def pairwise_vector_distances(vectors, save_file=False, normalize=False):
"""
Compare every pair of equal-sized vectors.
Parameters
----------
vectors : array of 1-D lists or arrays of integers or floats
save_file : bool
save file?
normalize : bool
normalize each element of the vectors?
Returns
-------
vector_distances : numpy array of integers or floats
distances between each pair of vectors
outfile : string [optional]
output filename for pairwise_vector_distances
Examples
--------
>>> import numpy as np
>>> from mindboggle.guts.compute import pairwise_vector_distances
>>> vectors = [[1,2,3],[0,3,5],[0,3.5,5],[1,1,1]]
>>> save_file = False
>>> normalize = False
>>> vector_distances, outfile = pairwise_vector_distances(vectors,
... save_file, normalize)
>>> print(np.array_str(np.array(vector_distances),
... precision=5, suppress_small=True))
[[ 0. 0.8165 0.89753 0.74536]
[ 0. 0. 0.16667 1.52753]
[ 0. 0. 0. 1.60728]
[ 0. 0. 0. 0. ]]
"""
import os
import numpy as np
from mindboggle.guts.compute import vector_distance
# Make sure argument is a numpy array
if not isinstance(vectors, np.ndarray):
vectors = np.array(vectors)
# Initialize output
vector_distances = np.zeros((len(vectors), len(vectors)))
#---------------------------------------------------------------------------
# Compute distance between each pair of vectors
#---------------------------------------------------------------------------
# Loop through every pair of vectors
for ihist1 in range(len(vectors)):
for ihist2 in range(len(vectors)):
if ihist2 >= ihist1:
# Store pairwise distances between histogram values
d = vector_distance(1.0*vectors[ihist1],
1.0*vectors[ihist2],
normalize=normalize)
vector_distances[ihist1, ihist2] = d
if save_file:
outfile = os.path.join(os.getcwd(), 'vector_distances.txt')
np.savetxt(outfile, vector_distances,
fmt=len(vectors) * '%.4f ', delimiter='\t', newline='\n')
if not os.path.exists(outfile):
raise IOError(outfile + " not found")
else:
outfile = ''
return vector_distances, outfile
def pairwise_vector_distances(vectors, save_file=False, normalize=False):
"""
Compare every pair of equal-sized vectors.
Parameters
----------
vectors : array of 1-D lists or arrays of integers or floats
save_file : Boolean
save file?
normalize : Boolean
normalize each element of the vectors?
Returns
-------
vector_distances : numpy array of integers or floats
distances between each pair of vectors
outfile : string [optional]
output filename for pairwise_vector_distances
Examples
--------
>>> from mindboggle.guts.compute import pairwise_vector_distances
>>> pairwise_vector_distances([[1,2,3],[0,3,5],[0,3.5,5],[1,1,1]])
(array([[ 0. , 0.81649658, 0.89752747, 0.74535599],
[ 0. , 0. , 0.16666667, 1.52752523],
[ 0. , 0. , 0. , 1.60727513],
[ 0. , 0. , 0. , 0. ]]),
'')
"""
import os
import numpy as np
from mindboggle.guts.compute import vector_distance
# Make sure argument is a numpy array
if not isinstance(vectors, np.ndarray):
vectors = np.array(vectors)
# Initialize output
vector_distances = np.zeros((len(vectors), len(vectors)))
#---------------------------------------------------------------------------
# Compute distance between each pair of vectors
#---------------------------------------------------------------------------
# Loop through every pair of vectors
for ihist1 in range(len(vectors)):
for ihist2 in range(len(vectors)):
if ihist2 >= ihist1:
# Store pairwise distances between histogram values
d = vector_distance(1.0 * vectors[ihist1],
1.0 * vectors[ihist2],
normalize=normalize)
vector_distances[ihist1, ihist2] = d
if save_file:
outfile = os.path.join(os.getcwd(), 'vector_distances.txt')
np.savetxt(outfile,
vector_distances,
fmt=len(vectors) * '%.4f ',
delimiter='\t',
newline='\n')
if not os.path.exists(outfile):
raise (IOError(outfile + " not found"))
else:
outfile = ''
return vector_distances, outfile
def pairwise_vector_distances(vectors, save_file=False, normalize=False):
"""
Compare every pair of equal-sized vectors.
Parameters
----------
vectors : array of 1-D lists or arrays of integers or floats
save_file : Boolean
save file?
normalize : Boolean
normalize each element of the vectors?
Returns
-------
vector_distances : numpy array of integers or floats
distances between each pair of vectors
outfile : string [optional]
output filename for pairwise_vector_distances
Examples
--------
>>> from mindboggle.guts.compute import pairwise_vector_distances
>>> pairwise_vector_distances([[1,2,3],[0,3,5],[0,3.5,5],[1,1,1]])
(array([[ 0. , 0.81649658, 0.89752747, 0.74535599],
[ 0. , 0. , 0.16666667, 1.52752523],
[ 0. , 0. , 0. , 1.60727513],
[ 0. , 0. , 0. , 0. ]]),
'')
"""
import os
import numpy as np
from mindboggle.guts.compute import vector_distance
# Make sure argument is a numpy array
if not isinstance(vectors, np.ndarray):
vectors = np.array(vectors)
# Initialize output
vector_distances = np.zeros((len(vectors), len(vectors)))
# ---------------------------------------------------------------------------
# Compute distance between each pair of vectors
# ---------------------------------------------------------------------------
# Loop through every pair of vectors
for ihist1 in range(len(vectors)):
for ihist2 in range(len(vectors)):
if ihist2 >= ihist1:
# Store pairwise distances between histogram values
d = vector_distance(1.0 * vectors[ihist1], 1.0 * vectors[ihist2], normalize=normalize)
vector_distances[ihist1, ihist2] = d
if save_file:
outfile = os.path.join(os.getcwd(), "vector_distances.txt")
np.savetxt(outfile, vector_distances, fmt=len(vectors) * "%.4f ", delimiter="\t", newline="\n")
if not os.path.exists(outfile):
raise (IOError(outfile + " not found"))
else:
outfile = ""
return vector_distances, outfile