def test_simple_svd(self): pm = SVDMapper() # train SVD pm.train(self.ndlin) self.failUnlessEqual(pm.proj.shape, (20, 20)) # now project data into PCA space p = pm.forward(self.ndlin) # only first eigenvalue significant self.failUnless(pm.sv[:1] > 1.0) self.failUnless((pm.sv[1:] < 0.0001).all()) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' pr = pm.reverse(p) self.failUnlessEqual(pr.shape, (40,20)) self.failUnless(np.abs(pm.reverse(p) - self.ndlin).sum() < 0.0001)
def test_simple_svd(self): pm = SVDMapper() # train SVD pm.train(self.ndlin) self.assertEqual(pm.proj.shape, (20, 20)) # now project data into PCA space p = pm.forward(self.ndlin) # only first eigenvalue significant self.assertTrue(pm.sv[:1] > 1.0) self.assertTrue((pm.sv[1:] < 0.0001).all()) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.assertTrue(var[:1] > 1.0) self.assertTrue((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' pr = pm.reverse(p) self.assertEqual(pr.shape, (40, 20)) self.assertTrue(np.abs(pm.reverse(p) - self.ndlin).sum() < 0.0001)
def test_more_svd(self): pm = SVDMapper() # train SVD pm.train(self.largefeat) # mixing matrix cannot be square self.failUnlessEqual(pm.proj.shape, (40, 10)) # only first singular value significant self.failUnless(pm.sv[:1] > 10) self.failUnless((pm.sv[1:] < 10).all()) # now project data into SVD space p = pm.forward(self.largefeat) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.failUnless(var[:1] > 1.0) self.failUnless((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' rp = pm.reverse(p) self.failUnlessEqual(rp.shape, self.largefeat.shape) self.failUnless((np.round(rp) == self.largefeat).all()) # copy mapper pm2 = deepcopy(pm) # now make new random data and do forward->reverse check data = np.random.normal(size=(98,40)) data_f = pm.forward(data) self.failUnlessEqual(data_f.shape, (98,10)) data_r = pm.reverse(data_f) self.failUnlessEqual(data_r.shape, (98,40))
def test_more_svd(self): pm = SVDMapper() # train SVD pm.train(self.largefeat) # mixing matrix cannot be square self.assertEqual(pm.proj.shape, (40, 10)) # only first singular value significant self.assertTrue(pm.sv[:1] > 10) self.assertTrue((pm.sv[1:] < 10).all()) # now project data into SVD space p = pm.forward(self.largefeat) # only variance of first component significant var = p.var(axis=0) # test that only one component has variance self.assertTrue(var[:1] > 1.0) self.assertTrue((var[1:] < 0.0001).all()) # check that the mapped data can be fully recovered by 'reverse()' rp = pm.reverse(p) self.assertEqual(rp.shape, self.largefeat.shape) self.assertTrue((np.round(rp) == self.largefeat).all()) # copy mapper pm2 = deepcopy(pm) # now make new random data and do forward->reverse check data = np.random.normal(size=(98, 40)) data_f = pm.forward(data) self.assertEqual(data_f.shape, (98, 10)) data_r = pm.reverse(data_f) self.assertEqual(data_r.shape, (98, 40))