MeanLinearVectorModel¶

class
menpo.model.
MeanLinearVectorModel
(components, mean)[source]¶ Bases:
LinearVectorModel
A Linear Model containing a matrix of vector components, each component vector being made up of features. The model additionally has a mean component which is handled accordingly when either:
 A component of the model is selected
 A projection operation is performed
Parameters:  components (
(n_components, n_features)
ndarray) – The components array.  mean (
(n_features,)
ndarray) – The mean vector.

component
(index, with_mean=True, scale=1.0)[source]¶ A particular component of the model, in vectorized form.
Parameters:  index (int) – The component that is to be returned
 with_mean (bool, optional) – If
True
, the component will be blended with the mean vector before being returned. If not, the component is returned on it’s own.  scale (float, optional) – A scale factor that should be directly applied to the component.
Only valid in the case where
with_mean == True
.
Returns: component_vector (
(n_features,)
ndarray) – The component vector.

copy
()¶ Generate an efficient copy of this object.
Note that Numpy arrays and other
Copyable
objects onself
will be deeply copied. Dictionaries and sets will be shallow copied, and everything else will be assigned (no copy will be made).Classes that store state other than numpy arrays and immutable types should overwrite this method to ensure all state is copied.
Returns: type(self)
– A copy of this object

instance
(weights)¶ Creates a new vector instance of the model by weighting together the components.
Parameters: weights ( (n_weights,)
ndarray or list) –The weightings for the first n_weights components that should be used.
weights[j]
is the linear contribution of the j’th principal component to the instance vector.Returns: vector ( (n_features,)
ndarray) – The instance vector for the weighting provided.

instance_vectors
(weights)¶ Creates new vectorized instances of the model using all the components of the linear model.
Parameters: weights ( (n_vectors, n_weights)
ndarray or list of lists) –The weightings for all components of the linear model. All components will be used to produce the instance.
weights[i, j]
is the linear contribution of the j’th principal component to the i’th instance vector produced.Raises: ValueError
– If n_weights > n_available_componentsReturns: vectors ( (n_vectors, n_features)
ndarray) – The instance vectors for the weighting provided.

orthonormalize_against_inplace
(linear_model)¶ Enforces that the union of this model’s components and another are both mutually orthonormal.
Both models keep its number of components unchanged or else a value error is raised.
Parameters: linear_model ( LinearVectorModel
) – A second linear model to orthonormalize this against.Raises: ValueError
– The number of features must be greater or equal than the sum of the number of components in both linear models ({} < {})

orthonormalize_inplace
()¶ Enforces that this model’s components are orthonormalized, s.t.
component_vector(i).dot(component_vector(j) = dirac_delta
.

project
(vector)¶ Projects the vector onto the model, retrieving the optimal linear reconstruction weights.
Parameters: vector ( (n_features,)
ndarray) – A vectorized novel instance.Returns: weights ( (n_components,)
ndarray) – A vector of optimal linear weights.

project_out
(vector)¶ Returns a version of vector where all the basis of the model have been projected out.
Parameters: vector ( (n_features,)
ndarray) – A novel vector.Returns: projected_out ( (n_features,)
ndarray) – A copy of vector with all basis of the model projected out.

project_out_vectors
(vectors)[source]¶ Returns a version of vectors where all the bases of the model have been projected out.
Parameters: vectors ( (n_vectors, n_features)
ndarray) – A matrix of novel vectors.Returns: projected_out ( (n_vectors, n_features)
ndarray) – A copy of vectors with all bases of the model projected out.

project_vectors
(vectors)[source]¶ Projects each of the vectors onto the model, retrieving the optimal linear reconstruction weights for each instance.
Parameters: vectors ( (n_samples, n_features)
ndarray) – Array of vectorized novel instances.Returns: projected ( (n_samples, n_components)
ndarray) – The matrix of optimal linear weights.

reconstruct
(vector)¶ Project a vector onto the linear space and rebuild from the weights found.
Parameters: vector ( (n_features, )
ndarray) – A vectorized novel instance to project.Returns: reconstructed ( (n_features,)
ndarray) – The reconstructed vector.

reconstruct_vectors
(vectors)¶ Projects the vectors onto the linear space and rebuilds vectors from the weights found.
Parameters: vectors ( (n_vectors, n_features)
ndarray) – A set of vectors to project.Returns: reconstructed ( (n_vectors, n_features)
ndarray) – The reconstructed vectors.

components
¶ The components matrix of the linear model.
Type: (n_available_components, n_features)
ndarray

n_components
¶ The number of bases of the model.
Type: int

n_features
¶ The number of elements in each linear component.
Type: int