Request branch2

examples/RequestedBBRCProblem.ipynb

@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RequestedBBRCProblem class use case\n",
+    "\n",
+    "This example presents a simple use of the RequestedBBRCProblem class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openturns as ot\n",
+    "import otbenchmark as otb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "RP8\n"
+    }
+   ],
+   "source": [
+    "problem = otb.RequestedBBRCProblem(\"testuser\", \"testpass\", -1, 1)\n",
+    "print(problem.name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "event = problem.getEvent()\n",
+    "g = event.getFunction()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "0.0007888456943755395"
+     },
+     "metadata": {},
+     "execution_count": 4
+    }
+   ],
+   "source": [
+    "problem.getProbability()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create the Monte-Carlo algorithm\n",
+    "algoProb = ot.ProbabilitySimulationAlgorithm(event)\n",
+    "# MaximumOuterSampling set to a very low value to make a fast example.\n",
+    "# More realistic parameter value (e.g. 1000).\n",
+    "algoProb.setMaximumOuterSampling(10)\n",
+    "algoProb.setMaximumCoefficientOfVariation(0.01)\n",
+    "algoProb.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "Number of function calls = 10\nFailure Probability = 0.0000\n"
+    }
+   ],
+   "source": [
+    "# Get the results\n",
+    "resultAlgo = algoProb.getResult()\n",
+    "neval = g.getEvaluationCallsNumber()\n",
+    "print(\"Number of function calls = %d\" % (neval))\n",
+    "# Because of the low value of the maximumOuterSampling, the estimate is not very accurate.\n",
+    "pf = resultAlgo.getProbabilityEstimate()\n",
+    "print(\"Failure Probability = %.4f\" % (pf))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3-final"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

examples/distributions/beta_results.csv

@@ -0,0 +1,30 @@
+set_id,problem_id,reliability_problem_id,beta
+-1,1,8,3.16
+-1,2,22,2.64
+1,1,14,2.42
+1,2,24,2.76
+1,3,28,5.11
+1,4,31,3.58
+1,5,38,2.48
+1,6,53,1.86
+1,7,54,3.09
+1,8,63,3.36
+1,9,75,2.33
+1,10,107,5.0
+1,11,111,4.81
+1,12,201,3.7
+1,13,203,4.92
+1,14,213,3.45
+1,15,300,3.88
+1,16,301,3.81
+2,1,25,4.36
+2,2,33,2.80
+2,3,35,2.70
+2,4,55,-0.15
+2,5,57,1.91
+2,6,60,1.70
+2,7,77,5.0
+2,8,89,2.55
+2,9,91,3.19
+2,10,110,4.0
+2,11,202,3.43