def setUp(self):
self.g1 = [
[2, 2],
[2, 5],
[6, 5],
[7, 3],
[4, 7],
[6, 4],
[5, 3],
[4, 6],
[2, 5],
[1, 3],
]
self.g2 = [
[6, 5],
[7, 4],
[8, 7],
[5, 6],
[5, 4],
]
self.h1 = list(linearalg.new_matrix(self.g1).tr())
self.h2 = list(linearalg.new_matrix(self.g2).tr())
self.cov1 = linearalg.new_matrix([ [3.89, 0.13],[0.13, 2.21] ])
self.cov2 = linearalg.new_matrix([ [1.36, 0.56],[0.56, 1.36] ])
self.expected_cov = [self.cov1, self.cov2]
def testMahalanobisDistance2d_MATLAB(self):
# MATLAB code:
# g1 = [ 2 2; 2 5; 6 5; 7 3; 4 7; 6 4; 5 3; 4 6; 2 5; 1 3; ];
# g2 = [ 6 5; 7 4; 8 7; 5 6; 5 4; ];
# mahal(g1, g2)
g1 = [ [2, 2], [2, 5], [6, 5], [7, 3], [4, 7], [6, 4], [5, 3], [4, 6], [2, 5], [1, 3], ]
g2 = [ [6, 5], [7, 4], [8, 7], [5, 6], [5, 4], ]
e = [ 11.9083, 12.0333, 0.0333, 4.9083, 8.0333, 0.9083, 2.9083, 4.9083, 12.0333, 15.9083 ]
s = linearalg.new_matrix(g2).covariance_by_cols(population_variance=False)
for i, g in enumerate(g1):
self.assertAlmostEqual(e[i], distance.squared_mahalanobis([g], g2, cov=s), 4)
def squared_mahalanobis_1d(u, v, cov=None, population_variance=False):
"""
Returns the *squared* Mahalanobis distance between vectors `u` and `v`.
`u` and `v` are assumed to be single-dimension, i.e., a simple list or
Vector of values.
`cov` is the covariance matrix. If not given, the pooled covariance of `u`
and `v` are used.
If the pooled covariance is calculated (i.e., `cov` is not given), then
if `population_variance` is False (default), the data are treated as samples
rather than a population.
If only relative distances are needed (as they are in most cases), then
this function should be preferred over `mahalanobis_1d(u, v)` which has the
added computational expense of taking the square root.
For more details and examples, see `dendropy.mathlib.distance.squared_mahalanobis()`.
>>> u = [1, 2, 2, 4, 1, 4]
>>> v = [2, 1, 1, 0, 2, 1]
>>> print squared_mahalanobis_1d(u, v)
1.3800154321
"""
u = linearalg.new_matrix([[i] for i in u])
v = linearalg.new_matrix([[i] for i in v])
return squared_mahalanobis(u, v, cov=cov, population_variance=population_variance)
def testCovarianceByRows(self):
for i, x in enumerate([self.h1, self.h2]):
x = linearalg.new_matrix(x)
s = x.covariance_by_rows(population_variance=True)
assert self.expected_cov[i] == s
def squared_mahalanobis(u, v, cov=None, population_variance=False):
"""
Returns the *squared* Mahalanobis distance between matrices `u` and `v`.
`u` and `v` must be 2-dimensional, and have the same number of columns
(though they can have different number of rows).
That is, they must be a list of lists, and the lengths of the inner lists
must be equal, though the length of the outer lists can be differnt). If
there simple vectors of values, they should be represented as lists of
multiple single-element lists, or use the convenience function
`squared_mahalanobis_1d(u, v)`.
`cov` is the covariance matrix. If not given, the pooled covariance of `u`
and `v` are used.
If the pooled covariance is calculated (i.e., `cov` is not given), then
if `population_variance` is False (default), the data are treated as samples
rather than a population.
If only relative distances are needed (as they are in most cases), then
this function should be preferred over `mahalanobis(u, v)` which has the
added computational expense of taking the square root.
The following examples calculate the Mahalanobis distances between
matrices, using the pooled covariances::
>>> u = [ [2, 3.14, 1.3], [1, 4, 5] ]
>>> v = [ [4, 1, 1], [5, 3, 2], [1, 3, 4], [1, 4, 4] ]
>>> print squared_mahalanobis(u, v)
2.15009570304
>>> u = [ [2, 5], [5, 7] ]
>>> v = [ [1, 5], [4, 8] ]
>>> print squared_mahalanobis(u, v)
28.8888888889
>>> # for single column, you can use
... `squared_mahalanobis_vec(u, v)` or:
>>> u = [ [1], [3], [5] ]
>>> v = [ [2], [3] ]
>>> print squared_mahalanobis(u, v)
0.425
The followed extended example shows a different approach. Here, we have
a known vector of means, and we are interested in calculating the distances
of different datasets to these means. Instead of using the pooled covariances
of the two matrices, only the covariance of the datasets are used (the means
are taken to be the truth)::
#! /usr/bin/env python
import random
from dendropy.mathlib import linearalg
from dendropy.mathlib import distance
nrows = 10
ncols = 4
v1 = []
for i in range(nrows):
v1.append([random.gauss(0, 10) for j in range(ncols)])
v2 = []
for i in range(nrows):
v2.append([random.gauss(10, 10) for j in range(ncols)])
v3 = []
for i in range(nrows):
v3.append([random.gauss(-10, 10) for j in range(ncols)])
c1 = [ [0] * 4 ]
c2 = [ [10] * 4 ]
c3 = [ [-10] * 4 ]
v1 = linearalg.new_matrix(v1)
s1 = v1.covariance_by_cols()
v2 = linearalg.new_matrix(v2)
s2 = v2.covariance_by_cols()
v3 = linearalg.new_matrix(v3)
s3 = v3.covariance_by_cols()
print
print "-- v1 --"
print "d(c1, v1) = {}".format(distance.squared_mahalanobis(c1, v1, cov=s1))
print "d(c2, v1) = {}".format(distance.squared_mahalanobis(c2, v1, cov=s1))
print "d(c3, v1) = {}".format(distance.squared_mahalanobis(c3, v1, cov=s1))
print
print "-- v2 --"
print "d(c1, v2) = {}".format(distance.squared_mahalanobis(c1, v2, cov=s2))
print "d(c2, v2) = {}".format(distance.squared_mahalanobis(c2, v2, cov=s2))
print "d(c3, v2) = {}".format(distance.squared_mahalanobis(c3, v2, cov=s2))
print
print "-- v3 --"
print "d(c1, v3) = {}".format(distance.squared_mahalanobis(c1, v3, cov=s3))
print "d(c2, v3) = {}".format(distance.squared_mahalanobis(c2, v3, cov=s3))
print "d(c3, v3) = {}".format(distance.squared_mahalanobis(c3, v3, cov=s3))
This results in::
-- v1 --
d(c1, v1) = 0.170092378552
d(c2, v1) = 8.59447583779
d(c3, v1) = 8.898973355
-- v2 --
d(c1, v2) = 30.7463150693
d(c2, v2) = 0.434906936737
d(c3, v2) = 121.212523478
-- v3 --
d(c1, v3) = 4.19154157624
d(c2, v3) = 18.4689462227
d(c3, v3) = 0.128840873046
The `mahal` function of MATLAB calculates the Mahalanobis distance as well.
Its implementation and usage are a little different:
d = mahal(Y,X) computes the Mahalanobis distance (in squared units) of
each observation in Y from the reference sample in matrix X. If Y is
n-by-m, where n is the number of observations and m is the dimension of
the data, d is n-by-1. X and Y must have the same number of columns,
but can have different numbers of rows. X must have more rows than
columns.
For observation I, the Mahalanobis distance is defined by d(I) =
(Y(I,:)-mu)*inv(SIGMA)*(Y(I,:)-mu)', where mu and SIGMA are the sample
mean and covariance of the data in X.
Thus, the following MATLAB code::
>> g1 = [ 2 2; 2 5; 6 5; 7 3; 4 7; 6 4; 5 3; 4 6; 2 5; 1 3; ];
>> g2 = [ 6 5; 7 4; 8 7; 5 6; 5 4; ];
>> mahal(g1, g2)
can be replicated by::
#! /usr/bin/env python
from dendropy.mathlib import distance
from dendropy.mathlib import linearalg
g1 = [ [2, 2], [2, 5], [6, 5], [7, 3], [4, 7], [6, 4], [5, 3], [4, 6], [2, 5], [1, 3], ]
g2 = [ [6, 5], [7, 4], [8, 7], [5, 6], [5, 4], ]
s = linearalg.new_matrix(g2).covariance_by_cols(population_variance=False)
for g in g1:
print distance.squared_mahalanobis([g], g2, cov=s)
"""
if not isinstance(u, linearalg.Matrix):
u = linearalg.new_matrix(u)
if not isinstance(v, linearalg.Matrix):
v = linearalg.new_matrix(v)
assert len(u[0]) == len(v[0])
if cov is None:
cov = linearalg.pooled_covariance(
u, v, population_variance=population_variance)
if len(cov) == 1:
cov_inv = cov
else:
cov_inv = cov.inverse()
# TODO: column means have already been calculated to
# get the covariances: avoid duplicating calcs
mean_diffs = linearalg.new_matrix([u.col_means() - v.col_means()])
# `mean_diffs` is now a row vector
# for consistency with the standard formulation, we should:
#
# mean_diffs = mean_diffs.tr()
# d = mean_diffs.tr().mmul(pooled_cov_inv).mmul(mean_diffs)
#
# Instead ...
d = mean_diffs.mmul(cov_inv).mmul(mean_diffs.tr())
return d[0][0]
def squared_mahalanobis(u, v, cov=None, population_variance=False):
"""
Returns the *squared* Mahalanobis distance between matrices `u` and `v`.
`u` and `v` must be 2-dimensional, and have the same number of columns
(though they can have different number of rows).
That is, they must be a list of lists, and the lengths of the inner lists
must be equal, though the length of the outer lists can be differnt). If
there simple vectors of values, they should be represented as lists of
multiple single-element lists, or use the convenience function
`squared_mahalanobis_1d(u, v)`.
`cov` is the covariance matrix. If not given, the pooled covariance of `u`
and `v` are used.
If the pooled covariance is calculated (i.e., `cov` is not given), then
if `population_variance` is False (default), the data are treated as samples
rather than a population.
If only relative distances are needed (as they are in most cases), then
this function should be preferred over `mahalanobis(u, v)` which has the
added computational expense of taking the square root.
The following examples calculate the Mahalanobis distances between
matrices, using the pooled covariances::
>>> u = [ [2, 3.14, 1.3], [1, 4, 5] ]
>>> v = [ [4, 1, 1], [5, 3, 2], [1, 3, 4], [1, 4, 4] ]
>>> print squared_mahalanobis(u, v)
2.15009570304
>>> u = [ [2, 5], [5, 7] ]
>>> v = [ [1, 5], [4, 8] ]
>>> print squared_mahalanobis(u, v)
28.8888888889
>>> # for single column, you can use
... `squared_mahalanobis_vec(u, v)` or:
>>> u = [ [1], [3], [5] ]
>>> v = [ [2], [3] ]
>>> print squared_mahalanobis(u, v)
0.425
The followed extended example shows a different approach. Here, we have
a known vector of means, and we are interested in calculating the distances
of different datasets to these means. Instead of using the pooled covariances
of the two matrices, only the covariance of the datasets are used (the means
are taken to be the truth)::
#! /usr/bin/env python
import random
from dendropy.mathlib import linearalg
from dendropy.mathlib import distance
nrows = 10
ncols = 4
v1 = []
for i in range(nrows):
v1.append([random.gauss(0, 10) for j in range(ncols)])
v2 = []
for i in range(nrows):
v2.append([random.gauss(10, 10) for j in range(ncols)])
v3 = []
for i in range(nrows):
v3.append([random.gauss(-10, 10) for j in range(ncols)])
c1 = [ [0] * 4 ]
c2 = [ [10] * 4 ]
c3 = [ [-10] * 4 ]
v1 = linearalg.new_matrix(v1)
s1 = v1.covariance_by_cols()
v2 = linearalg.new_matrix(v2)
s2 = v2.covariance_by_cols()
v3 = linearalg.new_matrix(v3)
s3 = v3.covariance_by_cols()
print
print "-- v1 --"
print "d(c1, v1) = {}".format(distance.squared_mahalanobis(c1, v1, cov=s1))
print "d(c2, v1) = {}".format(distance.squared_mahalanobis(c2, v1, cov=s1))
print "d(c3, v1) = {}".format(distance.squared_mahalanobis(c3, v1, cov=s1))
print
print "-- v2 --"
print "d(c1, v2) = {}".format(distance.squared_mahalanobis(c1, v2, cov=s2))
print "d(c2, v2) = {}".format(distance.squared_mahalanobis(c2, v2, cov=s2))
print "d(c3, v2) = {}".format(distance.squared_mahalanobis(c3, v2, cov=s2))
print
print "-- v3 --"
print "d(c1, v3) = {}".format(distance.squared_mahalanobis(c1, v3, cov=s3))
print "d(c2, v3) = {}".format(distance.squared_mahalanobis(c2, v3, cov=s3))
print "d(c3, v3) = {}".format(distance.squared_mahalanobis(c3, v3, cov=s3))
This results in::
-- v1 --
d(c1, v1) = 0.170092378552
d(c2, v1) = 8.59447583779
d(c3, v1) = 8.898973355
-- v2 --
d(c1, v2) = 30.7463150693
d(c2, v2) = 0.434906936737
d(c3, v2) = 121.212523478
-- v3 --
d(c1, v3) = 4.19154157624
d(c2, v3) = 18.4689462227
d(c3, v3) = 0.128840873046
The `mahal` function of MATLAB calculates the Mahalanobis distance as well.
Its implementation and usage are a little different:
d = mahal(Y,X) computes the Mahalanobis distance (in squared units) of
each observation in Y from the reference sample in matrix X. If Y is
n-by-m, where n is the number of observations and m is the dimension of
the data, d is n-by-1. X and Y must have the same number of columns,
but can have different numbers of rows. X must have more rows than
columns.
For observation I, the Mahalanobis distance is defined by d(I) =
(Y(I,:)-mu)*inv(SIGMA)*(Y(I,:)-mu)', where mu and SIGMA are the sample
mean and covariance of the data in X.
Thus, the following MATLAB code::
>> g1 = [ 2 2; 2 5; 6 5; 7 3; 4 7; 6 4; 5 3; 4 6; 2 5; 1 3; ];
>> g2 = [ 6 5; 7 4; 8 7; 5 6; 5 4; ];
>> mahal(g1, g2)
can be replicated by::
#! /usr/bin/env python
from dendropy.mathlib import distance
from dendropy.mathlib import linearalg
g1 = [ [2, 2], [2, 5], [6, 5], [7, 3], [4, 7], [6, 4], [5, 3], [4, 6], [2, 5], [1, 3], ]
g2 = [ [6, 5], [7, 4], [8, 7], [5, 6], [5, 4], ]
s = linearalg.new_matrix(g2).covariance_by_cols(population_variance=False)
for g in g1:
print distance.squared_mahalanobis([g], g2, cov=s)
"""
if not isinstance(u, linearalg.Matrix):
u = linearalg.new_matrix(u)
if not isinstance(v, linearalg.Matrix):
v = linearalg.new_matrix(v)
assert len(u[0]) == len(v[0])
if cov is None:
cov = linearalg.pooled_covariance(u, v, population_variance=population_variance)
if len(cov) == 1:
cov_inv = cov
else:
cov_inv = cov.inverse()
# TODO: column means have already been calculated to
# get the covariances: avoid duplicating calcs
mean_diffs = linearalg.new_matrix([u.col_means() - v.col_means()])
# `mean_diffs` is now a row vector
# for consistency with the standard formulation, we should:
#
# mean_diffs = mean_diffs.tr()
# d = mean_diffs.tr().mmul(pooled_cov_inv).mmul(mean_diffs)
#
# Instead ...
d = mean_diffs.mmul(cov_inv).mmul(mean_diffs.tr())
return d[0][0]
