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1 // Part 1 about Code Similarity |
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2 //============================== |
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3 |
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4 //(1) Complete the clean function below. It should find |
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5 // all words in a string using the regular expression |
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6 // \w+ and the library function |
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7 // |
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8 // some_regex.findAllIn(some_string) |
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9 // |
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10 // The words should be Returned as a list of strings. |
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11 |
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12 |
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13 //def clean(s: String) : List[String] = ... |
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14 |
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15 |
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16 |
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17 //(2) The function occurrences calculates the number of times |
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18 // strings occur in a list of strings. These occurrences should |
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19 // be calculated as a Map from strings to integers. |
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20 |
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21 |
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22 //def occurrences(xs: List[String]): Map[String, Int] = .. |
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23 |
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24 |
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25 //(3) This functions calculates the dot-product of two documents |
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26 // (list of strings). For this it calculates the occurrence |
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27 // maps from (2) and then multiplies the corresponding occurrences. |
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28 // If a string does not occur in a document, the product is zero. |
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29 // The function finally sums up all products. |
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30 |
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31 |
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32 //def prod(lst1: List[String], lst2: List[String]) : Int = .. |
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33 |
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34 |
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35 //(4) Complete the functions overlap and similarity. The overlap of |
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36 // two documents is calculated by the formula given in the assignment |
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37 // description. The similarity of two strings is given by the overlap |
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38 // of the cleaned strings (see (1)). |
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39 |
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40 |
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41 //def overlap(lst1: List[String], lst2: List[String]) : Double = ... |
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42 |
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43 //def similarity(s1: String, s2: String) : Double = ... |
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44 |
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45 |
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46 |
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47 |
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48 /* Test cases |
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49 |
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50 |
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51 val list1 = List("a", "b", "b", "c", "d") |
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52 val list2 = List("d", "b", "d", "b", "d") |
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53 |
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54 occurrences(List("a", "b", "b", "c", "d")) // Map(a -> 1, b -> 2, c -> 1, d -> 1) |
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55 occurrences(List("d", "b", "d", "b", "d")) // Map(d -> 3, b -> 2) |
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56 |
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57 prod(list1,list2) // 7 |
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58 |
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59 overlap(list1, list2) // 0.5384615384615384 |
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60 overlap(list2, list1) // 0.5384615384615384 |
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61 overlap(list1, list1) // 1.0 |
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62 overlap(list2, list2) // 1.0 |
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63 |
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64 // Plagiarism examples from |
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65 // https://desales.libguides.com/avoidingplagiarism/examples |
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66 |
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67 val orig1 = """There is a strong market demand for eco-tourism in |
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68 Australia. Its rich and diverse natural heritage ensures Australia's |
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69 capacity to attract international ecotourists and gives Australia a |
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70 comparative advantage in the highly competitive tourism industry.""" |
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71 |
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72 val plag1 = """There is a high market demand for eco-tourism in |
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73 Australia. Australia has a comparative advantage in the highly |
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74 competitive tourism industry due to its rich and varied natural |
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75 heritage which ensures Australia's capacity to attract international |
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76 ecotourists.""" |
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77 |
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78 similarity(orig1, plag1) // 0.8679245283018868 |
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79 |
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80 |
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81 // Plagiarism examples from |
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82 // https://www.utc.edu/library/help/tutorials/plagiarism/examples-of-plagiarism.php |
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83 |
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84 val orig2 = """No oil spill is entirely benign. Depending on timing and |
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85 location, even a relatively minor spill can cause significant harm to |
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86 individual organisms and entire populations. Oil spills can cause |
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87 impacts over a range of time scales, from days to years, or even |
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88 decades for certain spills. Impacts are typically divided into acute |
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89 (short-term) and chronic (long-term) effects. Both types are part of a |
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90 complicated and often controversial equation that is addressed after |
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91 an oil spill: ecosystem recovery.""" |
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92 |
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93 val plag2 = """There is no such thing as a "good" oil spill. If the |
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94 time and place are just right, even a small oil spill can cause damage |
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95 to sensitive ecosystems. Further, spills can cause harm days, months, |
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96 years, or even decades after they occur. Because of this, spills are |
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97 usually broken into short-term (acute) and long-term (chronic) |
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98 effects. Both of these types of harm must be addressed in ecosystem |
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99 recovery: a controversial tactic that is often implemented immediately |
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100 following an oil spill.""" |
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101 |
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102 overlap(clean(orig2), clean(plag2)) // 0.728 |
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103 similarity(orig2, plag2) // 0.728 |
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104 |
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105 |
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106 |
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107 // The punchline: everything above 0.6 looks suspicious and |
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108 // should be investigated by staff. |
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109 |
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110 */ |