mcmodels.models.HomogeneousModel

class mcmodels.models.HomogeneousModel(kappa=1000)

Homogeneous model similar to Oh et al. 2014.

Implements the Homogeneous model for fitting nonnegative weights at the regional level. There is an additional constraint on the features to ensure the regression is well conditioned.

Parameters:

kappa : float, optional, default: 1000

The maximum condition number allowed for input data arrays.

References

Oh et al. 2014. A mesoscale connectome of the mouse brain. Nature,

508(7495), 207-214. doi: 10.1038/nature13186

Examples

>>> from mcmodels.core import VoxelModelCache, RegionalData
>>> from mcmodels.models import HomogeneousModel
>>> # get data with whcich to fit model
>>> cache = VoxelModelCache()
>>> voxel_data = cache.get_experiment_data()
>>> regional_data = RegionalData.from_voxel_data(voxel_data)
>>> # fit model
>>> reg = HomogeneousModel()
>>> reg.fit(regional_data.injections, regional_data.projections)
HomogeneousModel(kappa=1000)

Attributes:

columns_ : array

The features that are included in the regression after the conditioning has occurred.

coef_ : array, shape (n_targets, n_features)

The model weights.

Methods

fit(self, X, y[, sample_weight])	Fit HomogeneousModel.
get_params(self[, deep])	Get parameters for this estimator.
predict(self, X)	Predict using the HomogeneousModel.
score(self, X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(self, \*\*params)	Set the parameters of this estimator.

__init__(self, kappa=1000)

Methods

__init__(self[, kappa])
fit(self, X, y[, sample_weight])	Fit HomogeneousModel.
get_params(self[, deep])	Get parameters for this estimator.
predict(self, X)	Predict using the HomogeneousModel.
score(self, X, y[, sample_weight])	Returns the coefficient of determination R^2 of the prediction.
set_params(self, \*\*params)	Set the parameters of this estimator.

fit(self, X, y, sample_weight=None)

Fit HomogeneousModel.

Parameters:

X : array, shape (n_samples, n_features)

Training data.

y : array, shape (n_samples, n_features)

Target values.

Returns:

self : returns an instance of self

predict(self, X)

Predict using the HomogeneousModel.

Parameters:

X : array, shape (n_samples, n_features)

Training data.

Returns:

C : array, shape (n_samples,) or (n_samples, n_targets)

Returns predicted values.

weights

Convenience property for pulling out regional matrix.