def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
lam = 1
x = cp.Variable(A.shape[1])
f = ep.hinge_loss(x, A, b) + lam * cp.sum_squares(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
lam = 1
x = cp.Variable(A.shape[1])
f = ep.hinge_loss(x, A, b) + lam*cp.sum_squares(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
ratio = float(np.sum(b == 1)) / len(b)
lambda_max = np.abs((1 - ratio) * A[b == 1, :].sum(axis=0) +
ratio * A[b == -1, :].sum(axis=0)).max()
lam = 0.5 * lambda_max
x = cp.Variable(A.shape[1])
f = ep.logistic_loss(x, A, b) + lam * cp.norm1(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
m = kwargs["m"]
n = kwargs["n"]
sigma = 0.05
mu = kwargs.get("mu", 1)
lam = 0.5 * sigma * np.sqrt(m * np.log(mu * n))
x = cp.Variable(A.shape[1])
f = ep.hinge_loss(x, A, b) + lam * cp.norm1(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
m = kwargs["m"]
n = kwargs["n"]
sigma = 0.05
mu = kwargs.get("mu", 1)
lam = 0.5*sigma*np.sqrt(m*np.log(mu*n))
x = cp.Variable(A.shape[1])
f = ep.hinge_loss(x, A, b) + lam*cp.norm1(x)
return cp.Problem(cp.Minimize(f))
def create(**kwargs):
A, b = problem_util.create_classification(**kwargs)
ratio = float(np.sum(b==1)) / len(b)
lambda_max = np.abs((1-ratio)*A[b==1,:].sum(axis=0) +
ratio*A[b==-1,:].sum(axis=0)).max()
lam = 0.5*lambda_max
x = cp.Variable(A.shape[1])
f = ep.logistic_loss(x, A, b) + lam*cp.norm1(x)
return cp.Problem(cp.Minimize(f))
