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IDSIA · May 26, 2021 · da250a4 · da250a4
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diff --git a/papers/neurips21/notebooks/experiments.ipynb b/papers/neurips21/notebooks/experiments.ipynb
@@ -607,7 +607,10 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "nohup java -cp /root/dev/credici/target/credici-0.1.3-SNAPSHOT-jar-with-dependencies.jar ./generate.java 0 > output0.out 2>&1 &\n",
+    "\n"
+   ]
   }
  ],
  "metadata": {

diff --git a/papers/neurips21/triangolo/output0.out b/papers/neurips21/triangolo/output0.out
@@ -0,0 +1,52 @@
+nohup: ignoring input
+seed=0
+
+Sampled
+{[0]=P([0, 2, 7, 8, 11]) [0.5, 0.5, 0.18181818181818182, 0.8181818181818182, 0.24528301886792453, 0.7547169811320755, 0.3684210526315789, 0.631578947368421, 0.5333333333333333, 0.4666666666666667, 0.19230769230769232, 0.8076923076923077, 0.3674698795180723, 0.6325301204819277, 0.11475409836065574, 0.8852459016393442, 0.5, 0.5, 0.5, 0.5, 0.0410958904109589, 0.958904109589041, 0.0, 1.0, 0.1797752808988764, 0.8202247191011236, 0.4375, 0.5625, 0.04245283018867924, 0.9575471698113207, 0.05555555555555555, 0.9444444444444444], [1]=P([1]) [0.617, 0.383], [2]=P([2, 9]) [0.7961165048543689, 0.20388349514563106, 0.6200716845878136, 0.37992831541218636], [3]=P([1, 3]) [0.7406807131280388, 0.9086161879895561, 0.2593192868719611, 0.09138381201044386], [4]=P([4]) [0.28300000000000003, 0.717], [5]=P([5, 8]) [0.32142857142857145, 0.6785714285714286, 0.36764705882352944, 0.6323529411764706], [6]=P([1, 3, 6, 10]) [0.359504132231405, 0.48109965635738833, 0.06593406593406594, 0.34615384615384615, 0.640495867768595, 0.5189003436426117, 0.9340659340659341, 0.6538461538461539, 0.30697674418604654, 0.40350877192982454, 0.17391304347826086, 0.1111111111111111, 0.6930232558139535, 0.5964912280701754, 0.8260869565217391, 0.8888888888888888], [7]=P([1, 5, 7]) [0.4318181818181818, 0.4852941176470588, 0.20906801007556675, 0.3076923076923077, 0.5681818181818182, 0.5147058823529411, 0.7909319899244333, 0.6923076923076923], [8]=P([1, 8, 10]) [0.2912912912912913, 0.10410094637223975, 0.7087087087087087, 0.8958990536277602, 0.3380281690140845, 0.3939393939393939, 0.6619718309859155, 0.6060606060606061], [9]=P([1, 9]) [0.7050243111831442, 0.7467362924281984, 0.29497568881685576, 0.25326370757180156], [10]=P([1, 4, 10]) [0.7213114754098361, 0.94, 0.4631336405529954, 0.7879858657243817, 0.2786885245901639, 0.06, 0.5368663594470046, 0.21201413427561838], [11]=P([6, 11]) [0.9825581395348837, 0.18292682926829268, 0.01744186046511628, 0.8170731707317073]}
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 13971116.ms
+tr0_0.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 13838258.ms
+tr0_1.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 14034875.ms
+tr0_2.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .
diff --git a/papers/neurips21/triangolo/output1.out b/papers/neurips21/triangolo/output1.out
@@ -0,0 +1,58 @@
+nohup: ignoring input
+seed=1
+
+Sampled
+{[0]=P([0, 2, 7, 8, 11]) [0.5, 0.5, 0.18181818181818182, 0.8181818181818182, 0.24528301886792453, 0.7547169811320755, 0.3684210526315789, 0.631578947368421, 0.5333333333333333, 0.4666666666666667, 0.19230769230769232, 0.8076923076923077, 0.3674698795180723, 0.6325301204819277, 0.11475409836065574, 0.8852459016393442, 0.5, 0.5, 0.5, 0.5, 0.0410958904109589, 0.958904109589041, 0.0, 1.0, 0.1797752808988764, 0.8202247191011236, 0.4375, 0.5625, 0.04245283018867924, 0.9575471698113207, 0.05555555555555555, 0.9444444444444444], [1]=P([1]) [0.617, 0.383], [2]=P([2, 9]) [0.7961165048543689, 0.20388349514563106, 0.6200716845878136, 0.37992831541218636], [3]=P([1, 3]) [0.7406807131280388, 0.9086161879895561, 0.2593192868719611, 0.09138381201044386], [4]=P([4]) [0.28300000000000003, 0.717], [5]=P([5, 8]) [0.32142857142857145, 0.6785714285714286, 0.36764705882352944, 0.6323529411764706], [6]=P([1, 3, 6, 10]) [0.359504132231405, 0.48109965635738833, 0.06593406593406594, 0.34615384615384615, 0.640495867768595, 0.5189003436426117, 0.9340659340659341, 0.6538461538461539, 0.30697674418604654, 0.40350877192982454, 0.17391304347826086, 0.1111111111111111, 0.6930232558139535, 0.5964912280701754, 0.8260869565217391, 0.8888888888888888], [7]=P([1, 5, 7]) [0.4318181818181818, 0.4852941176470588, 0.20906801007556675, 0.3076923076923077, 0.5681818181818182, 0.5147058823529411, 0.7909319899244333, 0.6923076923076923], [8]=P([1, 8, 10]) [0.2912912912912913, 0.10410094637223975, 0.7087087087087087, 0.8958990536277602, 0.3380281690140845, 0.3939393939393939, 0.6619718309859155, 0.6060606060606061], [9]=P([1, 9]) [0.7050243111831442, 0.7467362924281984, 0.29497568881685576, 0.25326370757180156], [10]=P([1, 4, 10]) [0.7213114754098361, 0.94, 0.4631336405529954, 0.7879858657243817, 0.2786885245901639, 0.06, 0.5368663594470046, 0.21201413427561838], [11]=P([6, 11]) [0.9825581395348837, 0.18292682926829268, 0.01744186046511628, 0.8170731707317073]}
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12810669.ms
+tr1_0.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12861426.ms
+tr1_1.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12799056.ms
+tr1_2.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12759057.ms
+tr1_3.uai
+....
diff --git a/papers/neurips21/triangolo/output2.out b/papers/neurips21/triangolo/output2.out
@@ -0,0 +1,61 @@
+seed=3
+
+Sampled
+{[0]=P([0, 2, 7, 8, 11]) [0.5, 0.5, 0.18181818181818182, 0.8181818181818182, 0.24528301886792453, 0.7547169811320755, 0.3684210526315789, 0.631578947368421, 0.5333333333333333, 0.4666666666666667, 0.19230769230769232, 0.8076923076923077, 0.3674698795180723, 0.6325301204819277, 0.11475409836065574, 0.8852459016393442, 0.5, 0.5, 0.5, 0.5, 0.0410958904109589, 0.958904109589041, 0.0, 1.0, 0.1797752808988764, 0.8202247191011236, 0.4375, 0.5625, 0.04245283018867924, 0.9575471698113207, 0.05555555555555555, 0.9444444444444444], [1]=P([1]) [0.617, 0.383], [2]=P([2, 9]) [0.7961165048543689, 0.20388349514563106, 0.6200716845878136, 0.37992831541218636], [3]=P([1, 3]) [0.7406807131280388, 0.9086161879895561, 0.2593192868719611, 0.09138381201044386], [4]=P([4]) [0.28300000000000003, 0.717], [5]=P([5, 8]) [0.32142857142857145, 0.6785714285714286, 0.36764705882352944, 0.6323529411764706], [6]=P([1, 3, 6, 10]) [0.359504132231405, 0.48109965635738833, 0.06593406593406594, 0.34615384615384615, 0.640495867768595, 0.5189003436426117, 0.9340659340659341, 0.6538461538461539, 0.30697674418604654, 0.40350877192982454, 0.17391304347826086, 0.1111111111111111, 0.6930232558139535, 0.5964912280701754, 0.8260869565217391, 0.8888888888888888], [7]=P([1, 5, 7]) [0.4318181818181818, 0.4852941176470588, 0.20906801007556675, 0.3076923076923077, 0.5681818181818182, 0.5147058823529411, 0.7909319899244333, 0.6923076923076923], [8]=P([1, 8, 10]) [0.2912912912912913, 0.10410094637223975, 0.7087087087087087, 0.8958990536277602, 0.3380281690140845, 0.3939393939393939, 0.6619718309859155, 0.6060606060606061], [9]=P([1, 9]) [0.7050243111831442, 0.7467362924281984, 0.29497568881685576, 0.25326370757180156], [10]=P([1, 4, 10]) [0.7213114754098361, 0.94, 0.4631336405529954, 0.7879858657243817, 0.2786885245901639, 0.06, 0.5368663594470046, 0.21201413427561838], [11]=P([6, 11]) [0.9825581395348837, 0.18292682926829268, 0.01744186046511628, 0.8170731707317073]}
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  c....
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 1272911..
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .......
+.
+60 iterations........
+.
+70 iterations........
+7
+80 iteration.........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12698167.ms
+tr2_1.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12905281.ms
+tr2_2.uai
+.........
+10 iterations .........
+20 iterations .........
+30 iterations .........
+40 iterations .........
+50 iterations .........
+60 iterations .........
+70 iterations .........
+80 iterations .........
+90 iterations .........
+100 iterations  calculated EM trajectory of 100
+Ellapsed time: 12794587.ms
+tr2_3.uai
+...........
+20 iterations .......
diff --git a/papers/neurips21/triangolo/tr0_0.uai b/papers/neurips21/triangolo/tr0_0.uai
diff --git a/papers/neurips21/triangolo/tr0_1.uai b/papers/neurips21/triangolo/tr0_1.uai
diff --git a/papers/neurips21/triangolo/tr0_2.uai b/papers/neurips21/triangolo/tr0_2.uai
diff --git a/papers/neurips21/triangolo/tr1_0.uai b/papers/neurips21/triangolo/tr1_0.uai
diff --git a/papers/neurips21/triangolo/tr1_1.uai b/papers/neurips21/triangolo/tr1_1.uai
diff --git a/papers/neurips21/triangolo/tr1_2.uai b/papers/neurips21/triangolo/tr1_2.uai
diff --git a/papers/neurips21/triangolo/tr1_3.uai b/papers/neurips21/triangolo/tr1_3.uai
diff --git a/papers/neurips21/triangolo/tr2_0.uai b/papers/neurips21/triangolo/tr2_0.uai
diff --git a/papers/neurips21/triangolo/tr2_1.uai b/papers/neurips21/triangolo/tr2_1.uai
diff --git a/papers/neurips21/triangolo/tr2_2.uai b/papers/neurips21/triangolo/tr2_2.uai
diff --git a/papers/neurips21/triangolo/tr2_3.uai b/papers/neurips21/triangolo/tr2_3.uai
diff --git a/papers/neurips21/triangolo/tr3_0.uai b/papers/neurips21/triangolo/tr3_0.uai
diff --git a/papers/neurips21/triangolo/tr3_1.uai b/papers/neurips21/triangolo/tr3_1.uai
diff --git a/papers/neurips21/triangolo/tr3_2.uai b/papers/neurips21/triangolo/tr3_2.uai
diff --git a/papers/neurips21/triangolo/tr3_3.uai b/papers/neurips21/triangolo/tr3_3.uai