def test_dirichlet(self):
a = Numeric.arange(10) * 0.1 + 0.1
d = self.rng.dirichlet(a)
array_check(d, Float, (a.shape[0],))
ra = Numeric.reshape(a, (a.shape[0], -1))
ra = Numeric.transpose(ra)
p = rngmodule.dirichlet_pdf(d, ra)
d = self.rng.dirichlet(a, 100)
array_check(d, Float, (100, a.shape[0]))
def test_dirichlet(self):
a = Numeric.arange(10) * .1 + .1
d = self.rng.dirichlet(a)
array_check(d, Float, (a.shape[0], ))
ra = Numeric.reshape(a, (a.shape[0], -1))
ra = Numeric.transpose(ra)
p = rngmodule.dirichlet_pdf(d, ra)
d = self.rng.dirichlet(a, 100)
array_check(d, Float, (100, a.shape[0]))
def setUp(self):
self.dim = 100
self.param = 2
self.a = 1000.1
self.b = 100
self.x = Numeric.arange(self.dim)
x = self.x
self.y = self.a + self.b * self.x
self.w = Numeric.ones((self.dim,))
self.ws = multifit.linear_workspace(self.dim, self.param)
self.X = Numeric.transpose(Numeric.array((Numeric.ones(self.dim,), x)))
def setUp(self):
self.dim = 100
self.param = 2
self.a = 1000.1
self.b = 100
self.x = Numeric.arange(self.dim)
x = self.x
self.y = self.a + self.b * self.x
self.w = Numeric.ones((self.dim, ))
self.ws = multifit.linear_workspace(self.dim, self.param)
self.X = Numeric.transpose(Numeric.array(
(Numeric.ones(self.dim, ), x)))
def exp_df(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t = params[0]
yi = params[1]
sigma = params[2]
e = exp(-lambda_ * t)
e_s = e / sigma
df = Numeric.array((e_s, -t * A * e_s, 1 / sigma))
df = Numeric.transpose(df)
return df
def exp_df(x, params):
A = x[0]
lambda_ = x[1]
b = x[2]
t = params[0]
yi = params[1]
sigma = params[2]
e = exp(-lambda_ * t)
e_s = e/sigma
df = numx.array((e_s, -t * A * e_s, 1/sigma))
df = numx.transpose(df)
return df
