def convert_to_givens_form(self):
"""
Convert from eigen to givens form
"""
rank = self.parameters['rank'][0]
Uc, E, Ur = giv.givensise(self.parameters['vec'])
EUr = np.dot(E[:rank, :rank], Ur)
D = np.dot(np.dot(EUr, np.diag(self.parameters['val'])), EUr.T)
U = Uc[:, :rank]
return U, D
def convert_to_givens_form(self):
"""
Convert from eigen to givens form
"""
rank = self.parameters['rank'][0]
Uc,E,Ur = giv.givensise(self.parameters['vec'])
EUr = np.dot(E[:rank,:rank],Ur)
D = np.dot(np.dot(EUr, np.diag(self.parameters['val'])), EUr.T)
U = Uc[:,:rank]
return U, D
def runTest(self):
for d in range(1,self.dMax+1):
for r in range(1,d):
# Random matrix of orthogonal columns
X = np.random.randn(d,d)
A = np.dot(X,X.T)
eVal,eVec = linalg.eigh(A)
U = eVec[:,:2]
# Factorise it
Uc,E,Ur = givensise(U)
# Reconstruct
shouldBeU = np.dot(Uc,np.dot(E,Ur))
# Test
np.testing.assert_almost_equal(U,shouldBeU)
def runTest(self):
for d in range(1, self.dMax + 1):
for r in range(1, d):
# Random matrix of orthogonal columns
X = np.random.randn(d, d)
A = np.dot(X, X.T)
eVal, eVec = linalg.eigh(A)
U = eVec[:, :2]
# Factorise it
Uc, E, Ur = givensise(U)
# Reconstruct
shouldBeU = np.dot(Uc, np.dot(E, Ur))
# Test
np.testing.assert_almost_equal(U, shouldBeU)
