testing2/docdiff.scala
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// Part 1 about Code Similarity
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//==============================
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//object CW7a { // for purposes of generating a jar
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//(1) Complete the clean function below. It should find
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//    all words in a string using the regular expression
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//    \w+  and the library function 
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//
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//         some_regex.findAllIn(some_string)
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//
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//    The words should be Returned as a list of strings.
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def clean(s: String) : List[String] = 
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  ("""\w+""".r).findAllIn(s).toList
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//(2) The function occurrences calculates the number of times  
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//    strings occur in a list of strings. These occurrences should 
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//    be calculated as a Map from strings to integers.
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def occurrences(xs: List[String]): Map[String, Int] =
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  (for (x <- xs.distinct) yield (x, xs.count(_ == x))).toMap
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//(3) This functions calculates the dot-product of two documents
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//    (list of strings). For this it calculates the occurrence
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//    maps from (2) and then multiplies the corresponding occurrences. 
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//    If a string does not occur in a document, the product is zero.
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//    The function finally sums up all products. 
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def prod(lst1: List[String], lst2: List[String]) : Int = {
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    val words = (lst1 ::: lst2).distinct
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    val occs1 = occurrences(lst1)
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    val occs2 = occurrences(lst2)
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    words.map{ w => occs1.getOrElse(w, 0) * occs2.getOrElse(w, 0) }.sum
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}
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//(4) Complete the functions overlap and similarity. The overlap of
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//    two documents is calculated by the formula given in the assignment
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//    description. The similarity of two strings is given by the overlap
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//    of the cleaned (see (1)) strings.  
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def overlap(lst1: List[String], lst2: List[String]) : Double = {
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    val m1 = prod(lst1, lst1)
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    val m2 = prod(lst2, lst2) 
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    prod(lst1, lst2).toDouble / (List(m1, m2).max)
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}
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def similarity(s1: String, s2: String) : Double =
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  overlap(clean(s1), clean(s2))
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/*
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val list1 = List("a", "b", "b", "c", "d") 
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val list2 = List("d", "b", "d", "b", "d")
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occurrences(List("a", "b", "b", "c", "d"))   // Map(a -> 1, b -> 2, c -> 1, d -> 1)
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occurrences(List("d", "b", "d", "b", "d"))   // Map(d -> 3, b -> 2)
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prod(list1,list2) // 7 
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overlap(list1, list2)   // 0.5384615384615384
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overlap(list2, list1)   // 0.5384615384615384
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overlap(list1, list1)   // 1.0
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overlap(list2, list2)   // 1.0
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// Plagiarism examples from 
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// https://desales.libguides.com/avoidingplagiarism/examples
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val orig1 = """There is a strong market demand for eco-tourism in
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Australia. Its rich and diverse natural heritage ensures Australia's
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capacity to attract international ecotourists and gives Australia a
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comparative advantage in the highly competitive tourism industry."""
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val plag1 = """There is a high market demand for eco-tourism in
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Australia. Australia has a comparative advantage in the highly
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competitive tourism industry due to its rich and varied natural
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heritage which ensures Australia's capacity to attract international
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ecotourists."""
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similarity(orig1, plag1)
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// Plagiarism examples from 
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// https://www.utc.edu/library/help/tutorials/plagiarism/examples-of-plagiarism.php
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val orig2 = """No oil spill is entirely benign. Depending on timing and
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location, even a relatively minor spill can cause significant harm to
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individual organisms and entire populations. Oil spills can cause
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impacts over a range of time scales, from days to years, or even
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decades for certain spills. Impacts are typically divided into acute
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(short-term) and chronic (long-term) effects. Both types are part of a
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complicated and often controversial equation that is addressed after
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an oil spill: ecosystem recovery."""
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val plag2 = """There is no such thing as a "good" oil spill. If the
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time and place are just right, even a small oil spill can cause damage
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to sensitive ecosystems. Further, spills can cause harm days, months,
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years, or even decades after they occur. Because of this, spills are
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usually broken into short-term (acute) and long-term (chronic)
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effects. Both of these types of harm must be addressed in ecosystem
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recovery: a controversial tactic that is often implemented immediately
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following an oil spill."""
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overlap(clean(orig2), clean(plag2))
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similarity(orig2, plag2)
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// The punchline: everything above 0.6 looks suspicious and 
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// should be looked at by staff.
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*/
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//}