Python nesta示例

示例#1

文件： test_nesta.py 项目： matthew-brett/regreg

def test_nesta_lasso():

    n, p = 1000, 20
    X = np.random.standard_normal((n, p))
    beta = np.zeros(p)
    beta[:4] = 30
    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X,Y)
    penalty = rr.l1norm(p, lagrange=2.)

    # using nesta
    z = rr.zero(p)
    primal, dual = rr.nesta(loss, z, penalty, tol=1.e-10,
                            epsilon=2.**(-np.arange(30)),
                            initial_dual=np.zeros(p))

    # using simple problem

    problem = rr.simple_problem(loss, penalty)
    problem.solve()
    nt.assert_true(np.linalg.norm(primal - problem.coefs) / np.linalg.norm(problem.coefs) < 1.e-3)

    # test None as smooth_atom

    rr.nesta(None, z, penalty, tol=1.e-10,
             epsilon=2.**(-np.arange(30)),
             initial_dual=np.zeros(p))

    # using coefficients to stop

    rr.nesta(loss, z, penalty, tol=1.e-10,
             epsilon=2.**(-np.arange(30)),
             initial_dual=np.zeros(p),
             coef_stop=True)

示例#2

文件： test_nesta.py 项目： vishalbelsare/regreg

def test_nesta_lasso():

    n, p = 1000, 20
    X = np.random.standard_normal((n, p))
    beta = np.zeros(p)
    beta[:4] = 30
    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X,Y)
    penalty = rr.l1norm(p, lagrange=2.)

    # using nesta
    z = rr.zero(p)
    primal, dual = rr.nesta(loss, z, penalty, tol=1.e-10,
                            epsilon=2.**(-np.arange(30)),
                            initial_dual=np.zeros(p))

    # using simple problem

    problem = rr.simple_problem(loss, penalty)
    problem.solve()
    nt.assert_true(np.linalg.norm(primal - problem.coefs) / np.linalg.norm(problem.coefs) < 1.e-3)

    # test None as smooth_atom

    rr.nesta(None, z, penalty, tol=1.e-10,
             epsilon=2.**(-np.arange(30)),
             initial_dual=np.zeros(p))

    # using coefficients to stop

    rr.nesta(loss, z, penalty, tol=1.e-10,
             epsilon=2.**(-np.arange(30)),
             initial_dual=np.zeros(p),
             coef_stop=True)

示例#3

文件： test_nesta.py 项目： matthew-brett/regreg

def test_nesta_nonnegative():

    state = np.random.get_state()
    np.random.seed(10)
    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    A = np.random.standard_normal((q,p))

    coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
    coef[:2] = -0.2
    beta = np.dot(np.linalg.pinv(A), coef)
    print(r'\beta', beta)
    print(r'A\beta', np.dot(A, beta))

    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X,Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss, penalty, constraint, max_iters=300, coef_tol=1.e-10, tol=1.e-10)

    print(r'A \hat{\beta}', np.dot(A, primal))
    assert_almost_nonnegative(np.dot(A,primal), tol=1.e-3)

    np.random.set_state(state)

示例#4

def test_nesta_nonnegative():

    state = np.random.get_state()
    np.random.seed(10)
    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    A = np.random.standard_normal((q, p))

    coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
    coef[:2] = -0.2
    beta = np.dot(np.linalg.pinv(A), coef)
    print(r'\beta', beta)
    print(r'A\beta', np.dot(A, beta))

    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X, Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss,
                            penalty,
                            constraint,
                            max_iters=300,
                            coef_tol=1.e-10,
                            tol=1.e-10)

    print(r'A \hat{\beta}', np.dot(A, primal))
    assert_almost_nonnegative(np.dot(A, primal), tol=1.e-3)

    np.random.set_state(state)

示例#5

文件： test_nesta.py 项目： Xiaoying-Tian/regreg

def test_nesta_nonnegative():

    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    A = np.random.standard_normal((q, p))

    coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
    coef[:2] = -0.2
    beta = np.dot(np.linalg.pinv(A), coef)

    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X, Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss,
                            penalty,
                            constraint,
                            max_iters=300,
                            coef_tol=1.e-4,
                            tol=1.e-4)

    print np.dot(A, primal)
    assert_almost_nonnegative(np.dot(A, primal), tol=1.e-3)

示例#6

文件： test_nesta.py 项目： sroet/regreg

def test_nesta_nonnegative():

    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    beta = np.zeros(p)
    beta[:4] = 3
    Y = np.random.standard_normal(n) + np.dot(X, beta)
    A = np.random.standard_normal((q, p))

    loss = rr.squared_error(X, Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss, penalty, constraint)

    assert_almost_nonnegative(np.dot(A, primal))

示例#7

文件： test_nesta.py 项目： amitibo/regreg

def test_nesta_nonnegative():

    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    beta = np.zeros(p)
    beta[:4] = 3
    Y = np.random.standard_normal(n) + np.dot(X, beta)
    A = np.random.standard_normal((q,p))

    loss = rr.squared_error(X,Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss, penalty, constraint)

    assert_almost_nonnegative(np.dot(A,primal))

示例#8

文件： test_nesta.py 项目： Xiaoying-Tian/regreg

def test_nesta_nonnegative():

    n, p, q = 1000, 20, 5
    X = np.random.standard_normal((n, p))
    A = np.random.standard_normal((q,p))

    coef = 10 * np.fabs(np.random.standard_normal(q)) + 1
    coef[:2] = -0.2
    beta = np.dot(np.linalg.pinv(A), coef)

    Y = np.random.standard_normal(n) + np.dot(X, beta)

    loss = rr.squared_error(X,Y)
    penalty = rr.l1norm(p, lagrange=0.2)
    constraint = rr.nonnegative.linear(A)

    primal, dual = rr.nesta(loss, penalty, constraint, max_iters=300, coef_tol=1.e-4, tol=1.e-4)

    print np.dot(A, primal)
    assert_almost_nonnegative(np.dot(A,primal), tol=1.e-3)