Package mdp :: Package nodes :: Class HitParadeNode

Class HitParadeNode

Collect the first n local maxima and minima of the training signal which are separated by a minimum gap d.

This is an analysis node, i.e. the data is analyzed during training and the results are stored internally. Use the get_maxima and get_minima methods to access them.

Instance Methods

[hide private]

__init__(self, n, d=1, input_dim=None, output_dim=None, dtype=None)
Initializes an object of type 'HitParadeNode'.

list

_get_supported_dtypes(self)
Return the data types supported by this node.

_set_input_dim(self, n)

_train(self, x)

tuple

get_maxima(self)
Return the tuple defining the maxima fulfilling specified criteria. If the training phase has not been completed yet, call stop_training.

tuple

get_minima(self)
Return the tuple defining the minima fulfilling specified criteria. If the training phase has not been completed yet, call stop_training.

train(self, x)
Update the internal structures according to the input data x.

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 PreserveDimNode

_set_output_dim(self, n)

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)

_check_train_args(self, x, *args, **kwargs)

_execute(self, x)

_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)

_stop_training(self, *args, **kwargs)

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

execute(self, x, *args, **kwargs)
Process the data contained in x.

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. 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.

stop_training(self, *args, **kwargs)
Stop the training phase.

Static Methods

[hide private]

Inherited from Node

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

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, n, d=1, input_dim=None, output_dim=None, dtype=None)
(Constructor)

Initializes an object of type 'HitParadeNode'.

Parameters:

n (int) - Number of maxima and minima to remember.
d (int) - Minimum gap between two hits.
input_dim (int) - The input dimensionality.
output_dim (int) - The output dimensionality.
dtype (numpy.dtype or str) - The datatype.

Overrides: object.__init__

_get_supported_dtypes(self)

Return the data types supported by this node.

Returns: list: The list of numpy.dtypes that this node supports.
Overrides: Node._get_supported_dtypes

_set_input_dim(self, n)

Overrides: Node._set_input_dim

_train(self, x)

Overrides: Node._train

get_maxima(self)

Return the tuple defining the maxima fulfilling specified criteria. If the training phase has not been completed yet, call stop_training.

Returns: tuple: A tuple containing maxima and their corresponding indices as numpy.ndarrays (see example in definition of the method OneDimensionalHitParade.update). The maxima are sorted in descending order.

get_minima(self)

Return the tuple defining the minima fulfilling specified criteria. If the training phase has not been completed yet, call stop_training.

Returns: tuple: A tuple containing minima and their corresponding indices as numpy.ndarrays (see example in definition of the OneDimensionalHitParade.update() function). The minima are sorted in descending order.

train(self, x)

Update the internal structures according to the input data x.

x is a matrix having different variables on different columns and observations on the rows.

By default, subclasses should overwrite _train to implement their training phase. The docstring of the _train method overwrites this docstring.

Note: a subclass supporting multiple training phases should implement the same signature for all the training phases and document the meaning of the arguments in the _train method doc-string. Having consistent signatures is a requirement to use the node in a flow.

Overrides: Node.train

Class HitParadeNode

__init__(self, n, d=1, input_dim=None, output_dim=None, dtype=None) (Constructor)

_get_supported_dtypes(self)

_set_input_dim(self, n)

_train(self, x)

get_maxima(self)

get_minima(self)

train(self, x)

init(self, n, d=1, input_dim=None, output_dim=None, dtype=None)
(Constructor)