def test_big(self):
from RandomArray import seed, random
seed(13, 17)
X = random((1024, 512))
Y = fwt2(X)
X1 = ifwt2(Y)
assert Numeric.allclose(X - X1, 0)
def testLinearLeastSquares(self):
"""
From bug #503733.
"""
# XXX not positive on this yet
import LinearAlgebra
from RandomArray import seed, random
seed(7,19)
(n, m) = (180, 35)
yp = random((n,m))
y = random(n)
x, residuals, rank, sv = LinearAlgebra.linear_least_squares(yp, y)
# shouldn't segfault.
assert rank == m
def test_ifwt2c(self):
from RandomArray import seed, random
seed(13, 17)
X = random((8, 4))
Y = ifwt2(X)
Y = ifwt2(Y)
Y = fwt2(Y)
X1 = fwt2(Y)
assert Numeric.allclose(X - X1, 0)
def simple_rdp(s, dir=None, vel=None, fraction=0.25, fgcolor=(255,255,255), bgcolor=(128,128,128), rseed=None): if rseed: old_seed = get_seed() seed(rseed[0], rseed[1]) if dir is None: for n in range(3): if n == 0: m = uniform(0.0, 1.0, shape=(s.w, s.h)) mc = where(greater(m, fraction), bgcolor[n], fgcolor[n]) s.array[:,:,n] = mc[::].astype(UnsignedInt8) else: dx = -int(round(vel * math.cos(math.pi * dir / 180.0))) dy = int(round(vel * math.sin(math.pi * dir / 180.0))) a = s.array[:,:,:] a = concatenate((a[dx:,:,:],a[:dx,:,:]), axis=0) a = concatenate((a[:,dy:,:],a[:,:dy,:]), axis=1) s.array[:,:,:] = a[::] if rseed: seed(old_seed[0], old_seed[1])
def initializeRandomNumbersFromTime():
random.seed()
seed(0, 0)
try:
if numeric == "Numeric":
import RNG
elif numeric == "NumPy":
import numpy.oldnumeric.rng as RNG
except ImportError:
pass
if RNG is None:
if numeric == "Numeric":
from RandomArray import uniform, seed
elif numeric == "NumPy":
from numpy.oldnumeric.random_array import uniform, seed
random = __import__('random')
seed(1, 1)
random.seed(1)
def initializeRandomNumbersFromTime():
random.seed()
seed(0, 0)
def gaussian(mean, std, shape=None):
if shape is None:
x = random.normalvariate(0., 1.)
else:
x = N.zeros(shape, N.Float)
xflat = N.ravel(x)
for i in range(len(xflat)):
xflat[i] = random.normalvariate(0., 1.)
return mean + std * x
def initializeRandomNumbersFromTime():
random.seed()
seed(0, 0)
if numeric == "Numeric":
import RNG
elif numeric == "NumPy":
import numpy.oldnumeric.rng as RNG
except ImportError:
pass
if RNG is None:
if numeric == "Numeric":
from RandomArray import uniform, seed
elif numeric == "NumPy":
from numpy.oldnumeric.random_array import uniform, seed
random = __import__('random')
seed(1, 1)
random.seed(1)
def initializeRandomNumbersFromTime():
random.seed()
seed(0, 0)
def gaussian(mean, std, shape=None):
if shape is None:
x = random.normalvariate(0., 1.)
else:
x = N.zeros(shape, N.Float)
xflat = N.ravel(x)
for i in range(len(xflat)):
xflat[i] = random.normalvariate(0., 1.)
return mean + std*x
