def PolytopProjection(data, T = 1.0, isProduct = False, solver = None):
if solver is None:
solver = 'cvxopt_qp'
#data = float128(data)
if isProduct:
H = data
n = data.shape[0]
m = len(T)
else:
H = dot(data, data.T)
n, m = data.shape
#print H.shape
#print 'PolytopProjection: n=%d, m=%d, H.shape[0]= %d, H.shape[1]= %d ' %(n, m, H.shape[0], H.shape[1])
#T = abs(dot(H, ones(n)))
f = -asfarray(T) *ones(n)
p = QP(H, f, lb = zeros(n), iprint = -1, maxIter = 150)
xtol = 1e-6
if max(T) < 1e5*xtol: xtol = max(T)/1e5
r = p._solve(solver, ftol = 1e-16, xtol = xtol, maxIter = 10000)
sol = r.xf
if isProduct:
return r.xf
else:
s = dot(data.T, r.xf)
return s.flatten(), r.xf
def PolytopProjection(data, T=1.0, isProduct=False, solver=None):
if solver is None:
solver = 'cvxopt_qp'
#data = float128(data)
if isProduct:
H = data
n = data.shape[0]
m = len(T)
else:
H = dot(data, data.T)
n, m = data.shape
#print H.shape
#print 'PolytopProjection: n=%d, m=%d, H.shape[0]= %d, H.shape[1]= %d ' %(n, m, H.shape[0], H.shape[1])
#T = abs(dot(H, ones(n)))
f = -asfarray(T) * ones(n)
p = QP(H, f, lb=zeros(n), iprint=-1, maxIter=150)
xtol = 1e-6
if max(T) < 1e5 * xtol: xtol = max(T) / 1e5
r = p._solve(solver, ftol=1e-16, xtol=xtol, maxIter=10000)
sol = r.xf
if isProduct:
return r.xf
else:
s = dot(data.T, r.xf)
return s.flatten(), r.xf
def test_cplex(self):
p = QP(diag([1, 2, 3]),
[15, 8, 80],
A = matrix('1 2 3; 8 15 80'),
b = [150, 800],
Aeq = [0, 1, -1],
beq = 25.5,
ub = [15,inf,inf])
r = p._solve('cplex', iprint = 0)
f_opt, x_opt = r.ff, r.xf
np.testing.assert_almost_equal(f_opt, -1190.35)
np.testing.assert_almost_equal(x_opt, [-15. , -2.3, -27.8 ])
def tilt(self):
######construct constraint weight matrix and optimize p############
ndim=self.ndim
wmatrix=self.ndim*self.m_fd()
wmatrix_2=self.ndim*self.k()*self.ydat
p0=ones(ndim)/ndim
lb=zeros(ndim)
ub=ones(ndim)
Aeq=ones(ndim)
beq=1.
#A=vstack((-wmatrix,-wmatrix_2,-wmatrix_2))
#b=hstack((zeros(len(wmatrix)),ones(len(wmatrix_2)),zeros(len(wmatrix_2))))
A=vstack((-wmatrix,-wmatrix_2))
b=hstack((zeros(len(wmatrix)),ones(len(wmatrix_2))))
p=QP(diag(ones(ndim)),2*p0*ones(ndim),lb=lb,ub=ub,Aeq=Aeq,beq=beq,A=A,b=b,name="find optimal p that satisfies m'>0 and m<1")
r=p._solve('cvxopt_qp')
solution=r.xf
self.solution=solution
self.ydat=ndim*solution*self.ydat
def test_cvxopt(self):
Q = matrix([[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [0.0, 0.0, 3.0]])
p = matrix([15.0, 8.0, 80.0])
G = matrix([[1.0, 2.0, 3.0], [8.0, 15.0, 80.0], [0, 0, 0]])
h = matrix([150.0, 800.0, 0], (3, 1))
A = matrix([0.0, 1.0, -1.0], (1,3))
b = matrix(25.5)
sol = cvxopt.solvers.qp(Q, p, G, h, A, b)
p = QP(diag([1, 2, 3]),
[15, 8, 80],
A = np.matrix("1 2 3; 8 15 80"),
b = [150, 800],
Aeq = [0, 1, -1],
beq = 25.5)
r = p._solve('cvxopt_qp', iprint = 0)
f_opt, x_opt = r.ff, r.xf
np.testing.assert_almost_equal(f_opt, sol['primal objective'], decimal=5)
np.testing.assert_almost_equal(x_opt, np.squeeze(sol['x']), decimal=5)
beq = 0.0
A = [1.0 for i in range(2 * tot_values)]
b = nu * float(C)
#coeff for 3rd constraint
#kernel=H
p = QP(np.asmatrix(kernel),
np.asmatrix(f),
lb=np.asmatrix(lower_limit),
ub=np.asmatrix(upper_limit),
Aeq=Aeq,
beq=beq,
A=A,
b=b)
# or p = QP(H=diag([1,2,3]), f=[15,8,80], A= ...)
r = p._solve('cvxopt_qp', iprint=0)
f_opt, x = r.ff, r.xf
support_vectors = []
support_vectors_Y = []
coeff = []
b = 0.0
#support vectors: points such that an-an' ! = 0
for i in range(tot_values):
if not ((x[i] - x[tot_values + i]) == 0):
support_vectors.append(X[i])
support_vectors_Y.append(Y[i])
coeff.append(x[i] - x[tot_values + i])
#bias_term=tn-eps-(support vectors)*corresponding kernel
support_vector = []
Aeq = [1.0 for i in range(2 * tot_values)]
for i in range(tot_values, 2 * tot_values):
Aeq[i] = -1.0
beq = 0.0
#----------------------------------------------------------------------------------------------------
#coeff for 3rd constraint
#kernel=H
eq = QP(np.asmatrix(kernel),
np.asmatrix(f),
lb=np.asmatrix(lower_limit),
ub=np.asmatrix(upper_limit),
Aeq=Aeq,
beq=beq)
p = eq._solve('cvxopt_qp', iprint=0)
f_optimized, x = p.ff, p.xf
#---------------------------------------------------------------------------------------
support_vectors = []
support_vectors_Y = []
support_vector = []
support_vector_Y = []
coeff = []
b = 0.0
#support vectors: points such that an-an' ! = 0
for i in range(tot_values):
if not ((x[i] - x[tot_values + i]) == 0):
support_vectors.append(X[i])
support_vectors_Y.append(Y[i])
def fit(self, X, Y):
xdim = X.shape[0]
ydim = Y.shape[0]
if xdim != ydim:
raise Exception('Input dimension does not match!')
kernel=[[0.0 for i in range(2*xdim)] for j in range(2*xdim)]
for i in range(xdim):
for j in range(xdim):
kernel[i][j] = self._kernel(X[i], X[j])
kernel[i + xdim][j + xdim] = self._kernel(X[i], X[j])
#----------------------------------------------------------------------------------------------------------------
#negating the values for a_n'
for i in range(xdim):
for j in range(xdim):
kernel[i + xdim][j] = (-1.0) * self._kernel(X[i], X[j])
kernel[i][j + xdim] = (-1.0) * self._kernel(X[i], X[j])
#--------------------------------------------------------------------------------------------------------------
#coeff of 2nd term to minimize
f=[0.0 for i in range(2 * xdim)]
for i in range(xdim):
f[i] = -float(Y[i]) + self.eps
for i in range(xdim, 2 * xdim):
f[i] = float(Y[i - xdim])+ self.eps
#-----------------------------------------------------------------------------------------------------
#constraints
lower_limit = [0.0 for i in range(2 * xdim)]
upper_limit = [float(self.C) for i in range(2 * xdim)]
Aeq = [1.0 for i in range(2 * xdim)]
for i in range(xdim, 2 * xdim):
Aeq[i]=-1.0
beq=0.0
#----------------------------------------------------------------------------------------------------
#coeff for 3rd constraint
#kernel=H
eq = QP(np.asmatrix(kernel),np.asmatrix(f),
lb=np.asmatrix(lower_limit),
ub=np.asmatrix(upper_limit),
Aeq=Aeq,beq=beq)
p = eq._solve('cvxopt_qp', iprint = 0)
f_opt, x = p.ff, p.xf
#---------------------------------------------------------------------------------------
self.support_vectors=[]
self.support_vectors_Y=[]
self.coeff=[]
#support vectors: points such that an-an' ! = 0
for i in range(xdim):
if not((x[i]-x[xdim+i])==0):
self.support_vectors.append( X[i] )
self.support_vectors_Y.append(Y[i])
self.coeff.append( x[i]-x[xdim+i] )
low=min(abs(x))
for i in range(xdim):
if not(abs(x[i]-x[xdim+i]) < low + 0.005):
self.support_vector.append( X[i] )
self.support_vector_Y.append(Y[i])
bias=0.0
for i in range(len(X)):
bias=bias+float(Y[i] - self.eps - self._product(coeff, self.support_vectors, X[i]))
#generally bias is average as written in the book
self.bias=bias/len(X)
"""
Example:
Concider the problem
0.5 * (x1^2 + 2x2^2 + 3x3^2) + 15x1 + 8x2 + 80x3 -> min (1)
subjected to
x1 + 2x2 + 3x3 <= 150 (2)
8x1 + 15x2 + 80x3 <= 800 (3)
x2 - x3 = 25.5 (4)
x1 <= 15 (5)
"""
from numpy import diag, matrix, inf
from openopt import QP
p = QP(diag([1, 2, 3]), [15, 8, 80], A = matrix('1 2 3; 8 15 80'), b = [150, 800], Aeq = [0, 1, -1], beq = 25.5, ub = [15,inf,inf])
# or p = QP(H=diag([1,2,3]), f=[15,8,80], A= ...)
r = p._solve('cvxopt_qp', iprint = 0)
f_opt, x_opt = r.ff, r.xf
# x_opt = array([-15. , -2.5, -28. ])
# f_opt = -1190.25
#-----------------------------------------------------------------------------------------------------
#constraints
lower_limit=[0.0 for i in range(2*tot_values)]
upper_limit=[float(C) for i in range(2*tot_values)]
Aeq = [1.0 for i in range(2*tot_values)]
for i in range(tot_values,2*tot_values):
Aeq[i]=-1.0
beq=0.0
#----------------------------------------------------------------------------------------------------
#coeff for 3rd constraint
#kernel=H
eq = QP(np.asmatrix(kernel),np.asmatrix(f),lb=np.asmatrix(lower_limit),ub=np.asmatrix(upper_limit),Aeq=Aeq,beq=beq)
p = eq._solve('cvxopt_qp', iprint = 0)
f_optimized, x = p.ff, p.xf
#---------------------------------------------------------------------------------------
support_vectors=[]
support_vectors_Y=[]
support_vector=[]
support_vector_Y=[]
coeff=[]
b=0.0
#support vectors: points such that an-an' ! = 0
for i in range(tot_values):
if not((x[i]-x[tot_values+i])==0):
support_vectors.append( X[i] )
support_vectors_Y.append(Y[i])
