(np.array(x).T, columns=['x1','x2','x3','y']) In [26]: from pandas.stats.api import ols In [27]: x = [ [4,2,3,4,5,4,5,6,7,4,8,9,8,8,6,6,5,5,5,5,5,5,5], [4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,7,7,7,7,7,6,5], [4,1,2,5,6,7,8,9,7,8,7,8,7,7,7,7,7,7,6,6,4,4,4] ] In [28]: y = [1,2,3,4,3,4,5,4,5,5,4,5,4,5,4,5,6,5,4,5,4,3,4] In [29]: x.append(y) In [30]: df = pd.DataFrame(np.array(x).T, columns=['x1','x2','x3','y']) In [31]: ols(y=df['y'], x=df[['x1', 'x2', 'x3']]) Out[31]: -------------------------Summary of Regression Analysis------------------------- Formula: Y ~ <x1> + <x2> + <x3> + <intercept> Number of Observations: 23 Number of Degrees of Freedom: 4 R-squared: 0.5348 Adj R-squared: 0.4614 Rmse: 0.8254 F-stat (3, 19): 7.2813, p-value: 0.0019 Degrees of Freedom: model 3, resid 19 -----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- x1 -0.0618 0.1446 -0.43 0.6741 -0.3453 0.2217 x2 0.2360 0.1487 1.59 0.1290 -0.0554 0.5274 x3 0.2424 0.1394 1.74 0.0983 -0.0309 0.5156 intercept 1.5704 0.6331 2.48 0.0226 0.3296 2.8113 ---------------------------------End of Summary--------------------------------- 

and

 In [34]: df_1 = pd.DataFrame(np.array([[3,4,5,6,7], [4,3,2,1,3]]).T, columns=['y', 'x']) In [35]: df_1 Out[35]: yx 0 3 4 1 4 3 2 5 2 3 6 1 4 7 3 [5 rows x 2 columns] In [36]: ols(y=df_1['y'], x=df_1['x']) Out[36]: -------------------------Summary of Regression Analysis------------------------- Formula: Y ~ <x> + <intercept> Number of Observations: 5 Number of Degrees of Freedom: 2 R-squared: 0.3077 Adj R-squared: 0.0769 Rmse: 1.5191 F-stat (1, 3): 1.3333, p-value: 0.3318 Degrees of Freedom: model 1, resid 3 -----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- x -0.7692 0.6662 -1.15 0.3318 -2.0749 0.5365 intercept 7.0000 1.8605 3.76 0.0328 3.3534 10.6466 ---------------------------------End of Summary--------------------------------- In [37]: df_1 = pd.DataFrame(np.array([[3,4,5,6,7], [4,3,2,1,3]]).T, columns=['y', 'x']) In [38]: ols(y=df_1['y'], x=df_1['x']) Out[38]: -------------------------Summary of Regression Analysis------------------------- Formula: Y ~ <x> + <intercept> Number of Observations: 5 Number of Degrees of Freedom: 2 R-squared: 0.3077 Adj R-squared: 0.0769 Rmse: 1.5191 F-stat (1, 3): 1.3333, p-value: 0.3318 Degrees of Freedom: model 1, resid 3 -----------------------Summary of Estimated Coefficients------------------------ Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5% -------------------------------------------------------------------------------- x -0.7692 0.6662 -1.15 0.3318 -2.0749 0.5365 intercept 7.0000 1.8605 3.76 0.0328 3.3534 10.6466 ---------------------------------End of Summary--------------------------------- 

Answer 2

The implementation of LSE (least square error) linear regression is quite simple.

Here's the implementation in JavaScript - this should be trivial for the port for Go.

---

Here is the (untested) port:

 package main import "fmt" type Point struct { X float64 Y float64 } func linearRegressionLSE(series []Point) []Point { q := len(series) if q == 0 { return make([]Point, 0, 0) } p := float64(q) sum_x, sum_y, sum_xx, sum_xy := 0.0, 0.0, 0.0, 0.0 for _, p := range series { sum_x += pX sum_y += pY sum_xx += pX * pX sum_xy += pX * pY } m := (p*sum_xy - sum_x*sum_y) / (p*sum_xx - sum_x*sum_x) b := (sum_y / p) - (m * sum_x / p) r := make([]Point, q, q) for i, p := range series { r[i] = Point{pX, (pX*m + b)} } return r } func main() { // ... } 

Answer 3

There is a project called gostat , which has a bike package that should be able to perform linear regressions.

Unfortunately, the documentation is somewhat lacking, so you may have to read the code to find out how to use it. I worked a little with him, but did not touch the package of bays.

Answer 4

My port Gentleman AS75 (online) linear regression algorithm is written in Go (golang). It does the usual regression of OLS Orlyinary Lessest Squares. The online part means that it can process unlimited data rows: if you use (nxp) to create the design matrix, this is a little different: you call Includ () n times (or more if you get more data), giving it a vector of p values time. This effectively handles the case when n grows, and you may have to transfer data from the disk because it will not fit into memory.

https://github.com/glycerine/zettalm