.idea/AlgoDatSoSe25.iml

@@ -5,7 +5,7 @@
       <excludeFolder url="file://$MODULE_DIR$/.venv" />
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.12 (AlgoDatSoSe25)" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.12 (SoSe25)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

praktika/03_quick_and_heap/priority_queue.py

@@ -1,150 +0,0 @@
-from utils.memory_array import MemoryArray
-from utils.memory_cell import MemoryCell
-from utils.memory_manager import MemoryManager
-from utils.literal import Literal
-import random
-
-class PriorityQueue:
-
-    def __init__(self, size: Literal):
-        self.items = MemoryArray(size)
-        self.heapsize = MemoryCell(0)
-
-    def __len__(self):
-        return self.heapsize.value
-
-    def insert(self, item: MemoryCell):
-        if self.heapsize == self.items.length():
-            raise Exception("Queue is full")
-        self.heapsize.set(self.heapsize.succ())
-        self.items[adjust_index(self.heapsize)].set(item.value)
-        heapyfy_up(self.items, self.heapsize)
-
-
-    def pop(self) -> MemoryCell | None:
-        if self.is_empty():
-            return None
-        result = MemoryCell(self.items[Literal(0)])
-        self.heapsize.set(self.heapsize.pred())
-        if self.heapsize > Literal(1):
-            swap(self.items, 0, int(self.heapsize))
-            max_heapyfy(self.items, Literal(1), self.heapsize)
-        return result
-
-    def peek(self) -> MemoryCell | None:
-        if self.is_empty():
-            return None
-        return MemoryCell(self.items[Literal(0)])
-
-    def is_empty(self) -> bool:
-        return self.heapsize == Literal(0)
-
-    def __str__(self):
-        result = "[ "
-        for i, cell in enumerate(self.items.cells):
-            if i == int(self.heapsize):
-                result += "| "
-            result += str(cell) + " "
-        result += "]"
-        return result
-
-def left_child(i: Literal):
-    return Literal(2 * int(i))
-
-
-def right_child(i: Literal):
-    return Literal(2 * int(i) + 1)
-
-
-def adjust_index(i: Literal):
-    return i.pred()
-
-def heapyfy_up(z: MemoryArray, i: Literal):
-    if i == Literal(1):
-        return
-    parent = Literal(int(i) // 2)
-    if z[adjust_index(parent)] >= z[adjust_index(i)]:
-        return
-    swap(z, int(i)-1, int(parent)-1)
-    heapyfy_up(z, parent)
-
-
-def max_heapyfy(z: MemoryArray, i: Literal, heapsize: Literal):
-    l = left_child(i)
-    r = right_child(i)
-    with MemoryCell(i) as max_value:
-        if l <= heapsize and z[adjust_index(l)] > z[adjust_index(i)]:
-            max_value.set(l)
-        if r <= heapsize and z[adjust_index(r)] > z[adjust_index(max_value)]:
-            max_value.set(r)
-        if max_value != i:
-            swap(z, int(i)-1, int(max_value)-1)
-            max_heapyfy(z, max_value, heapsize)
-
-
-def swap(z: MemoryArray, i: int, j: int):
-    tmp = z[Literal(i)].value
-    z[Literal(i)] = z[Literal(j)]
-    z[Literal(j)].set(tmp)
-
-
-def analyze_complexity_insert(sizes, presorted=False):
-    """
-    Analysiert die Komplexität der Insert-Funktion.
-    """
-    for size in sizes:
-        MemoryManager().purge()  # Speicher zurücksetzen
-        pq = PriorityQueue(Literal(size))
-        insert_list = [random.randint(-100, 100) for _ in range(size)]
-        if presorted:
-            insert_list.sort()
-        for insert_value in insert_list[:-1]:
-            pq.insert(MemoryCell(insert_value))
-        MemoryManager().reset()
-        pq.insert(MemoryCell(insert_list[-1]))
-        MemoryManager.save_stats(size)
-
-    MemoryManager.plot_stats(["cells", "compares", "writes"])
-
-
-def analyze_complexity_pop(sizes, presorted=False):
-    """
-    Analysiert die Komplexität der Pop-Funktion.
-    """
-    for size in sizes:
-        MemoryManager().purge()  # Speicher zurücksetzen
-        pq = PriorityQueue(Literal(size))
-        insert_list = [random.randint(-100, 100) for _ in range(size)]
-        if presorted:
-            insert_list.sort()
-        for insert_value in insert_list:
-            pq.insert(MemoryCell(insert_value))
-        MemoryManager().reset()
-        pq.pop()
-        MemoryManager.save_stats(size)
-
-    MemoryManager.plot_stats(["cells", "compares", "writes"])
-
-
-if __name__ == '__main__':
-    length = Literal(10)
-    pq = PriorityQueue(length)
-    for i in range(10):
-        space_left = int(length) - len(pq)
-        if space_left > 0:
-            insert_count = random.randint(1, space_left)
-            insert_sequence = [random.randint(1, int(length)) for _ in range(insert_count)]
-            print(f"-> {insert_sequence}")
-            for j in insert_sequence:
-                pq.insert(MemoryCell(j))
-            print(f"{pq}")
-        if not pq.is_empty():
-            pop_count = random.randint(1, len(pq))
-            output_sequence = [int(pq.pop()) for _ in range(pop_count)]
-            print(f"<- {output_sequence}")
-            print(f"{pq}")
-
-    sizes = range(10, 501, 5)
-    analyze_complexity_insert(sizes, presorted=True)
-    analyze_complexity_pop(sizes, presorted=True)
-

praktika/03_quick_and_heap/quick_sorting_enhanced.py

@@ -1,93 +0,0 @@
-from utils.memory_array import MemoryArray
-from utils.memory_cell import MemoryCell
-from utils.memory_manager import MemoryManager
-from utils.literal import Literal
-
-def quick_sort_stepwise(z: MemoryArray, l: Literal, r: Literal):
-    if l < r:
-        q = partition(z, l, r)
-        yield z
-        yield from quick_sort_stepwise(z, l, q.pred())
-        yield from quick_sort_stepwise(z, q.succ(), r)
-        yield z
-
-
-def partition(z: MemoryArray, l: Literal, r: Literal):
-    p = mid_index(z, l, r, Literal((int(l)+int(r))//2))
-    swap(z, p, r)
-    with MemoryCell(z[r]) as pivot, MemoryCell(l) as i, MemoryCell(r.pred()) as j:
-        while i < j:
-            while z[i] < pivot:
-                i.set(i.succ())
-            while j > l and z[j] >= pivot:
-                j.set(j.pred())
-            if i < j:
-                swap(z, int(i), int(j))
-                i.set(i.succ())
-                j.set(j.pred())
-        if i == j and z[i] < pivot:
-            i.set(i.succ())
-        if z[i] != pivot:
-            swap(z, int(i), int(r))
-        return Literal(i)
-
-def mid_index(z: MemoryArray, i1, i2, i3):
-    if z[i1] < z[i2] < z[i3]:
-        return i2
-    elif z[i3] < z[i2] < z[i1]:
-        return i2
-    elif z[i2] < z[i1] < z[i3]:
-        return i1
-    elif z[i3] < z[i1] < z[i2]:
-        return i1
-    else:
-        return i3
-
-def quick_sort(z: MemoryArray, l: Literal = None, r: Literal = None):
-    if l is None:
-        l = Literal(0)
-    if r is None:
-        r = z.length().pred()
-    sort_generator = quick_sort_stepwise(z, l, r)
-    while True:
-        try:
-            next(sort_generator)
-        except StopIteration:
-            break
-
-
-def sort_file(filename, sort_func):
-    z = MemoryArray.create_array_from_file(filename)
-    sort_func(z)
-    return z
-
-
-def analyze_complexity(sort_func, sizes, presorted=False):
-    """
-    Analysiert die Komplexität einer Sortierfunktion.
-
-    :param sort_func: Die Funktion, die analysiert wird.
-    :param sizes: Eine Liste von Eingabegrößen für die Analyse.
-    """
-    for size in sizes:
-        MemoryManager.purge()  # Speicher zurücksetzen
-        if presorted:
-            random_array = MemoryArray.create_sorted_array(size)
-        else:
-            random_array = MemoryArray.create_random_array(size, -100, 100)
-        sort_func(random_array)
-        MemoryManager.save_stats(size)
-
-    MemoryManager.plot_stats(["cells", "compares", "writes"])
-
-
-def swap(z: MemoryArray, i: int, j: int):
-    tmp = z[Literal(i)].value
-    z[Literal(i)] = z[Literal(j)]
-    z[Literal(j)].set(tmp)
-
-
-if __name__ == '__main__':
-    sizes = range(10, 101, 5)
-    analyze_complexity(quick_sort, sizes)
-    analyze_complexity(quick_sort, sizes, True)

praktika/04_bin_baum/bin_tree_node.py

@@ -1,15 +0,0 @@
-from utils.memory_cell import MemoryCell
-
-class BinaryTreeNode(MemoryCell):
-
-    def __init__(self, value):
-        super().__init__(value)
-        self.left = None
-        self.right = None
-
-    def __repr__(self):
-        return f"BinaryTreeNode(value={self.value}, left={self.left}, right={self.right})"
-
-    def __str__(self):
-        return str(self.value)
-

utils/memory_array.py

@@ -102,13 +102,6 @@ class MemoryArray:
         a.reset_counters()
         return a
 
-    def __str__(self):
-        result = "[ "
-        for cell in self.cells:
-            result += str(cell) + ", "
-        result += "]"
-        return result
-
 
 if __name__ == "__main__":
     import random

vorlesung/03_fortgeschrittenes_sortieren/quick_sorting.py

@@ -5,6 +5,7 @@ from utils.memory_range import mrange
 from utils.literal import Literal
 
 def quick_sort_stepwise(z: MemoryArray, l: Literal, r: Literal):
+    n = z.length()
     if l < r:
         q = partition(z, l, r)
         yield z
@@ -76,6 +77,6 @@ def swap(z: MemoryArray, i: int, j: int):
 
 
 if __name__ == '__main__':
-    sizes = range(10, 101, 5)
+    sizes = range(10, 101, 10)
-    #analyze_complexity(quick_sort, sizes)
+    analyze_complexity(quick_sort, sizes)
-    analyze_complexity(quick_sort, sizes, True)
+    # analyze_complexity(quick_sort, sizes, True)

vorlesung/04_besondere_sortierverfahren/count_sorting.py

@@ -1,60 +0,0 @@
-from utils.memory_array import MemoryArray
-from utils.memory_cell import MemoryCell
-from utils.memory_manager import MemoryManager
-from utils.memory_range import mrange
-from utils.literal import Literal
-
-
-
-def count_sort(a: MemoryArray, b: MemoryArray, k: int):
-    c = MemoryArray(Literal(k + 1))
-    for i in mrange(Literal(k + 1)):
-        c[i].set(Literal(0))
-
-    for j in mrange(a.length()):
-        c[a[j]].set(c[a[j]].succ())
-
-    for i in mrange(Literal(1), Literal(k + 1)):
-        c[i].set(int(c[i]) + int(c[i.pred()]))
-
-    for j in mrange(a.length().pred(), Literal(-1), Literal(-1)):
-        b[c[a[j]].pred()].set(a[j])
-        c[a[j]].set(c[a[j]].pred())
-
-
-
-def analyze_complexity(sizes, presorted=False):
-    """
-    Analysiert die Komplexität einer Sortierfunktion.
-
-    :param sizes: Eine Liste von Eingabegrößen für die Analyse.
-    """
-    for size in sizes:
-        MemoryManager.purge()  # Speicher zurücksetzen
-        if presorted:
-            random_array = MemoryArray.create_sorted_array(size, 0, 100)
-        else:
-            random_array = MemoryArray.create_random_array(size, 0, 100)
-        dest_array = MemoryArray(Literal(size))
-        count_sort(random_array, dest_array, 100)
-        MemoryManager.save_stats(size)
-
-    MemoryManager.plot_stats(["cells", "compares", "writes"])
-
-
-def swap(z: MemoryArray, i: int, j: int):
-    tmp = z[Literal(i)].value
-    z[Literal(i)] = z[Literal(j)]
-    z[Literal(j)].set(tmp)
-
-
-if __name__ == '__main__':
-
-    # Test the count_sort function
-    a = MemoryArray([2, 5, 3, 0, 2, 3, 0, 3])
-    b = MemoryArray(Literal(len(a)))
-    count_sort(a, b, 5)
-
-    sizes = range(10, 101, 10)
-    analyze_complexity(sizes)
-    # analyze_complexity(sizes, True)

vorlesung/05_binaere_baeume/bin_search.py

@@ -1,61 +0,0 @@
-import random
-
-from utils.memory_array import MemoryArray
-from utils.memory_cell import MemoryCell
-from utils.memory_manager import MemoryManager
-from utils.memory_range import mrange
-from utils.literal import Literal
-
-
-def binary_search(z: MemoryArray, s: MemoryCell, l: Literal = None, r: Literal = None):
-    """
-    Perform a binary search on the sorted array z for the value x.
-    """
-    if l is None:
-        l = Literal(0)
-    if r is None:
-        r = Literal(z.length().pred())
-    if l > r:
-        return None
-    with MemoryCell(l) as m:
-        m += r
-        m //= Literal(2)
-        if s < z[m]:
-            return binary_search(z, s, l, m.pred())
-        elif s > z[m]:
-            return binary_search(z, s, m.succ(), r)
-        else:
-            return m
-
-
-def analyze_complexity(sizes):
-    """
-    Analysiert die Komplexität
-
-    :param sizes: Eine Liste von Eingabegrößen für die Analyse.
-    """
-    for size in sizes:
-        MemoryManager.purge()  # Speicher zurücksetzen
-        random_array = MemoryArray.create_sorted_array(size)
-        search_value = random.randint(-100, 100)
-        binary_search(random_array, MemoryCell(search_value))
-        MemoryManager.save_stats(size)
-
-    MemoryManager.plot_stats(["cells", "compares", "adds"])
-
-
-
-
-if __name__ == "__main__":
-    # Example usage
-    arr = MemoryArray([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    search_value = MemoryCell(8)
-    result = binary_search(arr, search_value)
-    if result is not None:
-        print(f"Value {search_value} found at index {result}.")
-    else:
-        print(f"Value {search_value} not found in the array.")
-
-
-    sizes = range(1, 1001, 2)
-    analyze_complexity(sizes)