def distance_matrix(data, distance_constructor=Euclidean, progress_callback=None):
""" A helper function that computes an :obj:`Orange.misc.SymMatrix` of all
pairwise distances between instances in `data`.
:param data: A data table
:type data: :obj:`Orange.data.Table`
:param distance_constructor: An DistanceConstructor instance (defaults to :obj:`Euclidean`).
:type distance_constructor: :obj:`Orange.distances.DistanceConstructor`
:param progress_callback: A function (taking one argument) to use for
reporting the on the progress.
:type progress_callback: function
:rtype: :class:`Orange.misc.SymMatrix`
"""
matrix = Orange.misc.SymMatrix(len(data))
dist = distance_constructor(data)
iter_count = matrix.dim * (matrix.dim - 1) / 2
milestones = progress_bar_milestones(iter_count, 100)
for count, ((i, ex1), (j, ex2)) in enumerate(_pairs(enumerate(data))):
matrix[i, j] = dist(ex1, ex2)
if progress_callback and count in milestones:
progress_callback(100.0 * count / iter_count)
return matrix
def distance_matrix(data,
distance_constructor=Euclidean,
progress_callback=None):
""" A helper function that computes an :obj:`Orange.misc.SymMatrix` of all
pairwise distances between instances in `data`.
:param data: A data table
:type data: :obj:`Orange.data.Table`
:param distance_constructor: An DistanceConstructor instance (defaults to :obj:`Euclidean`).
:type distance_constructor: :obj:`Orange.distances.DistanceConstructor`
:param progress_callback: A function (taking one argument) to use for
reporting the on the progress.
:type progress_callback: function
:rtype: :class:`Orange.misc.SymMatrix`
"""
matrix = Orange.misc.SymMatrix(len(data))
dist = distance_constructor(data)
iter_count = matrix.dim * (matrix.dim - 1) / 2
milestones = progress_bar_milestones(iter_count, 100)
for count, ((i, ex1), (j, ex2)) in enumerate(_pairs(enumerate(data))):
matrix[i, j] = dist(ex1, ex2)
if progress_callback and count in milestones:
progress_callback(100.0 * count / iter_count)
return matrix
def compute_distances(self, separate_keys, partitions, data):
""" Compute the distances between genotypes.
"""
if separate_keys and partitions:
self.progressBarInit()
matrix = Orange.core.SymMatrix(len(partitions))
profiles = [linearize(data, indices) for _, indices in partitions]
dist_func = self.DISTANCE_FUNCTIONS[self.distance_measure][1]
from Orange.utils import progress_bar_milestones
count = (len(profiles) * len(profiles) - 1) / 2
milestones = progress_bar_milestones(count)
iter_count = 0
for i in range(len(profiles)):
for j in range(i + 1, len(profiles)):
matrix[i, j] = dist_func(profiles[i], profiles[j])
iter_count += 1
if iter_count in milestones:
self.progressBarSet(100.0 * iter_count / count)
self.progressBarFinished()
items = [
["{0}={1}".format(key, value) for key, value in zip(separate_keys, values)] for values, _ in partitions
]
items = [" | ".join(item) for item in items]
matrix.setattr("items", items)
else:
matrix = None
self.matrix = matrix
def compute_combined(self):
self.error(0)
dim = 0
matrices = self.matrices.values()
new = None
if matrices and len(set(m.dim for m in matrices)) != 1:
# Do dimensions match
self.error(0, "Matrices are of different dimensions.")
elif matrices:
dim = matrices[0].dim
method = self.METHODS[self.selected_method][1]
new = Orange.misc.SymMatrix(dim)
milestones = progress_bar_milestones(100, dim * (dim + 1) / 2)
iter = 0
for i in range(dim):
for j in range(i, dim):
vals = [m[i, j] for m in matrices]
new[i, j] = method(vals)
items = [getattr(m, "items") for m in matrices]
items = filter(None, items)
if items:
new.setattr("items", items[0])
self.send("Combined Matrix", new)
def compute_combined(self):
self.error(0)
dim = 0
matrices = self.matrices.values()
new = None
if matrices and len(set(m.dim for m in matrices)) != 1:
# Do dimensions match
self.error(0, "Matrices are of different dimensions.")
elif matrices:
dim = matrices[0].dim
method = self.METHODS[self.selected_method][1]
new = Orange.misc.SymMatrix(dim)
milestones = progress_bar_milestones(100, dim * (dim + 1) / 2)
iter = 0
for i in range(dim):
for j in range(i, dim):
vals = [m[i, j] for m in matrices]
new[i, j] = method(vals)
items = [getattr(m, "items") for m in matrices]
items = filter(None, items)
if items:
new.setattr("items", items[0])
self.send("Combined Matrix", new)
def compute_distances(self, separate_keys, partitions, data):
""" Compute the distances between genotypes.
"""
if separate_keys and partitions:
self.progressBarInit()
matrix = Orange.core.SymMatrix(len(partitions))
profiles = [linearize(data, indices) for _, indices in partitions]
dist_func = self.DISTANCE_FUNCTIONS[self.distance_measure][1]
from Orange.utils import progress_bar_milestones
count = (len(profiles) * len(profiles) - 1) / 2
milestones = progress_bar_milestones(count)
iter_count = 0
for i in range(len(profiles)):
for j in range(i + 1, len(profiles)):
matrix[i, j] = dist_func(profiles[i], profiles[j])
iter_count += 1
if iter_count in milestones:
self.progressBarSet(100.0 * iter_count / count)
self.progressBarFinished()
items = [["{0}={1}".format(key, value) for key, value in zip(separate_keys, values)] \
for values, _ in partitions]
items = [" | ".join(item) for item in items]
matrix.setattr("items", items)
else:
matrix = None
self.matrix = matrix
