def run_pla_vs_svm(nbruns=1, N=10):
solvers.options['show_progress'] = False
d = []
l = 0
f = 0
t_set = []
y = []
svm_vs_pla = []
for i in range(nbruns):
onBothSides = False
while (not onBothSides):
d = data(N)
l = randomline()
f = target_function(l)
t_set = build_training_set(d, f)
y = target_vector(t_set)
if (1 in y) and (-1 in y):
onBothSides = True
else:
onBothSides = False
w = [0, 0, 0]
w_pla, iteration = PLA(N, w, f, t_set)
plaEout = evaluate_diff_f_g(f, w_pla)
X_matrix = input_data_matrix(t_set)
dimension = len(X_matrix[0])
#identity matrix of size dim X dim matrix x,I,J,typecode double
P = spmatrix(1, range(dimension), range(dimension), tc='d')
#vector of zeros of size dim, typecode double
q = matrix([0] * (dimension), tc='d')
mat = []
for t in t_set:
y = t[1]
temp = [x * -1.0 * y for x in t[0]]
mat.append(temp)
G = matrix(mat, tc='d')
G = G.trans()
# vectors of -1 of size t_set
h = matrix([-1] * len(t_set), tc='d')
#http://abel.ee.ucla.edu/cvxopt/examples/tutorial/qp.html
qp_sln = solvers.qp(P, q, G, h)
wsvm = list(qp_sln['x'])
# number of support vectors you can get at each run
count_sv = 0
for t in t_set:
wsvm = array(wsvm)
x = array(t[0])
y = t[1]
res = fabs(y * dot(wsvm, x) - 1)
if res < 0.001:
count_sv = count_sv + 1
#print count_sv
# Eout of svm
svmEout = computeEout_svm(f, wsvm)
#print 'svmEout: %s'%svmEout
if (svmEout < plaEout):
svm_vs_pla.append([True, count_sv])
else:
svm_vs_pla.append([False, count_sv])
print "svm win pla %f" % (len(filter(lambda a: a[0] is True, svm_vs_pla)) *
1.0 / N)
percent_svm_won = len([r[0] for r in svm_vs_pla if r[0] is True
]) * 1.0 / len(svm_vs_pla)
print "question 9: svm beat pla %f percent of the time" % (
percent_svm_won * 100)
avg_sv = sum([a[1] for a in svm_vs_pla]) * 1.0 / len(svm_vs_pla)
print "avg sv:", avg_sv
def run_pla_vs_svm(nbruns = 1, N = 10):
solvers.options['show_progress'] = False
d = []
l = 0
f = 0
t_set = []
y = []
svm_vs_pla = []
for i in range(nbruns):
onBothSides = False
while(not onBothSides):
d = data(N)
l = randomline()
f = target_function(l)
t_set = build_training_set(d,f)
y = target_vector(t_set)
if (1 in y) and (-1 in y):
onBothSides = True
else:
onBothSides = False
w = [0,0,0]
w_pla,iteration = PLA(N,w,f,t_set)
plaEout = evaluate_diff_f_g(f,w_pla)
X_matrix = input_data_matrix(t_set)
dimension = len(X_matrix[0])
#identity matrix of size dim X dim matrix x,I,J,typecode double
P = spmatrix(1, range(dimension), range(dimension), tc='d')
#vector of zeros of size dim, typecode double
q = matrix([0]*(dimension), tc='d')
mat = []
for t in t_set:
y = t[1]
temp = [x * -1.0*y for x in t[0]]
mat.append(temp)
G = matrix(mat, tc='d')
G = G.trans()
# vectors of -1 of size t_set
h = matrix([-1]*len(t_set), tc='d')
#http://abel.ee.ucla.edu/cvxopt/examples/tutorial/qp.html
qp_sln = solvers.qp(P, q, G, h)
wsvm = list(qp_sln['x'])
# number of support vectors you can get at each run
count_sv = 0
for t in t_set:
wsvm = array(wsvm)
x = array(t[0])
y = t[1]
res = fabs(y*dot(wsvm,x)-1)
if res < 0.001:
count_sv = count_sv + 1
#print count_sv
# Eout of svm
svmEout = computeEout_svm(f,wsvm)
#print 'svmEout: %s'%svmEout
if(svmEout < plaEout):
svm_vs_pla.append([True,count_sv])
else:
svm_vs_pla.append([False,count_sv])
print "svm win pla %f" % (len(filter(lambda a: a[0] is True, svm_vs_pla))*1.0/N)
percent_svm_won = len([r[0] for r in svm_vs_pla if r[0] is True])*1.0/len(svm_vs_pla)
print "question 9: svm beat pla %f percent of the time" % (percent_svm_won*100)
avg_sv = sum([a[1] for a in svm_vs_pla])*1.0/len(svm_vs_pla)
print "avg sv:", avg_sv
def computeEout_svm(f, w):
return evaluate_diff_f_g(f, w)
def computeEout_svm(f,w):
return evaluate_diff_f_g(f,w)
