def run_reg_linear_reg_one_vs_all(dTrain,dTest): lda = 1.0 for i in range(0,10): dTrain_current = getDataOneVsAll(dTrain,i) t_set = [] # in sample for d in dTrain_current: t_set.append([[1,d[1],d[2]],d[0]]) # out of sample dTest_current = getDataOneVsAll(dTest,i) t_setout = [] for d in dTest_current: t_setout.append([[1,d[1],d[2]],d[0]]) # in sample with no transform wlin,X0,y0 = linear_regression(len(t_set),t_set) print 'For %s vs all Ein = %s'%(i,compute_Ein(wlin,X0,y0)) # out of sample with no transform wout,Xout,yout = linear_regression(len(t_setout),t_setout) print 'For %s vs all Eout = %s'%(i,compute_Ein(wlin,Xout,yout)) # in sample with transform t_set_trans = transform_t_set(t_set) wtrans,Xtrans,ytrans = linear_regression(len(t_set_trans),t_set_trans) # out of sample with transform t_setout = transform_t_set(t_setout) wt,xt,yt = linear_regression(len(t_setout),t_setout) print 'For %s vs all with transformation Eout = %s'%(i,compute_Ein(wtrans,xt,yt))
def run_reg_linear_reg_one_vs_all(dTrain, dTest): lda = 1.0 for i in range(0, 10): dTrain_current = getDataOneVsAll(dTrain, i) t_set = [] # in sample for d in dTrain_current: t_set.append([[1, d[1], d[2]], d[0]]) # out of sample dTest_current = getDataOneVsAll(dTest, i) t_setout = [] for d in dTest_current: t_setout.append([[1, d[1], d[2]], d[0]]) # in sample with no transform wlin, X0, y0 = linear_regression(len(t_set), t_set) print 'For %s vs all Ein = %s' % (i, compute_Ein(wlin, X0, y0)) # out of sample with no transform wout, Xout, yout = linear_regression(len(t_setout), t_setout) print 'For %s vs all Eout = %s' % (i, compute_Ein(wlin, Xout, yout)) # in sample with transform t_set_trans = transform_t_set(t_set) wtrans, Xtrans, ytrans = linear_regression(len(t_set_trans), t_set_trans) # out of sample with transform t_setout = transform_t_set(t_setout) wt, xt, yt = linear_regression(len(t_setout), t_setout) print 'For %s vs all with transformation Eout = %s' % ( i, compute_Ein(wtrans, xt, yt))
def run_reg_linear_reg_one_vs_one(dTrain, dTest): lda1 = 0.01 lda2 = 1 # 1 vs 5 dTrain_current = getDataOneVsOne(dTrain, 1, 5) t_set = [] # in sample for d in dTrain_current: t_set.append([[1, d[1], d[2]], d[0]]) # out of sample dTest_current = getDataOneVsOne(dTest, 1, 5) t_setout = [] t_setout2 = [] for d in dTest_current: t_setout.append([[1, d[1], d[2]], d[0]]) t_setout2.append([[1, d[1], d[2]], d[0]]) print '--------------------------------------------------' print 'lambda is: %s' % (lda1) # in sample with no transform wlin, X0, y0 = linear_regression(len(t_set), t_set, lda1) print 'For 1 vs 5 Ein = %s' % (compute_Ein(wlin, X0, y0)) # out of sample with no transform wout, Xout, yout = linear_regression(len(t_setout), t_setout, lda1) print 'For 1 vs 5 Eout = %s' % (compute_Ein(wlin, Xout, yout)) # in sample with transform t_set_trans = transform_t_set(t_set) wtrans, Xtrans, ytrans = linear_regression(len(t_set_trans), t_set_trans, lda1) # out of sample with transform t_setout = transform_t_set(t_setout) wt, xt, yt = linear_regression(len(t_setout), t_setout, lda1) print 'For 1 vs 5 with transformation Ein = %s' % (compute_Ein( wtrans, Xtrans, ytrans)) print 'For 1 vs 5 with transformation Eout = %s' % (compute_Ein( wtrans, xt, yt)) print '--------------------------------------------------' print 'lambda is: %s' % (lda2) # in sample with no transform wlin2, X02, y02 = linear_regression(len(t_set), t_set, lda2) print 'For 1 vs 5 Ein = %s' % (compute_Ein(wlin2, X02, y02)) # out of sample with no transform wout2, Xout2, yout2 = linear_regression(len(t_setout2), t_setout2, lda2) print 'For 1 vs 5 Eout = %s' % (compute_Ein(wlin2, Xout2, yout2)) # in sample with transform t_set_trans2 = transform_t_set(t_set) wtrans2, Xtrans2, ytrans2 = linear_regression(len(t_set_trans2), t_set_trans2, lda2) # out of sample with transform t_setout2 = transform_t_set(t_setout2) wt2, xt2, yt2 = linear_regression(len(t_setout2), t_setout2, lda2) print 'For 1 vs 5 with transformation Ein = %s' % (compute_Ein( wtrans2, Xtrans2, ytrans2)) print 'For 1 vs 5 with transformation Eout = %s' % (compute_Ein( wtrans2, xt2, yt2))
def run_reg_linear_reg_one_vs_one(dTrain,dTest): lda1 = 0.01 lda2 = 1 # 1 vs 5 dTrain_current = getDataOneVsOne(dTrain,1,5) t_set = [] # in sample for d in dTrain_current: t_set.append([[1,d[1],d[2]],d[0]]) # out of sample dTest_current = getDataOneVsOne(dTest,1,5) t_setout = [] t_setout2 = [] for d in dTest_current: t_setout.append([[1,d[1],d[2]],d[0]]) t_setout2.append([[1,d[1],d[2]],d[0]]) print '--------------------------------------------------' print 'lambda is: %s'%(lda1) # in sample with no transform wlin,X0,y0 = linear_regression(len(t_set),t_set,lda1) print 'For 1 vs 5 Ein = %s'%(compute_Ein(wlin,X0,y0)) # out of sample with no transform wout,Xout,yout = linear_regression(len(t_setout),t_setout,lda1) print 'For 1 vs 5 Eout = %s'%(compute_Ein(wlin,Xout,yout)) # in sample with transform t_set_trans = transform_t_set(t_set) wtrans,Xtrans,ytrans = linear_regression(len(t_set_trans),t_set_trans,lda1) # out of sample with transform t_setout = transform_t_set(t_setout) wt,xt,yt = linear_regression(len(t_setout),t_setout,lda1) print 'For 1 vs 5 with transformation Ein = %s'%(compute_Ein(wtrans,Xtrans,ytrans)) print 'For 1 vs 5 with transformation Eout = %s'%(compute_Ein(wtrans,xt,yt)) print '--------------------------------------------------' print 'lambda is: %s'%(lda2) # in sample with no transform wlin2,X02,y02 = linear_regression(len(t_set),t_set,lda2) print 'For 1 vs 5 Ein = %s'%(compute_Ein(wlin2,X02,y02)) # out of sample with no transform wout2,Xout2,yout2 = linear_regression(len(t_setout2),t_setout2,lda2) print 'For 1 vs 5 Eout = %s'%(compute_Ein(wlin2,Xout2,yout2)) # in sample with transform t_set_trans2 = transform_t_set(t_set) wtrans2,Xtrans2,ytrans2 = linear_regression(len(t_set_trans2),t_set_trans2,lda2) # out of sample with transform t_setout2 = transform_t_set(t_setout2) wt2,xt2,yt2 = linear_regression(len(t_setout2),t_setout2,lda2) print 'For 1 vs 5 with transformation Ein = %s'%(compute_Ein(wtrans2,Xtrans2,ytrans2)) print 'For 1 vs 5 with transformation Eout = %s'%(compute_Ein(wtrans2,xt2,yt2))