def test_cholesky(size, n_threads):
X = np.random.rand(size + 1, size)
x_t_x = np.cov(X.T)
x_t_x_2 = np.copy(x_t_x)
print "About to start cholesky"
start = time.time()
cholesky.cholesky(x_t_x, n_threads)
#ch_x = np.asarray(cholesky.cholesky(x_t_x, n_threads)).T
print "Cholesky time for size: ", size, time.time() - start
start = time.time()
real_ch_x = np.linalg.cholesky(x_t_x_2)
print "Theirs took: ", time.time() - start
assert(np.allclose(x_t_x.T, real_ch_x))
def parallel_cholesky_linear_regressiion(x_t_x,x_t_y):
"""
calls the paralleized Cholesky written in Cython/OpenMP
"""
L = cholesky.cholesky(x_t_x)
z = np.linalg.solve(L,x_t_y)
theta = np.linalg.solve(np.transpose(L),z)
return theta
def runTest(self):
aa = list(
map(lambda xs: list(map(Fraction, xs)),
[[5, 3, 3], [3, 3, 3], [3, 3, 8]]))
m = np.array(aa)
d, a = cholesky.cholesky(m)
atma = a.transpose().dot(m).dot(a)
self.assertEqual(sum((atma - d).ravel()), Fraction(0, 1))
def ResolCholesky(A, B):
n, m = np.shape(A)
n2, m2 = np.shape(B)
L = cholesky.cholesky(A)
L2 = L.T
Laug = np.concatenate((L, B), axis=1)
#print(Laug)
Y = np.reshape(ResolutionSystTriInferieur.ResolutionSystTriInferieur(Laug),
(n2, 1))
#print(Y)
T = np.concatenate((L2, Y), axis=1)
#print(T)
X = ResolutionSystTriSup.ResolutionSystTriSup(T)
return (X)
def predict(self, X_):
kern = kernels.Kernels()
K_a = kern.kern_matrix(self.X, self.X, self.func, self.lam)
K_b = kern.kern_matrix(self.X, X_, self.func)
K_c = kern.kern_matrix(X_, self.X, self.func)
K_d = kern.kern_matrix(X_, X_, self.func, self.lam)
m = np.dot(np.dot(K_c, np.linalg.inv(K_a)), self.Y)
m = np.reshape(m, (np.size(m), 1))
C = K_d - np.dot(np.dot(K_c, np.linalg.inv(K_a)), K_b)
L = ch.cholesky(C)
return m, L
def differences_finies(N, a, b, f):
#pas de subdivision
h = (b - a) / N
# segment x
x = np.linspace(a, b, N + 1)
# Discretisation
xi = np.array([(a + (i * h)) for i in range(0, N)])
#resoudre Au=b
#construction matrice tridiagonale
A = np.zeros((N - 1, N - 1))
for i in range(N - 1):
for j in range(N - 1):
if (i == j):
A[i][j] = 2
if (j == i + 1):
A[i][j] = -1
if (i == j + 1):
A[i][j] = -1
#construction vecteur_f
vecteur_f = np.zeros((N - 1, 1))
for i in range(N - 1):
vecteur_f[i] = f(xi[i])
#Résolution de l'équation linéaire à l'aide de Cholesky
t = time.time()
U = cholesky.cholesky((1 / h**2) * A, vecteur_f)
print(U)
elapsed = time.time() - t
print("temps écoulé Cholesky ", elapsed)
#Résolution avec la factorisation LU
t = time.time()
U = lusolve.lusolve((1 / h**2) * A, vecteur_f)
print(U)
elapsed = time.time() - t
print("temps écoulé méthode LU ", elapsed)
def differences_finies(N, a, b, f):
#pas de subdivision
h = (b - a) / N
# segment x
x = np.linspace(a, b, N - 1)
# Discretisation
xi = np.array([(a + (i * h)) for i in range(0, N - 1)])
#resoudre Au=b
#construction matrice tridiagonale
A = np.zeros((N - 1, N - 1))
for i in range(N - 1):
for j in range(N - 1):
if (i == j):
A[i][j] = 2 * (1 + h**2)
elif (j == i + 1):
A[i][j] = -1
elif (i == j + 1):
A[i][j] = -1
#construction vecteur_f
vecteur_f = np.zeros((N - 1, 1))
for i in range(N - 1):
if (i == 0):
vecteur_f[i] = f(xi[i]) + 1 / h**2
elif (i == N - 2):
vecteur_f[i] = f(xi[i]) + exp(1) / h**2
else:
vecteur_f[i] = f(xi[i])
#Résolution de l'équation linéaire à l'aide de Cholesky
U = cholesky.cholesky((1 / h**2) * A, vecteur_f)
#print(U)
U_exact = [f(xi[i]) for i in range(0, N - 1)]
#print(U_exact)
return U, U_exact, x, xi
for k in range(n):
if(k!=i):
sum += abs(A[i][k])
if(sum > abs(A[i][i])):
return False
return True
testDiagonaleDominante = diagonaleDominante(A)
print("A est elle à diagonale dominante?")
print(testDiagonaleDominante)
##Résolution de Ax=b avec la méthode Cholesky
print("Méthode de Cholesky")
x = cholesky.cholesky(A,b)
print("valeur obtenue de x")
print(x)
##Vérification résultat
test = np.linalg.solve(A, b)
print("valeur réelle résolution linéaire")
print(test)
##Résolution de Ax=b avec la méthode de Gauss-Seidel
print("Méthode de Gauss-Seidel")
x0 = np.ones((10,1))
x = gaussseidel(A,b,Imax,errSeuil,x0)
print("valeur obtenue")
print(x)
def stiffnessmatrix(element, node, support, supportinfo):
#หา existnode
constrained = []
unconstrained = []
a = []
n = []
equation2 = {}
numsupportforce = 0
degreefreedom = len(node) * 2
g = (len(node) - len(support)) * 2
z = len(node)
L = [[0] * 2 for i in range(z)]
k = 0
print(len(node), "len all node")
for i in range(len(node)):
if node[i] not in support:
L[i][0] = k * 2
L[i][1] = k * 2 + 1
k = k + 1
for x in range(len(support)):
if supportinfo["type"][x] == 2:
numsupportforce = numsupportforce + 1
if supportinfo["constrained"][x] == "xr" or supportinfo[
"constrained"][x] == "xl":
constrained.append((x, 0))
unconstrained.append((x, 1))
elif supportinfo["constrained"][x] == "y":
unconstrained.append((x, 0))
constrained.append((x, 1))
elif supportinfo["type"][x] == 1:
numsupportforce = numsupportforce + 2
constrained.append((x, 0))
constrained.append((x, 1))
for z, b in unconstrained:
y = node.index(support[z])
L[y][b] = g
g = g + 1
for j, r in constrained:
y = node.index(support[j])
L[y][r] = g
g = g + 1
print(L, "L")
for x in range(degreefreedom):
for i in range(degreefreedom):
equation2.setdefault((x, i), []).append(0)
print(equation2, "equation2")
for q in range(len(element)):
print(q, "q")
Nearendnode = element[q][0]
Farendnode = element[q][1]
Nx = L[Nearendnode][0]
Ny = L[Nearendnode][1]
Fx = L[Farendnode][0]
Fy = L[Farendnode][1]
X = node[Farendnode][0] - node[Nearendnode][0]
Y = node[Farendnode][1] - node[Nearendnode][1]
Magnitude = ((X * X) + (Y * Y))**0.5
lambdaX = X / Magnitude
lambdaY = Y / Magnitude
print(Fx, "Fx")
print(Fy, "Fy")
print(Nx, "Nx")
print(Ny, "Ny")
print(lambdaX, "lamdaX value")
print(lambdaY, "lambdaY value")
print(Magnitude, "Magnitude value")
equation2[(
Nx, Nx)][-1] = equation2[(Nx, Nx)][-1] + (lambdaX**2) / Magnitude
equation2[(Ny, Nx)][-1] = equation2[
(Ny, Nx)][-1] + (lambdaX * lambdaY) / Magnitude
equation2[(
Fx, Nx)][-1] = equation2[(Fx, Nx)][-1] - (lambdaX**2) / Magnitude
equation2[(Fy, Nx)][-1] = equation2[
(Fy, Nx)][-1] - (lambdaX * lambdaY) / Magnitude
equation2[(Nx, Ny)][-1] = equation2[
(Nx, Ny)][-1] + (lambdaX * lambdaY) / Magnitude
equation2[(
Ny, Ny)][-1] = equation2[(Ny, Ny)][-1] + (lambdaY**2) / Magnitude
equation2[(Fx, Ny)][-1] = equation2[
(Fx, Ny)][-1] - (lambdaX * lambdaY) / Magnitude
equation2[(
Fy, Ny)][-1] = equation2[(Fy, Ny)][-1] - (lambdaY**2) / Magnitude
equation2[(
Nx, Fx)][-1] = equation2[(Nx, Fx)][-1] - (lambdaX**2) / Magnitude
equation2[(Ny, Fx)][-1] = equation2[
(Ny, Fx)][-1] - (lambdaX * lambdaY) / Magnitude
equation2[(
Fx, Fx)][-1] = equation2[(Fx, Fx)][-1] + (lambdaX**2) / Magnitude
equation2[(Fy, Fx)][-1] = equation2[
(Fy, Fx)][-1] + (lambdaX * lambdaY) / Magnitude
equation2[(Nx, Fy)][-1] = equation2[
(Nx, Fy)][-1] - (lambdaX * lambdaY) / Magnitude
equation2[(
Ny, Fy)][-1] = equation2[(Ny, Fy)][-1] - (lambdaY**2) / Magnitude
equation2[(Fx, Fy)][-1] = equation2[
(Fx, Fy)][-1] + (lambdaX * lambdaY) / Magnitude
equation2[(
Fy, Fy)][-1] = equation2[(Fy, Fy)][-1] + (lambdaY**2) / Magnitude
#ตัดให้เหลือ K11 อิงจา่ก จำนวน reactionatsupport
i = len(node) * 2 - numsupportforce
for row in range(i):
s = []
for column in range(i):
s.append(equation2[row, column][-1])
a.append(s)
print(a, "a")
result = cholesky.cholesky(a)
return result
