Example about how to query the model after being trained using KNN ?

[ch-rigu]

Hi!
Please cold you give an example about how to query this model after being trained ?
like when the testData is pased here: cls.Predict(testData)
it is possible do to something like this :

csv example:
Sepal length, Sepal width,Petal length, Petal width, Species
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor

input := '5.1,3.5,1.4,0.2'
predictions, err := cls.Predict(input)
if err != nil {
panic(err)
}
and print/get the species type ?

I managed to this in python but and quite lost about how to do it in Go

Cheers!
Chris.

[Yushgoel]

@ch-rigu Going through the code, the cls.Predict() function requires a FixedDataGrid as input. So you will need to convert input to a Fixed Data Grid format to pass it into Predict()

[alexanderjophus]

I'm also trying to solve this problem. After converting the user input to a FixedDataGrid the CategoricalAttribute is missing from the input data, when using Predict(), it complains attributes not compatible.

As far as I understand it, I do not want to pass in the categorical info from my user input, I want that to be returned from the Predict() function

	cls, err := knn.ReloadKNNClassifier("model.cls")
	if err != nil {
		log.Fatal(fmt.Errorf("loading model: %w", err))
	}
	toPredict := strings.NewReader("5.1,3.5,1.4,0.2")

	rawData, err := base.ParseCSVToInstancesFromReader(toPredict, false)
	if err != nil {
		log.Fatal(fmt.Errorf("parse reader: %w", err))
	}

	fmt.Println(cls.TrainingData.AllAttributes())
	fmt.Println(rawData.AllAttributes())
	predictions, err := cls.Predict(rawData)
	if err != nil {
		log.Fatal(fmt.Errorf("predicting: %w", err))
	}

The output from the 2 print statements is;

[FloatAttribute(0) FloatAttribute(1) FloatAttribute(2) FloatAttribute(3) CategoricalAttribute("4", [Iris-setosa Iris-versicolor Iris-virginica])]
[FloatAttribute(0) FloatAttribute(1) FloatAttribute(2) FloatAttribute(3)]

Am I doing something wrong in the model?

[ymmemo]

I had the same question. Is there a solution to the problem?

[riverlaker]

邮件已收到，谢谢！                  ——赵淼