Package mdp :: Package nodes :: Class LinearRegressionNode

Class LinearRegressionNode

Compute least-square, multivariate linear regression on the input
data, i.e., learn coefficients ``b_j`` so that::

  y_i = b_0 + b_1 x_1 + ... b_N x_N ,

for ``i = 1 ... M``, minimizes the square error given the training ``x``'s
and ``y``'s.

This is a supervised learning node, and requires input data ``x`` and
target data ``y`` to be supplied during training (see ``train``
docstring).

**Internal variables of interest**

  ``self.beta``
      The coefficients of the linear regression

Instance Methods

[hide private]

__init__(self, with_bias=True, use_pinv=False, input_dim=None, output_dim=None, dtype=None)
:Arguments:

_add_constant(self, x)
Add a constant term to the vector 'x'.

_check_train_args(self, x, y)

_execute(self, x)

_stop_training(self)

_train(self, x, y)
**Additional input arguments**

execute(self, x)
Process the data contained in `x`.

stop_training(self)
Stop the training phase.

train(self, x, y)
**Additional input arguments**

Inherited from unreachable.newobject: __long__, __native__, __nonzero__, __unicode__, next

Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __setattr__, __sizeof__, __subclasshook__

Inherited from Node

__add__(self, other)

__call__(self, x, *args, **kwargs)
Calling an instance of `Node` is equivalent to calling its `execute` method.

__repr__(self)
repr(x)

__str__(self)
str(x)

_check_input(self, x)

_check_output(self, y)

_get_supported_dtypes(self)
Return the list of dtypes supported by this node.

_get_train_seq(self)

_if_training_stop_training(self)

_inverse(self, x)

_pre_execution_checks(self, x)
This method contains all pre-execution checks.

_pre_inversion_checks(self, y)
This method contains all pre-inversion checks.

_refcast(self, x)
Helper function to cast arrays to the internal dtype.

_set_dtype(self, t)

_set_input_dim(self, n)

_set_output_dim(self, n)

copy(self, protocol=None)
Return a deep copy of the node.

get_current_train_phase(self)
Return the index of the current training phase.

get_dtype(self)
Return dtype.

get_input_dim(self)
Return input dimensions.

get_output_dim(self)
Return output dimensions.

get_remaining_train_phase(self)
Return the number of training phases still to accomplish.

get_supported_dtypes(self)
Return dtypes supported by the node as a list of :numpy:`dtype` objects.

has_multiple_training_phases(self)
Return True if the node has multiple training phases.

inverse(self, y, *args, **kwargs)
Invert `y`.

is_training(self)
Return True if the node is in the training phase, False otherwise.

save(self, filename, protocol=-1)
Save a pickled serialization of the node to `filename`.

set_dtype(self, t)
Set internal structures' dtype.

set_input_dim(self, n)
Set input dimensions.

set_output_dim(self, n)
Set output dimensions.

Static Methods

[hide private]

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

Inherited from Node

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

Properties

[hide private]

Inherited from object: __class__

Inherited from Node

_train_seq
List of tuples::

dtype
dtype

input_dim
Input dimensions

output_dim
Output dimensions

supported_dtypes
Supported dtypes

Method Details

[hide private]

init(self, with_bias=True, use_pinv=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)


:Arguments:

  with_bias
    If true, the linear model includes a constant term

    - True:  y_i = b_0 + b_1 x_1 + ... b_N x_N
    - False: y_i =       b_1 x_1 + ... b_N x_N

    If present, the constant term is stored in the first
    column of ``self.beta``.

  use_pinv
    If true, uses the pseudo-inverse function to compute
    the linear regression coefficients, which is more robust
    in some cases

Overrides: object.__init__

_add_constant(self, x)

Add a constant term to the vector 'x'.
x -> [1 x]

_train(self, x, y)


**Additional input arguments**

y
  array of size (x.shape[0], output_dim) that contains the observed
  output to the input x's.

Overrides: Node._train

execute(self, x)

Process the data contained in `x`.

If the object is still in the training phase, the function
`stop_training` will be called.
`x` is a matrix having different variables on different columns
and observations on the rows.

By default, subclasses should overwrite `_execute` to implement
their execution phase. The docstring of the `_execute` method
overwrites this docstring.

Overrides: Node.execute

is_invertible()
Static Method

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

Overrides: Node.is_invertible: (inherited documentation)

stop_training(self)

Stop the training phase.

By default, subclasses should overwrite `_stop_training` to implement
this functionality. The docstring of the `_stop_training` method
overwrites this docstring.

Overrides: Node.stop_training

train(self, x, y)


**Additional input arguments**

y
  array of size (x.shape[0], output_dim) that contains the observed
  output to the input x's.

Overrides: Node.train

Class LinearRegressionNode

__init__(self, with_bias=True, use_pinv=False, input_dim=None, output_dim=None, dtype=None) (Constructor)

_add_constant(self, x)

_check_train_args(self, x, y)

_execute(self, x)

_stop_training(self)

_train(self, x, y)

execute(self, x)

is_invertible() Static Method

stop_training(self)

train(self, x, y)

init(self, with_bias=True, use_pinv=False, input_dim=None, output_dim=None, dtype=None)
(Constructor)

is_invertible()
Static Method