plot

Go simple-to-use library for plotting and interpreting data, including the Gaussian distribution and Taylor series approximations as well as statistical computations of datasets, vector and matrix visualizations and more.

The plot package is easy to use and it offers a vast ammount of data methods. You can upload your own dataset to plot or use a dataset with the correspondant output. Looking forward to get some feedback!

Let's take a look at some functionality!

Installation

Pretty straightforward.

go get github.com/timothy102/plot

Import the library in your Go file.

import "github.com/timothy102/plot"

Usage

Let's look at a dataset using the cosine function. Second and third argument to the function invoke the starting and end point of iteration. You can adjust the iterations parameter to get a more dense distribution.

points := plot.DefineDataset(math.Cos, -10.0, 10.0, 500)
plot.PlotPoints(points, "Cosine.png", false)

That is how simple it is. If you would like to import your dataset externally, use the ReadFromDatafile function. The rest is the same.

points,err:=plot.ReadFromDatafile(filepath)
//error handling
if err:=PlotPoints(points,"Graph.png",false);err!=nil{
  log.Fatalf("could not plot data :%v",err)
}

Taylor series approximation for the Tan function and its error with the real deal. The last argument indicates the polynomial degree to which the function approximates.

points := plot.DefineDatasetWithPolynomial(TanEstimate, -2.0, 2.0, 250, 15)
e := EstimationError(math.Tan, points)
fmt.Printf("%.9f\n", e)

And the error is:

0.000000059

Taylor series approximation of the 15th degree polynomial of Sine and the difference between Sine.

points := plot.DefineDatasetWithPolynomial(plot.SinusEstimate, -5.0, 5.0, 250, 14)
plot.PlotPoints(points, "SinusEstimate.png", false)

p2 := plot.DefineDataset(math.Sin, -5.0, 5.0, 250)
plot.PlotPoints(p2, "Sinus.png", false)
	
e := plot.ErrorBetweenPoints(points, p2)
fmt.Printf("%.7f", e)

With an average error of:

0.0000085

Let's take a look at creating a random dataset and then normalizing it so the data is centered and it has the standard deviation of 1 The DefineRandomPoints creates a 1000 points between 0 and 10.

pts := plot.DefineRandomPoints(1000, 0.0, 10.0)
pts = plot.Normalize(pts)
plot.PlotPoints(pts, "RandomPoints.png", true)

And finally, the Gaussian distribution. First parameter is the mean value, the second is the standard deviation and the third is the number of iterations. Adjust these parameters for experimentation.

PlotGaussian(4.2,1.2,1000)

Contact

Please, feel free to reach out on LinkedIn, gmail. For more, check my medium article.

https://towardsdatascience.com/golang-as-the-new-machine-learning-powerforce-e1b74b10b83b

https://www.linkedin.com/in/tim-cvetko-32842a1a6/

License

Licensed under the MIT LICENSE