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))
