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)
if xnames:
ax.set_xticks(np.arange(nvars)+0.5)
ax.set_xticklabels(xnames, minor=True, fontsize='small',rotation=45, horizontalalignment='right')
#some keywords don't work in previous line ?
plt.setp( ax.get_xticklabels(), fontsize='small', rotation=45,
horizontalalignment='right')
fig.colorbar(axim)
ax.set_title(title)
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)
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()
#plt.show()
#plt.close('all')
if xnames:
ax.set_xticks(np.arange(nvars)+0.5)
ax.set_xticklabels(xnames, minor=True, fontsize='small',rotation=45, horizontalalignment='right')
#some keywords don't work in previous line ?
plt.setp( ax.get_xticklabels(), fontsize='small', rotation=45,
horizontalalignment='right')
fig.colorbar(axim)
ax.set_title(title)
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)
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()
#plt.show()
#plt.close('all')