How to predict class of a instance in Waffles c++ API - c++

Question

my name is titiri and happy that I found waffle library to classification. I think waffle is a good library for machine learning algorithms.
I have a question about waffle library.
After training a model, I want print a prediction, for a instance:
my code is:
GMatrix Instance(1,8);//instance have 8 real attribute and
double out;// value in attribute 'class' is nomial
Instance[0][0]=6;
Instance[0][1]=148;
Instance[0][2]=72;
Instance[0][3]=35;
Instance[0][4]=0;
Instance[0][5]=33.6;
Instance[0][6]=0.62;
Instance[0][7]=50;
modell->predict(Instance[0],&out);
cout<<&out;
This code do not work true and does not print anything.
Please help me!
What do I need to predict class of a instance , then print its class,
have a good performance 'predict' method for classify a instance?
Or is there a better method for this work ?
thanks,
Be happy and win

Answer 1

I suspect the reason your code does not print anything is because you forgot the endl. (This is what Joachim Pileborg mentioned in his comment.)
If you are using Visual Studio, you may want to add a breakpoint at the end of your code (maybe on the return statement) because in certain modes it can close your application before you get to see the output, which can make it seem as if nothing happened.
Example
What follows is a full example that works fine for me. It includes your instance. It loads a K-nearest neighbors learner from 2blobs_knn.json and then evaluates your instance on it. You can replace that file name with the name of any trained supervised model generated by the waffles tools.
With the model I used, the program prints "1" and exits.
If you want to use the exact model that I tested my code with (in case your method of constructing your learner is the problem) see the section after the example code.
#include <GClasses/GMatrix.h>
#include <GClasses/GHolders.h>
#include <GClasses/GRand.h>
#include <GClasses/GLearner.h>
#include <GClasses/GDom.h>
#include <iostream>
#include <cassert>
using namespace GClasses;
using std::cout; using std::endl;
int main(int argc, char *argv[])
{
//Load my trained learner from a file named 2blobs_knn.json and put
//it in hModel which is a shared-pointer class.
GLearnerLoader ll(GRand::global());
GDom dom;
dom.loadJson("2blobs_knn.json");
Holder<GSupervisedLearner> hModel(ll.loadSupervisedLearner(dom.root()));
assert(hModel.get() != NULL);
//Here is your code
GMatrix Instance(1,8);// Instance has 8 real attributes and one row
double out; // The value in attribute 'class' is nominal
Instance[0][0]=6;
Instance[0][1]=148;
Instance[0][2]=72;
Instance[0][3]=35;
Instance[0][4]=0;
Instance[0][5]=33.6;
Instance[0][6]=0.62;
Instance[0][7]=50;
hModel.get()->predict(Instance[0],&out);
cout << out << endl;
return 0;
}
How the learner I used in the example was constructed
To get the learner, I used Matlab (Octave is the free imitator) to generate a CSV file in which class 0 was an 8-dimensional spherical unit Gaussian centered at (0,0,0,0,0,0,0,0) and class 1 had the same distribution but centered at (2,2,2,2,2,2,2,2)
m=[[randn(200,8);randn(200,8)+2], [repmat(0,200,1);repmat(1,200,1)]];
csvwrite('2blobs.csv',m)
Then, I took that CSV, converted it to ARFF using
waffles_transform import 2blobs.csv > 2blobs.arff
Next, I changed the last attribute from #ATTRIBUTE attr8 real to
#ATTRIBUTE class {0,1} in a text editor so it would be nominal.
Finally, I trained the model with
waffles_learn train 2blobs.arff knn -neighbors 10 > 2blobs_knn.json