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, 13)
assert_array_almost_equal(msign*factors_svd, factors, 12)
assert_array_almost_equal(xred_svd, xreduced, 13)
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)
print("Error with unpickling, a new pickle file can be created with findow_1")
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars,1)])
plt.title('Correlation Coefficients')
xreda, facta, evaa, evea = sbtools.pcasvd(rr)
evallcs = (evaa).cumsum()
print(evallcs/evallcs[-1])
xred, fact, eva, eve = sbtools.pcasvd(rr, keepdim=4)
pcacorr = np.corrcoef(xred, rowvar=0)
plot_corr(pcacorr, xnames=ticksym, title='Correlation PCA')
resid = rr-xred
residcorr = np.corrcoef(resid, rowvar=0)
plot_corr(residcorr, xnames=ticksym, title='Correlation Residuals')
plt.matshow(residcorr)
plt.imshow(residcorr, cmap=plt.cm.jet, interpolation='nearest',
extent=(0,30,0,30), vmin=-1.0, vmax=1.0)
plt.colorbar()
except Exception: #blanket for any unpickling error
print "Error with unpickling, a new pickle file can be created with findow_1"
raise
ticksym = rrdm.columns.tolist()
rr = rrdm.values[1:400]
rrcorr = np.corrcoef(rr, rowvar=0)
plot_corr(rrcorr, xnames=ticksym)
nvars = rrcorr.shape[0]
plt.figure()
plt.hist(rrcorr[np.triu_indices(nvars, 1)])
plt.title('Correlation Coefficients')
xreda, facta, evaa, evea = sbtools.pcasvd(rr)
evallcs = (evaa).cumsum()
print evallcs / evallcs[-1]
xred, fact, eva, eve = sbtools.pcasvd(rr, keepdim=4)
pcacorr = np.corrcoef(xred, rowvar=0)
plot_corr(pcacorr, xnames=ticksym, title='Correlation PCA')
resid = rr - xred
residcorr = np.corrcoef(resid, rowvar=0)
plot_corr(residcorr, xnames=ticksym, title='Correlation Residuals')
plt.matshow(residcorr)
plt.imshow(residcorr,
cmap=plt.cm.jet,
interpolation='nearest',
