def test_nonnegativeleastsquares():
n = 100
X = np.eye(n)
beta = np.random.rand(n)
y = np.dot(X, beta)
my_nnls = opt.NonNegativeLeastSquares()
my_nnls.fit(X, y)
npt.assert_equal(my_nnls.coef_, beta)
npt.assert_equal(my_nnls.predict(X), y)
def __init__(self,
gtab,
sphere=None,
response=[0.0015, 0.0005, 0.0005],
solver='ElasticNet',
l1_ratio=0.5,
alpha=0.001,
isotropic=None):
"""
Initialize a Sparse Fascicle Model
Parameters
----------
gtab : GradientTable class instance
sphere : Sphere class instance, optional
A sphere on which coefficients will be estimated. Default:
symmetric sphere with 362 points (from :mod:`dipy.data`).
response : (3,) array-like, optional
The eigenvalues of a canonical tensor to be used as the response
function of single-fascicle signals.
Default:[0.0015, 0.0005, 0.0005]
solver : string, or initialized linear model object.
This will determine the algorithm used to solve the set of linear
equations underlying this model. If it is a string it needs to be
one of the following: {'ElasticNet', 'NNLS'}. Otherwise, it can be
an object that inherits from `dipy.optimize.SKLearnLinearSolver`
or an object with a similar interface from Scikit Learn:
`sklearn.linear_model.ElasticNet`, `sklearn.linear_model.Lasso` or
`sklearn.linear_model.Ridge` and other objects that inherit from
`sklearn.base.RegressorMixin`.
Default: 'ElasticNet'.
l1_ratio : float, optional
Sets the balance betwee L1 and L2 regularization in ElasticNet
[Zou2005]_. Default: 0.5
alpha : float, optional
Sets the balance between least-squares error and L1/L2
regularization in ElasticNet [Zou2005]_. Default: 0.001
isotropic : IsotropicModel class instance
This is a class that implements the function that calculates the
value of the isotropic signal. This is a value of the signal that is
independent of direction, and therefore removed from both sides of
the SFM equation. The default is an instance of IsotropicModel, but
other functions can be inherited from IsotropicModel to implement
other fits to the aspects of the data that depend on b-value, but
not on direction.
Notes
-----
This is an implementation of the SFM, described in [Rokem2015]_.
.. [Rokem2014] Ariel Rokem, Jason D. Yeatman, Franco Pestilli, Kendrick
N. Kay, Aviv Mezer, Stefan van der Walt, Brian A. Wandell
(2014). Evaluating the accuracy of diffusion MRI models in white
matter. PLoS ONE 10(4): e0123272. doi:10.1371/journal.pone.0123272
.. [Zou2005] Zou H, Hastie T (2005). Regularization and variable
selection via the elastic net. J R Stat Soc B:301-320
"""
ReconstModel.__init__(self, gtab)
if sphere is None:
sphere = dpd.get_sphere()
self.sphere = sphere
self.response = np.asarray(response)
if isotropic is None:
isotropic = IsotropicModel
self.isotropic = isotropic
if solver == 'ElasticNet':
self.solver = lm.ElasticNet(l1_ratio=l1_ratio,
alpha=alpha,
positive=True,
warm_start=True)
elif solver == 'NNLS' or solver == 'nnls':
self.solver = opt.NonNegativeLeastSquares()
elif (isinstance(solver, opt.SKLearnLinearSolver) or has_sklearn
and isinstance(solver, sklearn.base.RegressorMixin)):
self.solver = solver
else:
e_s = "The `solver` key-word argument needs to be: "
e_s += "'ElasticNet', 'NNLS', or a "
e_s += "`dipy.optimize.SKLearnLinearSolver` object"
raise ValueError(e_s)