def bidiag_unpack_diag(diag, superdiag):
"""
returns B
This functions unpacks the bidiagonal matrix T of the diagonal
and superdiagonal obtained from gsl_linalg_bidiag_unpack[_B]
"""
n = diag.shape[0]
B = numx.identity(n) * diag
sub = numx.identity(n - 1) * superdiag
sub2 = numx.concatenate((numx.concatenate((zeros([n - 1, 1]), sub),
1), zeros([1, n])))
B = sub2 + B
return B
def bidiag_unpack_diag(diag, superdiag):
"""
returns B
This functions unpacks the bidiagonal matrix T of the diagonal
and superdiagonal obtained from gsl_linalg_bidiag_unpack[_B]
"""
n = diag.shape[0]
B = numx.identity(n)*diag
sub = numx.identity(n-1)*superdiag
sub2 = numx.concatenate(
(numx.concatenate((zeros([n-1,1]),sub), 1), zeros([1,n])))
B = sub2 + B
return B
def symmtd_unpack_diag(diag, subdiag):
"""
returns T
This functions unpacks the tridiagonal matrix T of the diagonal
and subdiagonal obtained from gsl_linalg_symmtd_unpack[_T]
"""
n = diag.shape[0]
T = numx.identity(n) * diag
sub = numx.identity(n - 1) * subdiag
sub1 = numx.concatenate((zeros(
(1, n)), numx.concatenate((sub, zeros((n - 1, 1))), 1)))
sub2 = numx.concatenate((numx.concatenate((zeros([n - 1, 1]), sub),
1), zeros([1, n])))
T = sub1 + sub2 + T
return T
def symmtd_unpack_diag(diag, subdiag):
"""
returns T
This functions unpacks the tridiagonal matrix T of the diagonal
and subdiagonal obtained from gsl_linalg_symmtd_unpack[_T]
"""
n = diag.shape[0]
T = numx.identity(n)*diag
sub = numx.identity(n-1)*subdiag
sub1 = numx.concatenate(
(zeros((1,n)), numx.concatenate((sub, zeros((n-1,1))), 1)))
sub2 = numx.concatenate(
(numx.concatenate((zeros([n-1,1]),sub), 1), zeros([1,n])))
T = sub1 + sub2 + T
return T
def run():
r = rng.rng()
# slope
a = 1.45
# intercept
b = 3.88
# Generate the data
n = 20
i = numx.arange(n - 3)
dx = 10.0 / (n - 1.0)
e = r.uniform(n)
x = -5.0 + i * dx
y = a * x + b
#outliers
xo = numx.array([4.1, 3.5, 4.7])
yo = numx.array([-6.0, -6.7, -8.3])
x = numx.concatenate((x, xo))
y = numx.concatenate((y, yo))
X = numx.array([x * 0 + 1, x]).transpose()
ws = fr.workspace(*((fr.ols, ) + X.shape))
c_ols, cov_ols = ws.fit(X, y)
ws = fr.workspace(*((fr.bisquare, ) + X.shape))
c, cov = ws.fit(X, y)
y_rob = numx.zeros(n, numx.float_)
y_ols = numx.zeros(n, numx.float_)
yerr_rob = numx.zeros(n, numx.float_)
yerr_ols = numx.zeros(n, numx.float_)
pygsl.set_debug_level(0)
fr.est(X[0, :], c, cov)
y_rob, yerr_rob = fr.est_vector(X, c, cov)
pygsl.set_debug_level(0)
y_ols, yerr_ols = fr.est_vector(X, c_ols, cov_ols)
return x, y, (y_rob, yerr_rob), (y_ols, yerr_ols)