From a4a1e5835d6c1f024dc8555884cca3da0c0a7f19 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Tue, 5 Jan 2010 15:10:56 +0000
Subject: [PATCH] Version 0.5, add custom confidence ellipsoids David
Cournapeau <david@ar.media.kyoto-u.ac.jp> | 2006-08-07 18:46:57 +0900 (Mon,
07 Aug 2006)
From: cdavid <cdavid@cb17146a-f446-4be1-a4f7-bd7c5bb65646>
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@74 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
scikits/learn/pyem/Changelog | 7 +++++++
scikits/learn/pyem/TODO | 7 +------
scikits/learn/pyem/pyem/__init__.py | 4 ++--
scikits/learn/pyem/pyem/densities.py | 20 ++++++++++++--------
scikits/learn/pyem/pyem/profile_densities.py | 15 ++++++++-------
5 files changed, 30 insertions(+), 23 deletions(-)
diff --git a/scikits/learn/pyem/Changelog b/scikits/learn/pyem/Changelog
index 17a022f1b1..fc57297919 100644
--- a/scikits/learn/pyem/Changelog
+++ b/scikits/learn/pyem/Changelog
@@ -1,3 +1,10 @@
+pyem (0.5) Fri, 04 Aug 2006 23:10:37 +0900
+
+ * put version to 0.5.0
+ * implement confidence interval using chi2
+
+-- David Cournapeau <david@ar.media.kyoto-u.ac.jp>
+
pyem (0.4) Fri, 04 Aug 2006 19:37:47 +0900
* put version to 0.4.2
diff --git a/scikits/learn/pyem/TODO b/scikits/learn/pyem/TODO
index 10b201cdfb..546bf2eb48 100644
--- a/scikits/learn/pyem/TODO
+++ b/scikits/learn/pyem/TODO
@@ -1,13 +1,8 @@
-# Last Change: Thu Jul 13 05:00 PM 2006 J
+# Last Change: Fri Aug 04 11:00 PM 2006 J
Things which must be implemented for a 1.0 version:
- test for various length and model size.
- - refactoring with a class modelling mixture models.
- - for Gaussian densities: compute level <-> confidence ellipses
- relationship with Chi2 model.
- a small help/tutorial
- - review the code, with the difference between generic numpy functions
- and blas ones
Things which would be nice:
- C implementation of Gaussian densities at least.
diff --git a/scikits/learn/pyem/pyem/__init__.py b/scikits/learn/pyem/pyem/__init__.py
index b66857e8ce..0b5e774d00 100644
--- a/scikits/learn/pyem/pyem/__init__.py
+++ b/scikits/learn/pyem/pyem/__init__.py
@@ -1,7 +1,7 @@
#! /usr/bin/env python
-# Last Change: Fri Aug 04 07:00 PM 2006 J
+# Last Change: Fri Aug 04 11:00 PM 2006 J
-version = '0.4.2'
+version = '0.5.0'
from gauss_mix import GmParamError, GM
from gmm_em import GmmParamError, GMM
diff --git a/scikits/learn/pyem/pyem/densities.py b/scikits/learn/pyem/pyem/densities.py
index 8d9427a4b9..0ac27b070b 100644
--- a/scikits/learn/pyem/pyem/densities.py
+++ b/scikits/learn/pyem/pyem/densities.py
@@ -1,11 +1,13 @@
#! /usr/bin/python
#
# Copyrighted David Cournapeau
-# Last Change: Fri Aug 04 07:00 PM 2006 J
+# Last Change: Fri Aug 04 11:00 PM 2006 J
import numpy as N
import numpy.linalg as lin
from numpy.random import randn
+from scipy.stats import chi2
+
# Error classes
class DenError(Exception):
@@ -163,15 +165,14 @@ def _full_gauss_den(x, mu, va, log):
return y
# To plot a confidence ellipse from multi-variate gaussian pdf
-def gauss_ell(mu, va, dim = [0, 1], npoints = 100):
+def gauss_ell(mu, va, dim = [0, 1], npoints = 100, level = 0.39):
""" Given a mean and covariance for multi-variate
gaussian, returns npoints points for the ellipse
- of confidence 0.39
+ of confidence given by level (all points will be inside
+ the ellipsoides with a probability equal to level)
Returns the coordinate x and y of the ellipse"""
- # TODO: Get a confidence interval using the Chi2 distribution
- # of points at a given mahalanobis distance...
mu = N.atleast_1d(mu)
va = N.atleast_1d(va)
c = N.array(dim)
@@ -187,11 +188,14 @@ def gauss_ell(mu, va, dim = [0, 1], npoints = 100):
else:
raise DenError("mean and variance are not dim conformant")
- level = 0.39
+ # TODO: Get a confidence interval using the Chi2 distribution
+ # of points at a given mahalanobis distance...
+ chi22d = chi2(2)
+ mahal = N.sqrt(chi22d.ppf(level))
# Generates a circle of npoints
theta = N.linspace(0, 2 * N.pi, npoints)
- circle = N.array([N.cos(theta), N.sin(theta)])
+ circle = mahal * N.array([N.cos(theta), N.sin(theta)])
# Get the dimension which we are interested in:
mu = mu[dim]
@@ -395,7 +399,7 @@ if __name__ == "__main__":
Yc = Yc.transpose() + mu
# Plotting
- Xe, Ye = gauss_ell(mu, va, npoints = 100)
+ Xe, Ye = gauss_ell(mu, va, npoints = 100, level=0.95)
pylab.figure()
pylab.plot(Yc[:, 0], Yc[:, 1], '.')
pylab.plot(Xe, Ye, 'r')
diff --git a/scikits/learn/pyem/pyem/profile_densities.py b/scikits/learn/pyem/pyem/profile_densities.py
index ee6f30f86c..786cbf25d2 100644
--- a/scikits/learn/pyem/pyem/profile_densities.py
+++ b/scikits/learn/pyem/pyem/profile_densities.py
@@ -1,4 +1,5 @@
import numpy as N
+from numpy.random import randn
import densities as D
import tables
@@ -7,19 +8,19 @@ def bench1(mode = 'diag'):
# Diag Gaussian of dimension 20
#===========================================
d = 20
- n = 1e5
- niter = 1
+ n = 1e4
+ niter = 20
mode = 'diag'
# Generate a model with k components, d dimensions
- mu = N.randn(1, d)
+ mu = randn(1, d)
if mode == 'diag':
- va = abs(N.randn(1, d))
+ va = abs(randn(1, d))
elif mode == 'full':
- va = N.randn(d, d)
- va = N.matrixmultiply(va, va.transpose())
+ va = randn(d, d)
+ va = N.dot(va, va.transpose())
- X = N.randn(n, d)
+ X = randn(n, d)
print "Compute %d times densities, %d dimension, %d frames" % (niter, d, n)
for i in range(niter):
Y = D.gauss_den(X, mu, va)
--
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