for i in range(m):
a = np.random.randn(n)
a = a / np.linalg.norm(a)
bi = 1 + np.random.rand(1)
A[i, :] = a
b[i] = bi
q = QCML()
q.parse('''
dimensions m n
variables x(n) r
parameters A(m,n) b(m)
maximize r
A*x + r <= b
''')
q.canonicalize()
q.dims = {'m': m, 'n': n}
q.codegen("python")
socp_data = q.prob2socp(locals())
# stuffed variable size
n = socp_data['G'].shape[1]
ecos_sol = ecos.solve(**socp_data)
socp_sol = ecos_sol['x']
prob_sol_x = q.socp2prob(ecos_sol['x'])['x']
prob_sol_r = q.socp2prob(ecos_sol['x'])['r']
# a QCML model is specified by strings
# the parser parses each model line by line and builds an internal
# representation of an SOCP
s = """
dimensions m n
variable a(n)
variable b
parameter X(m,n) # positive samples
parameter Y(m,n) # negative samples
parameter gamma positive
minimize (norm(a) + gamma*sum(pos(1 - X*a + b) + pos(1 + Y*a - b)))
"""
print s
raw_input("press ENTER to parse....")
p = QCML(debug=True)
p.parse(s)
raw_input("press ENTER to canonicalize....")
p.canonicalize()
raw_input("press ENTER to generate code....")
p.dims = {'n': n, 'm': m}
p.codegen("python")
raw_input("press ENTER to solve with ECOS....")
socp_data = p.prob2socp(params=locals())
import ecos
sol = ecos.solve(**socp_data)
for i in xrange(T):
objective += ["square(norm(Q*x%i)) + square(norm(R*u%i))" % (i,i)]
#objective += ["square(norm(Q*x%i))" % T]
problem += ["minimize (1/2)*(" + ' + '.join(objective) + ")"]
s = '\n'.join(map(lambda x:' ' + x, problem))
print s
raw_input("press ENTER to parse....")
p = QCML(debug=True)
p.parse(s)
raw_input("press ENTER to canonicalize....")
p.canonicalize()
raw_input("press ENTER to generate code....")
p.dims = {'n': n, 'm': m}
p.codegen("python")
raw_input("press ENTER to solve with ECOS....")
socp_data = p.prob2socp(params=locals())
import ecos
sol = ecos.solve(**socp_data)
b = np.random.randn(m)
q = QCML()
q.parse('''
dimensions m n
variable x(n)
parameters A(m,n) b(m)
minimize norm1(x)
A*x == b
''')
q.canonicalize()
q.dims = {'m': m, 'n': n}
q.codegen('python')
socp_vars = q.prob2socp(locals())
# convert to CSR for fast row slicing to distribute problem
socp_vars['A'] = socp_vars['A'].tocsr()
socp_vars['G'] = socp_vars['G'].tocsr()
# the size of the stuffed x or v
n = socp_vars['A'].shape[1]
ecos_sol = ecos.solve(**socp_vars)
# solution to transformed socp (stuffed)
socp_sol = ecos_sol['x']
# solution to original problem (unstuffed)
prob_sol = q.socp2prob(ecos_sol['x'])['x']
parameter D(two_mn, mn)
parameter blurmat(mn,mn)
minimize mu*norm(blurmat*x - g) + norm(D*x)
"""
D = sp.vstack([dxmat, dymat]).tocsc()
blurred = blurmat*imgvec + 5*np.random.randn(rows*cols)
from qcml import QCML
import ecos
p = QCML()
p.parse(text)
p.canonicalize()
dims = {'mn':rows*cols, 'two_mn':D.shape[0]}
p.codegen("python") # this creates a solver in Python calling CVXOPT
socp_data = p.prob2socp({'blurmat':blurmat,
'g':blurred,
'D':D,
'mu':25}, dims)
# sol = ecos.solve(**socp_data)
print socp_data
A = socp_data['G']
c = socp_data['c']
b = socp_data['h']
data = {'A':A, 'c':c, 'b':b}
cone = socp_data['dims']
sol = scs.solve(data, cone, opts = None, USE_INDIRECT = False)
my_vars = p.socp2prob(sol['x'], {'mn': rows*cols})
print my_vars
x = my_vars['x'].reshape(rows,cols)
#sol = ecos.solve(**socp_data)
#my_vars = p.socp2prob(sol['x'], dims)
