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from dionysus import Simplex, ZigzagPersistence , \ vertex_cmp , data_cmp \

MATH:7450 (22M:305) Topics in Topology: Scientific and Engineering Applications of Algebraic Topology Oct 21, 2013: Cohomology Fall 2013 course offered through the University of Iowa Division of Continuing Education Isabel K. Darcy, Department of Mathematics

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from dionysus import Simplex, ZigzagPersistence , \ vertex_cmp , data_cmp \

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  1. MATH:7450 (22M:305) Topics in Topology: Scientific and Engineering Applications of Algebraic Topology Oct 21, 2013: Cohomology Fall 2013 course offered through the University of Iowa Division of Continuing Education Isabel K. Darcy, Department of Mathematics Applied Mathematical and Computational Sciences, University of Iowa http://www.math.uiowa.edu/~idarcy/AppliedTopology.html

  2. triangle-zigzag.py from dionysus import Simplex, ZigzagPersistence, \ vertex_cmp, data_cmp \ # ,enable_log complex = {Simplex((0,), 0): None, # A Simplex((1,), 1): None, # B Simplex((2,), 2): None, # C Simplex((0,1), 2.5): None, # AB Simplex((1,2), 2.9): None, # BC Simplex((0,2), 3.5): None, # CA Simplex((0,1,2), 5): None} # ABC

  3. print "Complex:" for s in sorted(complex.keys()): print s print triangle idarcy$ python2.7 triangle-zigzag.py Complex: <0> <1> <2> <0, 1> 2.500000 <1, 2> 2.900000 <0, 2> 3.500000 <0, 1, 2>

  4. #enable_log("topology/persistence") zz = ZigzagPersistence() # Add all the simplices b = 1 for s in sorted(complex.keys(), data_cmp): print "%d: Adding %s" % (b, s) 1: Adding <0> 2: Adding <1> 3: Adding <2> 0 1 2

  5. # Add all the simplices b = 1 for s in sorted(complex.keys(), data_cmp): print "%d: Adding %s" % (b, s) i,d = zz.add([complex[ss] for ss in s.boundary], b) complex[s] = i if d: print "Interval (%d, %d)" % (d, b-1) b += 1

  6. # Add all the simplices b = 1 for s in sorted(complex.keys(), data_cmp): print "%d: Adding %s" % (b, s) i,d = zz.add([complex[ss] for ss in s.boundary], b) complex[s] = i if d: print "Interval (%d, %d)" % (d, b-1) b += 1 4: Adding <0, 1> 2.500000 Interval (2, 3) 5: Adding <1, 2> 2.900000 Interval (3, 4) 6: Adding <0, 2> 3.500000 7: Adding <0, 1, 2> Interval (6, 6) 0 1 2

  7. # Remove all the simplices for s in sorted(complex.keys(), data_cmp, reverse = True): print "%d: Removing %s" % (b, s) d = zz.remove(complex[s], b) del complex[s] if d: print "Interval (%d, %d)" % (d, b-1) b += 1 8: Removing <0, 1, 2> 9: Removing <0, 2> 3.500000 Interval (8, 8) 10: Removing <1, 2> 2.900000 11: Removing <0, 1> 2.500000 0 1 2

  8. 12: Removing <2> Interval (10, 11) 13: Removing <1> Interval (11, 12) 14: Removing <0> Interval (1, 13) 0 1 2

  9. http://link.springer.com/article/10.1007%2Fs00454-011-9344-x

  10. NKI (2002) breast cancer data: • gene expression levels of 24,000 from 272 tumors • Data = 272 points living in R24000

  11. NKI (2002) breast cancer data: • gene expression levels of 24,000 from 272 tumors • Data = 272 points living in R24000 • In reality one cleans the data first, removing unreliable data, etc. • One may also remove dimensions (genes) that appear irrelevant.

  12. NKI (2002) breast cancer data: • gene expression levels of 24,000 from 272 tumors • Data = 272 points living in R24000 • Since our dataset consists of only 272 points, it can’t be 24000-dimensional.

  13. For example, suppose our very fake data is { (9, 8, 7, 6, 5, 4, 3, 2, 3) ti | i = 1, 2, 3, … 1000 } = { (9, 8, 7, 6, 5, 4, 3, 2, 3), { (18, 16, 14, 12, 10, 8, 6, 4, 6), … }

  14. Dimensionality Reduction: • Given dataset D RN • Want: embedding f: D  Rn where n << N • which “preserves” the structure of the data. • Example: • { (9, 8, 7, 6, 5, 4, 3, 2, 3) ti | i = 1, 2, 3, … 1000 } R9 • f: D  R, f((9, 8, 7, 6, 5, 4, 3, 2, 3) ti) = ti U U

  15. Example: Principle component analysis (PCA) http://en.wikipedia.org/wiki/File:GaussianScatterPCA.png

  16. Many reduction methods: f1: D  R,f2: D  R, …fn: D  R (f1, f2, … fn): D  Rn Many are linear, M: RN  Rn, Mx = y But there are also non-linear dimensionality reduction algorithms.

  17. f1: D  R,f2: D  R, …fn: D  R (f1, f2, … fn): D  Rn

  18. Goal f1: D  S1,f2: D  S1, …fn: D  S1

  19. Note S1 is a ring:

  20. Cohomology and circular coordinates: Let X = finite simplicial complex. X0 = set of 0-simplices = vertices. X1 = set of 1-simplices = edges. X2 = set of 2-simplices = faces. . . . v2 e1 e2 v1 v3 e3

  21. Homology Let X = finite simplicial complex. C0 = set of 0-chains = linear combinations of vertices C1 = set of 1-chains = linear combinations of edges. C2 = set of 2-chains = linear combinations of faces. . . . v2 e1 e2 v1 v3 e3

  22. Cohomology and circular coordinates: Let X = finite simplicial complex, R a ring. C0 = set of 0-cochains = { f : C0 R | f homomorphism} C1 = set of 1-cochains = { f : C1 R | f homomorphism} C2 = set of 2-cochains = { f : C2 R | f homomorphism} . . .

  23. Cohomology and circular coordinates: Let X = finite simplicial complex, R a ring. C0(X, R) = set of 0-cochains = { f : X0 R } C1(X, R) = set of 1-cochains = { f : X1 R } C2(X, R) = set of 2-cochains = { f : X2 R } . . .

  24. Proposition 1: Let α ∈ C1(X;Z) be a cocycle. Then there exists a continuous function θ : X → S1 which maps each vertex to 0, and each edge ab around the entire circle with winding number α(ab). v2 → e1 e2 v1 v3 e3

  25. Proposition 2: 2 Let α ∈ C1(X;R) be a cocycle. Suppose there existsα ∈ C1(X;Z) and f ∈ C0(X;R) such that α = α + d0f . Then there exists a continuous function θ : X → S1 which maps each edge ablinearly to an interval of length α(ab), measured with sign. v2 → e1 e2 v1 v3 e3

  26. θ : X → R/Z = S1

  27. coboundarymaps d0: C0 = { f : X0 R } → C1 = { f : X1 R } (d0f )(ab) = f (b)− f (a) d1: C1 = { f : X1 R } → C2= { f : X2 R } (d1α)(abc) = α(bc)− α(ac)+α(ab).

  28. Let α ∈ Ci , di: Ci= { f : Xi R } → Ci+1= { f : Xi+1 R } α is a cocycle if diα = 0. α is a coboundary if there exists f in Ci-1 s.t. di-1f = α Note d1d0 f = 0 for any f ∈ C0. (d0f )(ab) = f (b) − f (a) (d1d0f )(abc) = d0f(bc)− d0f(ac)+ d0f(ab) = d0f(c) - d0f(b)-d0f(c) + d0f(a)+ d0f(b) - d0f(a)

  29. d1 d0 f = 0 implies Im (d0 ) ⊆ Ker (d1 ). H1 (X;A ) = Ker (d1 )/ Im (d0 ). Twococycles α,β arecohomologousif α − β is a coboundary.

  30. d1 d0 f = 0 implies Im (d0 ) ⊆ Ker (d1 ). H1 (X;A ) = Ker (d1 )/ Im (d0 ). v2 e1 e2 v1 v3 e3

  31. d1 d0 f = 0 implies Im (d0 ) ⊆ Ker (d1 ). H1 (X;A ) = Ker (d1 )/ Im (d0 ). v2 0  C0  C1  0 e1 e2 v1 v3 e3

  32. C0(X, R) = set of 0-cochains = { f : X0 R } v2 e1 e2 v1 v3 e3

  33. C0(X, R) = set of 0-cochains = { f : X0 R } = { f | f(v1) = r1, f(v2) = r2, f(v3) = r3; (r1, r2, r3) in R3 } C1(X, R) = set of 1-cochains = { f : X1 R } = { f | f(e1) = r1, f(e2) = r2, f(e3) = r3; (r1, r2, r3) in R3 } v2 e1 e2 v1 v3 e3

  34. C0(X, R) = set of 0-cochains = { f : X0 R } = { f | f(v1) = r1, f(v2) = r2, f(v3) = r3; (r1, r2, r3) in R3 } C1(X, R) = set of 1-cochains = { f : X1 R } = { f | f(e1) = r1, f(e2) = r2, f(e3) = r3; (r1, r2, r3) in R3 } Hi(X;A ) = Ker (di)/ Im (di-1). 0  C0 C1 0 v2 e1 e2 v1 v3 e3

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