Package mdp :: Package nodes :: Class LARSScikitsLearnNode

Class LARSScikitsLearnNode

Least Angle Regression model a.k.a. LAR This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LARS class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

n_features : int, optional: Number of selected active features
fit_intercept : boolean: whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).

Attributes

coef_ : array, shape = [n_features]: parameter vector (w in the fomulation formula)
intercept_ : float: independent term in decision function.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.LARS()
>>> clf.fit([[-1,1], [0, 0], [1, 1]], [-1, 0, -1], max_features=1)
LARS(verbose=False, fit_intercept=True)
>>> print clf.coef_
[ 0.         -0.81649658]

References

http://en.wikipedia.org/wiki/Least_angle_regression

init(self, input_dim=None, output_dim=None, dtype=None, **kwargs)
(Constructor)

n_features : int, optional: Number of selected active features
fit_intercept : boolean: whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).

Attributes

coef_ : array, shape = [n_features]: parameter vector (w in the fomulation formula)
intercept_ : float: independent term in decision function.

Examples

>>> from scikits.learn import linear_model
>>> clf = linear_model.LARS()
>>> clf.fit([[-1,1], [0, 0], [1, 1]], [-1, 0, -1], max_features=1)
LARS(verbose=False, fit_intercept=True)
>>> print clf.coef_
[ 0.         -0.81649658]

References

http://en.wikipedia.org/wiki/Least_angle_regression

_execute(self, x)

Overrides: Node._execute

_get_supported_dtypes(self)

Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.

Overrides: Node._get_supported_dtypes

_stop_training(self, **kwargs)

Concatenate the collected data in a single array.

Overrides: Node._stop_training

execute(self, x)

Predict using the linear model This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LARS class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : numpy array of shape [n_samples, n_features]

Returns

C : array, shape = [n_samples]: Returns predicted values.

Overrides: Node.execute

is_invertible()
Static Method

Return True if the node can be inverted, False otherwise.

Overrides: Node.is_invertible: (inherited documentation)

is_trainable()
Static Method

Return True if the node can be trained, False otherwise.

Returns: bool: A boolean indicating whether the node can be trained.
Overrides: Node.is_trainable

stop_training(self, **kwargs)

Fit the model using X, y as training data. This node has been automatically generated by wrapping the scikits.learn.linear_model.least_angle.LARS class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

X : array-like, shape = [n_samples, n_features]: Training data.
y : array-like, shape = [n_samples]: Target values.
precompute : True | False | 'auto' | array-like: Whether to use a precomputed Gram matrix to speed up calculations. If set to 'auto' let us decide. The Gram matrix can also be passed as argument.