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 <td style="vertical-align: top;"><a
href="aboutQuandles.html">Previous</a> 
 </td>
 <td
style="text-align: center; vertical-align: top; color: rgb(0, 0, 102);"><big>About HAP: Persistent Homology 
 </big></td>
 <td style="text-align: right; vertical-align: top;"><a
href="aboutMetrics.html">next</a> 
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 <big></big> 
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 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);"><big>1. Persistent Homology of Cubical and </big><big>Simplicial Complexes</big> 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255); text-align: left;">An
inclusion
of
pure
cubical
complexes
X1 --> X2
induces a natural homology homomorphism Hn(X1,F)
--> Hn(X2,F) for each positive n and any
coefficient module F. Taking F to be a field, the induced homomorphism
is a homomorphism of vector spaces and is completely determined by its
rank P1,2. 
 
A sequence of inclusions X1 --> X2 --> X3
--> ... --> Xk induces a sequence of homology
homomorphisms which, in each degree n, determine a kxk matrix of ranks Pi,j
(where
for
i>j
we
define
Pi,j=0). This matrix is referred
to as the n-th persistence matrix,
over
the
field
F,
for
the
sequence
of
pure
cubical
complexes. 
 
A possible scenario is that X1 is a sample from an unknown
manifold M, and that each space Xi+1 is obtained by
thickening Xi in some fashion. The hope is that the
persistence matrices describe the shape of the manifold M from which X1
was sampled. 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">Persistence
matrices
are
particularly
useful
when
analysing
high-dimensional
data
since
the
shape
of such data is hard to visualize. However, as a toy
example let us consider the
2-dimensional <a href="datacloud.eps">data cloud</a> 
 
 <div style="text-align: center;"><img alt=""
src="sample_from_circle.gif" style="width: 406px; height: 311px;"> 
 <div style="text-align: left;"> 
which, as we can see, was sampled (possibly with error) from an
annulus.
The following computations agree with this observation. 
 
The following commands produce a sequence of thickenings for this data
cloud and then compute the degree 1 persistence matrix over the field
of rational numbers. 
 </div>
 </div>
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
M:=ReadImageAsPureCubicalComplex("datacloud.eps",300); 
Pure cubical complex of dimension 2. 
 
gap>
T:=ThickeningFiltration(M,10);

 
Filtered pure cubical complex of dimension 2. 
 
gap> P:=PersistentHomologyOfFilteredCubicalComplex(T,1);; 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">The
persistence matrix P can be viewed as a barcode. The following command
displays this barcode. The single horizontal line at the bottom of the
barcode corresponds to a single persistent 1-dimensional homology. This
is consistent with the data having been sampled from an annulus - a
space with a single 1-dimensional hole. The various dots in the barcode
correspond to homologies that arise briefly at various stages in the
thickening process. 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
BarCodeDisplay(P); 
 
 <div style="text-align: center;"><img
style="width: 496px; height: 1086px;" alt="" src="brcd.gif"> 
 </div>
 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">As
a more realistic illustration of this approach to topological data
analysis let us suppose that we have made 400 experimental observations
v1, ..., v400 and that we are able to estimate
some metric distance d(vi,vj) between each pair
of observations. For instance, the observations might be vectors of a
given length and d(vi,vj) could be the
Euclidean distance. We could then build a filtered simplicial complex
K={K1<K2<...<KT} from an
increasing sequence of thresholds d1<...<dT.
Each
term
in
the
filtration
has 400 vertices v1, ..., v400,
and Kt is determined by the threshold dt: a
simplex belongs to Kt is and only if each pair of vertices
in the simplex satisfies d(vi,vj) < dt .
The
persistent
homology
of
the
filtered simplicial complex K could
provide information on the space from which our data was selected. 
 
The following data 
 <table
style="text-align: left; width: 400px; margin-left: auto; margin-right: auto; height: 178px;"
border="1" cellpadding="2" cellspacing="2">
 <tbody>
 <tr>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;">v1 
 </td>
 <td style="vertical-align: top; text-align: center;">v2 
 </td>
 <td style="vertical-align: top; text-align: center;">
... 
 </td>
 <td style="vertical-align: top; text-align: center;">v399 
 </td>
 <td style="vertical-align: top; text-align: center;">v400 
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top; text-align: center;">v1 
 </td>
 <td style="vertical-align: top; text-align: center;">0 
 </td>
 <td style="vertical-align: top; text-align: center;">66 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;">191 
 </td>
 <td style="vertical-align: top; text-align: center;">137 
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top; text-align: center;">v2 
 </td>
 <td style="vertical-align: top; text-align: center;">66 
 </td>
 <td style="vertical-align: top; text-align: center;">0 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;">125 
 </td>
 <td style="vertical-align: top; text-align: center;">71 
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top; text-align: center;">... 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top; text-align: center;">v399 
 </td>
 <td style="vertical-align: top; text-align: center;">191 
 </td>
 <td style="vertical-align: top; text-align: center;">125 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;">0 
 </td>
 <td style="vertical-align: top; text-align: center;">54 
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top; text-align: center;">v400 
 </td>
 <td style="vertical-align: top; text-align: center;">137 
 </td>
 <td style="vertical-align: top; text-align: center;">71 
 </td>
 <td style="vertical-align: top; text-align: center;"> 
 </td>
 <td style="vertical-align: top; text-align: center;">54 
 </td>
 <td style="vertical-align: top; text-align: center;">0 
 </td>
 </tr>
 </tbody>
 </table>
 
 is contained in a symmetric matrix in the file <a
href="symmetricMatrix.txt">symmetricMatrix.txt</a> . The following
commands read this matrix, compute the filtered simplicial complex, and
then compute the barcodes for 0-dimensional and 1-dimensional homology.
These barcodes suggest that the data points were samples from a space
with the homology of a disjoint union of two circles. 
 
We view the barcodes in compact form, where a line with label n
is used to denote n lines which start at the same point in
the filtration and end at the same point in the filtration. 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
Read("symmetricMatrix.txt"); 
gap> S:=SymmetricMat;; 
gap> G:=SymmetricMatrixToFilteredGraph(S,10,30);; #Take a filtration
with length T=10, and discard any distances greater than 30. 
gap> N:=SimplicialNerveOfFilteredGraph(G,2);; 
gap> C:=SparseFilteredChainComplexOfFilteredSimplicialComplex(N);; 
gap> P0:=PersistentHomologyOfFilteredSparseChainComplex(C,0);; 
gap> P1:=PersistentHomologyOfFilteredSparseChainComplex(C,1);; 
 
gap> BarCodeCompactDisplay(P0); 
 
 <div style="text-align: center;"><img
style="width: 549px; height: 201px;" alt="" src="percyl0.gif"> 
 </div>
 
gap>BarCodeCompactDisplay(P1); 
 
 <div style="text-align: center;"><img
style="width: 547px; height: 169px;" alt="" src="percyl1.gif"> 
 </div>
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">A
different scenario for the use of persitent homology of pure cubical
complexes is in the analysis of digital images. As a second toy example
consider the digital photo. 
 
 <div style="text-align: center;"><img
style="width: 499px; height: 376px;" alt="" src="../../../tst/examples/image1.3.2.png"> 
 <div style="text-align: left;"> 
The 20 longish lines in the following barcode for the degree 0
persistent homology correspond to the 20 objects in the photo. The 14
longish lines in the following barcode for the degree 1 persistent
homology correspond to the fact that 14 of the objects have holes in
them. We again view the barcodes in compact form, where a line with
label n
is used to denote n lines which start at the same point in
the filtration and end at the same point in the filtration. 
 </div>
 </div>
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
F:=ReadImageAsFilteredCubicalComplex("image1.3.2.png",20); 
Filtered pure cubical complex of dimension 2. 
 
gap> P0:=PersistentHomologyOfFilteredCubicalComplex(F,0);; 
gap> BarCodeCompactDisplay(P0); 
 
 <div style="text-align: center;"><img
style="width: 467px; height: 304px;" alt="" src="perhom0.gif"> 
 <div style="text-align: left;"> 
gap> P1:=PersistentHomologyOfFilteredCubicalComplex(F,1);; 
gap> BarCodeCompactDisplay(P1); 
 
 <div style="text-align: center;"><img
style="width: 567px; height: 572px;" alt="" src="perhom1.gif"> 
 </div>
 </div>
 </div>
 </td>
 </tr>
 <tr align="center">
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);"><big><a name="proteins"></a>2. Persistent
Homology of Proteins</big> 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">Given
a
pure
cubical
complex K, let us denote its centre of gravity by c(K).
Given an increasing sequence of positive numbers 0 < t1 <
t2 < ... , let us denote by FKn the
intersection of K with the Euclidean ball of radius tn
centred at c(K). This gives rise to a filtered pure cubical complex FK1
< FK2 < ... which we refer to as the concetric filtration on K. 
 
For a protein backbone K, the degree 0 persistent homology of FK should
contain useful
information on the geometric shape of K. 
 
The following commands compute this shape descriptor for the
T.thermophilus 1V2X protein and the H.sapiens <a>1XD3</a> protein
pictured on the <a href="aboutKnots.html#proteins">previous page</a>. 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
K:=ReadPDBfileAsPureCubicalComplex("1V2X.pdb");;

 
Reading chain containing 191 atoms. 
gap> FK:=ConcentricallyFilteredPureCubicalComplex(K,10);; 
gap> P:=PersistentHomologyOfFilteredCubicalComplex(FK,0);; 
gap> BarCodeCompactDisplay(P); 
 
 <div style="text-align: center;"><img
style="width: 595px; height: 446px;" alt="" src="bck1.gif"> 
 <div style="text-align: left;"> 
 
 </div>
 </div>
gap>
K:=ReadPDBfileAsPureCubicalComplex("1XD3.pdb");;

 
Reading chain containing 243 atoms. 
gap> FK:=ConcentricallyFilteredPureCubicalComplex(K,10);; 
gap> P:=PersistentHomologyOfFilteredCubicalComplex(FK,0);; 
gap> BarCodeCompactDisplay(P); 
 
 <div style="text-align: center;"><img
style="width: 679px; height: 584px;" alt="" src="bck2.gif"> 
 </div>
 </td>
 </tr>
 <tr align="center">
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);"><big>3. Persistent Homology of Groups</big> 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">Any
sequence of group homomorphisms G1 --> G2
--> ... --> Gk induces a sequence of homology
homomorphisms. In particular, the successive quotients of a group G by
the terms of its upper central series give a sequence of group
homomorphisms that induces an interesting sequence of homology
homomorphisms. 
 
For a finite p-group we take homology coefficients in the field of p
elements. The following commands compute and display the degree 3
homology barcode for the Sylow 2-subgroup of the Mathieu group M12.

 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
G:=SylowSubgroup(MathieuGroup(12),2);; 
 
gap> IdGroup(G); 
[ 64, 134 ] 
 
gap> P:=UniversalBarCode("UpperCentralSeries",64,134,3);; 
 
gap> BarCodeDisplay(P); 
 
 <div style="text-align: center;"><img
style="width: 229px; height: 309px;" alt="" src="m12per.gif"> 
 </div>
 </td>
 </tr>
 <tr align="center">
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);"><big>4. Persistent Homology of Filtered Chain
Complexes</big> 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 255);">The
Lyndon-Hochschild-Serre spectral sequence in group homology describes
the homology of a group G in terms of the homology of a normal subgroup
N and the homology of the quotient G/N. The spectral sequence arises
from a filtered chain complex. Barcodes can be used to represent the
differentials in this spectral sequence. 
 
For example, the following commands produce the degree 2 mod 2 homology
LHS barcode
for G the diherdal group of order 64 and N its centre. 
 </td>
 </tr>
 <tr>
 <td
style="vertical-align: top; background-color: rgb(255, 255, 204);">gap>
G:=DihedralGroup(64);; 
gap> N:=Center(G);; 
gap> R:=ResolutionNormalSeries([G,N],3);; 
gap> C:=FilteredTensorWithIntegersModP(R,2);; 
gap> P:=PersistentHomologyOfFilteredChainComplex(C,2,2);; 
gap> BarCodeDisplay(P); 
 
 <div style="text-align: center;"><img
style="width: 176px; height: 133px;" alt="" src="lhsbc.gif"> 
 </div>
 </td>
 </tr>
 <tr>
 <td style="vertical-align: top;">
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style="margin-left: auto; margin-right: auto; width: 100%; text-align: left;"
border="0" cellpadding="2" cellspacing="2">
 <tbody>
 <tr>
 <td style="vertical-align: top;"><a
style="color: rgb(0, 0, 102);" href="aboutQuandles.html">Previous
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 </td>
 <td style="text-align: center; vertical-align: top;"><a
href="aboutContents.html">Contents</a> 
 </td>
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