Added week6

.gitignore

@@ -21,14 +21,14 @@ cp/tests/unitgrade_data/Week01Palindrome.pkl
 cp/exam
 #cp/project1
 #cp/project2
-cp/project3
+#cp/project3
 cp/project4
 cp/project5
 cp/project6
 cp/tests/tests_week01.py
 #*04*
-*05*
-*06*
+#*05*
+#*06*
 *07*
 *08*
 *09*

cp/ex06/__init__.py

+"""DO NOT EDIT/ADD TO THIS FILE.""" 

cp/ex06/language.py

+"""Exercise 6.5: Languages."""
+
+def get_people_by_language(language: str, name_languages: dict)-> list:
+    """Return the names of people who speak a specific language.
+    
+    :param language: A string containing the desired language.
+    :param name_languages: A dictionary containing the names of people along with their spoken languages
+    :return: The names of people that speak the desired language.
+    """
+    # TODO: Code has been removed from here. 
+if __name__ == "__main__":
+    name_languages = {
+    'Peter': ['Danish', 'English'],
+    'Alice': ['English', 'French'],
+    'John': ['Spanish', 'English'],
+    'Maria': ['Danish', 'Spanish'],
+    'Anna': ['German', 'English']
+    }
+    print(get_people_by_language('English', name_languages))

cp/ex06/letter.py

+"""Exercise 6.2: Letter histogram."""
+
+def letter_histogram(input_string : str) -> dict:
+    """Return the histogram of letter occurrences.
+    
+    :param input_string: The word based on which the letter histogram is calculated.
+    :return: The alphabet characters as keys with their corresponding occurrences as values.
+    """
+    # TODO: Code has been removed from here. 
+
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    print("What is the letter histogram of the word banana?",  letter_histogram('banana'))

cp/ex06/multi_tap.py

+"""Exercise 6.9: Multi-tap."""
+
+def multi_tap(keys : list, times : list) -> str:
+    """Return the string corresponding to the multi-tap key presses.
+
+    :param keys: The list of keys pressed.
+    :param times: The list of times of when the keys were pressed.
+    :return: The string corresponding to the key presses.
+    """
+    # TODO: Code has been removed from here. 
+    
+
+
+
+if __name__ == "__main__":
+    keys = [7, 7, 7, 7, 6, 6, 6]
+    times = [0, 0.7, 0.8, 0.9, 1, 1.1, 1.2]
+    print(multi_tap(keys, times), '==', 'PRO')

cp/ex06/nato.py

+"""Exercise 6.1: The NATO alphabet."""
+
+def text_to_nato(plaintext : str) -> str:
+    """Return the NATO version of a word separated by dashes.
+
+    :param plaintext: The word to replace with its phrase according to the NATO alphabet.
+    :return: The NATO representation of the input word.
+    """
+    # TODO: Code has been removed from here. 
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    print("What is the NATO representation of hello?", text_to_nato('hello'))

cp/ex06/sentiment_analysis.py

+"""Exercise 6.7: Sentiment analysis."""
+
+def sentiment_analysis(text: str) -> int:
+    """Return the sentence sentiment score, according to the rules of words scoring, as described in the text above.
+
+    :param text: The sentence to check for sentiment scoring.
+    :return: The total sentiment scoring of the sentence.
+    """
+    # TODO: Code has been removed from here. 
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    text='I think the food was excellent and great, but the service was horrible '
+    sentiment_analysis(text)

cp/ex06/spell_check.py

+"""Exercise 6.8: Spell check."""
+
+def spell_check(text: str, corrections: dict) -> str:
+    """Return the corrected text for spelling errors according to a set of rules.
+
+    :param text: The sentence to check for spelling.
+    :param corrections: The dictionary of wrongly spelled words and their equivalent corrected version.
+    :return: The correctly spelled sentence.
+    """
+    # TODO: Code has been removed from here. 
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    corrections = {
+    'apsolute': 'absolute',
+    'teh': 'the',
+    'acess': 'access', 
+    'occured': 'occurred',
+    'exampel': 'example'
+    }
+    text = "The apsolute acsess to teh data occured in this exampel."
+
+    print(spell_check(text, corrections))

cp/ex06/truncate.py

+"""Exercise 6.6: Truncate and normalize."""
+
+def truncate_values(float_list : list, settings: dict) -> list: 
+    """Return a truncated list of floats given the initial list and the settings for truncating. If normalize is True, the values are first normalized to the [0,1] range and then truncated.
+    
+    :param float_list: list of floats
+    :param settings: Dictionary containing the keys vmin, vmax and normalize with their corresponding values.
+    :return: Truncated/Normalized+Truncated values.
+    """
+    # TODO: Code has been removed from here. 
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    settings = {'vmin': 0, 'vmax': 2, 'normalize': False}
+    float_list = [0.5,0.4,-0.3, 1.5, 2.5, 3.5]
+    result=truncate_values(float_list=float_list,settings=settings)
+    print(result)

cp/ex06/word_histogram.py

+"""Exercise 6.3-6.4."""
+
+def word_histogram(lines : list) -> list:
+    """Return the word count histogram from the input lines.
+
+    :param lines: The lines that are analyzed for word count.
+    :return: The histogram of word occurrences.
+    """
+    # TODO: Code has been removed from here. 
+    
+def extract_keyword(lines : str, ignore_list : list) -> dict:
+    """Return the five most frequent words that are not on the ignore list and their occurrences.
+
+    :param lines: The sentence to extract keywords from.
+    :param ignore_list: The words that should ignored.
+    :return: The five most frequent words in the sentence as keys with their count as values.
+    """
+    # TODO: Code has been removed from here. 
+
+
+if __name__ == "__main__":
+    # here you can try out your functions
+    print(word_histogram('I think therefore I am.'))
+
+    # Ignore list of common words
+    ignore_list = [
+    'a', 'an', 'the', 'above', 'across', 'against', 'along', 'among', 'around',
+    'at', 'before', 'behind', 'below', 'beneath', 'beside', 'between', 'by',
+    'down', 'from', 'in', 'into', 'near', 'of', 'off', 'on', 'to', 'toward',
+    'under', 'upon', 'with', 'within','function', 'for', 'and', 'nor', 'but', 'or', 'yet', 'so']
+
+    # Example usage:
+    lines = [    "Write the function word_histogram, which takes as argument a list containing lines of a text.",    "The function should ... ... ... ... ... make a histogram of words that occur in the text."]
+
+    keywords_result = extract_keyword(lines, ignore_list)
+
+    # Print the 5 most occurring keywords
+    print(keywords_result)

cp/project3/__init__.py

+"""DO NOT EDIT/ADD TO THIS FILE.""" 

cp/project3/project3_grade.py

+# cp/project3/project3_tests.py
+''' WARNING: Modifying, decompiling or otherwise tampering with this script, it's data or the resulting .token file will be investigated as a cheating attempt. '''
+import bz2, base64
+exec(bz2.decompress(base64.b64decode('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')))
\ No newline at end of file

cp/project3/project3_tests.py

+from unitgrade import UTestCase, Report, hide
+import string
+from cp import minput
+from unittest.mock import patch
+import io
+import unittest
+import math
+
+class WaterHeight(UTestCase):
+    def test_water_height(self):
+        from cp.ex05.water_height import water_height
+        self.assertEqual(water_height(5, [1,2,3]), 5)
+        self.assertEqual(water_height(2, [1,1]), 0)
+        self.assertEqual(water_height(0, [1,3]), 1)
+        self.assertEqual(water_height(1,[1,2,3,4,5]),6)
+        self.assertEqual(water_height(2,[1,12,8,4,5]),22)
+        self.assertEqual(water_height(5,[1,10,3,4,5]),18)
+
+class Week05BestBuy(UTestCase):
+    def test_bestbuy(self):
+        from cp.ex05.best_buy import best_buy
+        self.assertEqual(best_buy([3, 2, 1, 3, 5], 10, 0, False), 4)
+        self.assertEqual(best_buy([3, 2, 1, 3, 5], 3, 1, False), 2)
+        self.assertEqual(best_buy([3, 2, 1, 3, 5], 8, 4, True), 2)
+        self.assertEqual(best_buy([3, 2, 1, 3, 5], 15, 4, True), 5)
+
+
+
+class Week05TicTacToePrintBoard(UTestCase):
+    def test_print_board(self):
+        from cp.ex05.tictactoe import print_board
+        with unittest.mock.patch('sys.stdout', new=io.StringIO()) as mock_stdout:
+            print_board([['-','X','-'],['-','-','O'],['X','-','-']])
+            out = mock_stdout.getvalue().splitlines()
+            self.assertEqual(len(out), 3, msg="You did not print out 3 separate lines")
+            self.assertEqual(out[0], "-X-")
+            self.assertEqual(out[1], "--O")
+            self.assertEqual(out[2], "X--")
+
+class Week05TicTacToeGetGameState(UTestCase):
+    def test_get_game_state(self):
+        from cp.ex05.tictactoe import get_game_state
+        self.assertEqual(get_game_state([['-','-','-'],['-','-','-'],['-','-','-']]),'-') 
+        self.assertEqual(get_game_state([['X','X','X'],['-','-','-'],['-','-','-']]),'X')
+        self.assertEqual(get_game_state([['-','-','-'],['X','X','X'],['-','-','-']]),'X')
+        self.assertEqual(get_game_state([['X','-','-'],['-','X','-'],['O','O','X']]),'X')  
+        self.assertEqual(get_game_state([['X','-','-'],['-','X','-'],['O','O','O']]),'O') 
+        self.assertEqual(get_game_state([['X','O','X'],['X','O','O'],['O','X','X']]),'Draw')
+
+class Week05TicTacToeUpdateBoard(UTestCase):
+    def test_update_board(self):
+        from cp.ex05.tictactoe import update_board
+        with unittest.mock.patch('sys.stdout', new=io.StringIO()) as mock_stdout:
+            self.assertEqual(update_board([['-','-','-'],['-','-','-'],['-','-','-']],'X',[1,0]),[['-', '-', '-'], ['X', '-', '-'], ['-', '-', '-']])
+            self.assertEqual(update_board([['X','X','-'],['-','-','-'],['-','-','-']],'O',[0,2]),[['X', 'X', 'O'], ['-', '-', '-'], ['-', '-', '-']])
+            self.assertEqual(update_board([['-','-','-'],['X','X','X'],['-','-','-']],'X',[2,0]),[['-', '-', '-'], ['X', 'X', 'X'], ['X', '-', '-']])
+            self.assertEqual(update_board([['X','-','-'],['-','X','-'],['O','O','X']],'O',[1,2]),[['X', '-', '-'], ['-', 'X', 'O'], ['O', 'O', 'X']])  
+            self.assertEqual(update_board([['X','-','-'],['-','X','-'],['O','O','O']],'X',[0,2]),[['X', '-', 'X'], ['-', 'X', '-'], ['O', 'O', 'O']]) 
+            self.assertEqual(update_board([['O','-','-'],['O','X','-'],['O','X','X']],'O',[0,2]),[['O', '-', 'O'], ['O', 'X', '-'], ['O', 'X', 'X']])
+            out = mock_stdout.getvalue()
+            self.assertEqual(out, "", "You should not print anything for valid moves")
+        with unittest.mock.patch('sys.stdout', new=io.StringIO()) as mock_stdout:
+            update_board([['O','-','-'],['O','X','-'],['O','X','X']],'O',[0,0])
+            out = mock_stdout.getvalue().strip()
+            self.assertEqual(out, "Invalid move!")        
+
+class Week05TicTacToeMain(UTestCase):
+    def test_tictactoe_main(self):
+        from cp.ex05.tictactoe import tictactoe 
+        with unittest.mock.patch('sys.stdout', new=io.StringIO()) as mock_stdout:
+            tictactoe([['X','X','-'],['-','O','-'],['O','-','-']],'O',[0,2])
+            out = mock_stdout.getvalue().splitlines()
+            self.assertEqual(len(out), 4, msg="You did not print out 4 separate lines")
+            self.assertEqual(out[0], "XXO")
+            self.assertEqual(out[1], "-O-")
+            self.assertEqual(out[2], "O--")
+            self.assertEqual(out[3],"Player O won!")
+
+class Week06SentimentAnalysis(UTestCase):
+    def test_sentiment_analysis(self):
+        from cp.ex06.sentiment_analysis import sentiment_analysis
+        self.assertEqual(sentiment_analysis('I think the food was excellent and great, but the waiter service was horrible '),-5)
+        self.assertEqual(sentiment_analysis('When I woke up, I was feeling very bad, but then I had a coffee, and my day turned out to be excellent '),4)
+        self.assertEqual(sentiment_analysis('I know it is good not to complain, but the selection criteria were genuinely inadequate and unfair '),-1)
+
+
+class Week06MultiTap(UTestCase):
+    def test_multi_tap(self):
+        from cp.ex06.multi_tap import multi_tap 
+        self.assertEqual(multi_tap([7, 7, 7, 7, 6, 6, 6], [0, 0.7, 0.8, 0.9, 1, 1.1, 1.2]), 'PRO')
+        self.assertEqual(multi_tap([4, 4, 4, 0, 2, 6, 0, 4, 4, 2, 7, 7, 9, 9, 9], [0.1, 0.3, 0.6, 1.0, 1.2, 1.4, 1.7, 2.0, 2.2, 2.5, 2.9, 3.9, 4.3, 4.4, 4.8]), "I AM HAPPY")
+        self.assertEqual(multi_tap([7, 7, 7, 7, 7, 7, 7, 7, 6, 6, 6, 8, 8, 3], [0.1, 0.5, 0.8, 1.2, 2.6, 3.7, 4.1, 4.2, 4.5, 4.9, 5.1, 5.4, 5.6, 5.9]), "SPROUD")
+        self.assertEqual(multi_tap([4, 4, 4, 2, 2, 2, 3, 3, 0, 4, 4, 4, 2, 2, 2, 3, 3, 0, 2, 2, 2, 2, 2, 9, 9, 9], [0.3, 0.7, 0.8, 1.1, 1.3, 1.5, 1.7, 1.8, 2.1, 2.4, 2.7, 3.0, 3.4, 3.7, 3.9, 4.0, 4.3, 4.5, 4.7, 5.0, 6.4, 7.9, 8.3, 8.5, 8.7, 9.0]), "ICE ICE BABY")
+        self.assertEqual(multi_tap([5, 5, 5, 4, 4, 4, 8, 8, 8, 3, 3, 0, 5, 5, 5, 2, 8, 8, 4, 4, 4, 0, 5, 5, 5, 4, 4, 4, 7, 7, 7, 7, 8], [0.7, 0.9, 1.2, 1.6, 1.7, 2.0, 2.4, 2.5, 2.8, 3.2, 3.5, 3.7, 3.9, 4.3, 4.6, 4.8, 5.0, 5.3, 5.6, 6.3, 6.7, 6.8, 7.1, 7.3, 7.5, 7.8, 8.0, 8.1, 8.3, 8.5, 8.7, 8.9, 9.3]), "LIVE LAUGH LIST")
+
+        
+        
+class Project3(Report):
+    title = "Project 3"
+    remote_url = "https://cp.pages.compute.dtu.dk/02002public/_static/evaluation/"
+
+    abbreviate_questions = True
+    questions = [(Week05BestBuy,10),
+                (WaterHeight,10),
+                (Week05TicTacToePrintBoard,5),
+                (Week05TicTacToeGetGameState,10),
+                (Week05TicTacToeUpdateBoard,10),
+                (Week06SentimentAnalysis,20),
+                (Week06MultiTap,20),
+                ]
+    import cp
+    pack_imports = [cp]
+
+
+if __name__ == "__main__":
+    from unitgrade import evaluate_report_student
+    evaluate_report_student(Project3())