[Scipy-svn] r2948 - in trunk/Lib/cluster: . tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Mon Apr 30 10:50:51 EDT 2007
Author: stefan
Date: 2007-04-30 09:49:05 -0500 (Mon, 30 Apr 2007)
New Revision: 2948
Modified:
trunk/Lib/cluster/tests/test_vq.py
trunk/Lib/cluster/tests/vq_test.py
trunk/Lib/cluster/vq.py
Log:
Comply with the style guide (PEP08). Fix vq_test for numpy.
Modified: trunk/Lib/cluster/tests/test_vq.py
===================================================================
--- trunk/Lib/cluster/tests/test_vq.py 2007-04-30 13:55:42 UTC (rev 2947)
+++ trunk/Lib/cluster/tests/test_vq.py 2007-04-30 14:49:05 UTC (rev 2948)
@@ -19,98 +19,98 @@
set_local_path()
# import modules that are located in the same directory as this file.
import os.path
-DATAFILE1 = os.path.join(sys.path[0], "data.txt")
+DATAFILE1 = os.path.join(sys.path[0], "data.txt")
restore_path()
# Global data
X = N.array([[3.0, 3], [4, 3], [4, 2],
- [9, 2], [5, 1], [6, 2], [9, 4],
- [5, 2], [5, 4], [7, 4], [6, 5]])
+ [9, 2], [5, 1], [6, 2], [9, 4],
+ [5, 2], [5, 4], [7, 4], [6, 5]])
CODET1 = N.array([[3.0000, 3.0000],
- [6.2000, 4.0000],
- [5.8000, 1.8000]])
-
-CODET2 = N.array([[11.0/3, 8.0/3],
- [6.7500, 4.2500],
- [6.2500, 1.7500]])
+ [6.2000, 4.0000],
+ [5.8000, 1.8000]])
+CODET2 = N.array([[11.0/3, 8.0/3],
+ [6.7500, 4.2500],
+ [6.2500, 1.7500]])
+
LABEL1 = N.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1])
class test_vq(NumpyTestCase):
def check_py_vq(self, level=1):
- initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
- code = initc.copy()
- label1 = py_vq(X, initc)[0]
+ initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
+ code = initc.copy()
+ label1 = py_vq(X, initc)[0]
assert_array_equal(label1, LABEL1)
def check_py_vq2(self, level=1):
- initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
- code = initc.copy()
- label1 = py_vq2(X, initc)[0]
+ initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
+ code = initc.copy()
+ label1 = py_vq2(X, initc)[0]
assert_array_equal(label1, LABEL1)
def check_vq(self, level=1):
- initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
- code = initc.copy()
+ initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
+ code = initc.copy()
try:
import _vq
- label1 = _vq.double_vq(X, initc)[0]
+ label1 = _vq.double_vq(X, initc)[0]
assert_array_equal(label1, LABEL1)
except ImportError:
print "== Error while importing _vq, not testing C imp of vq =="
#def check_vq_1d(self, level=1):
- # data = X[:, 0]
- # initc = data[:3]
- # code = initc.copy()
+ # data = X[:, 0]
+ # initc = data[:3]
+ # code = initc.copy()
# print _py_vq_1d(data, initc)
class test_kmean(NumpyTestCase):
def check_kmeans_simple(self, level=1):
- initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
- code = initc.copy()
- code1 = kmeans(X, code, iter = 1)[0]
+ initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
+ code = initc.copy()
+ code1 = kmeans(X, code, iter = 1)[0]
assert_array_almost_equal(code1, CODET2)
def check_kmeans_lost_cluster(self, level=1):
"""This will cause kmean to have a cluster with no points."""
- data = N.fromfile(open(DATAFILE1), sep = ", ")
- data = data.reshape((200, 2))
- initk = N.array([[-1.8127404, -0.67128041],
- [ 2.04621601, 0.07401111],
- [-2.31149087,-0.05160469]])
+ data = N.fromfile(open(DATAFILE1), sep = ", ")
+ data = data.reshape((200, 2))
+ initk = N.array([[-1.8127404, -0.67128041],
+ [ 2.04621601, 0.07401111],
+ [-2.31149087,-0.05160469]])
- res = kmeans(data, initk)
+ res = kmeans(data, initk)
def check_kmeans2_simple(self, level=1):
"""Testing simple call to kmeans2 and its results."""
- initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
- code = initc.copy()
- code1 = kmeans2(X, code, niter = 1)[0]
- code2 = kmeans2(X, code, niter = 2)[0]
+ initc = N.concatenate(([[X[0]], [X[1]], [X[2]]]))
+ code = initc.copy()
+ code1 = kmeans2(X, code, niter = 1)[0]
+ code2 = kmeans2(X, code, niter = 2)[0]
assert_array_almost_equal(code1, CODET1)
assert_array_almost_equal(code2, CODET2)
#def check_kmeans2_rank1(self, level=1):
# """Testing simple call to kmeans2 with rank 1 data."""
- # data = N.fromfile(open(DATAFILE1), sep = ", ")
- # data = data.reshape((200, 2))
- # data1 = data[:, 0]
- # data2 = data[:, 1]
+ # data = N.fromfile(open(DATAFILE1), sep = ", ")
+ # data = data.reshape((200, 2))
+ # data1 = data[:, 0]
+ # data2 = data[:, 1]
- # initc = data1[:3]
- # code = initc.copy()
+ # initc = data1[:3]
+ # code = initc.copy()
# print _py_vq_1d(data1, code)
- # code1 = kmeans2(data1, code, niter = 1)[0]
- # code2 = kmeans2(data1, code, niter = 2)[0]
+ # code1 = kmeans2(data1, code, niter = 1)[0]
+ # code2 = kmeans2(data1, code, niter = 2)[0]
def check_kmeans2_init(self, level = 1):
"""Testing that kmeans2 init methods work."""
- data = N.fromfile(open(DATAFILE1), sep = ", ")
- data = data.reshape((200, 2))
+ data = N.fromfile(open(DATAFILE1), sep = ", ")
+ data = data.reshape((200, 2))
kmeans2(data, 3, minit = 'random')
kmeans2(data, 3, minit = 'points')
Modified: trunk/Lib/cluster/tests/vq_test.py
===================================================================
--- trunk/Lib/cluster/tests/vq_test.py 2007-04-30 13:55:42 UTC (rev 2947)
+++ trunk/Lib/cluster/tests/vq_test.py 2007-04-30 14:49:05 UTC (rev 2948)
@@ -1,5 +1,6 @@
-from numpy import *
-import vq_c as vq
+import numpy as N
+from scipy.cluster import vq
+#import vq_c as vq
def python_vq(all_data,code_book):
import time
@@ -11,8 +12,8 @@
print ' first dist:', dist1[:5]
print ' last codes:', codes1[-5:]
print ' last dist:', dist1[-5:]
- float_obs = all_data.astype(Float32)
- float_code = code_book.astype(Float32)
+ float_obs = all_data.astype(N.float32)
+ float_code = code_book.astype(N.float32)
t1 = time.time()
codes1,dist1 = vq.vq(float_obs,float_code)
t2 = time.time()
@@ -33,13 +34,12 @@
return array(data)
def main():
- import scipy.stats
- scipy.stats.seed(1000,1000)
+ N.random.seed((1000,1000))
Ncodes = 40
Nfeatures = 16
Nobs = 4000
- code_book = RandomArray.normal(0,1,(Ncodes,Nfeatures))
- features = RandomArray.normal(0,1,(Nobs,Nfeatures))
+ code_book = N.random.normal(0,1,(Ncodes,Nfeatures))
+ features = N.random.normal(0,1,(Nobs,Nfeatures))
codes,dist = python_vq(features,code_book)
if __name__ == '__main__':
Modified: trunk/Lib/cluster/vq.py
===================================================================
--- trunk/Lib/cluster/vq.py 2007-04-30 13:55:42 UTC (rev 2947)
+++ trunk/Lib/cluster/vq.py 2007-04-30 14:49:05 UTC (rev 2948)
@@ -66,6 +66,7 @@
array([[ 3.41250074, 2.20300046, 5.88897275],
[ 2.69407953, 2.39456571, 7.62102355],
[ 1.43684242, 0.57469577, 5.88897275]])
+
"""
std_dev = std(obs, axis=0)
return obs / std_dev
@@ -82,7 +83,7 @@
algorithm or something similar.
:Parameters:
- obs : ndarray
+ obs : ndarray
Each row of the 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.
@@ -143,7 +144,7 @@
""" Python version of vq algorithm.
The algorithm simply computes the euclidian distance between each
- observation and every frame in the code_book/
+ observation and every frame in the code_book.
:Parameters:
obs : ndarray
@@ -166,6 +167,7 @@
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
+
"""
# n = number of observations
# d = number of features
@@ -175,21 +177,20 @@
else:
return _py_vq_1d(obs, code_book)
else:
- (n, d) = shape(obs)
+ (n, d) = shape(obs)
# code books and observations should have same number of features and same shape
if not N.ndim(obs) == N.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)
+ 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 = N.sum((obs[i] - code_book) ** 2, 1)
- code[i] = argmin(dist)
+ dist = N.sum((obs[i] - code_book) ** 2, 1)
+ code[i] = argmin(dist)
min_dist[i] = dist[code[i]]
return code, sqrt(min_dist)
@@ -210,16 +211,17 @@
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 = N.zeros((n, nc))
+ n = obs.size
+ nc = code_book.size
+ dist = N.zeros((n, nc))
for i in range(nc):
- dist[:, i] = N.sum(obs - code_book[i])
+ dist[:,i] = N.sum(obs - code_book[i])
print dist
- code = argmin(dist)
- min_dist= dist[code]
+ code = argmin(dist)
+ min_dist = dist[code]
return code, sqrt(min_dist)
@@ -248,16 +250,17 @@
mind_dist : ndarray
min_dist[i] gives the distance between the ith observation and its
corresponding code.
+
"""
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
+ 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, :]
+
+ diff = obs[newaxis,:,:] - code_book[:,newaxis,:]
dist = sqrt(N.sum(diff * diff, -1))
code = argmin(dist, 0)
min_dist = minimum.reduce(dist, 0) #the next line I think is equivalent
@@ -324,7 +327,7 @@
return code_book, avg_dist[-1]
def kmeans(obs, k_or_guess, iter=20, thresh=1e-5):
- """ Generate a code book with minimum distortion.
+ """Generate a code book with minimum distortion.
:Parameters:
obs : ndarray
@@ -356,10 +359,8 @@
Examples
--------
- ("Not checked carefully for accuracy..." he said sheepishly)
-
>>> from numpy import array
- >>> from scipy.cluster.vq import vq, kmeans
+ >>> from scipy.cluster.vq import vq, kmeans, whiten
>>> features = array([[ 1.9,2.3],
... [ 1.5,2.5],
... [ 0.8,0.6],
@@ -375,8 +376,8 @@
(array([[ 2.3110306 , 2.86287398],
[ 0.93218041, 1.24398691]]), 0.85684700941625547)
- >>> import RandomArray
- >>> RandomArray.seed(1000,2000)
+ >>> from numpy import random
+ >>> random.seed((1000,2000))
>>> codes = 3
>>> kmeans(whitened,codes)
(array([[ 2.3110306 , 2.86287398],
@@ -387,8 +388,8 @@
if int(iter) < 1:
raise ValueError, 'iter must be >= to 1.'
if type(k_or_guess) == type(array([])):
- guess = k_or_guess
- result = _kmeans(obs, guess, thresh = thresh)
+ guess = k_or_guess
+ result = _kmeans(obs, guess, thresh = thresh)
else:
#initialize best distance value to a large value
best_dist = 100000
@@ -397,8 +398,8 @@
#print 'kmeans iter: ',
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)
+ 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
@@ -406,32 +407,33 @@
return result
def _kpoints(data, k):
- """Pick k points at random in data (one row = one observation).
-
+ """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
+ k : int
Number of samples to generate.
+
"""
if data.ndim > 1:
- n = data.shape[0]
+ n = data.shape[0]
else:
- n = data.size
+ n = data.size
- p = N.random.permutation(n)
- x = data[p[:k], :].copy()
+ p = N.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.
@@ -440,24 +442,25 @@
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
+ k : int
Number of samples to generate.
+
"""
- mu = N.mean(data, 0)
+ mu = N.mean(data, 0)
cov = N.cov(data, rowvar = 0)
# k rows, d cols (one row = one obs)
# Generate k sample of a random variable ~ Gaussian(mu, cov)
- x = N.random.randn(k, mu.size)
- x = N.dot(x, N.linalg.cholesky(cov).T) + mu
+ x = N.random.randn(k, mu.size)
+ x = N.dot(x, N.linalg.cholesky(cov).T) + mu
return x
-_valid_init_meth = {'random': _krandinit, 'points': _kpoints}
+_valid_init_meth = {'random': _krandinit, 'points': _kpoints}
-def kmeans2(data, k, minit = 'random', niter = 10):
+def kmeans2(data, k, minit='random', niter=10):
"""Classify a set of points into k clusters using kmean algorithm.
-
+
The algorithm works by minimizing the euclidian distance between data points
of cluster means. This version is more complete than kmean (has several
initialisation methods).
@@ -473,7 +476,7 @@
minit : string
Method for initialization. Available methods are random, points and
uniform:
-
+
random uses k points drawn from a Gaussian random generator which
mean and variances are estimated from the data.
@@ -496,9 +499,9 @@
# If data is rank 1, then we have 1 dimension problem.
nd = N.ndim(data)
if nd == 1:
- d = 1
+ d = 1
elif nd == 2:
- d = data.shape[1]
+ d = data.shape[1]
else:
raise ValueError("Input of rank > 2 not supported")
@@ -508,43 +511,43 @@
if not nd == N.ndim(k):
raise ValueError("k is not an int and has not same rank than data")
if d == 1:
- nc = len(k)
+ nc = len(k)
else:
- (nc, dc) = k.shape
+ (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()
+ clusters = k.copy()
else:
- nc = k
+ nc = k
try:
- init = _valid_init_meth[minit]
+ init = _valid_init_meth[minit]
except KeyError:
raise ValueError("unknown init method %s" % str(minit))
- clusters = init(data, k)
+ clusters = init(data, k)
assert not niter == 0
return _kmeans2(data, clusters, niter, nc)
def _kmeans2(data, code, niter, nc):
""" "raw" version of kmeans2. Do not use directly.
-
+
Run kmeans with a given initial codebook.
-
+
:undocumented
+
"""
for i in range(niter):
# Compute the nearest neighbour for each obs
# using the current code book
- label = vq(data, code)[0]
+ label = vq(data, code)[0]
# Update the code by computing centroids using the new code book
for j in range(nc):
mbs = N.where(label==j)
if mbs[0].size > 0:
- code[j, :] = N.mean(data[mbs], axis=0)
+ code[j,:] = N.mean(data[mbs], axis=0)
else:
- warnings.warn("one cluster has no member anymore ! You should"\
- " rerun kmean with different initialization !")
+ warnings.warn("One of the clusters are empty. " \
+ "Re-run kmean with a different initialization.")
return code, label
-
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