void MLearn::runML(){
Mat trainingData ( numTrainingPoints , 2 , CV_32FC1 ) ;
Mat testData ( numTestPoints , 2 , CV_32FC1 ) ;
randu ( trainingData ,0 ,1) ;
randu ( testData ,0 ,1) ;
Mat trainingClasses = labelData ( trainingData , eq ) ;
Mat testClasses = labelData ( testData , eq ) ;
plot_binary ( trainingData , trainingClasses , " Training Data " ) ;
plot_binary ( testData , testClasses , " Test Data " ) ;
svm ( trainingData , trainingClasses , testData , testClasses ) ;
mlp ( trainingData , trainingClasses , testData , testClasses ) ;
knn ( trainingData , trainingClasses , testData , testClasses , 3) ;
bayes ( trainingData , trainingClasses , testData , testClasses ) ;
decisiontree ( trainingData , trainingClasses , testData , testClasses ) ;
waitKey () ;
}
void testSequence(){
int i;
long *fib;
unsigned char *primeNumbmer;
pdf *dist;
dist=normalDistribution(10.0, 5.0, 4, 0.1, 400);
printf("\nSequence\n");
printf("Normal distribution\n");
for (i=0;i<400;i++){
printf("%f\t%f\n",(dist+i)->x,(dist+i)->fx);
}
printf("gamma 1 =%f\n",tgamma(4));
printf("gamma 1 =%f\n",gammaFunction(4));
printf("p(a|b) =%f\n",bayes(0.8, 0.01, 0.096));
printf("factorial =%d\n",factorial(5));
printf("double factorial =%d\n",doubleFactorial(5));
fib=fibonacci(20);
primeNumbmer=prime(20);
printf("fibonacci\n");
for (i=0;i<20;i++){
printf("%d ",i);
}
printf("\n");
for (i=0;i<20;i++){
printf("%ld ",fib[i]);
}
printf("\n");
printf("prime\n");
for (i=0;i<20;i++){
if (primeNumbmer[i] ==1)
printf("%d ",i);
}
printf("\n");
}
