For a given set of training data examples stored in a $.$CSV file, implement and demonstrate

the Candidate$-$Elimination algorithm to output a description of the set of all hypotheses

consistent with the training examples.

Task: The CANDIDATE$-$ELIMINATION algorithm computes the version space containing

all hypotheses from $H$ that are consistent with an observed sequence of training examples.

Dataset: EnjoySport training examples

$so$ larr$\{\}$

$GO-\{<?,?,?,?,?,?>\}$

$S0-\{\}$

$x1=$

$G1-\{<?,?,?,?,?,?>\}$

$S1-\{<$ Sunny, Warm, Normal, Strong, Warm, Same $>\}$

$x2=<$ Sunny, Warm, High, Strong, Warm, Same$>$

$G2\{<?,?,?,?,?,?>\}$

S$2$ larr $\{<$ Sunny, Warm, $?,$ Strong, Warm, Same $>\}$

Candidate$-$Elimination Aloorithm:

Initialize $G$ to the set of maximally general hypotheses in $H$

Initialize $S$ to the set of maximally specific hypotheses in $H$

For each training example $d,$ do

If $d$ is a positive example

Remove from $G$ any hypothesis inconsistent with $d$

For each hypothesis $s$ in $S$ that is not consistent with $d$

Remove s from $S$

Add to $S$ all minimal generalizations $h$ of $s$ such that

$h$ is consistent with $d,$ and some member of $G$ is more general than $h$

Remove from $S$ any hypothesis that is more general than another hypothesis in $S$

If $d$ is a negative example

Remove from $S$ any hypothesis inconsistent with $d$

For each hypothesis $g$ in $G$ that is not consistent with $d$

Remove $g$ from $G$

Add to $G$ all minimal specializations $h$ of $g$ such that

$h$ is consistent with $d,$ and some member of $S$ is more specific than $h$

Remove from $G$ any hypothesis that is less general than another hypothesis in $G$

Coding hints

$m$

Constructor of class Holder holding two parameters, self refers to the

instance of the class

$"\text{'}$

def init $($self$,$attr$)$:

$.$

def add$\_$values$($self$,$factor,values$)$:

class CandidateElimination:

Positive $=\{\}$ #Initialize positive empty dictionary Negative $=\{\}$ #Initialize

negative empty dictionary

def init $($self$,$data,fact$)$:

def run$\_$algorithm$($self$)$:

$"\text{'}$

Initialize the specific and general boundaries, and loop the dataset against

the algorithm

$m$

$"\text{'}$

Programmatically populate list in the iterating variable trial$\_$set

$|"$

if self.is$\_$positive$($trial$\_$set$)$: #if trial set$/$example

if not self.consistent$($s$,$trial$\_$set$[0])$:

else:#if it is negative

$...$

if specializations $[]$ :

$...$

def initializeS$($self$)$:$"\text{'}$ Initialize the specific boundary $"\text{'}$

$...$

def initializeG$($self$)$:

$"\text{'}$ Initialize the general boundary $"\text{'}$

$...$

def is$\_$positive$($self$,$trial$\_$set$)$:

$"\text{'}$ Check if a given training trial$\_$set is positive $"\text{'}$

$...$

def is$\_$negative$($self$,$trial$\_$set$)$:

$"\text{'}$ Check if a given training trial$\_$set is negative $"\text{'}$

$..$

def match$\_$factor$($self$,$value$1,$value$2)$:

$"\text{'}$ Check for the factors values match $,",$

$..$

def consistent$($self$,$hypothesis, instance$)$:

$"\text{'}$ Check whether the instance is part of the hypothesis $"\text{'}$

def remove$\_$inconsistent$\_$G$($self$,$hypotheses,instance$)$:

$"\text{'}$ For a positive trial$\_$set, the hypotheses in G $,,$

defremove$\_$inconsistent$\_$S$($self$,$hypotheses,instance$)$:

$|"\text{'}$ For a negative trial$\_$set, the hypotheses in S $,",$

def remove$\_$more$\_$general$($self$,$hypotheses$)$:

$"\text{'}$ After generalizing S for a positive trial$\_$set, the hypothesis in S $,,$

defremove$\_$more$\_$specific$($self$,$hypotheses$)$:

$"\text{'}$ After specializing G for a negative trial$\_$set, the hypothesis in G $,,$

def generalize$\_$inconsistent$\_$S$($self$,$hypothesis,instance$)$:

$"\text{'}$ When a inconsistent hypothesis for positive trial$\_$set is seen in the

specific boundary $S,,$

$...$def specialize$\_$inconsistent$\_$G$($self$,$hypothesis,instance$)$:

$"\text{'}$ When a inconsistent hypothesis for negative trial$\_$s