Adds fulljointdistribution models

Author: samagra14

notebooks/QuantifyingUncertainty.ipynb

@@ -16,7 +16,7 @@
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       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd9e14d3-276a-42e7-a4ee-d0b721a148aa",
+       "model_id": "a4d84936-b337-4880-963c-b61be6b8c440",
        "version_major": 2,
        "version_minor": 0
       },
@@ -400,23 +400,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Joint Distribution = <0.108, 0.012, 0.016, 0.064, 0.072, 0.008, 0.144, 0.576>\n"
+      "Joint Distribution = <0.108, 0.012, 0.016, 0.064, 0.072, 0.008, 0.144, 0.576>\n",
+      "Probability of having cavity and toothache but not of catch = 0.012\n"
      ]
     },
     {
-     "ename": "ERROR",
-     "evalue": " java.lang.IllegalArgumentException",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31mERROR: java.lang.IllegalArgumentException: Assignments passed in is not the same size as variables making up probability table.\u001b[0;0m"
-     ]
+     "data": {
+      "text/plain": [
+       "null"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -458,11 +461,111 @@
     "System.out.println(\"Joint Distribution = \"+jointDistribution.toString());\n",
     "\n",
     "// We can then ask about any subset of the jointDistribution\n",
-    "// Lets ask about the probability of having a toothache and cavity\n",
+    "// Lets ask about the probability of having a toothache and cavity but not of catch\n",
     "// We will use the AssignmentProposition class for assigning the values to the random variables.\n",
     "AssignmentProposition toothache = new AssignmentProposition(toothacheRv,true);\n",
     "AssignmentProposition cavity = new AssignmentProposition(cavityRv,true);\n",
-    "System.out.println(\"Probability of having cavity and toothache = \" + jointDistribution.getValue(toothache));"
+    "AssignmentProposition catchVal = new AssignmentProposition(catchRv,false);\n",
+    "System.out.println(\"Probability of having cavity and toothache but not of catch = \" +\n",
+    "                   jointDistribution.getValue(toothache,cavity,catchVal));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From the preceding definition of possible worlds, it follows that a probability model is\n",
+    "completely determined by the joint distribution for all of the random variables—the so-called\n",
+    "**full joint probability distribution**. For example, if the variables are Cavity, Toothache ,\n",
+    "and Weather , then the full joint distribution is given by P(Cavity, Toothache , Weather ).\n",
+    "This joint distribution can be represented as a 2 × 2 × 4 table with 16 entries. the above example represents the full joint distribution of a similar case. To encode a probability model using a full joint distribiution we can use the `FullJointDistributionModel` class from the code repo as follows :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The random variables in the model = [Toothache, Cavity, Catch]\n",
+      "The prior probability of having a toothache = 0.2\n",
+      "The prior probability of having a cavity = 0.2\n",
+      "The probability of having a cavity and toothache simultaneously is = 0.12\n",
+      "The probability of having a toothache given that the person has a cavity(causal direction) is = 0.6\n",
+      "The probability of having a cavity given that the person is experiencing toothache(diagnostic direction) is = 0.6\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "null"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "package aima.notebooks.quantifyinguncertainty;\n",
+    "\n",
+    "import aima.core.probability.domain.*;\n",
+    "import aima.core.probability.*;\n",
+    "import aima.core.probability.util.*;\n",
+    "import aima.core.probability.proposition.*;\n",
+    "import aima.core.probability.full.*;\n",
+    "\n",
+    "// First we define the random variables\n",
+    "RandVar cavityRv = new RandVar(\"Cavity\", new BooleanDomain());\n",
+    "RandVar toothacheRv = new RandVar(\"Toothache\", new BooleanDomain());\n",
+    "RandVar catchRv = new RandVar(\"Catch\", new BooleanDomain());\n",
+    "\n",
+    "\n",
+    "// Create the full joint distribution from the table given above.\n",
+    "FullJointDistributionModel model = new FullJointDistributionModel(\n",
+    "                            new double[] {\n",
+    "\t\t\t\t// Toothache = true, Cavity = true, Catch = true\n",
+    "\t\t\t\t0.108,\n",
+    "\t\t\t\t// Toothache = true, Cavity = true, Catch = false\n",
+    "\t\t\t\t0.012,\n",
+    "\t\t\t\t// Toothache = true, Cavity = false, Catch = true\n",
+    "\t\t\t\t0.016,\n",
+    "\t\t\t\t// Toothache = true, Cavity = false, Catch = false\n",
+    "\t\t\t\t0.064,\n",
+    "\t\t\t\t// Toothache = false, Cavity = true, Catch = true\n",
+    "\t\t\t\t0.072,\n",
+    "\t\t\t\t// Toothache = false, Cavity = true, Catch = false\n",
+    "\t\t\t\t0.008,\n",
+    "\t\t\t\t// Toothache = false, Cavity = false, Catch = true\n",
+    "\t\t\t\t0.144,\n",
+    "\t\t\t\t// Toothache = false, Cavity = false, Catch = false\n",
+    "\t\t\t\t0.576 },\n",
+    "                    toothacheRv,cavityRv,catchRv);\n",
+    "// Let's define a few assignments\n",
+    "AssignmentProposition toothache = new AssignmentProposition(toothacheRv,true);\n",
+    "AssignmentProposition cavity = new AssignmentProposition(cavityRv,true);\n",
+    "// Now let's have a look at what we can do with the model.\n",
+    "// To print the random variables in the model\n",
+    "System.out.println(\"The random variables in the model = \" + model.getRepresentation());\n",
+    "// We can calculate the prior probabilities of a variety of combinations of random variables\n",
+    "System.out.println(\"The prior probability of having a toothache = \"+ model.prior(toothache));\n",
+    "System.out.println(\"The prior probability of having a cavity = \"+ model.prior(cavity));\n",
+    "System.out.println(\"The probability of having a cavity and toothache simultaneously is = \"+ model.prior(toothache, cavity));\n",
+    "// We can also calculate a variety of posterior probabilities from the model as follows\n",
+    "System.out.println(\"The probability of having a toothache given that the person has a cavity(causal direction) is = \"+ \n",
+    "                  model.posterior(toothache,cavity));\n",
+    "System.out.println(\"The probability of having a cavity given that the person is experiencing toothache(diagnostic direction) is = \"\n",
+    "                  + model.posterior(cavity,toothache));\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inference using full joint distributions"
    ]
   },
   {