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numbers = [1, 2, 3, 4, 5]
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# Verdopplung aller Zahlen mittels map
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result = list(map(lambda x: x * 2, numbers))
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print("Ergebnis:", result)
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#assert result == [2, 4, 6, 8, 10]
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@@ -0,0 +1,7 @@
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words = ["huge", "great", "tremendous", "fake news"]
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# Strings in Großbuchstaben umwandeln und "!" anhängen
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result = list(map(lambda word: word.upper() + "!", words))
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print("Ergebnis:", result)
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#assert result == ["HUGE!", "GREAT!", "TREMENDOUS!", "FAKE NEWS!"]
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@@ -0,0 +1,7 @@
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numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
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# Nur gerade Zahlen (x % 2 == 0) herausfiltern
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result = list(filter(lambda x: x % 2 == 0, numbers))
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print("Ergebnis:", result)
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assert result == [2, 4, 6, 8, 10]
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@@ -0,0 +1,7 @@
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words = ["Kuchen", "Katze", "Apfel", "Kaffee", "Baum", "Kino"]
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# Einzeiler kombiniert filter, map und sum
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total_length = sum(map(len, filter(lambda w: w.lower().startswith('k'), words)))
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print("Gesamtlänge aller K-Wörter:", total_length)
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assert total_length == 6 + 5 + 6 + 4 # Kuchen (6) + Katze (5) + Kaffee (6) + Kino (4) = 21
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@@ -0,0 +1,16 @@
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from functools import reduce
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numbers = [2, 3, 4, 5]
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# Produkt berechnen
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result = reduce(lambda x, y: x * y, numbers, 1)
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print("Produkt:", result)
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assert result == 120
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# Verhalten bei einer leeren Liste:
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# Ohne Startwert (initializer) wirft reduce() einen TypeError: "reduce() of empty sequence with no initial value".
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# Behebung: Der optional paramter initializer=1 sorgt dafür, dass bei einer leeren Liste das neutrale Element 1 zurückgegeben wird.
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empty_list = []
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result_empty = reduce(lambda x, y: x * y, empty_list, 1)
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assert result_empty == 1
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+47
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class Particle:
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def __init__(self, position, velocity=(0, 0), acceleration=(0, 0)):
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self.position = position
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self.velocity = velocity
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self.acceleration = acceleration
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def update(self):
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"""Erstellt ein neues Particle-Objekt mit aktualisiertem Zustand."""
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new_velocity = (
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self.velocity[0] + self.acceleration[0],
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self.velocity[1] + self.acceleration[1]
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)
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new_position = (
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self.position[0] + new_velocity[0],
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self.position[1] + new_velocity[1]
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)
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return Particle(new_position, new_velocity, self.acceleration)
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# Simulation mit Historie (Immutable State erlaubt Undo)
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history = []
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# Initialzustand (Start bei (0, 0) mit Beschleunigung (1, 2))
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p0 = Particle(position=(0, 0), velocity=(0, 0), acceleration=(1, 2))
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history.append(p0)
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# 1. Update
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p1 = history[-1].update()
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history.append(p1)
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# 2. Update
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p2 = history[-1].update()
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history.append(p2)
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# Überprüfen der Zwischenzustände
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assert history[0].position == (0, 0)
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assert history[1].position == (1, 2)
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assert history[2].position == (3, 6)
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# UNDO-Schritte ausführen (Einfach das jeweils letzte Element entfernen)
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history.pop() # Undo von Schritt 2
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assert history[-1].position == (1, 2)
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history.pop() # Undo von Schritt 1
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assert history[-1].position == (0, 0)
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print("Undo-Tests für Particle erfolgreich bestanden!")
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@@ -0,0 +1,41 @@
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# Ausgangs- und Zielzustand
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state = {'A': 'B', 'B': '', 'C': ''}
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goal = {'A': 'C', 'B': 'A', 'C': ''}
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def is_clear(state, block):
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"""Prüft, ob ein Block frei ist (nichts liegt darauf)."""
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return all(v != block for v in state.values())
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def move(block, target):
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"""Gibt eine Action-Funktion zurück (Closure)."""
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def action(current_state):
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if not is_clear(current_state, block):
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raise ValueError(f"Auf Block {block} liegt etwas!")
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if target != '' and not is_clear(current_state, target):
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raise ValueError(f"Block {target} ist nicht frei!")
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new_state = current_state.copy()
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new_state[block] = target
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print(f"Moved {block} to {target}.")
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return new_state
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return action
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def chain(f, g):
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"""Funktionale Komposition: Erst f, dann g ausführen."""
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return lambda current_state: g(f(current_state))
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if __name__ == '__main__':
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# Erstellen des Ausführungsplans
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plan = chain(move('A', 'C'), move('B', 'A'))
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# Ausführen des Plans auf dem Ausgangszustand
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final = plan(state)
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print("Final state:", final)
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assert final == goal
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