72 lines
2.4 KiB
Python
72 lines
2.4 KiB
Python
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'''tests for pca and arma to ar and ma representation
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compared with matlab princomp, and garchar, garchma
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TODO:
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* convert to generators with yield to have individual tests
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* incomplete: test relationship of pca-evecs and pinv (adding constant)
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'''
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from statsmodels.sandbox.tools import pca, pcasvd
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from statsmodels.multivariate.tests.results.datamlw import (
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princomp1, princomp2, princomp3, data)
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def check_pca_princomp(pcares, princomp):
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factors, evals, evecs = pcares[1:]
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#res_princomp.coef, res_princomp.factors, res_princomp.values
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msign = (evecs/princomp.coef)[0]
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assert_array_almost_equal(msign*evecs, princomp.coef, 13)
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assert_array_almost_equal(msign*factors, princomp.factors, 13)
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assert_array_almost_equal(evals, princomp.values.ravel(), 13)
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def check_pca_svd(pcares, pcasvdres):
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xreduced, factors, evals, evecs = pcares
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xred_svd, factors_svd, evals_svd, evecs_svd = pcasvdres
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assert_array_almost_equal(evals_svd, evals, 14)
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msign = (evecs/evecs_svd)[0]
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assert_array_almost_equal(msign*evecs_svd, evecs, 13)
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assert_array_almost_equal(msign*factors_svd, factors, 13)
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assert_array_almost_equal(xred_svd, xreduced, 13)
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xf = data.xo/1000.
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def test_pca_princomp():
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pcares = pca(xf)
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check_pca_princomp(pcares, princomp1)
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pcares = pca(xf[:20,:])
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check_pca_princomp(pcares, princomp2)
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pcares = pca(xf[:20,:]-xf[:20,:].mean(0))
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check_pca_princomp(pcares, princomp3)
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pcares = pca(xf[:20,:]-xf[:20,:].mean(0), demean=0)
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check_pca_princomp(pcares, princomp3)
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def test_pca_svd():
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xreduced, factors, evals, evecs = pca(xf)
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factors_wconst = np.c_[factors, np.ones((factors.shape[0],1))]
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beta = np.dot(np.linalg.pinv(factors_wconst), xf)
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#np.dot(np.linalg.pinv(factors_wconst),x2/1000.).T[:,:4] - evecs
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assert_array_almost_equal(beta.T[:,:4], evecs, 14)
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xred_svd, factors_svd, evals_svd, evecs_svd = pcasvd(xf, keepdim=0)
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assert_array_almost_equal(evals_svd, evals, 14)
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msign = (evecs/evecs_svd)[0]
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assert_array_almost_equal(msign*evecs_svd, evecs, 13)
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assert_array_almost_equal(msign*factors_svd, factors, 12)
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assert_array_almost_equal(xred_svd, xreduced, 13)
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pcares = pca(xf, keepdim=2)
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pcasvdres = pcasvd(xf, keepdim=2)
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check_pca_svd(pcares, pcasvdres)
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#print np.dot(factors[:,:3], evecs.T[:3,:])[:5]
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if __name__ == '__main__':
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test_pca_svd()
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