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)
If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

_add_constant(self, x)
Add a constant term to the vector 'x'. x -> [1 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 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. If filename is None, return a string.

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)

If the input dimension and the output dimension are unspecified, they will be set when the train or execute method is called for the first time. If dtype is unspecified, it will be inherited from the data it receives at the first call of train or execute.

Every subclass must take care of up- or down-casting the internal structures to match this argument (use _refcast private method when possible).

Parameters:

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