Exemplo n.º 1
0
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))
Exemplo n.º 2
0
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))
Exemplo n.º 3
0
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))
Exemplo n.º 4
0
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))