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
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