probability.ipynb

   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**make_factor** is used to create the **cpt** and **variables** that will be passed to the constructor of **Factor**. We use **make_factor** for each variable. It takes in the arguments **var** the particular variable, **e** the evidence we want to do inference on, **bn** the bayes network.\n",
    "\n",
    "Here **variables** for each node refers to a list consisting of the variable itself and the parents minus any variables that are part of the evidence. This is created by finding the **node.parents** and filtering out those that are not part of the evidence.\n",
    "\n",
    "The **cpt** created is the one similar to the original **cpt** of the node with only rows that agree with the evidence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(all_events)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **all_events** function is a recursive generator function which yields a key for the orignal **cpt** which is part of the node. This works by extending evidence related to the node, thus all the output from **all_events** only includes events that support the evidence. Given **all_events** is a generator function one such event is returned on every call. \n",
    "\n",
    "We can try this out using the example on **Page 524** of the book. We will make **f**<sub>5</sub>(A) = P(m | A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f5 = make_factor('MaryCalls', {'JohnCalls': True, 'MaryCalls': True}, burglary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<probability.Factor at 0x7f4b1a69b080>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(False,): 0.01, (True,): 0.7}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f5.cpt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Alarm']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f5.variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here **f5.cpt** False key gives probability for **P(MaryCalls=True | Alarm = False)**. Due to our representation where we only store probabilities for only in cases where the node variable is True this is the same as the **cpt** of the BayesNode. Let us try a somewhat different example from the book where evidence is that the Alarm = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_factor = make_factor('MaryCalls', {'Alarm': True}, burglary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{(False,): 0.30000000000000004, (True,): 0.7}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_factor.cpt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here the **cpt** is for **P(MaryCalls | Alarm = True)**. Therefore the probabilities for True and False sum up to one. Note the difference between both the cases. Again the only rows included are those consistent with the evidence.\n",
    "\n",
    "#### Operations on Factors\n",
    "\n",
    "We are interested in two kinds of operations on factors. **Pointwise Product** which is used to created joint distributions and **Summing Out** which is used for marginalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(Factor.pointwise_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Factor.pointwise_product** implements a method of creating a joint via combining two factors. We take the union of **variables** of both the factors and then generate the **cpt** for the new factor using **all_events** function. Note that the given we have eliminated rows that are not consistent with the evidence. Pointwise product assigns new probabilities by multiplying rows similar to that in a database join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(pointwise_product)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pointwise_product** extends this operation to more than two operands where it is done sequentially in pairs of two."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(Factor.sum_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Factor.sum_out** makes a factor eliminating a variable by summing over its values. Again **events_all** is used to generate combinations for the rest of the variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(sum_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**sum_out** uses both **Factor.sum_out** and **pointwise_product** to finally eliminate a particular variable from all factors by summing over its values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Elimination Ask\n",
    "\n",
    "The algorithm described in **Figure 14.11** of the book is implemented by the function **elimination_ask**. We use this for inference. The key idea is that we eliminate the hidden variables by interleaving joining and marginalization. It takes in 3 arguments **X** the query variable, **e** the evidence variable and **bn** the Bayes network. \n",
    "\n",
    "The algorithm creates factors out of Bayes Nodes in reverse order and eliminates hidden variables using **sum_out**. Finally it takes a point wise product of all factors and normalizes. Let us finally solve the problem of inferring \n",
    "\n",
    "**P(Burglary=True | JohnCalls=True, MaryCalls=True)** using variable elimination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(elimination_ask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'False: 0.716, True: 0.284'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elimination_ask('Burglary', dict(JohnCalls=True, MaryCalls=True), burglary).show_approx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approximate Inference in Bayesian Networks\n",
    "\n",
    "Exact inference fails to scale for very large and complex Bayesian Networks. This section covers implementation of randomized sampling algorithms, also called Monte Carlo algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(BayesNode.sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we consider the different algorithms in this section let us look at the **BayesNode.sample** method. It samples from the distribution for this variable conditioned on event's values for parent_variables. That is, return True/False at random according to with the conditional probability given the parents. The **probability** function is a simple helper from **utils** module which returns True with the probability passed to it.\n",
    "\n",
    "### Prior Sampling\n",
    "\n",
    "The idea of Prior Sampling is to sample from the Bayesian Network in a topological order. We start at the top of the network and sample as per **P(X<sub>i</sub> | parents(X<sub>i</sub>)** i.e. the probability distribution from which the value is sampled is conditioned on the values already assigned to the variable's parents. This can be thought of as a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(prior_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function **prior_sample** implements the algorithm described in **Figure 14.13** of the book. Nodes are sampled in the topological order. The old value of the event is passed as evidence for parent values. We will use the Bayesian Network in **Figure 14.12** to try out the **prior_sample**\n",
    "\n",
    "<img src=\"files/images/sprinklernet.jpg\" height=\"500\" width=\"500\">\n",
    "\n",
    "We store the samples on the observations. Let us find **P(Rain=True)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 1000\n",
    "all_observations = [prior_sample(sprinkler) for x in range(N)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we filter to get the observations where Rain = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rain_true = [observation for observation in all_observations if observation['Rain'] == True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can find **P(Rain=True)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.508\n"
     ]
    }
   ],
   "source": [
    "answer = len(rain_true) / N\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate a conditional distribution. We can use a two-step filtering process. We first separate out the variables that are consistent with the evidence. Then for each value of query variable, we can find probabilities. For example to find **P(Cloudy=True | Rain=True)**. We have already filtered out the values consistent with our evidence in **rain_true**. Now we apply a second filtering step on **rain_true** to find **P(Rain=True and Cloudy=True)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7755905511811023\n"
     ]
    }
   ],
   "source": [
    "rain_and_cloudy = [observation for observation in rain_true if observation['Cloudy'] == True]\n",
    "answer = len(rain_and_cloudy) / len(rain_true)\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rejection Sampling\n",
    "\n",
    "Rejection Sampling is based on an idea similar to what we did just now. First, it generates samples from the prior distribution specified by the network. Then, it rejects all those that do not match the evidence. The function **rejection_sampling** implements the algorithm described by **Figure 14.14**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(rejection_sampling)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function keeps counts of each of the possible values of the Query variable and increases the count when we see an observation consistent with the evidence. It takes in input parameters **X** - The Query Variable, **e** - evidence, **bn** - Bayes net and **N** - number of prior samples to generate.\n",
    "\n",
    "**consistent_with** is used to check consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(consistent_with)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To answer **P(Cloudy=True | Rain=True)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7835249042145593"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = rejection_sampling('Cloudy', dict(Rain=True), sprinkler, 1000)\n",
    "p[True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Likelihood Weighting\n",
    "\n",
    "Rejection sampling tends to reject a lot of samples if our evidence consists of a large number of variables. Likelihood Weighting solves this by fixing the evidence (i.e. not sampling it) and then using weights to make sure that our overall sampling is still consistent.\n",
    "\n",
    "The pseudocode in **Figure 14.15** is implemented as **likelihood_weighting** and **weighted_sample**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(weighted_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "**weighted_sample** samples an event from Bayesian Network that's consistent with the evidence **e** and returns the event and its weight, the likelihood that the event accords to the evidence. It takes in two parameters **bn** the Bayesian Network and **e** the evidence.\n",
    "\n",
    "The weight is obtained by multiplying **P(x<sub>i</sub> | parents(x<sub>i</sub>))** for each node in evidence. We set the values of **event = evidence** at the start of the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'Cloudy': True, 'Rain': True, 'Sprinkler': False, 'WetGrass': True}, 0.8)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weighted_sample(sprinkler, dict(Rain=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(likelihood_weighting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**likelihood_weighting** implements the algorithm to solve our inference problem. The code is similar to **rejection_sampling** but instead of adding one for each sample we add the weight obtained from **weighted_sampling**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "likelihood_weighting('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gibbs Sampling\n",
    "\n",
    "In likelihood sampling, it is possible to obtain low weights in cases where the evidence variables reside at the bottom of the Bayesian Network. This can happen because influence only propagates downwards in likelihood sampling.\n",
    "\n",
    "Gibbs Sampling solves this. The implementation of **Figure 14.16** is provided in the function **gibbs_ask** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(gibbs_ask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In **gibbs_ask** we initialize the non-evidence variables to random values. And then select non-evidence variables and sample it from **P(Variable | value in the current state of all remaining vars) ** repeatedly sample. In practice, we speed this up by using **markov_blanket_sample** instead. This works because terms not involving the variable get canceled in the calculation. The arguments for **gibbs_ask** are similar to **likelihood_weighting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'False: 0.17, True: 0.83'"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gibbs_ask('Cloudy', dict(Rain=True), sprinkler, 200).show_approx()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference in Temporal Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start, it will be helpful to understand the structure of a temporal model. We will use the example of the book with the guard and the umbrella. In this example, the state $\\textbf{X}$ is whether it is a rainy day (`X = True`) or not (`X = False`) at Day $\\textbf{t}$. In the sensor or observation model, the observation or evidence $\\textbf{U}$ is whether the professor holds an umbrella (`U = True`) or not (`U = False`) on **Day** $\\textbf{t}$. Based on that, the transition model is \n",
    "\n",
    "| $X_{t-1}$         | $X_{t}$         | **P**$(X_{t}| X_{t-1})$|  \n",
    "| -------------     |-------------    | ----------------------------------|\n",
    "| ***${False}$***  | ***${False}$***  | 0.7                         |\n",
    "| ***${False}$***  | ***${True}$***   | 0.3                         |\n",
    "| ***${True}$***   | ***${False}$***  | 0.3                         |\n",
    "| ***${True}$***   | ***${True}$***   | 0.7                         |\n",
    "\n",
    "And the the sensor model will be,\n",
    "\n",
    "| $X_{t}$          | $U_{t}$         | **P**$(U_{t}|X_{t})$|  \n",
    "| :-------------:  |:-------------:  | :------------------------:|\n",
    "| ***${False}$***  | ***${True}$***  | 0.2     |\n",
    "| ***${False}$***  | ***${False}$*** | 0.8     |\n",
    "| ***${True}$***   | ***${True}$***  | 0.9     |\n",
    "| ***${True}$***   | ***${False}$*** | 0.1     |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the filtering task we are given evidence **U** in each time **t** and we want to compute the belief $B_{t}(x)= P(X_{t}|U_{1:t})$. \n",
    "We can think of it as a three step process:\n",
    "1. In every step we start with the current belief $P(X_{t}|e_{1:t})$\n",
    "2. We update it for time\n",
    "3. We update it for evidence\n",
    "\n",
    "The forward algorithm performs the step 2 and 3 at once. It updates, or better say reweights, the initial belief using the transition and the sensor model. Let's see the umbrella example. On  **Day 0** no observation is available, and for that reason we will assume that we have equal possibilities to rain or not. In the **`HiddenMarkovModel`** class, the prior probabilities for **Day 0** are by default [0.5, 0.5]. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource HiddenMarkovModel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We instantiate the object **`hmm`** of the class using a list of lists for both the transition and the sensor model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "umbrella_transition_model = [[0.7, 0.3], [0.3, 0.7]]\n",
    "umbrella_sensor_model = [[0.9, 0.2], [0.1, 0.8]]\n",
    "hmm = HiddenMarkovModel(umbrella_transition_model, umbrella_sensor_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **`sensor_dist()`** method returns a list with the conditional probabilities of the sensor model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9, 0.2]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hmm.sensor_dist(ev=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The observation update is calculated with the **`forward()`** function. Basically, we update our belief using the observation model. The function returns a list with the probabilities of **raining or not** on **Day 1**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(forward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of raining on day 1 is 0.82\n"
     ]
    }
   ],
   "source": [
    "belief_day_1 = forward(hmm, umbrella_prior, ev=True)\n",
    "print ('The probability of raining on day 1 is {:.2f}'.format(belief_day_1[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In **Day 2** our initial belief is the updated belief of **Day 1**. Again using the **`forward()`** function we can compute the probability of raining in **Day 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of raining in day 2 is 0.88\n"
     ]
    }
   ],
   "source": [
    "belief_day_2 = forward(hmm, belief_day_1, ev=True)\n",
    "print ('The probability of raining in day 2 is {:.2f}'.format(belief_day_2[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the smoothing part we are interested in computing the distribution over past states given evidence up to the present. Assume that we want to compute the distribution for the time **k**, for $0\\leq k<t $, the computation can be divided in two parts: \n",
    "1. The forward message will be computed till and by filtering forward from 1 to **k**.\n",
    "2. The backward message can be computed by a recusive process that runs from **k** to **t**. \n",
    "\n",
    "Rather than starting at time 1, the algorithm starts at time **t**. In the umbrella example, we can compute the backward message from **Day 2** to **Day 1** by using the `backward` function. The `backward` function has as parameters the object created by the **`HiddenMarkovModel`** class, the evidence in **Day 2** (in our case is **True**), and the initial probabilities of being in state in time t+1. Since no observation is available then it will be [1, 1]. The `backward` function will return a list with the conditional probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(backward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6272727272727272, 0.37272727272727274]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = [1, 1]\n",
    "backward(hmm, b, ev=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some may notice that the result is not the same as in the book. The main reason is that in the book the normalization step is not used. If we want to normalize the result, one can use the **`normalize()`** helper function.\n",
    "\n",
    "In order to find the smoothed estimate for raining in **Day k**, we will use the **`forward_backward()`** function. As in the example in the book, the umbrella is observed in both days and the prior distribution is [0.5, 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pseudocode('Forward-Backward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of raining in Day 0 is 0.65 and in Day 1 is 0.88\n"
     ]
    }
   ],
   "source": [
    "umbrella_prior = [0.5, 0.5]\n",
    "prob = forward_backward(hmm, ev=[T, T], prior=umbrella_prior)\n",
    "print ('The probability of raining in Day 0 is {:.2f} and in Day 1 is {:.2f}'.format(prob[0][0], prob[1][0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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