Python scipy.randn() Examples
The following are 8
code examples of scipy.randn().
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Example #1
| Source File: shared_ops.py From strawberryfields with Apache License 2.0 | 6 votes |
|
def haar_measure(n):
"""A Random matrix distributed with the Haar measure.
For more details, see :cite:`mezzadri2006`.
Args:
n (int): matrix size
Returns:
array: an nxn random matrix
"""
z = (sp.randn(n, n) + 1j * sp.randn(n, n)) / np.sqrt(2.0)
q, r = qr(z)
d = sp.diagonal(r)
ph = d / np.abs(d)
q = np.multiply(q, ph, q)
return q
Example #2
| Source File: test_decompositions.py From strawberryfields with Apache License 2.0 | 6 votes |
|
def haar_measure(n):
"""A Random matrix distributed with the Haar measure.
For more details, see :cite:`mezzadri2006`.
Args:
n (int): matrix size
Returns:
array: an nxn random matrix
"""
z = (sp.randn(n, n) + 1j * sp.randn(n, n)) / np.sqrt(2.0)
q, r = qr(z)
d = sp.diagonal(r)
ph = d / np.abs(d)
q = np.multiply(q, ph, q)
return q
Example #3
| Source File: gp.py From GPPVAE with Apache License 2.0 | 6 votes |
|
def generate_data(N, S, L):
# generate genetics
G = 1.0 * (sp.rand(N, S) < 0.2)
G -= G.mean(0)
G /= G.std(0) * sp.sqrt(G.shape[1])
# generate latent phenotypes
Zg = sp.dot(G, sp.randn(G.shape[1], L))
Zn = sp.randn(N, L)
# generate variance exapleind
vg = sp.linspace(0.8, 0, L)
# rescale and sum
Zg *= sp.sqrt(vg / Zg.var(0))
Zn *= sp.sqrt((1 - vg) / Zn.var(0))
Z = Zg + Zn
return Z, G
Example #4
| Source File: warm_start_test.py From osqp-python with Apache License 2.0 | 5 votes |
|
def test_warm_start(self):
# Big problem
sp.random.seed(2)
self.n = 100
self.m = 200
self.A = sparse.random(self.m, self.n, density=0.9, format='csc')
self.l = -sp.rand(self.m) * 2.
self.u = sp.rand(self.m) * 2.
P = sparse.random(self.n, self.n, density=0.9)
self.P = sparse.triu(P.dot(P.T), format='csc')
self.q = sp.randn(self.n)
# Setup solver
self.model = osqp.OSQP()
self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
**self.opts)
# Solve problem with OSQP
res = self.model.solve()
# Store optimal values
x_opt = res.x
y_opt = res.y
tot_iter = res.info.iter
# Warm start with zeros and check if number of iterations is the same
self.model.warm_start(x=np.zeros(self.n), y=np.zeros(self.m))
res = self.model.solve()
self.assertEqual(res.info.iter, tot_iter)
# Warm start with optimal values and check that number of iter < 10
self.model.warm_start(x=x_opt, y=y_opt)
res = self.model.solve()
self.assertLess(res.info.iter, 10)
Example #5
| Source File: polishing_test.py From osqp-python with Apache License 2.0 | 5 votes |
|
def test_polish_unconstrained(self):
# Unconstrained QP problem
sp.random.seed(4)
self.n = 30
self.m = 0
P = sparse.diags(np.random.rand(self.n)) + 0.2*sparse.eye(self.n)
self.P = P.tocsc()
self.q = np.random.randn(self.n)
self.A = sparse.csc_matrix((self.m, self.n))
self.l = np.array([])
self.u = np.array([])
self.model = osqp.OSQP()
self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
**self.opts)
# Solve problem
res = self.model.solve()
# Assert close
nptest.assert_array_almost_equal(
res.x, np.array([
-0.61981415, -0.06174194, 0.83824061, -0.0595013, -0.17810828,
2.90550031, -1.8901713, -1.91191741, -3.73603446, 1.7530356,
-1.67018181, 3.42221944, 0.61263403, -0.45838347, -0.13194248,
2.95744794, 5.2902277, -1.42836238, -8.55123842, -0.79093815,
0.43418189, -0.69323554, 1.15967924, -0.47821898, 3.6108927,
0.03404309, 0.16322926, -2.17974795, 0.32458796, -1.97553574]))
nptest.assert_array_almost_equal(res.y, np.array([]))
nptest.assert_array_almost_equal(res.info.obj_val, -35.020288603855825)
Example #6
| Source File: polishing_test.py From osqp-python with Apache License 2.0 | 5 votes |
|
def test_polish_random(self):
# Random QP problem
sp.random.seed(6)
self.n = 30
self.m = 50
Pt = sp.randn(self.n, self.n)
self.P = sparse.triu(np.dot(Pt.T, Pt), format='csc')
self.q = sp.randn(self.n)
self.A = sparse.csc_matrix(sp.randn(self.m, self.n))
self.l = -3 + sp.randn(self.m)
self.u = 3 + sp.randn(self.m)
self.model = osqp.OSQP()
self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
**self.opts)
# Solve problem
res = self.model.solve()
# Assert close
nptest.assert_array_almost_equal(
res.x, np.array([
-0.58549607, 0.0030388, -0.07154039, -0.0406463, -0.13349925,
-0.1354755, -0.17417362, 0.0165324, -0.12213118, -0.10477034,
-0.51748662, -0.05310921, 0.07862616, 0.53663003, -0.01459859,
0.40678716, -0.03496123, 0.25722838, 0.06335071, 0.29908295,
-0.6223218, -0.07614658, -0.3892153, -0.18111635, 0.56301768,
0.10429917, 0.09821862, -0.30881928, 0.24430531, 0.06597486]))
nptest.assert_array_almost_equal(
res.y, np.array([
0., -2.11407101e-01, 0., 0., 0., 0., 0., 0., 0.,
0., -3.78854588e-02, 0., -1.58346998e-02, 0., 0.,
-6.88711599e-02, 0., 0., 0., 0., 0., 0., 0., 0.,
6.04385132e-01, 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1.37995470e-01, 0., 0., 0., -2.04427802e-02,
0., -1.32983915e-01, 0., 2.94425952e-02, 0., 0.,
0., 0., 0., -6.53409219e-02, 0.]))
nptest.assert_array_almost_equal(res.info.obj_val, -3.262280663471232)
Example #7
| Source File: primal_infeasibility_test.py From osqp-python with Apache License 2.0 | 5 votes |
|
def test_primal_infeasible_problem(self):
# Simple QP problem
sp.random.seed(4)
self.n = 50
self.m = 500
# Generate random Matrices
Pt = sparse.random(self.n, self.n)
self.P = sparse.triu(Pt.T.dot(Pt), format='csc')
self.q = sp.randn(self.n)
self.A = sparse.random(self.m, self.n).tolil() # Lil for efficiency
self.u = 3 + sp.randn(self.m)
self.l = -3 + sp.randn(self.m)
# Make random problem primal infeasible
self.A[int(self.n/2), :] = self.A[int(self.n/2)+1, :]
self.l[int(self.n/2)] = self.u[int(self.n/2)+1] + 10 * sp.rand()
self.u[int(self.n/2)] = self.l[int(self.n/2)] + 0.5
# Convert A to csc
self.A = self.A.tocsc()
self.model = osqp.OSQP()
self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,
**self.opts)
# Solve problem with OSQP
res = self.model.solve()
# Assert close
self.assertEqual(res.info.status_val,
constant('OSQP_PRIMAL_INFEASIBLE'))
Example #8
| Source File: test.py From PyCurvelab with MIT License | 4 votes |
|
def test(dim=2, clen=10):
for i in range(clen):
print("-----------------------------------")
if dim == 2:
sz = np.arange(256, 513)
elif dim == 3:
sz = np.arange(64, 128)
np.random.shuffle(sz)
iscplx = [True, False]
np.random.shuffle(iscplx)
if iscplx[0]:
print("Complex input")
f = np.array(sc.randn(*sz[:dim])+sc.randn(*sz[:dim])*1j)
else:
print("Real input")
f = np.array(sc.randn(*sz[:dim]))
isac = [True, False]
np.random.shuffle(isac)
if isac[0]:
print("All curvelets")
else:
print("Wavelets at finest scale")
print(f.shape)
if dim == 2:
A = ct.fdct2(f.shape, 6, 32, isac[0], cpx=iscplx[0])
elif dim == 3:
A = ct.fdct3(f.shape, 4, 8, isac[0], cpx=iscplx[0])
x = A.fwd(f)
if np.allclose(norm(f.flatten(), ord=2), norm(x, ord=2)):
print('Energy check ok!')
else:
print('Problem w energy test')
fr = A.inv(x)
if np.allclose(f.flatten(), fr.flatten()):
print('Inverse check ok!')
else:
print('Problem w inverse test')
print("||f|| = ", norm(f.flatten(), ord=2), f.dtype)
print("||x|| = ", norm(x, ord=2), x.dtype)
print("||fr|| = ", norm(fr.flatten(), ord=2), fr.dtype)
