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 run():
r = rng.rng()
bw = bspline(4, nbreak)
# Data to be fitted
x = 15. / (N - 1) * numx.arange(N)
y = numx.cos(x) * numx.exp(0.1 * x)
sigma = .1
w = 1.0 / sigma**2 * numx.ones(N)
dy = r.gaussian(sigma, N)
y = y + dy
# use uniform breakpoints on [0, 15]
bw.knots_uniform(0.0, 15.0)
X = numx.zeros((N, ncoeffs))
for i in range(N):
B = bw.eval(x[i])
X[i, :] = B
# do the fit
c, cov, chisq = multifit.wlinear(X, w, y,
multifit.linear_workspace(N, ncoeffs))
# output the smoothed curve
res_y = []
res_y_err = []
for i in range(N):
B = bw.eval(x[i])
yi, yi_err = multifit.linear_est(B, c, cov)
res_y.append(yi)
res_y_err.append(yi_err)
#print yi, yerr
res_y = numx.array(res_y)
res_y_err = numx.array(res_y_err)
return (
x,
y,
), (x, res_y), res_y_err
def run():
r = rng.rng()
bw = bspline(4, nbreak)
# Data to be fitted
x = 15. / (N-1) * numx.arange(N)
y = numx.cos(x) * numx.exp(0.1 * x)
sigma = .1
w = 1.0 / sigma**2 * numx.ones(N)
dy = r.gaussian(sigma, N)
y = y + dy
# use uniform breakpoints on [0, 15]
bw.knots_uniform(0.0, 15.0)
X = numx.zeros((N, ncoeffs))
for i in range(N):
B = bw.eval(x[i])
X[i,:] = B
# do the fit
c, cov, chisq = multifit.wlinear(X, w, y, multifit.linear_workspace(N, ncoeffs))
# output the smoothed curve
res_y = []
res_y_err = []
for i in range(N):
B = bw.eval(x[i])
yi, yi_err = multifit.linear_est(B, c, cov)
res_y.append(yi)
res_y_err.append(yi_err)
#print yi, yerr
res_y = numx.array(res_y)
res_y_err = numx.array(res_y_err)
return (x, y,), (x, res_y), res_y_err
