diff options
Diffstat (limited to 'Metrics')
38 files changed, 885 insertions, 104 deletions
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend index cf871ead..1745bc35 100644 --- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend +++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/Main.xtend | |||
@@ -35,7 +35,7 @@ class Main { | |||
35 | 35 | ||
36 | //human input has different package declaration | 36 | //human input has different package declaration |
37 | // reader = new GraphReader(Yakindumm2PackageImpl.eINSTANCE); | 37 | // reader = new GraphReader(Yakindumm2PackageImpl.eINSTANCE); |
38 | val human = new RWInformation("inputs/Random/", "outputs/", 1); | 38 | val human = new RWInformation("inputs/Fake_Random_Random/", "outputs/", 1); |
39 | calculateAllModels(human.inputFolder, human.outputFolder,human.numRuns, reader); | 39 | calculateAllModels(human.inputFolder, human.outputFolder,human.numRuns, reader); |
40 | 40 | ||
41 | 41 | ||
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend index cdd06027..71fa5fed 100644 --- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend +++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/app/PartialInterpretationMetric.xtend | |||
@@ -1,5 +1,6 @@ | |||
1 | package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app | 1 | package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app |
2 | 2 | ||
3 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.JSDistance | ||
3 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance | 4 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance.KSDistance |
4 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph | 5 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.graph.PartialInterpretationGraph |
5 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter | 6 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.CsvFileWriter |
@@ -17,13 +18,14 @@ import org.eclipse.emf.ecore.util.EcoreUtil | |||
17 | import org.eclipse.viatra.dse.api.Solution | 18 | import org.eclipse.viatra.dse.api.Solution |
18 | 19 | ||
19 | class PartialInterpretationMetric { | 20 | class PartialInterpretationMetric { |
20 | var static state = 0; | ||
21 | var static KSDistance ks; | 21 | var static KSDistance ks; |
22 | var static JSDistance js; | ||
22 | 23 | ||
23 | def static void initPaths(){ | 24 | def static void initPaths(){ |
24 | new File("debug/metric/").mkdir(); | 25 | new File("debug/metric/").mkdir(); |
25 | new File("debug/metric/trajectories/").mkdir(); | 26 | new File("debug/metric/trajectories/").mkdir(); |
26 | ks = new KSDistance(Domain.Yakinduum); | 27 | ks = new KSDistance(Domain.Yakinduum); |
28 | js = new JSDistance(Domain.Yakinduum); | ||
27 | } | 29 | } |
28 | 30 | ||
29 | def static MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){ | 31 | def static MetricDistanceGroup calculateMetricDistance(PartialInterpretation partial){ |
@@ -35,6 +37,22 @@ class PartialInterpretationMetric { | |||
35 | val metricCalculator = new PartialInterpretationGraph(partial, metrics, null); | 37 | val metricCalculator = new PartialInterpretationGraph(partial, metrics, null); |
36 | var metricSamples = metricCalculator.evaluateAllMetricsToSamples(); | 38 | var metricSamples = metricCalculator.evaluateAllMetricsToSamples(); |
37 | 39 | ||
40 | var mpc = js.mpcDistance(metricSamples.mpcSamples); | ||
41 | var na = js.naDistance(metricSamples.naSamples); | ||
42 | var outDegree = js.outDegreeDistance(metricSamples.outDegreeSamples); | ||
43 | |||
44 | return new MetricDistanceGroup(mpc, na, outDegree); | ||
45 | } | ||
46 | |||
47 | def static MetricDistanceGroup calculateMetricDistanceKS(PartialInterpretation partial){ | ||
48 | val metrics = new ArrayList<Metric>(); | ||
49 | metrics.add(new OutDegreeMetric()); | ||
50 | metrics.add(new NodeActivityMetric()); | ||
51 | metrics.add(new MultiplexParticipationCoefficientMetric()); | ||
52 | |||
53 | val metricCalculator = new PartialInterpretationGraph(partial, metrics, null); | ||
54 | var metricSamples = metricCalculator.evaluateAllMetricsToSamples(); | ||
55 | |||
38 | var mpc = ks.mpcDistance(metricSamples.mpcSamples); | 56 | var mpc = ks.mpcDistance(metricSamples.mpcSamples); |
39 | var na = ks.naDistance(metricSamples.naSamples); | 57 | var na = ks.naDistance(metricSamples.naSamples); |
40 | var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples); | 58 | var outDegree = ks.outDegreeDistance(metricSamples.outDegreeSamples); |
@@ -52,7 +70,6 @@ class PartialInterpretationMetric { | |||
52 | //make dir since the folder can be none existing | 70 | //make dir since the folder can be none existing |
53 | new File(path).mkdir(); | 71 | new File(path).mkdir(); |
54 | val filename = path + "/state_"+currentStateId+"-"+counter+".csv"; | 72 | val filename = path + "/state_"+currentStateId+"-"+counter+".csv"; |
55 | state++; | ||
56 | val metricCalculator = new PartialInterpretationGraph(partial, metrics, currentStateId); | 73 | val metricCalculator = new PartialInterpretationGraph(partial, metrics, currentStateId); |
57 | 74 | ||
58 | CsvFileWriter.write(metricCalculator.evaluateAllMetrics(), filename); | 75 | CsvFileWriter.write(metricCalculator.evaluateAllMetrics(), filename); |
@@ -60,20 +77,22 @@ class PartialInterpretationMetric { | |||
60 | 77 | ||
61 | def static void outputTrajectories(PartialInterpretation empty, List<Solution> solutions){ | 78 | def static void outputTrajectories(PartialInterpretation empty, List<Solution> solutions){ |
62 | for(solution : solutions){ | 79 | for(solution : solutions){ |
80 | |||
63 | //need to copy the empty solution because the transition directly worked on the graph | 81 | //need to copy the empty solution because the transition directly worked on the graph |
64 | val emptySolutionCopy = EcoreUtil.copy(empty) | 82 | val emptySolutionCopy = EcoreUtil.copy(empty) |
65 | val trajectory = solution.shortestTrajectory; | 83 | val trajectory = solution.shortestTrajectory; |
66 | trajectory.modelWithEditingDomain = emptySolutionCopy | 84 | trajectory.model = emptySolutionCopy |
67 | 85 | ||
68 | // state codes that will record the trajectory | 86 | // state codes that will record the trajectory |
69 | val stateCodes = newArrayList() | 87 | val stateCodes = newArrayList() |
70 | |||
71 | var counter = 0 | 88 | var counter = 0 |
89 | |||
72 | //transform and record the state codes for each state | 90 | //transform and record the state codes for each state |
73 | while(trajectory.doNextTransformation){ | 91 | while(trajectory.doNextTransformation){ |
74 | //println(trajectory.stateCoder.createStateCode) | 92 | //println(trajectory.stateCoder.createStateCode) |
75 | val stateId = trajectory.stateCoder.createStateCode.toString | 93 | val stateId = trajectory.stateCoder.createStateCode.toString |
76 | val interpretation = trajectory.getModel(); | 94 | val interpretation = trajectory.getModel(); |
95 | println(stateId) | ||
77 | //calculate metrics of current state | 96 | //calculate metrics of current state |
78 | calculateMetric(interpretation as PartialInterpretation, "debug/metric/output", stateId, counter) | 97 | calculateMetric(interpretation as PartialInterpretation, "debug/metric/output", stateId, counter) |
79 | stateCodes.add(stateId) | 98 | stateCodes.add(stateId) |
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend new file mode 100644 index 00000000..ced9eadb --- /dev/null +++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/JSDistance.xtend | |||
@@ -0,0 +1,95 @@ | |||
1 | package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance | ||
2 | |||
3 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain | ||
4 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader | ||
5 | import com.google.common.collect.Sets | ||
6 | import java.text.DecimalFormat | ||
7 | import java.util.HashMap | ||
8 | import java.util.List | ||
9 | |||
10 | class JSDistance { | ||
11 | var HashMap<String, Double> mpcPMF; | ||
12 | var HashMap<String, Double> naPMF; | ||
13 | var HashMap<String, Double> outDegreePMF; | ||
14 | var DecimalFormat formatter; | ||
15 | |||
16 | new(Domain d){ | ||
17 | var metrics = RepMetricsReader.read(d); | ||
18 | var mpcSamples = metrics.mpcSamples; | ||
19 | var naSamples = metrics.naSamples.stream.mapToDouble([it]).toArray(); | ||
20 | var outDegreeSamples = metrics.outDegreeSamples.stream.mapToDouble([it]).toArray(); | ||
21 | |||
22 | //needs to format the number to string avoid precision issue | ||
23 | formatter = new DecimalFormat("#0.00000"); | ||
24 | |||
25 | mpcPMF = pmfFromSamples(mpcSamples, formatter); | ||
26 | naPMF = pmfFromSamples(naSamples, formatter); | ||
27 | outDegreePMF = pmfFromSamples(outDegreeSamples, formatter); | ||
28 | } | ||
29 | |||
30 | def private pmfFromSamples(double[] samples, DecimalFormat formatter){ | ||
31 | var length = samples.length; | ||
32 | var pmfMap = new HashMap<String, Double>(); | ||
33 | |||
34 | for(sample : samples){ | ||
35 | pmfMap.put(formatter.format(sample), pmfMap.getOrDefault(formatter.format(sample), 0.0) + 1.0 / length); | ||
36 | } | ||
37 | |||
38 | return pmfMap; | ||
39 | } | ||
40 | |||
41 | def private combinePMF(HashMap<String, Double> pmf1, HashMap<String, Double> pmf2){ | ||
42 | var pmfMap = new HashMap<String, Double>(); | ||
43 | |||
44 | var union = Sets.union(pmf1.keySet(), pmf2.keySet()); | ||
45 | |||
46 | for(key : union){ | ||
47 | // corresponding to M in JS distance | ||
48 | var value = 1.0/2 * (pmf1.getOrDefault(key, 0.0) + pmf2.getOrDefault(key, 0.0)); | ||
49 | pmfMap.put(key, value); | ||
50 | } | ||
51 | return pmfMap; | ||
52 | } | ||
53 | |||
54 | def private jsDivergence(HashMap<String, Double> p, HashMap<String, Double> q){ | ||
55 | val m = combinePMF(q, p); | ||
56 | var distance = 1.0/2 * klDivergence(p, m) + 1.0/2 * klDivergence(q, m); | ||
57 | return distance; | ||
58 | } | ||
59 | |||
60 | def klDivergence(HashMap<String, Double> p, HashMap<String, Double> q){ | ||
61 | var distance = 0.0; | ||
62 | for(key : q.keySet()){ | ||
63 | //need to convert log e to log 2 | ||
64 | if(p.containsKey(key)){ | ||
65 | distance -= p.get(key) * Math.log(q.get(key) / p.get(key)) / Math.log(2); | ||
66 | } | ||
67 | } | ||
68 | return distance; | ||
69 | } | ||
70 | |||
71 | def double mpcDistance(List<Double> samples){ | ||
72 | // map list to array | ||
73 | var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter); | ||
74 | //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1 | ||
75 | if(map.size < 2) return 1; | ||
76 | return jsDivergence(map, mpcPMF); | ||
77 | } | ||
78 | |||
79 | def double naDistance(List<Double> samples){ | ||
80 | // map list to array | ||
81 | var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter); | ||
82 | |||
83 | //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1 | ||
84 | if(map.size < 2) return 1; | ||
85 | return jsDivergence(map, naPMF); | ||
86 | } | ||
87 | |||
88 | def double outDegreeDistance(List<Double> samples){ | ||
89 | // map list to array | ||
90 | var map = pmfFromSamples(samples.stream().mapToDouble([it]).toArray(), formatter); | ||
91 | //if the size of array is smaller than 2, ks distance cannot be performed, simply return 1 | ||
92 | if(map.size < 2) return 1; | ||
93 | return jsDivergence(map, outDegreePMF); | ||
94 | } | ||
95 | } \ No newline at end of file | ||
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend index 1fb21529..58e0a8a3 100644 --- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend +++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/distance/KSDistance.xtend | |||
@@ -1,9 +1,9 @@ | |||
1 | package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance | 1 | package ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.distance |
2 | 2 | ||
3 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain | 3 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.app.Domain |
4 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader | ||
4 | import java.util.List | 5 | import java.util.List |
5 | import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest | 6 | import org.apache.commons.math3.stat.inference.KolmogorovSmirnovTest |
6 | import ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator.io.RepMetricsReader | ||
7 | 7 | ||
8 | class KSDistance { | 8 | class KSDistance { |
9 | var static ksTester = new KolmogorovSmirnovTest(); | 9 | var static ksTester = new KolmogorovSmirnovTest(); |
diff --git a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend index bed356e9..01e3940b 100644 --- a/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend +++ b/Metrics/Metrics-Calculation/ca.mcgill.ecse.dslreasoner.realistic.metrics.calculator/src/ca/mcgill/ecse/dslreasoner/realistic/metrics/calculator/io/CsvFileWriter.xtend | |||
@@ -21,7 +21,7 @@ class CsvFileWriter { | |||
21 | output.append(datarow.get(i) + ','); | 21 | output.append(datarow.get(i) + ','); |
22 | } | 22 | } |
23 | 23 | ||
24 | if(datarow.size > 1){ | 24 | if(datarow.size >= 1){ |
25 | output.append(datarow.get(datarow.size() - 1)); | 25 | output.append(datarow.get(datarow.size() - 1)); |
26 | output.append('\n'); | 26 | output.append('\n'); |
27 | } | 27 | } |
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg new file mode 100644 index 00000000..6b987b3e --- /dev/null +++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/output/controled_viatra_all/Node Activity.jpg | |||
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diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb index 4c7fecb3..000822bf 100644 --- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb +++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/metrics_distance_with_selector.ipynb | |||
@@ -16,7 +16,7 @@ | |||
16 | }, | 16 | }, |
17 | { | 17 | { |
18 | "cell_type": "code", | 18 | "cell_type": "code", |
19 | "execution_count": 2, | 19 | "execution_count": 1, |
20 | "metadata": {}, | 20 | "metadata": {}, |
21 | "outputs": [], | 21 | "outputs": [], |
22 | "source": [ | 22 | "source": [ |
@@ -51,7 +51,7 @@ | |||
51 | }, | 51 | }, |
52 | { | 52 | { |
53 | "cell_type": "code", | 53 | "cell_type": "code", |
54 | "execution_count": 3, | 54 | "execution_count": 2, |
55 | "metadata": {}, | 55 | "metadata": {}, |
56 | "outputs": [], | 56 | "outputs": [], |
57 | "source": [ | 57 | "source": [ |
@@ -81,7 +81,7 @@ | |||
81 | }, | 81 | }, |
82 | { | 82 | { |
83 | "cell_type": "code", | 83 | "cell_type": "code", |
84 | "execution_count": 4, | 84 | "execution_count": 3, |
85 | "metadata": {}, | 85 | "metadata": {}, |
86 | "outputs": [], | 86 | "outputs": [], |
87 | "source": [ | 87 | "source": [ |
@@ -111,7 +111,7 @@ | |||
111 | }, | 111 | }, |
112 | { | 112 | { |
113 | "cell_type": "code", | 113 | "cell_type": "code", |
114 | "execution_count": 38, | 114 | "execution_count": 4, |
115 | "metadata": {}, | 115 | "metadata": {}, |
116 | "outputs": [], | 116 | "outputs": [], |
117 | "source": [ | 117 | "source": [ |
@@ -144,7 +144,7 @@ | |||
144 | }, | 144 | }, |
145 | { | 145 | { |
146 | "cell_type": "code", | 146 | "cell_type": "code", |
147 | "execution_count": 6, | 147 | "execution_count": 5, |
148 | "metadata": {}, | 148 | "metadata": {}, |
149 | "outputs": [], | 149 | "outputs": [], |
150 | "source": [ | 150 | "source": [ |
@@ -154,7 +154,7 @@ | |||
154 | }, | 154 | }, |
155 | { | 155 | { |
156 | "cell_type": "code", | 156 | "cell_type": "code", |
157 | "execution_count": 7, | 157 | "execution_count": 6, |
158 | "metadata": {}, | 158 | "metadata": {}, |
159 | "outputs": [], | 159 | "outputs": [], |
160 | "source": [ | 160 | "source": [ |
@@ -168,7 +168,7 @@ | |||
168 | }, | 168 | }, |
169 | { | 169 | { |
170 | "cell_type": "code", | 170 | "cell_type": "code", |
171 | "execution_count": 8, | 171 | "execution_count": 7, |
172 | "metadata": {}, | 172 | "metadata": {}, |
173 | "outputs": [], | 173 | "outputs": [], |
174 | "source": [ | 174 | "source": [ |
@@ -182,7 +182,7 @@ | |||
182 | }, | 182 | }, |
183 | { | 183 | { |
184 | "cell_type": "code", | 184 | "cell_type": "code", |
185 | "execution_count": 25, | 185 | "execution_count": 8, |
186 | "metadata": {}, | 186 | "metadata": {}, |
187 | "outputs": [], | 187 | "outputs": [], |
188 | "source": [ | 188 | "source": [ |
@@ -198,7 +198,7 @@ | |||
198 | }, | 198 | }, |
199 | { | 199 | { |
200 | "cell_type": "code", | 200 | "cell_type": "code", |
201 | "execution_count": 43, | 201 | "execution_count": 9, |
202 | "metadata": {}, | 202 | "metadata": {}, |
203 | "outputs": [], | 203 | "outputs": [], |
204 | "source": [ | 204 | "source": [ |
@@ -214,7 +214,7 @@ | |||
214 | }, | 214 | }, |
215 | { | 215 | { |
216 | "cell_type": "code", | 216 | "cell_type": "code", |
217 | "execution_count": 33, | 217 | "execution_count": 10, |
218 | "metadata": {}, | 218 | "metadata": {}, |
219 | "outputs": [], | 219 | "outputs": [], |
220 | "source": [ | 220 | "source": [ |
@@ -248,7 +248,7 @@ | |||
248 | }, | 248 | }, |
249 | { | 249 | { |
250 | "cell_type": "code", | 250 | "cell_type": "code", |
251 | "execution_count": 42, | 251 | "execution_count": 11, |
252 | "metadata": {}, | 252 | "metadata": {}, |
253 | "outputs": [], | 253 | "outputs": [], |
254 | "source": [ | 254 | "source": [ |
@@ -272,7 +272,7 @@ | |||
272 | }, | 272 | }, |
273 | { | 273 | { |
274 | "cell_type": "code", | 274 | "cell_type": "code", |
275 | "execution_count": 15, | 275 | "execution_count": 12, |
276 | "metadata": {}, | 276 | "metadata": {}, |
277 | "outputs": [], | 277 | "outputs": [], |
278 | "source": [ | 278 | "source": [ |
@@ -283,7 +283,7 @@ | |||
283 | }, | 283 | }, |
284 | { | 284 | { |
285 | "cell_type": "code", | 285 | "cell_type": "code", |
286 | "execution_count": 46, | 286 | "execution_count": 13, |
287 | "metadata": {}, | 287 | "metadata": {}, |
288 | "outputs": [], | 288 | "outputs": [], |
289 | "source": [ | 289 | "source": [ |
@@ -297,18 +297,18 @@ | |||
297 | }, | 297 | }, |
298 | { | 298 | { |
299 | "cell_type": "code", | 299 | "cell_type": "code", |
300 | "execution_count": 77, | 300 | "execution_count": 14, |
301 | "metadata": {}, | 301 | "metadata": {}, |
302 | "outputs": [ | 302 | "outputs": [ |
303 | { | 303 | { |
304 | "data": { | 304 | "data": { |
305 | "application/vnd.jupyter.widget-view+json": { | 305 | "application/vnd.jupyter.widget-view+json": { |
306 | "model_id": "9519be563fbc41c28921c77ef6481b17", | 306 | "model_id": "a8471e4dd66a47ecb6abb2371be43321", |
307 | "version_major": 2, | 307 | "version_major": 2, |
308 | "version_minor": 0 | 308 | "version_minor": 0 |
309 | }, | 309 | }, |
310 | "text/plain": [ | 310 | "text/plain": [ |
311 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | 311 | "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…" |
312 | ] | 312 | ] |
313 | }, | 313 | }, |
314 | "metadata": {}, | 314 | "metadata": {}, |
@@ -320,7 +320,7 @@ | |||
320 | "<function __main__.plot_out_degree(lines)>" | 320 | "<function __main__.plot_out_degree(lines)>" |
321 | ] | 321 | ] |
322 | }, | 322 | }, |
323 | "execution_count": 77, | 323 | "execution_count": 14, |
324 | "metadata": {}, | 324 | "metadata": {}, |
325 | "output_type": "execute_result" | 325 | "output_type": "execute_result" |
326 | } | 326 | } |
@@ -333,18 +333,18 @@ | |||
333 | }, | 333 | }, |
334 | { | 334 | { |
335 | "cell_type": "code", | 335 | "cell_type": "code", |
336 | "execution_count": 78, | 336 | "execution_count": 15, |
337 | "metadata": {}, | 337 | "metadata": {}, |
338 | "outputs": [ | 338 | "outputs": [ |
339 | { | 339 | { |
340 | "data": { | 340 | "data": { |
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342 | "model_id": "c896725e542c4bf8a1bc76ba66819b20", | 342 | "model_id": "ad6e466cc3fe44d393d2c82d48244d83", |
343 | "version_major": 2, | 343 | "version_major": 2, |
344 | "version_minor": 0 | 344 | "version_minor": 0 |
345 | }, | 345 | }, |
346 | "text/plain": [ | 346 | "text/plain": [ |
347 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | 347 | "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…" |
348 | ] | 348 | ] |
349 | }, | 349 | }, |
350 | "metadata": {}, | 350 | "metadata": {}, |
@@ -356,7 +356,7 @@ | |||
356 | "<function __main__.plot_out_na(lines)>" | 356 | "<function __main__.plot_out_na(lines)>" |
357 | ] | 357 | ] |
358 | }, | 358 | }, |
359 | "execution_count": 78, | 359 | "execution_count": 15, |
360 | "metadata": {}, | 360 | "metadata": {}, |
361 | "output_type": "execute_result" | 361 | "output_type": "execute_result" |
362 | } | 362 | } |
@@ -369,18 +369,18 @@ | |||
369 | }, | 369 | }, |
370 | { | 370 | { |
371 | "cell_type": "code", | 371 | "cell_type": "code", |
372 | "execution_count": 79, | 372 | "execution_count": 16, |
373 | "metadata": {}, | 373 | "metadata": {}, |
374 | "outputs": [ | 374 | "outputs": [ |
375 | { | 375 | { |
376 | "data": { | 376 | "data": { |
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378 | "model_id": "880410d675624545ab73977a463bb5c9", | 378 | "model_id": "d88ebc8e4062473a96ac35fe800028ef", |
379 | "version_major": 2, | 379 | "version_major": 2, |
380 | "version_minor": 0 | 380 | "version_minor": 0 |
381 | }, | 381 | }, |
382 | "text/plain": [ | 382 | "text/plain": [ |
383 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | 383 | "interactive(children=(SelectMultiple(description='Trajectory:', options={'../input/viatra_nocon_output/traject…" |
384 | ] | 384 | ] |
385 | }, | 385 | }, |
386 | "metadata": {}, | 386 | "metadata": {}, |
@@ -392,7 +392,7 @@ | |||
392 | "<function __main__.plot_out_mpc(lines)>" | 392 | "<function __main__.plot_out_mpc(lines)>" |
393 | ] | 393 | ] |
394 | }, | 394 | }, |
395 | "execution_count": 79, | 395 | "execution_count": 16, |
396 | "metadata": {}, | 396 | "metadata": {}, |
397 | "output_type": "execute_result" | 397 | "output_type": "execute_result" |
398 | } | 398 | } |
@@ -412,7 +412,7 @@ | |||
412 | }, | 412 | }, |
413 | { | 413 | { |
414 | "cell_type": "code", | 414 | "cell_type": "code", |
415 | "execution_count": 50, | 415 | "execution_count": 17, |
416 | "metadata": {}, | 416 | "metadata": {}, |
417 | "outputs": [], | 417 | "outputs": [], |
418 | "source": [ | 418 | "source": [ |
@@ -427,13 +427,13 @@ | |||
427 | }, | 427 | }, |
428 | { | 428 | { |
429 | "cell_type": "code", | 429 | "cell_type": "code", |
430 | "execution_count": 51, | 430 | "execution_count": 18, |
431 | "metadata": {}, | 431 | "metadata": {}, |
432 | "outputs": [ | 432 | "outputs": [ |
433 | { | 433 | { |
434 | "data": { | 434 | "data": { |
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436 | "model_id": "0d04d6db770a49f4a160ff55cc7131f6", | 436 | "model_id": "1eb2ba5848a048389bca8d804fc8340a", |
437 | "version_major": 2, | 437 | "version_major": 2, |
438 | "version_minor": 0 | 438 | "version_minor": 0 |
439 | }, | 439 | }, |
@@ -450,7 +450,7 @@ | |||
450 | "<function __main__.plot_out_degree(lines)>" | 450 | "<function __main__.plot_out_degree(lines)>" |
451 | ] | 451 | ] |
452 | }, | 452 | }, |
453 | "execution_count": 51, | 453 | "execution_count": 18, |
454 | "metadata": {}, | 454 | "metadata": {}, |
455 | "output_type": "execute_result" | 455 | "output_type": "execute_result" |
456 | } | 456 | } |
@@ -463,13 +463,13 @@ | |||
463 | }, | 463 | }, |
464 | { | 464 | { |
465 | "cell_type": "code", | 465 | "cell_type": "code", |
466 | "execution_count": 52, | 466 | "execution_count": 19, |
467 | "metadata": {}, | 467 | "metadata": {}, |
468 | "outputs": [ | 468 | "outputs": [ |
469 | { | 469 | { |
470 | "data": { | 470 | "data": { |
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472 | "model_id": "96eebad1f6274d79ad377c8c54b44615", | 472 | "model_id": "6e5840f7a5ad4515bce9080088b644f2", |
473 | "version_major": 2, | 473 | "version_major": 2, |
474 | "version_minor": 0 | 474 | "version_minor": 0 |
475 | }, | 475 | }, |
@@ -486,7 +486,7 @@ | |||
486 | "<function __main__.plot_na(lines)>" | 486 | "<function __main__.plot_na(lines)>" |
487 | ] | 487 | ] |
488 | }, | 488 | }, |
489 | "execution_count": 52, | 489 | "execution_count": 19, |
490 | "metadata": {}, | 490 | "metadata": {}, |
491 | "output_type": "execute_result" | 491 | "output_type": "execute_result" |
492 | } | 492 | } |
@@ -499,13 +499,13 @@ | |||
499 | }, | 499 | }, |
500 | { | 500 | { |
501 | "cell_type": "code", | 501 | "cell_type": "code", |
502 | "execution_count": 53, | 502 | "execution_count": 20, |
503 | "metadata": {}, | 503 | "metadata": {}, |
504 | "outputs": [ | 504 | "outputs": [ |
505 | { | 505 | { |
506 | "data": { | 506 | "data": { |
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508 | "model_id": "4fc2714a3cd3440daf5014bb4b942b9a", | 508 | "model_id": "9e30f267b092491ba1ffe8f83c5f68ce", |
509 | "version_major": 2, | 509 | "version_major": 2, |
510 | "version_minor": 0 | 510 | "version_minor": 0 |
511 | }, | 511 | }, |
@@ -522,7 +522,7 @@ | |||
522 | "<function __main__.plot_mpc(lines)>" | 522 | "<function __main__.plot_mpc(lines)>" |
523 | ] | 523 | ] |
524 | }, | 524 | }, |
525 | "execution_count": 53, | 525 | "execution_count": 20, |
526 | "metadata": {}, | 526 | "metadata": {}, |
527 | "output_type": "execute_result" | 527 | "output_type": "execute_result" |
528 | } | 528 | } |
@@ -542,7 +542,7 @@ | |||
542 | }, | 542 | }, |
543 | { | 543 | { |
544 | "cell_type": "code", | 544 | "cell_type": "code", |
545 | "execution_count": 59, | 545 | "execution_count": 21, |
546 | "metadata": {}, | 546 | "metadata": {}, |
547 | "outputs": [], | 547 | "outputs": [], |
548 | "source": [ | 548 | "source": [ |
@@ -557,13 +557,13 @@ | |||
557 | }, | 557 | }, |
558 | { | 558 | { |
559 | "cell_type": "code", | 559 | "cell_type": "code", |
560 | "execution_count": 60, | 560 | "execution_count": 22, |
561 | "metadata": {}, | 561 | "metadata": {}, |
562 | "outputs": [ | 562 | "outputs": [ |
563 | { | 563 | { |
564 | "data": { | 564 | "data": { |
565 | "application/vnd.jupyter.widget-view+json": { | 565 | "application/vnd.jupyter.widget-view+json": { |
566 | "model_id": "4401931533b5497f864f146d7b4dcd3c", | 566 | "model_id": "cc1f64c92e814c32a81cd5ec5d4e50dc", |
567 | "version_major": 2, | 567 | "version_major": 2, |
568 | "version_minor": 0 | 568 | "version_minor": 0 |
569 | }, | 569 | }, |
@@ -580,7 +580,7 @@ | |||
580 | "<function __main__.plot_out_degree(lines)>" | 580 | "<function __main__.plot_out_degree(lines)>" |
581 | ] | 581 | ] |
582 | }, | 582 | }, |
583 | "execution_count": 60, | 583 | "execution_count": 22, |
584 | "metadata": {}, | 584 | "metadata": {}, |
585 | "output_type": "execute_result" | 585 | "output_type": "execute_result" |
586 | } | 586 | } |
@@ -593,13 +593,13 @@ | |||
593 | }, | 593 | }, |
594 | { | 594 | { |
595 | "cell_type": "code", | 595 | "cell_type": "code", |
596 | "execution_count": 61, | 596 | "execution_count": 23, |
597 | "metadata": {}, | 597 | "metadata": {}, |
598 | "outputs": [ | 598 | "outputs": [ |
599 | { | 599 | { |
600 | "data": { | 600 | "data": { |
601 | "application/vnd.jupyter.widget-view+json": { | 601 | "application/vnd.jupyter.widget-view+json": { |
602 | "model_id": "fb7bdedff841420bb8f817013f565020", | 602 | "model_id": "75021f4f68db4a809ce7c86c0d25ef1b", |
603 | "version_major": 2, | 603 | "version_major": 2, |
604 | "version_minor": 0 | 604 | "version_minor": 0 |
605 | }, | 605 | }, |
@@ -616,7 +616,7 @@ | |||
616 | "<function __main__.plot_node_activity(lines)>" | 616 | "<function __main__.plot_node_activity(lines)>" |
617 | ] | 617 | ] |
618 | }, | 618 | }, |
619 | "execution_count": 61, | 619 | "execution_count": 23, |
620 | "metadata": {}, | 620 | "metadata": {}, |
621 | "output_type": "execute_result" | 621 | "output_type": "execute_result" |
622 | } | 622 | } |
@@ -629,13 +629,13 @@ | |||
629 | }, | 629 | }, |
630 | { | 630 | { |
631 | "cell_type": "code", | 631 | "cell_type": "code", |
632 | "execution_count": 62, | 632 | "execution_count": 24, |
633 | "metadata": {}, | 633 | "metadata": {}, |
634 | "outputs": [ | 634 | "outputs": [ |
635 | { | 635 | { |
636 | "data": { | 636 | "data": { |
637 | "application/vnd.jupyter.widget-view+json": { | 637 | "application/vnd.jupyter.widget-view+json": { |
638 | "model_id": "6b0c349c4a3b4813825513f739ea30da", | 638 | "model_id": "86f5c376905a4759a7b44ad52804424d", |
639 | "version_major": 2, | 639 | "version_major": 2, |
640 | "version_minor": 0 | 640 | "version_minor": 0 |
641 | }, | 641 | }, |
@@ -652,7 +652,7 @@ | |||
652 | "<function __main__.plot_mpc(lines)>" | 652 | "<function __main__.plot_mpc(lines)>" |
653 | ] | 653 | ] |
654 | }, | 654 | }, |
655 | "execution_count": 62, | 655 | "execution_count": 24, |
656 | "metadata": {}, | 656 | "metadata": {}, |
657 | "output_type": "execute_result" | 657 | "output_type": "execute_result" |
658 | } | 658 | } |
@@ -672,7 +672,7 @@ | |||
672 | }, | 672 | }, |
673 | { | 673 | { |
674 | "cell_type": "code", | 674 | "cell_type": "code", |
675 | "execution_count": 67, | 675 | "execution_count": 25, |
676 | "metadata": {}, | 676 | "metadata": {}, |
677 | "outputs": [], | 677 | "outputs": [], |
678 | "source": [ | 678 | "source": [ |
@@ -687,13 +687,13 @@ | |||
687 | }, | 687 | }, |
688 | { | 688 | { |
689 | "cell_type": "code", | 689 | "cell_type": "code", |
690 | "execution_count": 74, | 690 | "execution_count": 26, |
691 | "metadata": {}, | 691 | "metadata": {}, |
692 | "outputs": [ | 692 | "outputs": [ |
693 | { | 693 | { |
694 | "data": { | 694 | "data": { |
695 | "application/vnd.jupyter.widget-view+json": { | 695 | "application/vnd.jupyter.widget-view+json": { |
696 | "model_id": "b76901ba9d44433984032e0dc5679fa9", | 696 | "model_id": "57ba4d8443c145ad845fb862e3ef7519", |
697 | "version_major": 2, | 697 | "version_major": 2, |
698 | "version_minor": 0 | 698 | "version_minor": 0 |
699 | }, | 699 | }, |
@@ -710,7 +710,7 @@ | |||
710 | "<function __main__.plot_out_degree(lines)>" | 710 | "<function __main__.plot_out_degree(lines)>" |
711 | ] | 711 | ] |
712 | }, | 712 | }, |
713 | "execution_count": 74, | 713 | "execution_count": 26, |
714 | "metadata": {}, | 714 | "metadata": {}, |
715 | "output_type": "execute_result" | 715 | "output_type": "execute_result" |
716 | } | 716 | } |
@@ -723,13 +723,13 @@ | |||
723 | }, | 723 | }, |
724 | { | 724 | { |
725 | "cell_type": "code", | 725 | "cell_type": "code", |
726 | "execution_count": 75, | 726 | "execution_count": 27, |
727 | "metadata": {}, | 727 | "metadata": {}, |
728 | "outputs": [ | 728 | "outputs": [ |
729 | { | 729 | { |
730 | "data": { | 730 | "data": { |
731 | "application/vnd.jupyter.widget-view+json": { | 731 | "application/vnd.jupyter.widget-view+json": { |
732 | "model_id": "9e0d61e29b02467cb52618860a1bde7f", | 732 | "model_id": "c020ecb466c14f3ca1bfc0fd2fe03b7b", |
733 | "version_major": 2, | 733 | "version_major": 2, |
734 | "version_minor": 0 | 734 | "version_minor": 0 |
735 | }, | 735 | }, |
@@ -746,7 +746,7 @@ | |||
746 | "<function __main__.plot_na(lines)>" | 746 | "<function __main__.plot_na(lines)>" |
747 | ] | 747 | ] |
748 | }, | 748 | }, |
749 | "execution_count": 75, | 749 | "execution_count": 27, |
750 | "metadata": {}, | 750 | "metadata": {}, |
751 | "output_type": "execute_result" | 751 | "output_type": "execute_result" |
752 | } | 752 | } |
@@ -759,13 +759,13 @@ | |||
759 | }, | 759 | }, |
760 | { | 760 | { |
761 | "cell_type": "code", | 761 | "cell_type": "code", |
762 | "execution_count": 76, | 762 | "execution_count": 28, |
763 | "metadata": {}, | 763 | "metadata": {}, |
764 | "outputs": [ | 764 | "outputs": [ |
765 | { | 765 | { |
766 | "data": { | 766 | "data": { |
767 | "application/vnd.jupyter.widget-view+json": { | 767 | "application/vnd.jupyter.widget-view+json": { |
768 | "model_id": "70074805fee44a1aa5b9ccb3770b5c0c", | 768 | "model_id": "2165668057fd47ad92459e749ec68bad", |
769 | "version_major": 2, | 769 | "version_major": 2, |
770 | "version_minor": 0 | 770 | "version_minor": 0 |
771 | }, | 771 | }, |
@@ -782,7 +782,7 @@ | |||
782 | "<function __main__.plot_mpc(lines)>" | 782 | "<function __main__.plot_mpc(lines)>" |
783 | ] | 783 | ] |
784 | }, | 784 | }, |
785 | "execution_count": 76, | 785 | "execution_count": 28, |
786 | "metadata": {}, | 786 | "metadata": {}, |
787 | "output_type": "execute_result" | 787 | "output_type": "execute_result" |
788 | } | 788 | } |
@@ -802,7 +802,7 @@ | |||
802 | }, | 802 | }, |
803 | { | 803 | { |
804 | "cell_type": "code", | 804 | "cell_type": "code", |
805 | "execution_count": 80, | 805 | "execution_count": 29, |
806 | "metadata": {}, | 806 | "metadata": {}, |
807 | "outputs": [], | 807 | "outputs": [], |
808 | "source": [ | 808 | "source": [ |
@@ -817,13 +817,13 @@ | |||
817 | }, | 817 | }, |
818 | { | 818 | { |
819 | "cell_type": "code", | 819 | "cell_type": "code", |
820 | "execution_count": 82, | 820 | "execution_count": 30, |
821 | "metadata": {}, | 821 | "metadata": {}, |
822 | "outputs": [ | 822 | "outputs": [ |
823 | { | 823 | { |
824 | "data": { | 824 | "data": { |
825 | "application/vnd.jupyter.widget-view+json": { | 825 | "application/vnd.jupyter.widget-view+json": { |
826 | "model_id": "912ba2fdfd7c46848065f174aa6177e0", | 826 | "model_id": "907d7824033b4dfe980c391db0da63eb", |
827 | "version_major": 2, | 827 | "version_major": 2, |
828 | "version_minor": 0 | 828 | "version_minor": 0 |
829 | }, | 829 | }, |
@@ -840,7 +840,7 @@ | |||
840 | "<function __main__.plot_out_degree(lines)>" | 840 | "<function __main__.plot_out_degree(lines)>" |
841 | ] | 841 | ] |
842 | }, | 842 | }, |
843 | "execution_count": 82, | 843 | "execution_count": 30, |
844 | "metadata": {}, | 844 | "metadata": {}, |
845 | "output_type": "execute_result" | 845 | "output_type": "execute_result" |
846 | } | 846 | } |
@@ -853,13 +853,13 @@ | |||
853 | }, | 853 | }, |
854 | { | 854 | { |
855 | "cell_type": "code", | 855 | "cell_type": "code", |
856 | "execution_count": 83, | 856 | "execution_count": 31, |
857 | "metadata": {}, | 857 | "metadata": {}, |
858 | "outputs": [ | 858 | "outputs": [ |
859 | { | 859 | { |
860 | "data": { | 860 | "data": { |
861 | "application/vnd.jupyter.widget-view+json": { | 861 | "application/vnd.jupyter.widget-view+json": { |
862 | "model_id": "0ba621dd0e7d4957aaff2cf209bba165", | 862 | "model_id": "08a32c21d0b64217a556715caa8db7b5", |
863 | "version_major": 2, | 863 | "version_major": 2, |
864 | "version_minor": 0 | 864 | "version_minor": 0 |
865 | }, | 865 | }, |
@@ -876,7 +876,7 @@ | |||
876 | "<function __main__.plot_na(lines)>" | 876 | "<function __main__.plot_na(lines)>" |
877 | ] | 877 | ] |
878 | }, | 878 | }, |
879 | "execution_count": 83, | 879 | "execution_count": 31, |
880 | "metadata": {}, | 880 | "metadata": {}, |
881 | "output_type": "execute_result" | 881 | "output_type": "execute_result" |
882 | } | 882 | } |
@@ -889,13 +889,13 @@ | |||
889 | }, | 889 | }, |
890 | { | 890 | { |
891 | "cell_type": "code", | 891 | "cell_type": "code", |
892 | "execution_count": 84, | 892 | "execution_count": 32, |
893 | "metadata": {}, | 893 | "metadata": {}, |
894 | "outputs": [ | 894 | "outputs": [ |
895 | { | 895 | { |
896 | "data": { | 896 | "data": { |
897 | "application/vnd.jupyter.widget-view+json": { | 897 | "application/vnd.jupyter.widget-view+json": { |
898 | "model_id": "d432bbae1c6f48c3acd1767f2e2b13c7", | 898 | "model_id": "9dad041ff05d46ce969cfacb07c2ba98", |
899 | "version_major": 2, | 899 | "version_major": 2, |
900 | "version_minor": 0 | 900 | "version_minor": 0 |
901 | }, | 901 | }, |
@@ -912,7 +912,7 @@ | |||
912 | "<function __main__.plot_mpc(lines)>" | 912 | "<function __main__.plot_mpc(lines)>" |
913 | ] | 913 | ] |
914 | }, | 914 | }, |
915 | "execution_count": 84, | 915 | "execution_count": 32, |
916 | "metadata": {}, | 916 | "metadata": {}, |
917 | "output_type": "execute_result" | 917 | "output_type": "execute_result" |
918 | } | 918 | } |
@@ -924,6 +924,651 @@ | |||
924 | ] | 924 | ] |
925 | }, | 925 | }, |
926 | { | 926 | { |
927 | "cell_type": "markdown", | ||
928 | "metadata": {}, | ||
929 | "source": [ | ||
930 | "## Controlled Viatra with Out Degree" | ||
931 | ] | ||
932 | }, | ||
933 | { | ||
934 | "cell_type": "code", | ||
935 | "execution_count": 33, | ||
936 | "metadata": {}, | ||
937 | "outputs": [], | ||
938 | "source": [ | ||
939 | "con_viatra_stats = readStats('../input/controlled_viatra_out_degree/',10000)\n", | ||
940 | "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n", | ||
941 | "\n", | ||
942 | "# trajectories and colors\n", | ||
943 | "trajectories = {}\n", | ||
944 | "w = createSelectionWidge(trajectories)\n", | ||
945 | "colors = createRandomColors(len(trajectories))" | ||
946 | ] | ||
947 | }, | ||
948 | { | ||
949 | "cell_type": "code", | ||
950 | "execution_count": 34, | ||
951 | "metadata": {}, | ||
952 | "outputs": [ | ||
953 | { | ||
954 | "data": { | ||
955 | "application/vnd.jupyter.widget-view+json": { | ||
956 | "model_id": "cd77560284d9419daec57192a64b75ec", | ||
957 | "version_major": 2, | ||
958 | "version_minor": 0 | ||
959 | }, | ||
960 | "text/plain": [ | ||
961 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
962 | ] | ||
963 | }, | ||
964 | "metadata": {}, | ||
965 | "output_type": "display_data" | ||
966 | }, | ||
967 | { | ||
968 | "data": { | ||
969 | "text/plain": [ | ||
970 | "<function __main__.plot_out_degree(lines)>" | ||
971 | ] | ||
972 | }, | ||
973 | "execution_count": 34, | ||
974 | "metadata": {}, | ||
975 | "output_type": "execute_result" | ||
976 | } | ||
977 | ], | ||
978 | "source": [ | ||
979 | "def plot_out_degree(lines):\n", | ||
980 | " plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_out_degree/')\n", | ||
981 | "interact(plot_out_degree, lines=w)" | ||
982 | ] | ||
983 | }, | ||
984 | { | ||
985 | "cell_type": "code", | ||
986 | "execution_count": 35, | ||
987 | "metadata": {}, | ||
988 | "outputs": [ | ||
989 | { | ||
990 | "data": { | ||
991 | "application/vnd.jupyter.widget-view+json": { | ||
992 | "model_id": "ab11afebf7674cebae8d7318c661cf3c", | ||
993 | "version_major": 2, | ||
994 | "version_minor": 0 | ||
995 | }, | ||
996 | "text/plain": [ | ||
997 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
998 | ] | ||
999 | }, | ||
1000 | "metadata": {}, | ||
1001 | "output_type": "display_data" | ||
1002 | }, | ||
1003 | { | ||
1004 | "data": { | ||
1005 | "text/plain": [ | ||
1006 | "<function __main__.plot_na(lines)>" | ||
1007 | ] | ||
1008 | }, | ||
1009 | "execution_count": 35, | ||
1010 | "metadata": {}, | ||
1011 | "output_type": "execute_result" | ||
1012 | } | ||
1013 | ], | ||
1014 | "source": [ | ||
1015 | "def plot_na(lines):\n", | ||
1016 | " plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_out_degree/')\n", | ||
1017 | "interact(plot_na, lines=w)" | ||
1018 | ] | ||
1019 | }, | ||
1020 | { | ||
1021 | "cell_type": "code", | ||
1022 | "execution_count": 36, | ||
1023 | "metadata": { | ||
1024 | "scrolled": false | ||
1025 | }, | ||
1026 | "outputs": [ | ||
1027 | { | ||
1028 | "data": { | ||
1029 | "application/vnd.jupyter.widget-view+json": { | ||
1030 | "model_id": "c20b42abcba646c18d7caa6eeb54c403", | ||
1031 | "version_major": 2, | ||
1032 | "version_minor": 0 | ||
1033 | }, | ||
1034 | "text/plain": [ | ||
1035 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1036 | ] | ||
1037 | }, | ||
1038 | "metadata": {}, | ||
1039 | "output_type": "display_data" | ||
1040 | }, | ||
1041 | { | ||
1042 | "data": { | ||
1043 | "text/plain": [ | ||
1044 | "<function __main__.plot_mpc(lines)>" | ||
1045 | ] | ||
1046 | }, | ||
1047 | "execution_count": 36, | ||
1048 | "metadata": {}, | ||
1049 | "output_type": "execute_result" | ||
1050 | } | ||
1051 | ], | ||
1052 | "source": [ | ||
1053 | "def plot_mpc(lines):\n", | ||
1054 | " plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_out_degree/')\n", | ||
1055 | "interact(plot_mpc, lines=w)" | ||
1056 | ] | ||
1057 | }, | ||
1058 | { | ||
1059 | "cell_type": "markdown", | ||
1060 | "metadata": {}, | ||
1061 | "source": [ | ||
1062 | "## Controlled Viatra with Node Activity" | ||
1063 | ] | ||
1064 | }, | ||
1065 | { | ||
1066 | "cell_type": "code", | ||
1067 | "execution_count": 37, | ||
1068 | "metadata": {}, | ||
1069 | "outputs": [], | ||
1070 | "source": [ | ||
1071 | "con_viatra_stats = readStats('../input/controlled_viatra_out_degree_node_activity/',20000)\n", | ||
1072 | "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n", | ||
1073 | "\n", | ||
1074 | "# trajectories and colors\n", | ||
1075 | "trajectories = {}\n", | ||
1076 | "w = createSelectionWidge(trajectories)\n", | ||
1077 | "colors = createRandomColors(len(trajectories))" | ||
1078 | ] | ||
1079 | }, | ||
1080 | { | ||
1081 | "cell_type": "code", | ||
1082 | "execution_count": 38, | ||
1083 | "metadata": {}, | ||
1084 | "outputs": [ | ||
1085 | { | ||
1086 | "data": { | ||
1087 | "application/vnd.jupyter.widget-view+json": { | ||
1088 | "model_id": "902b580a11fa4c8db9d03508ad629067", | ||
1089 | "version_major": 2, | ||
1090 | "version_minor": 0 | ||
1091 | }, | ||
1092 | "text/plain": [ | ||
1093 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1094 | ] | ||
1095 | }, | ||
1096 | "metadata": {}, | ||
1097 | "output_type": "display_data" | ||
1098 | }, | ||
1099 | { | ||
1100 | "data": { | ||
1101 | "text/plain": [ | ||
1102 | "<function __main__.plot_out_degree(lines)>" | ||
1103 | ] | ||
1104 | }, | ||
1105 | "execution_count": 38, | ||
1106 | "metadata": {}, | ||
1107 | "output_type": "execute_result" | ||
1108 | } | ||
1109 | ], | ||
1110 | "source": [ | ||
1111 | "def plot_out_degree(lines):\n", | ||
1112 | " plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_with_node_activity/')\n", | ||
1113 | "interact(plot_out_degree, lines=w)" | ||
1114 | ] | ||
1115 | }, | ||
1116 | { | ||
1117 | "cell_type": "code", | ||
1118 | "execution_count": 39, | ||
1119 | "metadata": {}, | ||
1120 | "outputs": [ | ||
1121 | { | ||
1122 | "data": { | ||
1123 | "application/vnd.jupyter.widget-view+json": { | ||
1124 | "model_id": "851b567e745940288b577d9bd27e6f08", | ||
1125 | "version_major": 2, | ||
1126 | "version_minor": 0 | ||
1127 | }, | ||
1128 | "text/plain": [ | ||
1129 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1130 | ] | ||
1131 | }, | ||
1132 | "metadata": {}, | ||
1133 | "output_type": "display_data" | ||
1134 | }, | ||
1135 | { | ||
1136 | "data": { | ||
1137 | "text/plain": [ | ||
1138 | "<function __main__.plot_na(lines)>" | ||
1139 | ] | ||
1140 | }, | ||
1141 | "execution_count": 39, | ||
1142 | "metadata": {}, | ||
1143 | "output_type": "execute_result" | ||
1144 | } | ||
1145 | ], | ||
1146 | "source": [ | ||
1147 | "def plot_na(lines):\n", | ||
1148 | " plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_with_node_activity/')\n", | ||
1149 | "interact(plot_na, lines=w)" | ||
1150 | ] | ||
1151 | }, | ||
1152 | { | ||
1153 | "cell_type": "code", | ||
1154 | "execution_count": 40, | ||
1155 | "metadata": {}, | ||
1156 | "outputs": [ | ||
1157 | { | ||
1158 | "data": { | ||
1159 | "application/vnd.jupyter.widget-view+json": { | ||
1160 | "model_id": "7de173291f394b10b5113e3312b7b2e1", | ||
1161 | "version_major": 2, | ||
1162 | "version_minor": 0 | ||
1163 | }, | ||
1164 | "text/plain": [ | ||
1165 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1166 | ] | ||
1167 | }, | ||
1168 | "metadata": {}, | ||
1169 | "output_type": "display_data" | ||
1170 | }, | ||
1171 | { | ||
1172 | "data": { | ||
1173 | "text/plain": [ | ||
1174 | "<function __main__.plot_mpc(lines)>" | ||
1175 | ] | ||
1176 | }, | ||
1177 | "execution_count": 40, | ||
1178 | "metadata": {}, | ||
1179 | "output_type": "execute_result" | ||
1180 | } | ||
1181 | ], | ||
1182 | "source": [ | ||
1183 | "def plot_mpc(lines):\n", | ||
1184 | " plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_with_node_activity/')\n", | ||
1185 | "interact(plot_mpc, lines=w)" | ||
1186 | ] | ||
1187 | }, | ||
1188 | { | ||
1189 | "cell_type": "markdown", | ||
1190 | "metadata": {}, | ||
1191 | "source": [ | ||
1192 | "# Random EMF With Normal(2,1)" | ||
1193 | ] | ||
1194 | }, | ||
1195 | { | ||
1196 | "cell_type": "code", | ||
1197 | "execution_count": 41, | ||
1198 | "metadata": {}, | ||
1199 | "outputs": [], | ||
1200 | "source": [ | ||
1201 | "random_emf_stats = readStats('../input/random_emf_normal/',6000)\n", | ||
1202 | "random_emf_dic = calDistanceDic(random_emf_stats, human_rep)\n", | ||
1203 | "\n", | ||
1204 | "# trajectories and colors\n", | ||
1205 | "trajectories = {}\n", | ||
1206 | "w = createSelectionWidge(trajectories)\n", | ||
1207 | "colors = createRandomColors(len(trajectories))" | ||
1208 | ] | ||
1209 | }, | ||
1210 | { | ||
1211 | "cell_type": "code", | ||
1212 | "execution_count": 42, | ||
1213 | "metadata": {}, | ||
1214 | "outputs": [ | ||
1215 | { | ||
1216 | "data": { | ||
1217 | "application/vnd.jupyter.widget-view+json": { | ||
1218 | "model_id": "6b9ee873d9ca41649cf05f3b713d9142", | ||
1219 | "version_major": 2, | ||
1220 | "version_minor": 0 | ||
1221 | }, | ||
1222 | "text/plain": [ | ||
1223 | "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…" | ||
1224 | ] | ||
1225 | }, | ||
1226 | "metadata": {}, | ||
1227 | "output_type": "display_data" | ||
1228 | }, | ||
1229 | { | ||
1230 | "data": { | ||
1231 | "text/plain": [ | ||
1232 | "<function __main__.plot_out_degree(lines)>" | ||
1233 | ] | ||
1234 | }, | ||
1235 | "execution_count": 42, | ||
1236 | "metadata": {}, | ||
1237 | "output_type": "execute_result" | ||
1238 | } | ||
1239 | ], | ||
1240 | "source": [ | ||
1241 | "def plot_out_degree(lines):\n", | ||
1242 | " plot(random_emf_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out degree', '../output/random_emf_normal/')\n", | ||
1243 | "interact(plot_out_degree, lines=[[]])" | ||
1244 | ] | ||
1245 | }, | ||
1246 | { | ||
1247 | "cell_type": "code", | ||
1248 | "execution_count": 43, | ||
1249 | "metadata": {}, | ||
1250 | "outputs": [ | ||
1251 | { | ||
1252 | "data": { | ||
1253 | "application/vnd.jupyter.widget-view+json": { | ||
1254 | "model_id": "88f258a0b0ac4417aba320beca7508cf", | ||
1255 | "version_major": 2, | ||
1256 | "version_minor": 0 | ||
1257 | }, | ||
1258 | "text/plain": [ | ||
1259 | "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…" | ||
1260 | ] | ||
1261 | }, | ||
1262 | "metadata": {}, | ||
1263 | "output_type": "display_data" | ||
1264 | }, | ||
1265 | { | ||
1266 | "data": { | ||
1267 | "text/plain": [ | ||
1268 | "<function __main__.plot_node_activity(lines)>" | ||
1269 | ] | ||
1270 | }, | ||
1271 | "execution_count": 43, | ||
1272 | "metadata": {}, | ||
1273 | "output_type": "execute_result" | ||
1274 | } | ||
1275 | ], | ||
1276 | "source": [ | ||
1277 | "def plot_node_activity(lines):\n", | ||
1278 | " plot(random_emf_dic, lines, 0, lambda a: a.na_distance, colors, 'node activity', '../output/random_emf_normal/')\n", | ||
1279 | "interact(plot_node_activity, lines=[[]])" | ||
1280 | ] | ||
1281 | }, | ||
1282 | { | ||
1283 | "cell_type": "code", | ||
1284 | "execution_count": 44, | ||
1285 | "metadata": {}, | ||
1286 | "outputs": [ | ||
1287 | { | ||
1288 | "data": { | ||
1289 | "application/vnd.jupyter.widget-view+json": { | ||
1290 | "model_id": "d71cf26018184ee6953c50b74908f52d", | ||
1291 | "version_major": 2, | ||
1292 | "version_minor": 0 | ||
1293 | }, | ||
1294 | "text/plain": [ | ||
1295 | "interactive(children=(Dropdown(description='lines', options=([],), value=[]), Output()), _dom_classes=('widget…" | ||
1296 | ] | ||
1297 | }, | ||
1298 | "metadata": {}, | ||
1299 | "output_type": "display_data" | ||
1300 | }, | ||
1301 | { | ||
1302 | "data": { | ||
1303 | "text/plain": [ | ||
1304 | "<function __main__.plot_mpc(lines)>" | ||
1305 | ] | ||
1306 | }, | ||
1307 | "execution_count": 44, | ||
1308 | "metadata": {}, | ||
1309 | "output_type": "execute_result" | ||
1310 | } | ||
1311 | ], | ||
1312 | "source": [ | ||
1313 | "def plot_mpc(lines):\n", | ||
1314 | " plot(random_emf_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/random_emf_normal/')\n", | ||
1315 | "interact(plot_mpc, lines=[[]])" | ||
1316 | ] | ||
1317 | }, | ||
1318 | { | ||
1319 | "cell_type": "code", | ||
1320 | "execution_count": 45, | ||
1321 | "metadata": {}, | ||
1322 | "outputs": [], | ||
1323 | "source": [ | ||
1324 | "con_viatra_stats = readStats('../input/controlled_viatra_all/',20000)\n", | ||
1325 | "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n", | ||
1326 | "\n", | ||
1327 | "# trajectories and colors\n", | ||
1328 | "trajectories = {}\n", | ||
1329 | "w = createSelectionWidge(trajectories)\n", | ||
1330 | "colors = createRandomColors(len(trajectories))" | ||
1331 | ] | ||
1332 | }, | ||
1333 | { | ||
1334 | "cell_type": "code", | ||
1335 | "execution_count": 46, | ||
1336 | "metadata": {}, | ||
1337 | "outputs": [ | ||
1338 | { | ||
1339 | "data": { | ||
1340 | "application/vnd.jupyter.widget-view+json": { | ||
1341 | "model_id": "db15ac26aad84683b9da99fc54749850", | ||
1342 | "version_major": 2, | ||
1343 | "version_minor": 0 | ||
1344 | }, | ||
1345 | "text/plain": [ | ||
1346 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1347 | ] | ||
1348 | }, | ||
1349 | "metadata": {}, | ||
1350 | "output_type": "display_data" | ||
1351 | }, | ||
1352 | { | ||
1353 | "data": { | ||
1354 | "text/plain": [ | ||
1355 | "<function __main__.plot_out_degree(lines)>" | ||
1356 | ] | ||
1357 | }, | ||
1358 | "execution_count": 46, | ||
1359 | "metadata": {}, | ||
1360 | "output_type": "execute_result" | ||
1361 | } | ||
1362 | ], | ||
1363 | "source": [ | ||
1364 | "def plot_out_degree(lines):\n", | ||
1365 | " plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/controled_viatra_all/')\n", | ||
1366 | "interact(plot_out_degree, lines=w)" | ||
1367 | ] | ||
1368 | }, | ||
1369 | { | ||
1370 | "cell_type": "code", | ||
1371 | "execution_count": 47, | ||
1372 | "metadata": {}, | ||
1373 | "outputs": [ | ||
1374 | { | ||
1375 | "data": { | ||
1376 | "application/vnd.jupyter.widget-view+json": { | ||
1377 | "model_id": "30bfaf8dd45d4b21b0b43afe5e9fdb8a", | ||
1378 | "version_major": 2, | ||
1379 | "version_minor": 0 | ||
1380 | }, | ||
1381 | "text/plain": [ | ||
1382 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1383 | ] | ||
1384 | }, | ||
1385 | "metadata": {}, | ||
1386 | "output_type": "display_data" | ||
1387 | }, | ||
1388 | { | ||
1389 | "data": { | ||
1390 | "text/plain": [ | ||
1391 | "<function __main__.plot_na(lines)>" | ||
1392 | ] | ||
1393 | }, | ||
1394 | "execution_count": 47, | ||
1395 | "metadata": {}, | ||
1396 | "output_type": "execute_result" | ||
1397 | } | ||
1398 | ], | ||
1399 | "source": [ | ||
1400 | "def plot_na(lines):\n", | ||
1401 | " plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/controled_viatra_all/')\n", | ||
1402 | "interact(plot_na, lines=w)" | ||
1403 | ] | ||
1404 | }, | ||
1405 | { | ||
1406 | "cell_type": "code", | ||
1407 | "execution_count": 48, | ||
1408 | "metadata": {}, | ||
1409 | "outputs": [ | ||
1410 | { | ||
1411 | "data": { | ||
1412 | "application/vnd.jupyter.widget-view+json": { | ||
1413 | "model_id": "5636d37b4416474db5441fe47e7a8a30", | ||
1414 | "version_major": 2, | ||
1415 | "version_minor": 0 | ||
1416 | }, | ||
1417 | "text/plain": [ | ||
1418 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1419 | ] | ||
1420 | }, | ||
1421 | "metadata": {}, | ||
1422 | "output_type": "display_data" | ||
1423 | }, | ||
1424 | { | ||
1425 | "data": { | ||
1426 | "text/plain": [ | ||
1427 | "<function __main__.plot_mpc(lines)>" | ||
1428 | ] | ||
1429 | }, | ||
1430 | "execution_count": 48, | ||
1431 | "metadata": {}, | ||
1432 | "output_type": "execute_result" | ||
1433 | } | ||
1434 | ], | ||
1435 | "source": [ | ||
1436 | "def plot_mpc(lines):\n", | ||
1437 | " plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/controled_viatra_all/')\n", | ||
1438 | "interact(plot_mpc, lines=w)" | ||
1439 | ] | ||
1440 | }, | ||
1441 | { | ||
1442 | "cell_type": "markdown", | ||
1443 | "metadata": {}, | ||
1444 | "source": [ | ||
1445 | "### Viatra With Both metric and consistency" | ||
1446 | ] | ||
1447 | }, | ||
1448 | { | ||
1449 | "cell_type": "code", | ||
1450 | "execution_count": 53, | ||
1451 | "metadata": {}, | ||
1452 | "outputs": [], | ||
1453 | "source": [ | ||
1454 | "con_viatra_stats = readStats('../input/viatra_control_all_with_consistency_1/',20000)\n", | ||
1455 | "con_viatra_dic = calDistanceDic(con_viatra_stats, human_rep)\n", | ||
1456 | "\n", | ||
1457 | "# trajectories and colors\n", | ||
1458 | "trajectories = {}\n", | ||
1459 | "w = createSelectionWidge(trajectories)\n", | ||
1460 | "colors = createRandomColors(len(trajectories))" | ||
1461 | ] | ||
1462 | }, | ||
1463 | { | ||
1464 | "cell_type": "code", | ||
1465 | "execution_count": 54, | ||
1466 | "metadata": {}, | ||
1467 | "outputs": [ | ||
1468 | { | ||
1469 | "data": { | ||
1470 | "application/vnd.jupyter.widget-view+json": { | ||
1471 | "model_id": "e5c7231686544d959527cff36c1f1a5e", | ||
1472 | "version_major": 2, | ||
1473 | "version_minor": 0 | ||
1474 | }, | ||
1475 | "text/plain": [ | ||
1476 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1477 | ] | ||
1478 | }, | ||
1479 | "metadata": {}, | ||
1480 | "output_type": "display_data" | ||
1481 | }, | ||
1482 | { | ||
1483 | "data": { | ||
1484 | "text/plain": [ | ||
1485 | "<function __main__.plot_out_degree(lines)>" | ||
1486 | ] | ||
1487 | }, | ||
1488 | "execution_count": 54, | ||
1489 | "metadata": {}, | ||
1490 | "output_type": "execute_result" | ||
1491 | } | ||
1492 | ], | ||
1493 | "source": [ | ||
1494 | "def plot_out_degree(lines):\n", | ||
1495 | " plot(con_viatra_dic, lines, 0, lambda a: a.out_d_distance, colors, 'out_degree', '../output/viatra_control_all_with_consistency_1/')\n", | ||
1496 | "interact(plot_out_degree, lines=w)" | ||
1497 | ] | ||
1498 | }, | ||
1499 | { | ||
1500 | "cell_type": "code", | ||
1501 | "execution_count": 55, | ||
1502 | "metadata": {}, | ||
1503 | "outputs": [ | ||
1504 | { | ||
1505 | "data": { | ||
1506 | "application/vnd.jupyter.widget-view+json": { | ||
1507 | "model_id": "e043705333bb474e89582ea9358c57c3", | ||
1508 | "version_major": 2, | ||
1509 | "version_minor": 0 | ||
1510 | }, | ||
1511 | "text/plain": [ | ||
1512 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1513 | ] | ||
1514 | }, | ||
1515 | "metadata": {}, | ||
1516 | "output_type": "display_data" | ||
1517 | }, | ||
1518 | { | ||
1519 | "data": { | ||
1520 | "text/plain": [ | ||
1521 | "<function __main__.plot_na(lines)>" | ||
1522 | ] | ||
1523 | }, | ||
1524 | "execution_count": 55, | ||
1525 | "metadata": {}, | ||
1526 | "output_type": "execute_result" | ||
1527 | } | ||
1528 | ], | ||
1529 | "source": [ | ||
1530 | "def plot_na(lines):\n", | ||
1531 | " plot(con_viatra_dic, lines, 0, lambda a: a.na_distance, colors, 'Node Activity', '../output/viatra_control_all_with_consistency_1/')\n", | ||
1532 | "interact(plot_na, lines=w)" | ||
1533 | ] | ||
1534 | }, | ||
1535 | { | ||
1536 | "cell_type": "code", | ||
1537 | "execution_count": 56, | ||
1538 | "metadata": {}, | ||
1539 | "outputs": [ | ||
1540 | { | ||
1541 | "data": { | ||
1542 | "application/vnd.jupyter.widget-view+json": { | ||
1543 | "model_id": "ee4723b62293402e87e6a3f798019b36", | ||
1544 | "version_major": 2, | ||
1545 | "version_minor": 0 | ||
1546 | }, | ||
1547 | "text/plain": [ | ||
1548 | "interactive(children=(SelectMultiple(description='Trajectory:', options={}, value=()), Output()), _dom_classes…" | ||
1549 | ] | ||
1550 | }, | ||
1551 | "metadata": {}, | ||
1552 | "output_type": "display_data" | ||
1553 | }, | ||
1554 | { | ||
1555 | "data": { | ||
1556 | "text/plain": [ | ||
1557 | "<function __main__.plot_mpc(lines)>" | ||
1558 | ] | ||
1559 | }, | ||
1560 | "execution_count": 56, | ||
1561 | "metadata": {}, | ||
1562 | "output_type": "execute_result" | ||
1563 | } | ||
1564 | ], | ||
1565 | "source": [ | ||
1566 | "def plot_mpc(lines):\n", | ||
1567 | " plot(con_viatra_dic, lines, 0, lambda a: a.mpc_distance, colors, 'mpc', '../output/viatra_control_all_with_consistency_1/')\n", | ||
1568 | "interact(plot_mpc, lines=w)" | ||
1569 | ] | ||
1570 | }, | ||
1571 | { | ||
927 | "cell_type": "code", | 1572 | "cell_type": "code", |
928 | "execution_count": null, | 1573 | "execution_count": null, |
929 | "metadata": {}, | 1574 | "metadata": {}, |
diff --git a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb index 9653b2a0..78f408fc 100644 --- a/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb +++ b/Metrics/Metrics-Calculation/metrics_plot/model_evolve_comparison/src/representative_selector .ipynb | |||
@@ -63,16 +63,16 @@ | |||
63 | }, | 63 | }, |
64 | { | 64 | { |
65 | "cell_type": "code", | 65 | "cell_type": "code", |
66 | "execution_count": 4, | 66 | "execution_count": 3, |
67 | "metadata": {}, | 67 | "metadata": {}, |
68 | "outputs": [ | 68 | "outputs": [ |
69 | { | 69 | { |
70 | "data": { | 70 | "data": { |
71 | "text/plain": [ | 71 | "text/plain": [ |
72 | "1253" | 72 | "304" |
73 | ] | 73 | ] |
74 | }, | 74 | }, |
75 | "execution_count": 4, | 75 | "execution_count": 3, |
76 | "metadata": {}, | 76 | "metadata": {}, |
77 | "output_type": "execute_result" | 77 | "output_type": "execute_result" |
78 | } | 78 | } |
@@ -90,7 +90,7 @@ | |||
90 | ")\n", | 90 | ")\n", |
91 | "\n", | 91 | "\n", |
92 | "\n", | 92 | "\n", |
93 | "humanFiles = reader.readmultiplefiles('../input/humanOutput/', 1300, False)\n", | 93 | "humanFiles = reader.readmultiplefiles('../input/human_output_100/', 1300, False)\n", |
94 | "modelToFileName = {}\n", | 94 | "modelToFileName = {}\n", |
95 | "for name in humanFiles:\n", | 95 | "for name in humanFiles:\n", |
96 | " modelToFileName[GraphStat(name)] = name\n", | 96 | " modelToFileName[GraphStat(name)] = name\n", |
@@ -115,7 +115,7 @@ | |||
115 | }, | 115 | }, |
116 | { | 116 | { |
117 | "cell_type": "code", | 117 | "cell_type": "code", |
118 | "execution_count": 5, | 118 | "execution_count": 4, |
119 | "metadata": {}, | 119 | "metadata": {}, |
120 | "outputs": [], | 120 | "outputs": [], |
121 | "source": [ | 121 | "source": [ |
@@ -144,21 +144,26 @@ | |||
144 | "cell_type": "markdown", | 144 | "cell_type": "markdown", |
145 | "metadata": {}, | 145 | "metadata": {}, |
146 | "source": [ | 146 | "source": [ |
147 | "#### For all human models\n", | ||
147 | "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n", | 148 | "* the rep found is ../input/humanOutput\\R_20158_run_1.csv\n", |
148 | "* the average distance between it and others is 0.05515988287586802" | 149 | "* the average distance between it and others is 0.05515988287586802\n", |
150 | "\n", | ||
151 | "#### For human models with $100 \\pm 10$ nodes\n", | ||
152 | "* the rep found is ../input/human_output_100\\R_2015225_run_1.csv\n", | ||
153 | "* the average distance between it and others is $0.046150929558524685$" | ||
149 | ] | 154 | ] |
150 | }, | 155 | }, |
151 | { | 156 | { |
152 | "cell_type": "code", | 157 | "cell_type": "code", |
153 | "execution_count": 6, | 158 | "execution_count": 5, |
154 | "metadata": {}, | 159 | "metadata": {}, |
155 | "outputs": [ | 160 | "outputs": [ |
156 | { | 161 | { |
157 | "name": "stdout", | 162 | "name": "stdout", |
158 | "output_type": "stream", | 163 | "output_type": "stream", |
159 | "text": [ | 164 | "text": [ |
160 | "../input/humanOutput\\R_20158_run_1.csv\n", | 165 | "../input/human_output_100\\R_2015225_run_1.csv\n", |
161 | "../input/humanOutput\\R_20158_run_1.csv\n" | 166 | "../input/human_output_100\\R_2015225_run_1.csv\n" |
162 | ] | 167 | ] |
163 | } | 168 | } |
164 | ], | 169 | ], |
@@ -171,14 +176,14 @@ | |||
171 | }, | 176 | }, |
172 | { | 177 | { |
173 | "cell_type": "code", | 178 | "cell_type": "code", |
174 | "execution_count": 19, | 179 | "execution_count": 6, |
175 | "metadata": {}, | 180 | "metadata": {}, |
176 | "outputs": [ | 181 | "outputs": [ |
177 | { | 182 | { |
178 | "name": "stdout", | 183 | "name": "stdout", |
179 | "output_type": "stream", | 184 | "output_type": "stream", |
180 | "text": [ | 185 | "text": [ |
181 | "0.05515988287586802\n" | 186 | "0.046150929558524685\n" |
182 | ] | 187 | ] |
183 | } | 188 | } |
184 | ], | 189 | ], |
@@ -201,41 +206,46 @@ | |||
201 | "cell_type": "markdown", | 206 | "cell_type": "markdown", |
202 | "metadata": {}, | 207 | "metadata": {}, |
203 | "source": [ | 208 | "source": [ |
209 | "#### For all human models\n", | ||
204 | "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n", | 210 | "* the rep found is ../input/humanOutput\\R_2016176_run_1.csv\n", |
205 | "* the average distance between it and others is 0.05275267434589047" | 211 | "* the average distance between it and others is 0.05275267434589047\n", |
212 | "\n", | ||
213 | "#### For human models with $100 \\pm 10$ nodes\n", | ||
214 | "* the rep found is ../input/human_output_100\\R_2017419_run_1.csv\n", | ||
215 | "* the average distance between it and others is $0.04679429311806747$" | ||
206 | ] | 216 | ] |
207 | }, | 217 | }, |
208 | { | 218 | { |
209 | "cell_type": "code", | 219 | "cell_type": "code", |
210 | "execution_count": 7, | 220 | "execution_count": 13, |
211 | "metadata": {}, | 221 | "metadata": {}, |
212 | "outputs": [ | 222 | "outputs": [ |
213 | { | 223 | { |
214 | "name": "stdout", | 224 | "name": "stdout", |
215 | "output_type": "stream", | 225 | "output_type": "stream", |
216 | "text": [ | 226 | "text": [ |
217 | "../input/humanOutput\\R_2016176_run_1.csv\n", | 227 | "../input/human_output_100\\R_2017419_run_1.csv\n", |
218 | "../input/humanOutput\\R_2016176_run_1.csv\n" | 228 | "../input/human_output_100\\R_2017419_run_1.csv\n" |
219 | ] | 229 | ] |
220 | } | 230 | } |
221 | ], | 231 | ], |
222 | "source": [ | 232 | "source": [ |
223 | "total_distance = 0\n", | 233 | "na_rep_index = findRep(models, lambda m: m.na)\n", |
224 | "for model in models:\n", | 234 | "print(list(modelToFileName.values())[na_rep_index])\n", |
225 | " total_distance += ks_value(od_rep_model.mpc, model.mpc)\n", | 235 | "na_rep_model = models[na_rep_index]\n", |
226 | "print(total_distance / len(models))" | 236 | "print(modelToFileName[na_rep_model])\n" |
227 | ] | 237 | ] |
228 | }, | 238 | }, |
229 | { | 239 | { |
230 | "cell_type": "code", | 240 | "cell_type": "code", |
231 | "execution_count": 18, | 241 | "execution_count": 14, |
232 | "metadata": {}, | 242 | "metadata": {}, |
233 | "outputs": [ | 243 | "outputs": [ |
234 | { | 244 | { |
235 | "name": "stdout", | 245 | "name": "stdout", |
236 | "output_type": "stream", | 246 | "output_type": "stream", |
237 | "text": [ | 247 | "text": [ |
238 | "0.05275267434589047\n" | 248 | "0.04679429311806747\n" |
239 | ] | 249 | ] |
240 | } | 250 | } |
241 | ], | 251 | ], |
@@ -243,7 +253,7 @@ | |||
243 | "total_distance = 0\n", | 253 | "total_distance = 0\n", |
244 | "count = 0\n", | 254 | "count = 0\n", |
245 | "for model in models:\n", | 255 | "for model in models:\n", |
246 | " total_distance += ks_value(od_rep_model.na, model.na)\n", | 256 | " total_distance += ks_value(na_rep_model.na, model.na)\n", |
247 | "print(total_distance / len(models))" | 257 | "print(total_distance / len(models))" |
248 | ] | 258 | ] |
249 | }, | 259 | }, |
@@ -258,21 +268,26 @@ | |||
258 | "cell_type": "markdown", | 268 | "cell_type": "markdown", |
259 | "metadata": {}, | 269 | "metadata": {}, |
260 | "source": [ | 270 | "source": [ |
271 | "#### For all human models\n", | ||
261 | "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n", | 272 | "* the rep found is ../input/humanOutput\\R_2015246_run_1.csv\n", |
262 | "* the average distance between it and others is 0.08556632702185384" | 273 | "* the average distance between it and others is 0.08556632702185384\n", |
274 | "\n", | ||
275 | "#### For human models with $100 \\pm 10$ nodes\n", | ||
276 | "* the rep found is ../input/human_output_100\\R_2016324_run_1.csv\n", | ||
277 | "* the average distance between it and others is $0.07028909225833631$" | ||
263 | ] | 278 | ] |
264 | }, | 279 | }, |
265 | { | 280 | { |
266 | "cell_type": "code", | 281 | "cell_type": "code", |
267 | "execution_count": 8, | 282 | "execution_count": 16, |
268 | "metadata": {}, | 283 | "metadata": {}, |
269 | "outputs": [ | 284 | "outputs": [ |
270 | { | 285 | { |
271 | "name": "stdout", | 286 | "name": "stdout", |
272 | "output_type": "stream", | 287 | "output_type": "stream", |
273 | "text": [ | 288 | "text": [ |
274 | "../input/humanOutput\\R_2015246_run_1.csv\n", | 289 | "../input/human_output_100\\R_2016324_run_1.csv\n", |
275 | "../input/humanOutput\\R_2015246_run_1.csv\n" | 290 | "../input/human_output_100\\R_2016324_run_1.csv\n" |
276 | ] | 291 | ] |
277 | } | 292 | } |
278 | ], | 293 | ], |
@@ -285,14 +300,14 @@ | |||
285 | }, | 300 | }, |
286 | { | 301 | { |
287 | "cell_type": "code", | 302 | "cell_type": "code", |
288 | "execution_count": 20, | 303 | "execution_count": 18, |
289 | "metadata": {}, | 304 | "metadata": {}, |
290 | "outputs": [ | 305 | "outputs": [ |
291 | { | 306 | { |
292 | "name": "stdout", | 307 | "name": "stdout", |
293 | "output_type": "stream", | 308 | "output_type": "stream", |
294 | "text": [ | 309 | "text": [ |
295 | "0.08556632702185384\n" | 310 | "0.07028909225833631\n" |
296 | ] | 311 | ] |
297 | } | 312 | } |
298 | ], | 313 | ], |
@@ -300,7 +315,7 @@ | |||
300 | "total_distance = 0\n", | 315 | "total_distance = 0\n", |
301 | "count = 0\n", | 316 | "count = 0\n", |
302 | "for model in models:\n", | 317 | "for model in models:\n", |
303 | " total_distance += ks_value(od_rep_model.mpc, model.mpc)\n", | 318 | " total_distance += ks_value(mpc_rep_model.mpc, model.mpc)\n", |
304 | "print(total_distance / len(models))" | 319 | "print(total_distance / len(models))" |
305 | ] | 320 | ] |
306 | }, | 321 | }, |
@@ -310,6 +325,13 @@ | |||
310 | "metadata": {}, | 325 | "metadata": {}, |
311 | "outputs": [], | 326 | "outputs": [], |
312 | "source": [] | 327 | "source": [] |
328 | }, | ||
329 | { | ||
330 | "cell_type": "code", | ||
331 | "execution_count": null, | ||
332 | "metadata": {}, | ||
333 | "outputs": [], | ||
334 | "source": [] | ||
313 | } | 335 | } |
314 | ], | 336 | ], |
315 | "metadata": { | 337 | "metadata": { |
diff --git a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py index ce9d4255..aba81b13 100644 --- a/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py +++ b/Metrics/Metrics-Calculation/metrics_plot/utils/constants.py | |||
@@ -18,8 +18,8 @@ METAMODEL = 'Meta Mode' | |||
18 | 18 | ||
19 | STATE_ID = 'State Id' | 19 | STATE_ID = 'State Id' |
20 | 20 | ||
21 | HUMAN_OUT_D_REP = '../input/humanOutput/R_20158_run_1.csv' | 21 | HUMAN_OUT_D_REP = '../input/humanOutput/R_2015225_run_1.csv' |
22 | 22 | ||
23 | HUMAN_MPC_REP = '../input/humanOutput/R_2015246_run_1.csv' | 23 | HUMAN_MPC_REP = '../input/humanOutput/R_2016324_run_1.csv' |
24 | 24 | ||
25 | HUMAN_NA_REP = '../input/humanOutput/R_2016176_run_1.csv' | 25 | HUMAN_NA_REP = '../input/humanOutput/R_2017419_run_1.csv' |