def simple_test():
# examples from
# http://en.wikipedia.org/wiki/Gaussian_elimination
# 2x + y - z = 8.0
#-3x - y + 2z = -11.0
#-2x + y + 2z = -3.0
b = [8.0,-11.0,-3.0]
A = [[2.0,1.0,-1.0],
[-3.0,-1.0,2.0],
[-2.0,1.0,2.0]]
b = np.array([[8.0],
[-11.0],
[-3.0]])
A = np.array([[2.0,1.0,-1.0],
[-3.0,-1.0,2.0],
[-2.0,1.0,2.0]])
print 'True: [2.0, 3.0, -1.0]'
print 'Gaussian elimination: '
starttime = time.clock()
print Gau_Elim(A,b)
endtime = time.clock()
print ('running time %.6f',endtime-starttime)
b = np.array([[8.0],
[-11.0],
[-3.0]])
A = np.array([[2.0,1.0,-1.0],
[-3.0,-1.0,2.0],
[-2.0,1.0,2.0]])
A1 = sparse.lil_matrix(np.dot(A.T,A))
b1 = np.dot(A.T,b)
#print linalg.cg(A1,b1)[0]
print 'conjugate gradient: '
starttime = time.clock()
print conj_grad(A1,b1).T[0]
endtime = time.clock()
print ('running time %.6f',endtime-starttime)
b = np.array([cols,1])
starttime = time.clock()
csvreader = csv.reader(open('A_sparse_matrix.csv'))
for line in csvreader:
A[int(line[1]),int(line[2])] = line[0]
b = np.genfromtxt('b_vector.csv',delimiter=',')
endtime = time.clock()
print ('loading time %.6f',endtime-starttime)
if cg_test == 1:
A1 = sparse.lil_matrix(A.T*A)
bb = np.reshape(b,(20000,1))
b1 = A.T*bb
print 'conjugate gradient: '
starttime = time.clock()
ans_cg,iters = conj_grad(A1,b1)
print ans_cg.T[0]
endtime = time.clock()
print ('running time %.6f',endtime-starttime)
print iters
if gau_elim_test == 1:
A2 = A.T*A
bb = np.reshape(b,(20000,1))
b2 = A.T*bb
print 'Gaussian elimination: '
starttime = time.clock()
print Gau_Elim(A2.toarray(),b2)
endtime = time.clock()
print ('Gaussian elimination runing time %.6f',endtime-starttime)