dcase_framework.features.FeatureNormalizer

class dcase_framework.features.FeatureNormalizer(stat=None, feature_matrix=None)

Feature normalizer

Accumulates feature statistics

Examples

>>> normalizer = FeatureNormalizer()
>>> for feature_matrix in training_items:
>>>     normalizer.accumulate(feature_matrix)
>>>
>>> normalizer.finalize()

>>> for feature_matrix in test_items:
>>>     feature_matrix_normalized = normalizer.normalizer(feature_matrix)
>>>     # used the features

__init__ method.

Parameters:

stat : dict or None

Pre-calculated statistics in dict to initialize internal state

feature_matrix : numpy.ndarray [shape=(frames, number of feature values)] or None

Feature matrix to be used in the initialization

__init__(stat=None, feature_matrix=None)

__init__ method.

Parameters:

stat : dict or None

Pre-calculated statistics in dict to initialize internal state

feature_matrix : numpy.ndarray [shape=(frames, number of feature values)] or None

Feature matrix to be used in the initialization

Methods

__init__([stat, feature_matrix])	__init__ method.
accumulate(feature_container)	Accumulate statistics
clear(() -> None.  Remove all items from D.)
copy(() -> a shallow copy of D)
detect_file_format(filename)	Detect file format from extension
empty()	Check if file is empty
exists()	Checks that file exists
finalize()	Finalize statistics calculation
fromkeys(...)	v defaults to None.
get((k[,d]) -> D[k] if k in D, ...)
get_dump_content(data)	Clean internal content for saving
get_file_information()	Get file information, filename
get_hash([data])	Get unique hash string (md5) for given parameter dict
get_hash_for_path([dotted_path])
get_path(dotted_path[, default, data])	Get value from nested dict with dotted path
has_key((k) -> True if D has a key k, else False)
items(() -> list of D’s (key, value) pairs, ...)
iteritems(() -> an iterator over the (key, ...)
iterkeys(() -> an iterator over the keys of D)
itervalues(...)
keys(() -> list of D’s keys)
load(\*args, \*\*kwargs)	Load file
log([level])	Log container content
merge(override[, target])	Recursive dict merge
normalize(feature_container[, channel])	Normalize feature matrix with internal statistics of the class
pop((k[,d]) -> v, ...)	If key is not found, d is returned if given, otherwise KeyError is raised
popitem(() -> (k, v), ...)	2-tuple; but raise KeyError if D is empty.
process(feature_data)	Normalize feature matrix with internal statistics of the class
save(\*args, \*\*kwargs)	Save file
set_path(dotted_path, new_value[, data])	Set value in nested dict with dotted path
setdefault((k[,d]) -> D.get(k,d), ...)
show()	Print container content
update(([E, ...)	If E present and has a .keys() method, does: for k in E: D[k] = E[k]
values(() -> list of D’s values)
viewitems(...)
viewkeys(...)
viewvalues(...)

Attributes

valid_formats