adjust polynomial fitting demo

demos/polynomial_fit.py

@@ -12,14 +12,14 @@ def polynomial(coefficients, sampling_points):
 
     Parameters
     ----------
-    coefficients: Model
+    coefficients: Field
     sampling_points: Numpy array
     """
 
-    if not (isinstance(coefficients, ift.Model)
+    if not (isinstance(coefficients, ift.Field)
             and isinstance(sampling_points, np.ndarray)):
         raise TypeError
-    params = coefficients.value.to_global_data()
+    params = coefficients.to_global_data()
     out = np.zeros_like(sampling_points)
     for ii in range(len(params)):
         out += params[ii] * sampling_points**ii
@@ -88,8 +88,7 @@ y[5] -= 0
 
 # Set up minimization problem
 p_space = ift.UnstructuredDomain(N_params)
-params = ift.Variable(ift.MultiField.from_dict(
-    {'params': ift.full(p_space, 0.)}))['params']
+params = ift.full(p_space, 0.)
 R = PolynomialResponse(p_space, x)
 ift.extra.consistency_check(R)
 
@@ -98,7 +97,9 @@ d = ift.from_global_data(d_space, y)
 N = ift.DiagonalOperator(ift.from_global_data(d_space, var))
 
 IC = ift.GradientNormController(tol_abs_gradnorm=1e-8)
-H = ift.Hamiltonian(ift.GaussianEnergy(R(params), d, N), IC)
+likelihood = lambda inp: ift.GaussianEnergy(d, N)(R(inp))
+H = ift.Hamiltonian(likelihood, IC)
+H = ift.EnergyAdapter(params, H)
 H = H.make_invertible(IC)
 
 # Minimize
@@ -116,13 +117,13 @@ xs = np.linspace(xmin, xmax, 100)
 
 sc = ift.StatCalculator()
 for ii in range(len(samples)):
-    sc.add(params.at(samples[ii]).value)
-    ys = polynomial(params.at(samples[ii]), xs)
+    sc.add(samples[ii])
+    ys = polynomial(samples[ii], xs)
     if ii == 0:
         plt.plot(xs, ys, 'k', alpha=.05, label='Posterior samples')
         continue
     plt.plot(xs, ys, 'k', alpha=.05)
-ys = polynomial(params.at(H.position), xs)
+ys = polynomial(H.position, xs)
 plt.plot(xs, ys, 'r', linewidth=2., label='Interpolation')
 plt.legend()
 plt.savefig('fit.png')