def _calc_betas(self, x, y):
N = len(self._index)
K = len(self._x.columns)
betas = np.empty((N, K), dtype=float)
betas[:] = np.NaN
valid = self._time_has_obs
enough = self._enough_obs
window = self._window
# Use transformed (demeaned) Y, X variables
cum_xx = self._cum_xx(x)
cum_xy = self._cum_xy(x, y)
for i in range(N):
if not valid[i] or not enough[i]:
continue
xx = cum_xx[i]
xy = cum_xy[i]
if self._is_rolling and i >= window:
xx = xx - cum_xx[i - window]
xy = xy - cum_xy[i - window]
betas[i] = math.solve(xx, xy)
mask = ~np.isnan(betas).any(axis=1)
have_betas = np.arange(N)[mask]
return betas, have_betas, mask
def _calc_betas(self, x, y):
N = len(self._index)
K = len(self._x.columns)
betas = np.empty((N, K), dtype=float)
betas[:] = np.NaN
valid = self._time_has_obs
enough = self._enough_obs
window = self._window
# Use transformed (demeaned) Y, X variables
cum_xx = self._cum_xx(x)
cum_xy = self._cum_xy(x, y)
for i in xrange(N):
if not valid[i] or not enough[i]:
continue
xx = cum_xx[i]
xy = cum_xy[i]
if self._is_rolling and i >= window:
xx = xx - cum_xx[i - window]
xy = xy - cum_xy[i - window]
betas[i] = math.solve(xx, xy)
mask = -np.isnan(betas).any(axis=1)
have_betas = np.arange(N)[mask]
return betas, have_betas, mask
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest("no statsmodels")
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assert_(np.allclose(result, expected))
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assert_(np.allclose(result, expected))
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest("no statsmodels")
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assertTrue(np.allclose(result, expected))
def test_solve_rect(self):
if not _have_statsmodels:
raise nose.SkipTest("no statsmodels")
b = Series(np.random.randn(N), self.frame.index)
result = pmath.solve(self.frame, b)
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
expected = ols(y=b, x=self.frame, intercept=False).beta
self.assertTrue(np.allclose(result, expected))
