def __init__(self, database=[], features='all', perform_splits=1, num_k=1):
"""**kNNInteractive** (ImageList *database* = ``[]``, *features* = 'all', bool *perform_splits* = ``True``, int *num_k* = ``1``)
Creates a new kNN interactive classifier instance.
*database*
Must be a list (or Python interable) containing glyphs to use
as training data for the classifier.
Any images in the list that were manually classified (have
classification_state == MANUAL) will be used as training data
for the classifier. Any UNCLASSIFIED or AUTOMATICALLY
classified images will be ignored.
When initializing a noninteractive classifier, the database
*must* be non-empty.
*features*
A list of feature function names to use for classification.
These feature names
correspond to the `feature plugin methods`__. To use all
available feature functions, pass in ``'all'``.
.. __: plugins.html#features
*perform_splits*
If ``perform_splits`` is ``True``, glyphs trained with names
beginning with ``_split.`` are run through a given splitting
algorithm. For instance, glyphs that need to be broken into
upper and lower halves for further classification of those
parts would be trained as ``_split.splity``. When the
automatic classifier encounters glyphs that most closely
match those trained as ``_split``, it will perform the
splitting algorithm and then continue to recursively
classify its parts.
The `splitting algorithms`__ are documented in the plugin documentation.
.. __: plugins.html#segmentation
New splitting algorithms can be created by `writing plugin`__ methods
in the category ``Segmentation``.
.. __: writing_plugins.html
"""
self.features = features
self.feature_functions = core.ImageBase.get_feature_functions(features)
num_features = features_module.get_features_length(features)
_kNNBase.__init__(self, num_features=num_features, num_k=num_k)
classify.InteractiveClassifier.__init__(self, database, perform_splits)
def __init__(self, database=[], features='all', perform_splits=1, num_k=1):
"""**kNNInteractive** (ImageList *database* = ``[]``, *features* = 'all', bool *perform_splits* = ``True``, int *num_k* = ``1``)
Creates a new kNN interactive classifier instance.
*database*
Must be a list (or Python interable) containing glyphs to use
as training data for the classifier.
Any images in the list that were manually classified (have
classification_state == MANUAL) will be used as training data
for the classifier. Any UNCLASSIFIED or AUTOMATICALLY
classified images will be ignored.
When initializing a noninteractive classifier, the database
*must* be non-empty.
*features*
A list of feature function names to use for classification.
These feature names
correspond to the `feature plugin methods`__. To use all
available feature functions, pass in ``'all'``.
.. __: plugins.html#features
*perform_splits*
If ``perform_splits`` is ``True``, glyphs trained with names
beginning with ``_split.`` are run through a given splitting
algorithm. For instance, glyphs that need to be broken into
upper and lower halves for further classification of those
parts would be trained as ``_split.splity``. When the
automatic classifier encounters glyphs that most closely
match those trained as ``_split``, it will perform the
splitting algorithm and then continue to recursively
classify its parts.
The `splitting algorithms`__ are documented in the plugin documentation.
.. __: plugins.html#segmentation
New splitting algorithms can be created by `writing plugin`__ methods
in the category ``Segmentation``.
.. __: writing_plugins.html
"""
self.features = features
self.feature_functions = core.ImageBase.get_feature_functions(features)
num_features = features_module.get_features_length(features)
_kNNBase.__init__(self, num_features=num_features, num_k=num_k)
classify.InteractiveClassifier.__init__(self, database, perform_splits)
def change_feature_set(self, f):
"""**change_feature_set** (*features*)
Changes the set of features used in the classifier to the given list of feature names.
*features*
These feature names correspond to the `feature plugin methods`__.
To use all available feature functions, pass in ``'all'``.
.. __: plugins.html#features"""
self.features = f
self.feature_functions = core.ImageBase.get_feature_functions(f)
self.num_features = features_module.get_features_length(f)
if len(self.database):
self.is_dirty = True
self.generate_features_on_glyphs(self.database)
def change_feature_set(self, f):
"""**change_feature_set** (*features*)
Changes the set of features used in the classifier to the given list of feature names.
*features*
These feature names correspond to the `feature plugin methods`__.
To use all available feature functions, pass in ``'all'``.
.. __: plugins.html#features"""
self.features = f
self.feature_functions = core.ImageBase.get_feature_functions(f)
self.num_features = features_module.get_features_length(f)
if len(self.database):
self.is_dirty = True
self.generate_features_on_glyphs(self.database)
