def test_lstsqjackknifeslow(self):
x = np.atleast_2d(np.arange(10)).T
y = np.atleast_2d(2 * np.arange(10)).T
reg = jk.LstsqJackknifeSlow(x, y, n_blocks=10)
regnn = jk.LstsqJackknifeSlow(x, y, n_blocks=10, nn=True)
assert_array_almost_equal(reg.est, [[2.]])
assert_array_almost_equal(regnn.est, [[2.]])
def test_eq_slow(self):
x = np.atleast_2d(np.random.normal(size=(100, 2)))
y = np.atleast_2d(np.random.normal(size=(100, 1)))
print(x.shape)
for n_blocks in range(2, 49):
b1 = jk.LstsqJackknifeFast(x, y, n_blocks=n_blocks).est
b2 = jk.LstsqJackknifeSlow(x, y, n_blocks=n_blocks).est
assert_array_almost_equal(b1, b2)
def irwls(cls,
x,
y,
update_func,
n_blocks,
w,
slow=False,
separators=None):
'''
Iteratively re-weighted least squares (IRWLS).
Parameters
----------
x : np.matrix with shape (n, p)
Independent variable.
y : np.matrix with shape (n, 1)
Dependent variable.
update_func: function
Transforms output of np.linalg.lstsq to new weights.
n_blocks : int
Number of jackknife blocks (for estimating SE via block jackknife).
w : np.matrix with shape (n, 1)
Initial regression weights.
slow : bool
Use slow block jackknife? (Mostly for testing)
separators : list or None
Block jackknife block boundaries (optional).
Returns
-------
jknife : jk.LstsqJackknifeFast
Block jackknife regression with the final IRWLS weights.
'''
(n, p) = x.shape
if y.shape != (n, 1):
raise ValueError(
'y has shape {S}. y must have shape ({N}, 1).'.format(
S=y.shape, N=n))
if w.shape != (n, 1):
raise ValueError(
'w has shape {S}. w must have shape ({N}, 1).'.format(
S=w.shape, N=n))
w = np.sqrt(w)
for i in range(2): # update this later
new_w = np.sqrt(update_func(cls.wls(x, y, w)))
if new_w.shape != w.shape:
print('IRWLS update:', new_w.shape, w.shape)
raise ValueError('New weights must have same shape.')
else:
w = new_w
x = cls._weight(x, w)
y = cls._weight(y, w)
if slow:
jknife = jk.LstsqJackknifeSlow(x,
y,
n_blocks,
separators=separators)
else:
jknife = jk.LstsqJackknifeFast(x,
y,
n_blocks,
separators=separators)
return jknife