def _generate(l, k, g, beta, M, e, A, mu, intercept):
p = beta.shape[0]
if intercept:
gradL1 = grad_l1(beta[1:, :])
gradL2 = grad_l2_squared(beta[1:, :])
gradTVmu = grad_tvmu(beta[1:, :], A, mu)
else:
gradL1 = grad_l1(beta)
gradL2 = grad_l2_squared(beta)
gradTVmu = grad_tvmu(beta, A, mu)
alpha = -(l * gradL1 + k * gradL2 + g * gradTVmu)
Mte = np.dot(M.T, e)
if intercept:
alpha = np.divide(alpha, Mte[1:, :])
else:
alpha = np.divide(alpha, Mte)
X = np.ones(M.shape)
if intercept:
for i in xrange(p - 1):
X[:, i + 1] = M[:, i + 1] * alpha[i, 0]
else:
for i in xrange(p):
X[:, i] = M[:, i] * alpha[i, 0]
y = np.dot(X, beta) - e
return X, y
def _generate(l, k, g, beta, M, e, A, mu, intercept):
p = beta.shape[0]
if intercept:
gradL1 = grad_l1(beta[1:, :])
gradL2 = grad_l2_squared(beta[1:, :])
gradTVmu = grad_tvmu(beta[1:, :], A, mu)
else:
gradL1 = grad_l1(beta)
gradL2 = grad_l2_squared(beta)
gradTVmu = grad_tvmu(beta, A, mu)
alpha = -(l * gradL1 + k * gradL2 + g * gradTVmu)
Mte = np.dot(M.T, e)
if intercept:
alpha = np.divide(alpha, Mte[1:, :])
else:
alpha = np.divide(alpha, Mte)
X = np.ones(M.shape)
if intercept:
for i in xrange(p - 1):
X[:, i + 1] = M[:, i + 1] * alpha[i, 0]
else:
for i in xrange(p):
X[:, i] = M[:, i] * alpha[i, 0]
y = np.dot(X, beta) - e
return X, y
def _generate(l, k, g, beta, M, e, mu, shape):
p = np.prod(shape)
gradL1mu = grad_l1mu(beta, mu)
gradL2 = grad_l2_squared(beta)
gradTVmu = grad_tvmu(beta, shape, mu)
alpha = -(l * gradL1mu + k * gradL2 + g * gradTVmu)
alpha = np.divide(alpha, np.dot(M.T, e))
X = np.zeros(M.shape)
for i in xrange(p):
X[:, i] = M[:, i] * alpha[i, 0]
y = np.dot(X, beta) - e
return X, y
def _generate(l, k, g, beta, M, e, mu, shape):
p = np.prod(shape)
gradL1mu = grad_l1mu(beta, mu)
gradL2 = grad_l2_squared(beta)
gradTVmu = grad_tvmu(beta, shape, mu)
alpha = -(l * gradL1mu + k * gradL2 + g * gradTVmu)
alpha = np.divide(alpha, np.dot(M.T, e))
X = np.zeros(M.shape)
for i in xrange(p):
X[:, i] = M[:, i] * alpha[i, 0]
y = np.dot(X, beta) - e
return X, y
