def __init__(self, tobjects, distance, find_closest_points, k,
get_labels_of_record):
'''
Constructor.
@type tobjects: list of records
@param tobjects: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific tobjects parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between rec1 and rec2
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance)
@param distance: finding closest points,
@type k: integer
@param k: no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.tobjects = tobjects()
self.distance = distance
self.find_closest_points = find_closest_points
self.k = k
self.get_labels_of_record = get_labels_of_record
self.list_of_all_labels = find_all_labels(self.tobjects, self.get_labels_of_record)
#compute the counts:
self.c, self.c_prim = self.__calculate_label_counts()
self.fraction_knn_thresholds, self.fmeasure_per_class = self.__calculate_thresholds()
def __init__(self, frecords, distance, find_closest_points, k_list,
get_labels_of_record):
'''
Constructor.
@type frecords: list of records
@param frecords: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific frecords parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between 2 records
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance);
It returns training objects which are closest to the sample, in a sorted form, increasing
order in terms of their distance from sample.
@param distance: finding closest points,
@type k_list: list of integers
@param k_list: list of no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.frecords = list(frecords(
)) #TODO: zrobic podawanie listy skoro i tak robi sie liste z tego!
self.distance = distance
self.find_closest_points = find_closest_points
self.k_list = k_list
self.k_max = max(k_list)
PRINTER('[MlKnnFractionalEnsembledStrongest: init] max k: ' +
str(self.k_max))
self.get_labels_of_record = get_labels_of_record
self.list_of_all_labels = find_all_labels(self.frecords,
self.get_labels_of_record)
PRINTER('[MlKnnFractionalEnsembledStrongest: init] labels: ' +
str(self.list_of_all_labels))
#compute the counts of the form c[k][label][i]
#<- how many times for k, label i-neighbouring codes codes have the same label:
self.c, self.c_prim = self.__calculate_label_counts()
self.fraction_knn_thresholds = {}
self.fraction_knn_fmeasures = {}
for k in self.k_list:
self.fraction_knn_thresholds[k], self.fraction_knn_fmeasures[
k] = self.__calculate_thresholds(self.c[k], self.c_prim[k])
def __init__(self, frecords, distance, find_closest_points, k_list,
get_labels_of_record):
'''
Constructor.
@type frecords: list of records
@param frecords: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific frecords parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between 2 records
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance);
It returns training objects which are closest to the sample, in a sorted form, increasing
order in terms of their distance from sample.
@param distance: finding closest points,
@type k_list: list of integers
@param k_list: list of no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.frecords = list(frecords())#TODO: zrobic podawanie listy skoro i tak robi sie liste z tego!
self.distance = distance
self.find_closest_points = find_closest_points
self.k_list = k_list
self.k_max = max(k_list)
PRINTER('[MlKnnFractionalEnsembledStrongest: init] max k: '+str(self.k_max))
self.get_labels_of_record = get_labels_of_record
self.list_of_all_labels = find_all_labels(self.frecords, self.get_labels_of_record)
PRINTER('[MlKnnFractionalEnsembledStrongest: init] labels: '+str(self.list_of_all_labels))
#compute the counts of the form c[k][label][i]
#<- how many times for k, label i-neighbouring codes codes have the same label:
self.c, self.c_prim = self.__calculate_label_counts()
self.fraction_knn_thresholds = {}
self.fraction_knn_fmeasures = {}
for k in self.k_list:
self.fraction_knn_thresholds[k], self.fraction_knn_fmeasures[k] = self.__calculate_thresholds(self.c[k], self.c_prim[k])
def __init__(self, frecords, distance, find_closest_points, k_list,
get_labels_of_record):
'''
Constructor.
@type frecords: list of records
@param frecords: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific frecords parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between 2 records
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance);
It returns training objects which are closest to the sample, in a sorted form, increasing
order in terms of their distance from sample.
@param distance: finding closest points,
@type k_list: list of integers
@param k_list: list of no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.list_of_all_labels = find_all_labels(list(frecords()),
get_labels_of_record)
self.k_list = k_list
PRINTER('[MlKnnFractionalEnsembledStrongest: init] labels: ' +
str(self.list_of_all_labels))
PRINTER(
'[MlKnnFractionalEnsembledStrongest: init]: START OF TRAINING...')
self.mlknn_fractionals = {}
for k in self.k_list:
self.mlknn_fractionals[k] = MlKnnFractional(
frecords, distance, find_closest_points, k,
get_labels_of_record)
PRINTER(
'[MlKnnFractionalEnsembledStrongest: init]: END OF TRAINING...')
def __init__(self, tobjects, distance, find_closest_points, k,
smoothing_param, get_labels_of_record):
'''
Constructor.
@type tobjects: list of training objects
@param tobjects: used to calculate parameters (probabilities)
and nearest neighbours amongst the training objects it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific tobjects parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between rec1 and rec2
@type find_closest_points: function of signature:
find_closest_points(sample, objects, excluding, how_many, distance)
@param distance: finding closest points for sample amongst the objects,
@type k: integer
@param k: no. of neighbours taken into consideration
@type k: smoothing_param
@param smoothing_param - min number of occurences of each label = as in the algorithm
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.tobjects = tobjects #list(tobjects())
self.distance = distance
self.find_closest_points = find_closest_points
self.k = k
self.smoothing_param = smoothing_param
self.get_labels_of_record = get_labels_of_record
self.labels = find_all_labels(self.tobjects, self.get_labels_of_record)
PRINTER('[MlKnn: init] labels: ' + str(self.labels))
self.threshold = self.init_zero_thresholds(self.labels)
#compute the probabilities:
self.labelprobabilities, self.labelcounterprobabilities = self.__get_label_probabilities(
)
self.posteriorprobabilities = self.__get_posterior_probabilities()
def __init__(self, tobjects, distance, find_closest_points, k, smoothing_param, get_labels_of_record):
'''
Constructor.
@type tobjects: list of training objects
@param tobjects: used to calculate parameters (probabilities)
and nearest neighbours amongst the training objects it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific tobjects parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between rec1 and rec2
@type find_closest_points: function of signature:
find_closest_points(sample, objects, excluding, how_many, distance)
@param distance: finding closest points for sample amongst the objects,
@type k: integer
@param k: no. of neighbours taken into consideration
@type k: smoothing_param
@param smoothing_param - min number of occurences of each label = as in the algorithm
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.tobjects = tobjects#list(tobjects())
self.distance = distance
self.find_closest_points = find_closest_points
self.k = k
self.smoothing_param = smoothing_param
self.get_labels_of_record = get_labels_of_record
self.labels = find_all_labels(self.tobjects, self.get_labels_of_record)
PRINTER('[MlKnn: init] labels: '+str(self.labels))
self.threshold = self.init_zero_thresholds(self.labels)
#compute the probabilities:
self.labelprobabilities, self.labelcounterprobabilities = self.__get_label_probabilities()
self.posteriorprobabilities = self.__get_posterior_probabilities()
def __init__(self, frecords, distance, find_closest_points, k_list, get_labels_of_record):
'''
Constructor.
@type frecords: list of records
@param frecords: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific frecords parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between 2 records
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance);
It returns training objects which are closest to the sample, in a sorted form, increasing
order in terms of their distance from sample.
@param distance: finding closest points,
@type k_list: list of integers
@param k_list: list of no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.list_of_all_labels = find_all_labels(list(frecords()), get_labels_of_record)
self.k_list = k_list
PRINTER('[MlKnnFractionalEnsembledStrongest: init] labels: '+str(self.list_of_all_labels))
PRINTER('[MlKnnFractionalEnsembledStrongest: init]: START OF TRAINING...')
self.mlknn_fractionals = {}
for k in self.k_list:
self.mlknn_fractionals[k] = MlKnnFractional(frecords, distance, find_closest_points, k,
get_labels_of_record)
PRINTER('[MlKnnFractionalEnsembledStrongest: init]: END OF TRAINING...')
def __init__(self, tobjects, distance, find_closest_points, k,
get_labels_of_record):
'''
Constructor.
@type tobjects: list of records
@param tobjects: used to calculate parameters (probabilities)
and nearest neighbours amongst the records it returns;
NOTE: if a user wants to manipulate, which codes to consider(e.g. higher or lower level)
it is good to give a specific tobjects parameter
@type distance: object that contains a method of signature: distance(rec1, rec2)
@param distance: returns distance measure between rec1 and rec2
@type find_closest_points: function of signature:
find_closest_points(sample, records, excluding, how_many, distance)
@param distance: finding closest points,
@type k: integer
@param k: no. of neighbours taken into consideration
@type get_labels_of_record: function
@param get_labels_of_record: returns list of labels assigned to a record
'''
self.tobjects = tobjects()
self.distance = distance
self.find_closest_points = find_closest_points
self.k = k
self.get_labels_of_record = get_labels_of_record
self.list_of_all_labels = find_all_labels(self.tobjects,
self.get_labels_of_record)
#compute the counts:
self.c, self.c_prim = self.__calculate_label_counts()
self.fraction_knn_thresholds, self.fmeasure_per_class = self.__calculate_thresholds(
)
def find_all_labels(self, tobjects, get_labels):
return find_all_labels.find_all_labels(tobjects, get_labels)
def find_all_labels(self, tobjects, get_labels):
return find_all_labels.find_all_labels(tobjects, get_labels)
