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"""
====================================================================
K-means clustering and vector quantization (:mod:`scipy.cluster.vq`)
====================================================================
Provides routines for k-means clustering, generating code books
from k-means models, and quantizing vectors by comparing them with
centroids in a code book.
.. autosummary::
:toctree: generated/
whiten -- Normalize a group of observations so each feature has unit variance
vq -- Calculate code book membership of a set of observation vectors
kmeans -- Performs k-means on a set of observation vectors forming k clusters
kmeans2 -- A different implementation of k-means with more methods
-- for initializing centroids
Background information
======================
The k-means algorithm takes as input the number of clusters to
generate, k, and a set of observation vectors to cluster. It
returns a set of centroids, one for each of the k clusters. An
observation vector is classified with the cluster number or
centroid index of the centroid closest to it.
A vector v belongs to cluster i if it is closer to centroid i than
any other centroids. If v belongs to i, we say centroid i is the
dominating centroid of v. The k-means algorithm tries to
minimize distortion, which is defined as the sum of the squared distances
between each observation vector and its dominating centroid. Each
step of the k-means algorithm refines the choices of centroids to
reduce distortion. The change in distortion is used as a
stopping criterion: when the change is lower than a threshold, the
k-means algorithm is not making sufficient progress and
terminates. One can also define a maximum number of iterations.
Since vector quantization is a natural application for k-means,
information theory terminology is often used. The centroid index
or cluster index is also referred to as a "code" and the table
mapping codes to centroids and vice versa is often referred as a
"code book". The result of k-means, a set of centroids, can be
used to quantize vectors. Quantization aims to find an encoding of
vectors that reduces the expected distortion.
All routines expect obs to be a M by N array where the rows are
the observation vectors. The codebook is a k by N array where the
i'th row is the centroid of code word i. The observation vectors
and centroids have the same feature dimension.
As an example, suppose we wish to compress a 24-bit color image
(each pixel is represented by one byte for red, one for blue, and
one for green) before sending it over the web. By using a smaller
8-bit encoding, we can reduce the amount of data by two
thirds. Ideally, the colors for each of the 256 possible 8-bit
encoding values should be chosen to minimize distortion of the
color. Running k-means with k=256 generates a code book of 256
codes, which fills up all possible 8-bit sequences. Instead of
sending a 3-byte value for each pixel, the 8-bit centroid index
(or code word) of the dominating centroid is transmitted. The code
book is also sent over the wire so each 8-bit code can be
translated back to a 24-bit pixel value representation. If the
image of interest was of an ocean, we would expect many 24-bit
blues to be represented by 8-bit codes. If it was an image of a
human face, more flesh tone colors would be represented in the
code book.
"""
from __future__ import division, print_function, absolute_import
__docformat__ = 'restructuredtext'
__all__ = ['whiten', 'vq', 'kmeans', 'kmeans2']
# TODO:
# - implements high level method for running several times k-means with
# different initialialization
# - warning: what happens if different number of clusters ? For now, emit a
# warning, but it is not great, because I am not sure it really make sense to
# succeed in this case (maybe an exception is better ?)
import warnings
from numpy.random import randint
from numpy import shape, zeros, sqrt, argmin, minimum, array, \
newaxis, arange, compress, equal, common_type, single, double, take, \
std, mean
import numpy as np
class ClusterError(Exception):
pass
def whiten(obs):
"""
Normalize a group of observations on a per feature basis.
Before running k-means, it is beneficial to rescale each feature
dimension of the observation set with whitening. Each feature is
divided by its standard deviation across all observations to give
it unit variance.
Parameters
----------
obs : ndarray
Each row of the array is an observation. The
columns are the features seen during each observation.
>>> # f0 f1 f2
>>> obs = [[ 1., 1., 1.], #o0
... [ 2., 2., 2.], #o1
... [ 3., 3., 3.], #o2
... [ 4., 4., 4.]]) #o3
Returns
-------
result : ndarray
Contains the values in `obs` scaled by the standard deviation
of each column.
Examples
--------
>>> from scipy.cluster.vq import whiten
>>> features = np.array([[1.9, 2.3, 1.7],
... [1.5, 2.5, 2.2],
... [0.8, 0.6, 1.7,]])
>>> whiten(features)
array([[ 4.17944278, 2.69811351, 7.21248917],
[ 3.29956009, 2.93273208, 9.33380951],
[ 1.75976538, 0.7038557 , 7.21248917]])
"""
std_dev = std(obs, axis=0)
return obs / std_dev
def vq(obs, code_book):
"""
Assign codes from a code book to observations.
Assigns a code from a code book to each observation. Each
observation vector in the 'M' by 'N' `obs` array is compared with the
centroids in the code book and assigned the code of the closest
centroid.
The features in `obs` should have unit variance, which can be
acheived by passing them through the whiten function. The code
book can be created with the k-means algorithm or a different
encoding algorithm.
Parameters
----------
obs : ndarray
Each row of the 'N' x 'M' array is an observation. The columns are
the "features" seen during each observation. The features must be
whitened first using the whiten function or something equivalent.
code_book : ndarray
The code book is usually generated using the k-means algorithm.
Each row of the array holds a different code, and the columns are
the features of the code.
>>> # f0 f1 f2 f3
>>> code_book = [
... [ 1., 2., 3., 4.], #c0
... [ 1., 2., 3., 4.], #c1
... [ 1., 2., 3., 4.]]) #c2
Returns
-------
code : ndarray
A length N array holding the code book index for each observation.
dist : ndarray
The distortion (distance) between the observation and its nearest
code.
Notes
-----
This currently forces 32-bit math precision for speed. Anyone know
of a situation where this undermines the accuracy of the algorithm?
Examples
--------
>>> from numpy import array
>>> from scipy.cluster.vq import vq
>>> code_book = array([[1.,1.,1.],
... [2.,2.,2.]])
>>> features = array([[ 1.9,2.3,1.7],
... [ 1.5,2.5,2.2],
... [ 0.8,0.6,1.7]])
>>> vq(features,code_book)
(array([1, 1, 0],'i'), array([ 0.43588989, 0.73484692, 0.83066239]))
"""
try:
from . import _vq
ct = common_type(obs, code_book)
c_obs = obs.astype(ct)
c_code_book = code_book.astype(ct)
if ct is single:
results = _vq.vq(c_obs, c_code_book)
elif ct is double:
results = _vq.vq(c_obs, c_code_book)
else:
results = py_vq(obs, code_book)
except ImportError:
results = py_vq(obs, code_book)
return results
def py_vq(obs, code_book):
""" Python version of vq algorithm.
The algorithm computes the euclidian distance between each
observation and every frame in the code_book.
Parameters
----------
obs : ndarray
Expects a rank 2 array. Each row is one observation.
code_book : ndarray
Code book to use. Same format than obs. Should have same number of
features (eg columns) than obs.
Returns
-------
code : ndarray
code[i] gives the label of the ith obversation, that its code is
code_book[code[i]].
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
Notes
-----
This function is slower than the C version but works for
all input types. If the inputs have the wrong types for the
C versions of the function, this one is called as a last resort.
It is about 20 times slower than the C version.
"""
# n = number of observations
# d = number of features
if np.ndim(obs) == 1:
if not np.ndim(obs) == np.ndim(code_book):
raise ValueError(
"Observation and code_book should have the same rank")
else:
return _py_vq_1d(obs, code_book)
else:
(n, d) = shape(obs)
# code books and observations should have same number of features and same
# shape
if not np.ndim(obs) == np.ndim(code_book):
raise ValueError("Observation and code_book should have the same rank")
elif not d == code_book.shape[1]:
raise ValueError("Code book(%d) and obs(%d) should have the same " \
"number of features (eg columns)""" %
(code_book.shape[1], d))
code = zeros(n, dtype=int)
min_dist = zeros(n)
for i in range(n):
dist = np.sum((obs[i] - code_book) ** 2, 1)
code[i] = argmin(dist)
min_dist[i] = dist[code[i]]
return code, sqrt(min_dist)
def _py_vq_1d(obs, code_book):
""" Python version of vq algorithm for rank 1 only.
Parameters
----------
obs : ndarray
Expects a rank 1 array. Each item is one observation.
code_book : ndarray
Code book to use. Same format than obs. Should rank 1 too.
Returns
-------
code : ndarray
code[i] gives the label of the ith obversation, that its code is
code_book[code[i]].
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
"""
raise RuntimeError("_py_vq_1d buggy, do not use rank 1 arrays for now")
n = obs.size
nc = code_book.size
dist = np.zeros((n, nc))
for i in range(nc):
dist[:, i] = np.sum(obs - code_book[i])
print(dist)
code = argmin(dist)
min_dist = dist[code]
return code, sqrt(min_dist)
def py_vq2(obs, code_book):
"""2nd Python version of vq algorithm.
The algorithm simply computes the euclidian distance between each
observation and every frame in the code_book/
Parameters
----------
obs : ndarray
Expect a rank 2 array. Each row is one observation.
code_book : ndarray
Code book to use. Same format than obs. Should have same number of
features (eg columns) than obs.
Returns
-------
code : ndarray
code[i] gives the label of the ith obversation, that its code is
code_book[code[i]].
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
Notes
-----
This could be faster when number of codebooks is small, but it
becomes a real memory hog when codebook is large. It requires
N by M by O storage where N=number of obs, M = number of
features, and O = number of codes.
"""
d = shape(obs)[1]
# code books and observations should have same number of features
if not d == code_book.shape[1]:
raise ValueError("""
code book(%d) and obs(%d) should have the same
number of features (eg columns)""" % (code_book.shape[1], d))
diff = obs[newaxis, :, :] - code_book[:,newaxis,:]
dist = sqrt(np.sum(diff * diff, -1))
code = argmin(dist, 0)
min_dist = minimum.reduce(dist, 0) #the next line I think is equivalent
# - and should be faster
#min_dist = choose(code,dist) # but in practice, didn't seem to make
# much difference.
return code, min_dist
def _kmeans(obs, guess, thresh=1e-5):
""" "raw" version of k-means.
Returns
-------
code_book :
the lowest distortion codebook found.
avg_dist :
the average distance a observation is from a code in the book.
Lower means the code_book matches the data better.
See Also
--------
kmeans : wrapper around k-means
XXX should have an axis variable here.
Examples
--------
Note: not whitened in this example.
>>> from numpy import array
>>> from scipy.cluster.vq import _kmeans
>>> features = array([[ 1.9,2.3],
... [ 1.5,2.5],
... [ 0.8,0.6],
... [ 0.4,1.8],
... [ 1.0,1.0]])
>>> book = array((features[0],features[2]))
>>> _kmeans(features,book)
(array([[ 1.7 , 2.4 ],
[ 0.73333333, 1.13333333]]), 0.40563916697728591)
"""
code_book = array(guess, copy = True)
avg_dist = []
diff = thresh+1.
while diff > thresh:
nc = code_book.shape[0]
#compute membership and distances between obs and code_book
obs_code, distort = vq(obs, code_book)
avg_dist.append(mean(distort, axis=-1))
#recalc code_book as centroids of associated obs
if(diff > thresh):
has_members = []
for i in arange(nc):
cell_members = compress(equal(obs_code, i), obs, 0)
if cell_members.shape[0] > 0:
code_book[i] = mean(cell_members, 0)
has_members.append(i)
#remove code_books that didn't have any members
code_book = take(code_book, has_members, 0)
if len(avg_dist) > 1:
diff = avg_dist[-2] - avg_dist[-1]
#print avg_dist
return code_book, avg_dist[-1]
def kmeans(obs, k_or_guess, iter=20, thresh=1e-5):
"""
Performs k-means on a set of observation vectors forming k clusters.
The k-means algorithm adjusts the centroids until sufficient
progress cannot be made, i.e. the change in distortion since
the last iteration is less than some threshold. This yields
a code book mapping centroids to codes and vice versa.
Distortion is defined as the sum of the squared differences
between the observations and the corresponding centroid.
Parameters
----------
obs : ndarray
Each row of the M by N array is an observation vector. The
columns are the features seen during each observation.
The features must be whitened first with the `whiten` function.
k_or_guess : int or ndarray
The number of centroids to generate. A code is assigned to
each centroid, which is also the row index of the centroid
in the code_book matrix generated.
The initial k centroids are chosen by randomly selecting
observations from the observation matrix. Alternatively,
passing a k by N array specifies the initial k centroids.
iter : int, optional
The number of times to run k-means, returning the codebook
with the lowest distortion. This argument is ignored if
initial centroids are specified with an array for the
``k_or_guess`` parameter. This parameter does not represent the
number of iterations of the k-means algorithm.
thresh : float, optional
Terminates the k-means algorithm if the change in
distortion since the last k-means iteration is less than
or equal to thresh.
Returns
-------
codebook : ndarray
A k by N array of k centroids. The i'th centroid
codebook[i] is represented with the code i. The centroids
and codes generated represent the lowest distortion seen,
not necessarily the globally minimal distortion.
distortion : float
The distortion between the observations passed and the
centroids generated.
See Also
--------
kmeans2 : a different implementation of k-means clustering
with more methods for generating initial centroids but without
using a distortion change threshold as a stopping criterion.
whiten : must be called prior to passing an observation matrix
to kmeans.
Examples
--------
>>> from numpy import array
>>> from scipy.cluster.vq import vq, kmeans, whiten
>>> features = array([[ 1.9,2.3],
... [ 1.5,2.5],
... [ 0.8,0.6],
... [ 0.4,1.8],
... [ 0.1,0.1],
... [ 0.2,1.8],
... [ 2.0,0.5],
... [ 0.3,1.5],
... [ 1.0,1.0]])
>>> whitened = whiten(features)
>>> book = array((whitened[0],whitened[2]))
>>> kmeans(whitened,book)
(array([[ 2.3110306 , 2.86287398],
[ 0.93218041, 1.24398691]]), 0.85684700941625547)
>>> from numpy import random
>>> random.seed((1000,2000))
>>> codes = 3
>>> kmeans(whitened,codes)
(array([[ 2.3110306 , 2.86287398],
[ 1.32544402, 0.65607529],
[ 0.40782893, 2.02786907]]), 0.5196582527686241)
"""
if int(iter) < 1:
raise ValueError('iter must be at least 1.')
if type(k_or_guess) == type(array([])):
guess = k_or_guess
if guess.size < 1:
raise ValueError("Asked for 0 cluster ? initial book was %s" % \
guess)
result = _kmeans(obs, guess, thresh = thresh)
else:
#initialize best distance value to a large value
best_dist = np.inf
No = obs.shape[0]
k = k_or_guess
if k < 1:
raise ValueError("Asked for 0 cluster ? ")
for i in range(iter):
#the intial code book is randomly selected from observations
guess = take(obs, randint(0, No, k), 0)
book, dist = _kmeans(obs, guess, thresh = thresh)
if dist < best_dist:
best_book = book
best_dist = dist
result = best_book, best_dist
return result
def _kpoints(data, k):
"""Pick k points at random in data (one row = one observation).
This is done by taking the k first values of a random permutation of 1..N
where N is the number of observation.
Parameters
----------
data : ndarray
Expect a rank 1 or 2 array. Rank 1 are assumed to describe one
dimensional data, rank 2 multidimensional data, in which case one
row is one observation.
k : int
Number of samples to generate.
"""
if data.ndim > 1:
n = data.shape[0]
else:
n = data.size
p = np.random.permutation(n)
x = data[p[:k], :].copy()
return x
def _krandinit(data, k):
"""Returns k samples of a random variable which parameters depend on data.
More precisely, it returns k observations sampled from a Gaussian random
variable which mean and covariances are the one estimated from data.
Parameters
----------
data : ndarray
Expect a rank 1 or 2 array. Rank 1 are assumed to describe one
dimensional data, rank 2 multidimensional data, in which case one
row is one observation.
k : int
Number of samples to generate.
"""
def init_rank1(data):
mu = np.mean(data)
cov = np.cov(data)
x = np.random.randn(k)
x *= np.sqrt(cov)
x += mu
return x
def init_rankn(data):
mu = np.mean(data, 0)
cov = np.atleast_2d(np.cov(data, rowvar = 0))
# k rows, d cols (one row = one obs)
# Generate k sample of a random variable ~ Gaussian(mu, cov)
x = np.random.randn(k, mu.size)
x = np.dot(x, np.linalg.cholesky(cov).T) + mu
return x
nd = np.ndim(data)
if nd == 1:
return init_rank1(data)
else:
return init_rankn(data)
_valid_init_meth = {'random': _krandinit, 'points': _kpoints}
def _missing_warn():
"""Print a warning when called."""
warnings.warn("One of the clusters is empty. "
"Re-run kmean with a different initialization.")
def _missing_raise():
"""raise a ClusterError when called."""
raise ClusterError("One of the clusters is empty. "
"Re-run kmean with a different initialization.")
_valid_miss_meth = {'warn': _missing_warn, 'raise': _missing_raise}
def kmeans2(data, k, iter = 10, thresh = 1e-5, minit = 'random',
missing = 'warn'):
"""
Classify a set of observations into k clusters using the k-means algorithm.
The algorithm attempts to minimize the Euclidian distance between
observations and centroids. Several initialization methods are
included.
Parameters
----------
data : ndarray
A 'M' by 'N' array of 'M' observations in 'N' dimensions or a length
'M' array of 'M' one-dimensional observations.
k : int or ndarray
The number of clusters to form as well as the number of
centroids to generate. If `minit` initialization string is
'matrix', or if a ndarray is given instead, it is
interpreted as initial cluster to use instead.
iter : int
Number of iterations of the k-means algrithm to run. Note
that this differs in meaning from the iters parameter to
the kmeans function.
thresh : float
(not used yet)
minit : string
Method for initialization. Available methods are 'random',
'points', 'uniform', and 'matrix':
'random': generate k centroids from a Gaussian with mean and
variance estimated from the data.
'points': choose k observations (rows) at random from data for
the initial centroids.
'uniform': generate k observations from the data from a uniform
distribution defined by the data set (unsupported).
'matrix': interpret the k parameter as a k by M (or length k
array for one-dimensional data) array of initial centroids.
Returns
-------
centroid : ndarray
A 'k' by 'N' array of centroids found at the last iteration of
k-means.
label : ndarray
label[i] is the code or index of the centroid the
i'th observation is closest to.
"""
if missing not in _valid_miss_meth.keys():
raise ValueError("Unkown missing method: %s" % str(missing))
# If data is rank 1, then we have 1 dimension problem.
nd = np.ndim(data)
if nd == 1:
d = 1
#raise ValueError("Input of rank 1 not supported yet")
elif nd == 2:
d = data.shape[1]
else:
raise ValueError("Input of rank > 2 not supported")
if np.size(data) < 1:
raise ValueError("Input has 0 items.")
# If k is not a single value, then it should be compatible with data's
# shape
if np.size(k) > 1 or minit == 'matrix':
if not nd == np.ndim(k):
raise ValueError("k is not an int and has not same rank than data")
if d == 1:
nc = len(k)
else:
(nc, dc) = k.shape
if not dc == d:
raise ValueError("k is not an int and has not same rank than\
data")
clusters = k.copy()
else:
try:
nc = int(k)
except TypeError:
raise ValueError("k (%s) could not be converted to an integer " % str(k))
if nc < 1:
raise ValueError("kmeans2 for 0 clusters ? (k was %s)" % str(k))
if not nc == k:
warnings.warn("k was not an integer, was converted.")
try:
init = _valid_init_meth[minit]
except KeyError:
raise ValueError("unknown init method %s" % str(minit))
clusters = init(data, k)
if int(iter) < 1:
raise ValueError("iter = %s is not valid. iter must be a positive integer." % iter)
return _kmeans2(data, clusters, iter, nc, _valid_miss_meth[missing])
def _kmeans2(data, code, niter, nc, missing):
""" "raw" version of kmeans2. Do not use directly.
Run k-means with a given initial codebook.
"""
for i in range(niter):
# Compute the nearest neighbour for each obs
# using the current code book
label = vq(data, code)[0]
# Update the code by computing centroids using the new code book
for j in range(nc):
mbs = np.where(label==j)
if mbs[0].size > 0:
code[j] = np.mean(data[mbs], axis=0)
else:
missing()
return code, label
if __name__ == '__main__':
pass
#import _vq
#a = np.random.randn(4, 2)
#b = np.random.randn(2, 2)
#print _vq.vq(a, b)
#print _vq.vq(np.array([[1], [2], [3], [4], [5], [6.]]),
# np.array([[2.], [5.]]))
#print _vq.vq(np.array([1, 2, 3, 4, 5, 6.]), np.array([2., 5.]))
#_vq.vq(a.astype(np.float32), b.astype(np.float32))
#_vq.vq(a, b.astype(np.float32))
#_vq.vq([0], b)
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