This function generates some multinomial toy-data for classification Input a:column vector repenting decision boundary in the form: f(x)=a[0]+a[1]x1+a[2]x2+a[3]x1x2+a[4]x1^2+a[5]x2^3 NumberSamples: number of samples Output Out[1]:y lables such that f(xi)<=0 yi=1 and f(xi)>0 yi=2 Out[0]:X 2d feature vectors with rows corresponding to x Needs:numpy
This function generates some multinomial toy-data for classification Input a:column vector repenting decision boundary in the form: f(x)=a[0]+a[1]x1+a[2]x2+a[3]x1x2+a[4]x1^2+a[5]x2^3 NumberSamples: number of samples Output Out[1]:y lables such that f(xi)<=0 yi=1 and f(xi)>0 yi=2 Out[0]:X 2d feature vectors with rows corresponding to x Needs:numpy
This function generates some multinomial toy-data for classification Input a:column vector repenting decision boundary in the form: f(x)=a[0]+a[1]x1+a[2]x2+a[3]x1x2+a[4]x1^2+a[5]x2^3 NumberSamples: number of samples Output Out[1]:y lables such that f(xi)<=0 yi=1 and f(xi)>0 yi=2 Out[0]:X 2d feature vectors with rows corresponding to x Needs:numpy