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--- project-3-draft [2021/04/19 11:01]
pdmatei
+++ project-3-draft [2021/04/25 16:07] (current)
roxana_elena.stiuca prerequisite for taskset3
@@ Line 26: / Line 26: @@
     | Cartesian (Row -> Row -> Row) [String] Query Query
     | Projection [String] Query
---    | forall a. Filter (FilterCondition a) Query
+    | forall a. FEval a => Filter (FilterCondition a) Query
-    | Graph EdgeOp query
+    | Graph EdgeOp Query
 -- where EdgeOp is defined:
-type EdgeOp = Row -> Row -> Value
+type EdgeOp = Row -> Row -> Maybe Value
  </code>
 **Don't worry about Graph or Filter queries yet.**
+==== Prerequisite ====
+Add the following lines at the beginning of your .hs files:
+<code haskell>
+{-# LANGUAGE ExistentialQuantification #-}
+{-# LANGUAGE FlexibleInstances #-}
+</code>
+The first line allows ''forall a''.
+The second allows ''instance FEval String''.
 ===== Query Evaluation =====
@@ Line 101: / Line 110: @@
 === FilterCondition Evaluation ===
-Let's take a look at FilterCondition. It is used in a Filter query, in order
-to filter the entries (rows) in a table, based on a condition.
+A ''FilterCondition'' must evaluate to an actual filtering function, which has type:
-We are going to define class FEval, which contains function feval, through
+<code haskell>
-which we evaluate a FilterCondition to a function of type FilterOp (defined
+type FilterOp = Row -> Bool
-also below). In order to do so, feval will also receive the column names
+</code>
-(the table head).
+Since such filtering functions work differently for ''FilterCondition Float'' and ''FilterCondition String'', we need a class ''FEval'' which contains function ''feval''. The latter is used to evaluate a ''FilterCondition a'' to a function of type ''FilterOp''. In order to do so, ''feval'' needs to have information about column names (the table head), hence it's type is shown below.
 <code haskell>
 class FEval a where
     feval :: [String] -> (FilterCondition a) -> FilterOp
-type FilterOp = Row -> Bool
 </code>
-Your task is to write the instances for (FEval Float) and (FEval String).
+**Task 3.x.**: Your task is to write the instances for ''(FEval Float)'' and ''(FEval String)''.
 <code haskell>
 instance FEval Float where
@@ Line 119: / Line 129: @@
     ...
 </code>
-Now you can write the evaluation for Filter query (**eval**).
-=== Graph Query ===
+Now you can write the evaluation for the data constructor ''Filter query'' (see function **eval** from the previous section).
-We define a graph as a table with column names: ["From", "To", "Value"].
-Each row defines a weighted edge between node "From" and node "To".
-- **Graph edgeop query**: creates a graph starting from the table
+===== Graph queries =====
-query evaluates to.
-The nodes are the rows in table T.
+A **graph** is a special kind of table which has precisely the following column names: ''["From", "To", "Value"]''. Each row defines a **weighted edge** between node ''From'' and node ''To''.
-The weight of an edge between 2 nodes is given by edgeop. We will only
-keep the edge between row1 and row2 if (edgeop row1 row2) > 0.
+The query ''Graph edgeop query'': creates such a table starting from the result of the evaluation of ''query''. Suppose the query evaluates to a table **T**.
-In the resulting table, a row describes an edge between node_i and node_j
-and will have the values:
+  * The nodes are the **rows** in table **T**.
-"From" = first column from node_i
+  * The weight of an edge between 2 nodes is given by ''edgeop'', which returns a ''Maybe Value''. If ''edgeop row1 row2'' returns ''Nothing'', then we don't have an edge between those 2 nodes. If it returns ''Just val'' then we have an edge between ''row1'' and ''row2'' of weight ''val''.
-"To" = first column from node_j
+  * In the resulting table, each row describes an edge between node_i and node_j and will have the values:
-"Value" = edgeop node_i node_j
+    * "From" = first column from node_i
-The edge node_i-node_j is the same as node_j-node_i, so it should only
+    * "To" = first column from node_j
-appear once. "From" value should be lexicographically before "To".
+    * "Value" = edgeop node_i node_j
-=== Similarities graph, using queries ===
+The edge //node_i-node_j// is the same as //node_j-node_i//, so it should only appear once (graphs are unoriented). "From" value should be lexicographically before "To".
+**Example:** Suppose **T** is the table shown below:
+<code>
+Name      Grade      Class
+Mihai     9          321
+Andrei    8          322
+Stefan    10         321
+Ana       9          322
+</code>
+If we would like to build a graph that connects all students in the same class, then:
+<code haskell>
+edgeop [_,_,z] [_,_,c]
+   | z == c = Just c
+   | otherwise = Nothing
+</code>
+and the resulting graph will be:
+<code>
+From      To      Value
+Mihai     Stefan  321
+Ana       Andrei  322
+</code>
+If we would like to build a graph that connects students with grades equal or with a difference of **at least** a point, then:
+<code haskell>
+edgeop [_,x,_] [_,y,_]
+   | abs $ (read x :: Int) - (read y :: Int) <= 1 = Just "similar"
+   | otherwise = Nothing
+</code>
+and the resulting graph is:
+<code>
+From      To      Value
+Andrei    Mihai   similar
+Mihai     Stefan  similar
+Ana       Mihai   similar
+Ana       Andrei  similar
+Ana       Stefan  similar
+</code>
+==== Similarities graph, using queries ====
 We want to check the similarities between students lecture points.
-For that, we want to obtain a graph where "From" and "To" are students'
+  * For that, we want to obtain a graph where "From" and "To" are students' emails and "Value" is the distance between the 2 students' points.
-emails and "Value" is the distance between the 2 students' points.
+  * We define the distance between stud1 and stud2 as ''the sum of questions where they both received the same points''. Keep only the rows with ''distance >= 5''.
-We define the distance between stud1 and stud2 as the sum of questions
+  * The edges in the resulting graph (the rows in the resulting table) should be sorted by the "Value" column. If email is missing, don't include that entry.
-where they both received the same points.
-Also, the edges in the resulting graph (the rows in the resulting table)
+Your task is to write ''similarities_query'' as a **sequence of queries**, that once evaluated results in the graph described above.
-should be sorted by the "Value" column. Keep only the rows with
-distance >= 5. If email is missing, ignore that entry.
+**Note**: ''similarities_query'' is a Query. The checker applies ''eval'' on it.
-Your task is to write **similarities_query** as a sequence of
-queries, that once evaluated results in the graph described above.
+===== TL;DR Tasks =====
-=== TL;DR Tasks ===
+  - Enroll ''Query'' in class ''Eval'' (without ''Filter'' or ''Graph''). **0.3p**
-. Enroll Query in class Eval (without Filter or Graph). 0.2p
+  - Enroll ''FilterCondition'' in class ''FEval'' and implement ''eval'' for ''Filter'' query. **0.2p**
-. Enroll FilterCondition in class FEval and implement eval for Filter query. 0.2p
+  - Implement ''eval'' for ''Graph'' query. 0.2p
-. Implement eval for Graph query. 0.2p
+  - Extract similarity graph. 0.3p
-. Get graph for similarities. 0.3p
-=== Checker ===
+===== Checker =====
-=== Submit ===
+===== Submit =====
 **Deadline**: 16.05, 23:50.
 **Vmchecker**: TBA.