Indentation error correction

Author: samagra14

md/Gibbs-Ask.md

@@ -17,19 +17,19 @@ __function__ GIBBS-ASK(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_
 __Figure__ ?? The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.  
 
 ---
-
-## AIMA4e
-__function__ GIBBS-ASK(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_X_ | __e__)
- __local variables__: __N__, a vector of counts for each value of _X_, initially zero
-        __Z__, the nonevidence variables in _bn_
-        __x__, the current state of the network, initially copied from __e__
-
- initialize __x__ with random values for the variables in __Z__
- __for__ _j_ = 1 to _N_ __do__
-&emsp;&emsp;&emsp;__choose__ any variable _Z<sub>i</sub>_ from __Z__ acoording to any distribution _&rho;(i)_
-&emsp;&emsp;&emsp;&emsp;&emsp;set the value of _Z<sub>i</sub>_ in __x__ by sampling from __P__(_Z<sub>i</sub>_ &vert; _mb_(_Z<sub>i</sub>_))
-&emsp;&emsp;&emsp;&emsp;&emsp;__N__\[_x_\] &larr; __N__\[_x_\] &plus; 1 where _x_ is the value of _X_ in __x__
-&emsp;__return__ NORMALIZE(__N__)
+  
+## AIMA4e  
+__function__ GIBBS-ASK(_X_, __e__, _bn_, _N_) __returns__ an estimate of __P__(_X_ &vert; __e__)  
+&emsp;__local variables__: __N__, a vector of counts for each value of _X_, initially zero  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__Z__, the nonevidence variables in _bn_  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;__x__, the current state of the network, initially copied from __e__  
+  
+&emsp;initialize __x__ with random values for the variables in __Z__  
+&emsp;__for__ _j_ = 1 to _N_ __do__  
+&emsp;&emsp;&emsp;__choose__ any variable _Z<sub>i</sub>_ from __Z__ acoording to any distribution _&rho;(i)_  
+&emsp;&emsp;&emsp;&emsp;&emsp;set the value of _Z<sub>i</sub>_ in __x__ by sampling from __P__(_Z<sub>i</sub>_ &vert; _mb_(_Z<sub>i</sub>_))  
+&emsp;&emsp;&emsp;&emsp;&emsp;__N__\[_x_\] &larr; __N__\[_x_\] &plus; 1 where _x_ is the value of _X_ in __x__  
+&emsp;__return__ NORMALIZE(__N__)  
 
 ---
-__Figure__ ?? The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.
+__Figure__ ?? The Gibbs sampling algorithm for approximate inference in Bayesian networks; this version cycles through the variables, but choosing variables at random also works.  

md/Policy-Iteration.md

@@ -20,22 +20,22 @@ __function__ POLICY-ITERATION(_mdp_) __returns__ a policy
 __Figure ??__ The policy iteration algorithm for calculating an optimal policy.
 
 ---
-
-## AIMA4e
-__function__ POLICY-ITERATION(_mdp_) __returns__ a policy
- __inputs__: _mdp_, an MDP with states _S_, actions _A_(_s_), transition model _P_(_s′_ | _s_, _a_)
- __local variables__: _U_, a vector of utilities for states in _S_, initially zero
-        _π_, a policy vector indexed by state, initially random
-
- __repeat__
-   _U_ ← POLICY\-EVALUATION(_π_, _U_, _mdp_)
-   _unchanged?_ ← true
-   __for each__ state _s_ __in__ _S_ __do__
-&emsp;&emsp;&emsp;&emsp;&emsp;_a <sup> &#x2a; </sup>_ &larr; argmax<sub>_a_ &isin; _A_(_s_)</sub> Q-VALUE(_mdp_,_s_,_a_,_U_)
-&emsp;&emsp;&emsp;&emsp;&emsp;__if__ Q-VALUE(_mdp_,_s_,_a<sup>&#x2a;</sup>_,_U_) &gt; Q-VALUE(_mdp_,_s_,_&pi;_\[_s_\],_U_) __then do__  
+  
+## AIMA4e  
+__function__ POLICY-ITERATION(_mdp_) __returns__ a policy  
+&emsp;__inputs__: _mdp_, an MDP with states _S_, actions _A_(_s_), transition model _P_(_s&prime;_ &vert; _s_, _a_)  
+&emsp;__local variables__: _U_, a vector of utilities for states in _S_, initially zero  
+&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;_&pi;_, a policy vector indexed by state, initially random  
+  
+&emsp;__repeat__  
+&emsp;&emsp;&emsp;_U_ &larr; POLICY\-EVALUATION(_&pi;_, _U_, _mdp_)  
+&emsp;&emsp;&emsp;_unchanged?_ &larr; true  
+&emsp;&emsp;&emsp;__for each__ state _s_ __in__ _S_ __do__  
+&emsp;&emsp;&emsp;&emsp;&emsp;_a <sup> &#x2a; </sup>_ &larr; argmax<sub>_a_ &isin; _A_(_s_)</sub> Q-VALUE(_mdp_,_s_,_a_,_U_)  
+&emsp;&emsp;&emsp;&emsp;&emsp;__if__ Q-VALUE(_mdp_,_s_,_a<sup>&#x2a;</sup>_,_U_) &gt; Q-VALUE(_mdp_,_s_,_&pi;_\[_s_\],_U_) __then do__    
 &emsp;&emsp;&emsp;&emsp;&emsp;&emsp;&emsp;_&pi;_\[_s_\] &larr; _a<sup>&#x2a;</sup>_ ; _unchanged?_ &larr; false  
-&emsp;__until__ _unchanged?_
-&emsp;__return__ _&pi;_
+&emsp;__until__ _unchanged?_  
+&emsp;__return__ _&pi;_  
 
 ---
-__Figure ??__ The policy iteration algorithm for calculating an optimal policy.
+__Figure ??__ The policy iteration algorithm for calculating an optimal policy.  

md/Value-Iteration.md

@@ -21,21 +21,21 @@ __Figure ??__ The value iteration algorithm for calculating utilities of states.
 
 ---
 
-## AIMA4e
-__function__ VALUE-ITERATION(_mdp_, _ε_) __returns__ a utility function
- __inputs__: _mdp_, an MDP with states _S_, actions _A_(_s_), transition model _P_(_s′_ | _s_, _a_),
-      rewards _R_(_s_,_a_,_s′_), discount _γ_
-   _ε_, the maximum error allowed in the utility of any state
- __local variables__: _U_, _U′_, vectors of utilities for states in _S_, initially zero
-        _δ_, the maximum change in the utility of any state in an iteration
+## AIMA4e  
+__function__ VALUE-ITERATION(_mdp_, _ε_) __returns__ a utility function  
+ __inputs__: _mdp_, an MDP with states _S_, actions _A_(_s_), transition model _P_(_s′_ | _s_, _a_),  
+      rewards _R_(_s_,_a_,_s′_), discount _γ_  
+   _ε_, the maximum error allowed in the utility of any state  
+ __local variables__: _U_, _U′_, vectors of utilities for states in _S_, initially zero  
+        _δ_, the maximum change in the utility of any state in an iteration  
 
- __repeat__
-   _U_ ← _U′_; _δ_ ← 0
-   __for each__ state _s_ in _S_ __do__
-&emsp;&emsp;&emsp;&emsp;&emsp;_U&prime;_\[_s_\] &larr; max<sub>_a_ &isin; _A_(_s_)</sub> Q-VALUE(_mdp_,_s_,_a_,_U_)
-&emsp;&emsp;&emsp;&emsp;&emsp;__if__ &vert; _U&prime;_\[_s_\] &minus; _U_\[_s_\]  &vert; &gt; _&delta;_ __then__ _&delta;_ &larr; &vert; _U&prime;_\[_s_\] &minus; _U_\[_s_\]  &vert;
-&emsp;__until__ _&delta;_ &lt; _&epsi;_(1 &minus; _&gamma;_)&sol;_&gamma;_
-&emsp;__return__ _U_
+&emsp;__repeat__  
+&emsp;&emsp;&emsp;_U_ &larr; _U&prime;_; _&delta;_ &larr; 0  
+&emsp;&emsp;&emsp;__for each__ state _s_ in _S_ __do__  
+&emsp;&emsp;&emsp;&emsp;&emsp;_U&prime;_\[_s_\] &larr; max<sub>_a_ &isin; _A_(_s_)</sub> Q-VALUE(_mdp_,_s_,_a_,_U_)  
+&emsp;&emsp;&emsp;&emsp;&emsp;__if__ &vert; _U&prime;_\[_s_\] &minus; _U_\[_s_\]  &vert; &gt; _&delta;_ __then__ _&delta;_ &larr; &vert; _U&prime;_\[_s_\] &minus; _U_\[_s_\]  &vert;  
+&emsp;__until__ _&delta;_ &lt; _&epsi;_(1 &minus; _&gamma;_)&sol;_&gamma;_  
+&emsp;__return__ _U_  
 
 ---
 __Figure ??__ The value iteration algorithm for calculating utilities of states. The termination condition is from Equation (__??__).