def runlmpla(n=10): import pla as p fw = genline() (X,y) = genxy(n, fw) wini = lm(X,y) errratio = geterr(X,y,wini) # use the initial weights from regression res = p.pla(X[:,(1,2)], y, w=wini[1:3], b=wini[0]) # target function values res['lmw'] = wini res['fw'] = fw[1:3] res['fb'] = fw[0] return(res)
def runlmpla(n=10): import pla as p fw = genline() (X, y) = genxy(n, fw) wini = lm(X, y) errratio = geterr(X, y, wini) # use the initial weights from regression res = p.pla(X[:, (1, 2)], y, w=wini[1:3], b=wini[0]) # target function values res['lmw'] = wini res['fw'] = fw[1:3] res['fb'] = fw[0] return (res)
def naive_pla(points): from pla import pla return pla(points)