master > master: code py - config mit Bsp. von euklid + pollard
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@ -115,15 +115,86 @@
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]
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costs: [30, 10, 50, 10, 80]
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values: [17, 14, 17, 5, 25]
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- name: RUCKSACK
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algorithm: BRANCH-AND-BOUND
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max-cost: 900
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items: [
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'Sellerie',
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'Sonnenblumenkerne',
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'Rote Beete',
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'Hirse',
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'Buchweizen',
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]
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costs: [600, 100, 800, 100, 200]
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values: [10, 15, 20, 5, 15]
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Beispiele für Seminarwoche 12
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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- name: RANDOM-WALK
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algorithm: GRADIENT
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one-based: true
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coords-init: [3, 3]
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landscape: &ref_landscape1
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neighbourhoods:
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radius: 1
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# metric: MANHATTAN
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metric: MAXIMUM
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labels:
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- x
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- y
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values:
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- [5, 2, 1, 3, 4, 7]
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- [8, 4, 3, 5, 5, 6]
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- [9, 1, 2, 6, 8, 4]
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- [7, 4, 4, 3, 7, 3]
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- [6, 4, 2, 1, 0, 7]
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- [4, 3, 5, 2, 1, 8]
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optimise: MAX
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- name: RANDOM-WALK
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algorithm: ADAPTIVE
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one-based: true
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coords-init: [3, 3]
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landscape: *ref_landscape1
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optimise: MAX
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- name: RANDOM-WALK
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algorithm: METROPOLIS
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annealing: false
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temperature-init: 3.
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one-based: true
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coords-init: [5, 3]
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landscape: *ref_landscape1
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optimise: MAX
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- name: RANDOM-WALK
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algorithm: METROPOLIS
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annealing: false
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temperature-init: 3.
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one-based: false
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coords-init: [0]
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landscape:
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neighbourhoods:
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radius: 1
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metric: MANHATTAN
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labels:
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- x
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values: [4, 6.5, 2]
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optimise: MAX
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- name: GENETIC
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population:
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- [3, 5, 4, 1, 6, 7, 2, 8, 9]
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- [4, 5, 3, 2, 1, 6, 7, 8, 9]
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Beispiele für Seminarwoche 13
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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- name: EUKLID
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numbers:
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- 2017
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- 58
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- name: POLLARD-RHO
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growth: LINEAR
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# growth: EXPONENTIAL
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number: 534767
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x-init: 5
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