diff -r b84ea52bfd8f -r cdfb2ce30a3d testing2/docdiff.scala --- a/testing2/docdiff.scala Tue Nov 12 10:47:27 2019 +0000 +++ b/testing2/docdiff.scala Tue Nov 19 00:40:27 2019 +0000 @@ -2,7 +2,8 @@ //======================================== -object CW7a { // for purposes of generating a jar +object CW7a { + //(1) Complete the clean function below. It should find // all words in a string using the regular expression @@ -12,16 +13,39 @@ // // The words should be Returned as a list of strings. -def clean(s: String) : List[String] = - ("""\w+""".r).findAllIn(s).toList + +def clean(s: String) : List[String] = { + val regex = """\w+""".r; + val list_of_words = s.split(" ").toList + for(word <- list_of_words; + actual_word <- divide_string_where_different(word, regex.findAllIn(word).mkString, 0)) yield actual_word +} +/* + A secondary function that takes as parameters @param original which is the original word, @param returned which is thea word after the process of removing + some characters not allowed by a regular expression, and @param i which is the index where to start compare the characters of the two words. + It @return a List of strings which represents all the substrings of returned which were previously divided by characters not allowed by the regular expression applied on it. +*/ +def divide_string_where_different(original: String, returned: String, i : Int): List[String] ={ + val max_i = original.length -1 + if(original(i) != returned(i)) returned.substring(0, i)::divide_string_where_different(original.substring(i+1), returned.substring(i), 0).filter(_.nonEmpty) + else if (i == max_i) List(returned) + else divide_string_where_different(original,returned, i +1) + +} //(2) The function occurrences calculates the number of times // strings occur in a list of strings. These occurrences should // be calculated as a Map from strings to integers. -def occurrences(xs: List[String]): Map[String, Int] = - (for (x <- xs.distinct) yield (x, xs.count(_ == x))).toMap + +def occurrences(xs: List[String]): Map[String, Int] = { + val lst = xs.distinct + val word_pairs = (for (word <- lst) yield (word, xs.count(_==word))).toList + word_pairs.toMap +} + + //(3) This functions calculates the dot-product of two documents // (list of strings). For this it calculates the occurrence @@ -29,29 +53,33 @@ // If a string does not occur in a document, the product is zero. // The function finally sums up all products. + def prod(lst1: List[String], lst2: List[String]) : Int = { - val words = (lst1 ::: lst2).distinct - val occs1 = occurrences(lst1) - val occs2 = occurrences(lst2) - words.map{ w => occs1.getOrElse(w, 0) * occs2.getOrElse(w, 0) }.sum + val map1 = occurrences(lst1) + val map2 = occurrences(lst2) + print(s"map1 is $map1 \n and map2 is $map2") + val pairs = (for(pair1 <- map1 if(map2.get(pair1._1) != None)) yield (pair1._2, map2.get(pair1._1).get)).toList + print(s"\n pairs are $pairs") + val products = (for(pair <- pairs) yield pair._1 * pair._2).toList + products.sum + } + //(4) Complete the functions overlap and similarity. The overlap of // two documents is calculated by the formula given in the assignment // description. The similarity of two strings is given by the overlap -// of the cleaned (see (1)) strings. - -def overlap(lst1: List[String], lst2: List[String]) : Double = { - val m1 = prod(lst1, lst1) - val m2 = prod(lst2, lst2) - prod(lst1, lst2).toDouble / (List(m1, m2).max) -} - -def similarity(s1: String, s2: String) : Double = - overlap(clean(s1), clean(s2)) +// of the cleaned strings (see (1)). -/* +//def overlap(lst1: List[String], lst2: List[String]) : Double = ... + +//def similarity(s1: String, s2: String) : Double = ... + + + + +/* Test cases val list1 = List("a", "b", "b", "c", "d") @@ -61,6 +89,8 @@ occurrences(List("d", "b", "d", "b", "d")) // Map(d -> 3, b -> 2) prod(list1,list2) // 7 +prod(list1,list1) +prod(list2,list2) overlap(list1, list2) // 0.5384615384615384 overlap(list2, list1) // 0.5384615384615384 @@ -81,7 +111,7 @@ heritage which ensures Australia's capacity to attract international ecotourists.""" -similarity(orig1, plag1) +similarity(orig1, plag1) // 0.8679245283018868 // Plagiarism examples from @@ -105,13 +135,15 @@ recovery: a controversial tactic that is often implemented immediately following an oil spill.""" -overlap(clean(orig2), clean(plag2)) -similarity(orig2, plag2) +overlap(clean(orig2), clean(plag2)) // 0.728 +similarity(orig2, plag2) // 0.728 + + // The punchline: everything above 0.6 looks suspicious and -// should be looked at by staff. +// should be investigated by staff. */ +} -}