def main():
# read the file with elements, strip unnecessary pieces of strings, close the file
file = open("elements.txt", "r")
element_list = file.readlines()
for index in range(len(element_list)):
element_list[index] = element_list[index].strip("\n").lower()
file.close()
print("Greetings! It is time to check your knowledge of ATOMIC ELEMENTS.")
print(
"Enter any 5 of the first 20 atomic elements from Period Table and I will give you a grade."
)
user_input = input("Are you ready (y/n)?\n")
if user_input.lower() == "y" or user_input.lower() == "yes":
# get user input collected in a list
answer_list = get_names()
# distribute correct and incorrect answers in 2 separate lists
correct_answers = []
incorrect_answers = []
for answer in answer_list:
if answer in element_list:
correct_answers.append(answer)
else:
incorrect_answers.append(answer)
# calculate points
points = calculate_score(correct_answers)
# print results
print_results(points, correct_answers, incorrect_answers)
else:
print("Sorry to see you go :(")
#print the instructions for a user
print("Greetings! It is time to check your knowledge about ATOMIC ELEMENTS.")
print(
"Enter any 5 of the first 20 atomic elements from Period Table and I will give you a grade."
)
user_input = input("Are you ready (y/n)?\n")
print("******************************************************************")
if user_input.lower() == "y" or user_input.lower() == "yes":
# get user input collected in a list
answer_list = get_names()
# distribute correct and incorrect answers in 2 separate lists
correct_answers = []
incorrect_answers = []
for answer in answer_list:
if answer in element_list:
correct_answers.append(answer)
else:
incorrect_answers.append(answer)
# calculate points
points = calculate_score(correct_answers)
# print results
print_results(points, correct_answers, incorrect_answers)
else:
print("Sorry to see you go :(")
rmse_test = []
r2_train = []
r2_test = []
for i in range(20):
# split the data into training and test set
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.3,
random_state=None)
w = f.ridge_regression(X_train, y_train,
0.001) # compute the best parameter w
pred_train = f.predictions(X_train,
w) # compute predictions on training set
pred_test = f.predictions(X_test, w) # compute predictions on test set
# results
r2_train.append(f.r2_score(
y_train, pred_train)) # compute r2 score for training predictions
rmse_train.append(f.root_mse(y_train,
pred_train)) # compute training error
r2_test.append(f.r2_score(
y_test, pred_test)) # compute r2 score for test predictions
rmse_test.append(f.root_mse(y_test, pred_test)) # compute test error
# print results
f.print_results(r2_train, r2_test, rmse_train, rmse_test)
plt.show()
print("Scrapper lancé...")
arr_quotes = [] # tableau des citations + auteurs + tags
URL = "https://quotes.toscrape.com"
go_scrapping = True
url = URL
# parcours les pages tant qu'il y a des pages suivantes
while go_scrapping:
r = requests.get(url)
soup = BeautifulSoup(r.content, "html.parser")
arr_quotes = get_quotes(arr_quotes, soup, url)
page = soup.find("li", attrs={"class": "next"})
go_scrapping = page is not None
if go_scrapping:
link = page.find("a")
url = URL + link.get('href')
print_quotes(arr_quotes)
print_authors(arr_quotes)
print_tags(arr_quotes)
print_results(arr_quotes)
# affichage des résultats
for q in arr_quotes:
print(q.content, q.author, q.tags, "\n")
from functions import is_valid, calculate_mark_needed, print_results
# User Inputs
current_mark = raw_input("Enter current mark (%): ")
while is_valid(current_mark) == False:
current_mark = raw_input("Number not valid.\nEnter current mark (%):")
desired_mark = raw_input("Enter desired mark (%):")
while is_valid(desired_mark) == False:
desired_mark = raw_input("Number not valid.\nEnter desired mark (%):")
exam_weight = raw_input("Enter exam weight (%):")
while is_valid(exam_weight) == False or exam_weight == '0':
exam_weight = raw_input("Number not valid.\nEnter exam weight (%):")
# Convert values to floats now that they have been validated
current_mark = float(current_mark)
desired_mark = float(desired_mark)
exam_weight = float(exam_weight)
# Calculate the mark needed
mark_needed = calculate_mark_needed(current_mark, desired_mark,
exam_weight / 100)
# Print results
print_results(mark_needed, desired_mark)
from resolver import find_solution
# MAIN
print("Bienvenue dans ExSy !")
filetext = sys.argv[1:]
if not filetext or len(filetext) > 1:
print("Erreur Merci de ne passer qu'un seul parametre a ExSy")
sys.exit(0)
try:
f = open(filetext[0], 'r')
except IOError:
print ("Le fichier {0} n'existe pas".format(filetext[0]))
sys.exit(0)
print("J'analyse votre fichier !")
with open(filetext[0], 'r') as file:
line = file.readline()
while line:
if not read_a_line(line):
success = 0
sys.exit(0)
line = file.readline()
print_results()
print("\n")
find_solution(letter_value, facts_list, conditions_list)
print_results_facts()
# resolve