def test_pca_princomp():
pcares = pca(xf)
check_pca_princomp(pcares, princomp1)
pcares = pca(xf[:20, :])
check_pca_princomp(pcares, princomp2)
pcares = pca(xf[:20, :] - xf[:20, :].mean(0))
check_pca_princomp(pcares, princomp3)
pcares = pca(xf[:20, :] - xf[:20, :].mean(0), demean=0)
check_pca_princomp(pcares, princomp3)
def test_pca_princomp():
pcares = pca(xf)
check_pca_princomp(pcares, princomp1)
pcares = pca(xf[:20,:])
check_pca_princomp(pcares, princomp2)
pcares = pca(xf[:20,:]-xf[:20,:].mean(0))
check_pca_princomp(pcares, princomp3)
pcares = pca(xf[:20,:]-xf[:20,:].mean(0), demean=0)
check_pca_princomp(pcares, princomp3)
def test_pca_svd():
xreduced, factors, evals, evecs = pca(xf)
factors_wconst = np.c_[factors, np.ones((factors.shape[0], 1))]
beta = np.dot(np.linalg.pinv(factors_wconst), xf)
#np.dot(np.linalg.pinv(factors_wconst),x2/1000.).T[:,:4] - evecs
assert_array_almost_equal(beta.T[:, :4], evecs, 14)
xred_svd, factors_svd, evals_svd, evecs_svd = pcasvd(xf, keepdim=0)
assert_array_almost_equal(evals_svd, evals, 14)
msign = (evecs / evecs_svd)[0]
assert_array_almost_equal(msign * evecs_svd, evecs, 14)
assert_array_almost_equal(msign * factors_svd, factors, 13)
assert_array_almost_equal(xred_svd, xreduced, 14)
pcares = pca(xf, keepdim=2)
pcasvdres = pcasvd(xf, keepdim=2)
check_pca_svd(pcares, pcasvdres)
def test_pca_svd():
xreduced, factors, evals, evecs = pca(xf)
factors_wconst = np.c_[factors, np.ones((factors.shape[0],1))]
beta = np.dot(np.linalg.pinv(factors_wconst), xf)
#np.dot(np.linalg.pinv(factors_wconst),x2/1000.).T[:,:4] - evecs
assert_array_almost_equal(beta.T[:,:4], evecs, 14)
xred_svd, factors_svd, evals_svd, evecs_svd = pcasvd(xf, keepdim=0)
assert_array_almost_equal(evals_svd, evals, 14)
msign = (evecs/evecs_svd)[0]
assert_array_almost_equal(msign*evecs_svd, evecs, 14)
assert_array_almost_equal(msign*factors_svd, factors, 13)
assert_array_almost_equal(xred_svd, xreduced, 14)
pcares = pca(xf, keepdim=2)
pcasvdres = pcasvd(xf, keepdim=2)
check_pca_svd(pcares, pcasvdres)
def calc_factors(self, x=None, keepdim=0, addconst=True):
'''get factor decomposition of exogenous variables
This uses principal component analysis to obtain the factors. The number
of factors kept is the maximum that will be considered in the regression.
'''
if x is None:
x = self.exog
else:
x = np.asarray(x)
xred, fact, evals, evecs = pca(x, keepdim=keepdim, normalize=1)
self.exog_reduced = xred
#self.factors = fact
if addconst:
self.factors = sm.add_constant(fact, prepend=True)
self.hasconst = 1 #needs to be int
else:
self.factors = fact
self.hasconst = 0 #needs to be int
self.evals = evals
self.evecs = evecs
def calc_factors(self, x=None, keepdim=0, addconst=True):
'''get factor decomposition of exogenous variables
This uses principal component analysis to obtain the factors. The number
of factors kept is the maximum that will be considered in the regression.
'''
if x is None:
x = self.exog
else:
x = np.asarray(x)
xred, fact, evals, evecs = pca(x, keepdim=keepdim, normalize=1)
self.exog_reduced = xred
#self.factors = fact
if addconst:
self.factors = sm.add_constant(fact, prepend=True)
self.hasconst = 1 #needs to be int
else:
self.factors = fact
self.hasconst = 0 #needs to be int
self.evals = evals
self.evecs = evecs
