janniclas.loosen/Softwaretechnik-II
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add llm chapter

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Jan-Niclas Loosen
2025-01-30 11:12:08 +01:00
parent bd30888c3f
commit ce17502029
22 changed files with 443 additions and 26 deletions
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22
boa/analysis/aufgabe_02.py
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@@ -8,11 +8,14 @@ import matplotlib.pyplot as plt
import matplotlib
matplotlib.use('TkAgg')
JAVA_INPUT = './data/try_Java_Jan_2022_last_revision.boa.output.txt'
PYTHON_INPUT = './data/try_Python_Feb_2022_last_revision.boa.output.txt'
# Input Pfade
JAVA_INPUT = './boa-output/try_Java_Jan_2022_last_revision.boa.output.txt'
PYTHON_INPUT = './boa-output/try_Python_Feb_2022_last_revision.boa.output.txt'
SHOW_PLOTS = False
SEED = 3
# Konfiguration von read_sample
SEED = 1
SHOW_PLOTS = True
SAMPLES = 1000
def read_sample(file_path, title='', sample_size=0, show=True):
# filename kann auch ein URL sein: "https://..../example.csv"
@@ -50,12 +53,11 @@ def cohen_d(x, y):
# Stichprobengrößen der Gruppen
size_x, size_y = len(x), len(y)
# Gepoolte Varianz und Standardabweichung
pool_var = ((size_x - 1) * var_x + (size_y - 1) * var_y) / (size_x + size_y - 2)
pool_var = math.sqrt(pool_var)
# Gepoolte Standardabweichung
pool_std = math.sqrt(((size_x - 1) * var_x + (size_y - 1) * var_y) / (size_x + size_y - 2))
# Cohen's d
d_val = mean_diff / pool_var
d_val = mean_diff / pool_std
return d_val
def find_barrier(x_sample_path, y_sample_path, alpha=0.1, lower_limit=2, upper_limit=10000):
@@ -95,12 +97,12 @@ def main():
print('Statistische Berechnungen zu Häufigkeiten (Übung 5)')
print('\nEinlesen der ersten Stichprobe (Python)')
python_sample = read_sample(PYTHON_INPUT, sample_size=1000, show=SHOW_PLOTS, title="Python")
python_sample = read_sample(PYTHON_INPUT, sample_size=SAMPLES, show=SHOW_PLOTS, title="Python")
print('Mean:' + str(python_sample['Ratio'].mean()))
print('Variance:' + str(python_sample['Ratio'].var()))
print('\nEinlesen der zweiten Stichprobe (Java)')
java_sample = read_sample(JAVA_INPUT, sample_size=1000, show=SHOW_PLOTS, title="Java")
java_sample = read_sample(JAVA_INPUT, sample_size=SAMPLES, show=SHOW_PLOTS, title="Java")
print('Mean:' + str(java_sample['Ratio'].mean()))
print('Variance:' + str(java_sample['Ratio'].var()))

0
boa/analysis/data/try_Java_Jan_2022_last_revision.boa.output.txt → boa/analysis/boa-output/try_Java_Jan_2022_last_revision.boa.output.txt
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0
boa/analysis/data/try_Python_Feb_2022_last_revision.boa.output.txt → boa/analysis/boa-output/try_Python_Feb_2022_last_revision.boa.output.txt
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9
boa/analysis/test.py
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@@ -1,9 +0,0 @@
import random
random.seed(1)
print(random.randint(0, 10))
print(random.randint(0, 10))
print(random.randint(0, 10))
print(random.randint(0, 10))
print(random.randint(0, 10))
print(random.randint(0, 10))