def __remove_row_from_file(self, value):
file = read_file()
record = [row for row in file if value in row]
file.remove(record[0])
file = [';'.join(row) for row in file]
rewrite_file(file)
def solve(identifier):
user = myself()
challenge = next((x for x in CHALLENGES if x.identifier == identifier),
None)
if challenge is None:
raise InvalidUsage('challenge not found', status_code=404)
records = user.records.where(Record.challenge == identifier).limit(1)
record = records[0]
record.start()
codechallenge = {
"user": user.json,
"challenge": challenge.json,
}
readme = read_file(challenge.readme)
return render_template("solve.html",
codechallenge=json.dumps(codechallenge,
indent=4,
sort_keys=True),
fullname=user.email,
readme=readme,
challenge=challenge)
def read_order_file(input_file_name):
"""
Reads input order file invalid (InvalidOrder) orders
:param input_file_name: Path to orders input file
:return: 1 - list of valid orders (ValidOrder)
2 - list of invalid orders (InvalidOrder)
3 - read_successful True if reading file is successful, else False
"""
# read & validate orders file (valid_orders and/or invalid_orders may be length = 0)
valid_orders = []
invalid_orders = []
read_successful = True
try:
valid_orders, invalid_orders = model.read_file(input_file_name)
except FileNotFoundError as ex:
msg = 'FileNotFoundError (check file path) occurred while reading {path}. Exception=[{exception}'. \
format(path=input_file_name, exception=ex)
view.display_msg(msg)
read_successful = False
except StopIteration:
msg = 'StopIteration (check file size) occurred while reading {path}.'. \
format(path=input_file_name)
view.display_msg(msg)
read_successful = False
except Exception as ex:
msg = 'Unknown exception {ex} occurred while reading {path}.'. \
format(ex=ex, path=input_file_name)
view.display_msg(msg)
read_successful = False
finally:
return valid_orders, invalid_orders, read_successful
def all(self, sort=None):
file = read_file()
if sort:
model = ['NOME', 'CPF', 'EMAIL', 'TELEFONE']
file = sorted(file, key=(lambda item: item[model.index(sort)]))
return file
def handle_request(request):
if request.GET.button:
if __is_authenticated(request):
return successfully_authenticated(request.GET.user)
write_file(request.GET.user)
attempts = ', '.join(read_file())
return access_denied(attempts)
return login_page()
def handle_request(request):
if request.GET.button:
if __is_authenticated(request):
return successfully_authenticated(request.GET.user)
write_file(request.GET.user)
file_content = read_file()
users = set(file_content)
user_attempts = []
for user in users:
attemps = file_content.count(user)
user_attempts.append((user, attemps))
return access_denied(user_attempts)
return login_page()
def handle_add_contacts_request():
if request.GET.button:
name = request.GET.name
age = request.GET.age
gender = request.GET.gender
if name or age or gender:
if name and age and gender:
write_file(f'{name};{age};{gender}')
else:
return add_contact('Preencha todos os dados')
file = read_file()
return (redirect('/list-contacts')
if file else add_contact('O arquivo está vazio'))
return add_contact()
def handle_add_contacts_request():
if request.GET.button:
name = request.GET.name
id = request.GET.id
email = request.GET.email
phone = request.GET.phone
if name or id or email or phone:
if name and id and email and phone:
write_file(f'{name};{id};{email};{phone}')
else:
return add_contact('Preencha todos os dados')
file = read_file()
return (redirect('/list-contacts') if file
else add_contact('O arquivo está vazio'))
return add_contact()
def test_model_read_file():
#
# test successful read file
#
input_file_name = os.path.join('../input', 'orders.csv')
valid_orders, invalid_orders = model.read_file(input_file_name)
assert len(valid_orders) == 4
assert len(invalid_orders) == 0
#
# test missing file exception is raised
#
input_file_name = os.path.join('../input', 'orders_xxx.csv')
pytest.raises(FileNotFoundError, model.read_file, input_file_name)
#
# test zero length file exception is raised
#
input_file_name = 'file_size_zero.txt'
os.system('touch {0}'.format(input_file_name))
os.truncate(input_file_name, 0)
pytest.raises(StopIteration, model.read_file, input_file_name)
os.system('rm {0}'.format(input_file_name))
def get(self, value):
file = read_file()
file = [row for row in file if value in str(row)]
return file
def handle_list_contacts_request():
return list_contacts(read_file())
# Karl Jiang, karljiang
#
# Generate plots and print text answers for the CITY data
#
import sys
from model import linear_regression, apply_beta, read_file, Model, r_squared_table\
, compute_r_squared, print_model, get_highest_pair, forward_selection_r_squared, \
output_sel, select_best_k, test_foward_select
# useful defined constants for the city data
COMPLAINT_COLS = range(0, 7)
CRIME_TOTAL_COL = 7
file = "city"
variables, data = read_file("data/{}/training.csv".format(file))
if __name__ == "__main__":
#Task 1a
print(file, "Task", "1A")
print(r_squared_table(variables, data, CRIME_TOTAL_COL) )
print() #\n
#Task 1b
print(file, "Task", "1B")
model_1b = Model(variables, data, CRIME_TOTAL_COL, list(COMPLAINT_COLS) )
r2 = compute_r_squared(model_1b.beta, model_1b.pre_col, model_1b.dep_col)
print(print_model (model_1b.pre_var, r2) )
print()#\n
# CS121 Linear regression assignment
#
# Kristen Witte, kwitte
#
# Generate plots and print text answers for the STOCK data
#
import sys
import model
# useful defined constants for the stock data
#STOCKS = range(0, 11)
#DJIA = 11
#### Did not use
col_names, data = model.read_file("data/stock/training.csv")
col_names, test_data = model.read_file("data/stock/testing.csv")
##TASK 1A
print("Task 1A")
all_r2 = model.get_R2(col_names, data, "Single")
names_1A = col_names[:7]
model.make_table(all_r2, names_1A)
##TASK 1B
print("Task 1B")
combined_r2 = model.get_R2(col_names, data, "Multi")
names_1B = ["All"]
model.make_table(combined_r2, names_1B)
# CS121 Linear regression assignment
#
# Manuel Aragones and CNET ID(s) 446154
#
# Generate plots and print text answers for the ELECTION data
#
import sys
import numpy as np
import model
from model import *
# create a list of names and numpy array
col_names, data_train = model.read_file('data/election/training.csv')
col_names_test, data_test = model.read_file('data/election/testing.csv')
print(col_names)
print(col_names[11])
# useful defined constants for the election data
COUNTY_DATA_COLS = range(0, 11)
VOTE_DIFFERENCE_COL = 11
# define the dependant variable
Y_NAME = 'VOTES_DIFF'
# get the index of the variable
y_index = col_names.index(Y_NAME)
# slice the data with a mechanism compatible with linear_regression
# and apply_beta
y = data_train[:,y_index]
# CS121 Linear regression assignment
#
# Manuel Aragones and CNET ID(s) 446154
#
# Generate plots and print text answers for the CITY data
#
import sys
import numpy as np
import model
from model import *
# create a list of names and numpy array
col_names, data_train = model.read_file('data/city/training.csv')
col_names_test, data_test = model.read_file('data/city/testing.csv')
# define the dependant variable
Y_NAME = 'CRIME_TOTALS'
# get the index of the variable
y_index = col_names.index(Y_NAME)
# slice the data with a mechanism compatible with linear_regression
# and apply_beta
y = data_train[:,y_index]
y_test = data_test[:,y_index]
# define the independant variables. This method is better than using
# numbers as you see the name of the variables
X_NAMES = ['GRAFFITI',
'POT_HOLES',
'RODENTS',
'GARBAGE',
def all(self, filter=None):
return read_file()
from __future__ import print_function
import numpy as np
import tensorflow as tf
import time
import model
# Parameter definitions
batch_size = 200
learning_rate = 0.05
max_steps = 1000
# Prepare data
X_train, Y_train = model.read_file("../data/downgesture_train.list")
X_test, Y_test = model.read_file("../data/downgesture_test.list")
#print(Y_train)
# input placeholders
images_placeholder = tf.placeholder(tf.float32, shape=[None, 960])
labels_placeholder = tf.placeholder(tf.int64, shape=[None],name="label")
# variables (these are the values we want to optimize)
w1 = tf.Variable(tf.random_normal([960, 100]) * 0.01)
b1 = tf.Variable(tf.random_normal([100]) * 0.01)
y1 = tf.matmul(images_placeholder, w1) + b1
w2 = tf.Variable(tf.random_normal([100,2]) * 0.01)
b2 = tf.Variable(tf.random_normal([2]) * 0.01)
##################
# Tests for PA6
#
#
import numpy as np
import model
#data = np.array([[0, 0 , 0, 6], [0, 1, 0, 0], [0, 1, 1, 96]])
#col_names = ["G", "R", "T", "CR"]
col_names, data_training = model.read_file("data/city/training.csv")
col_names, data_testing = model.read_file("data/city/testing.csv")
#all_r2 = model.get_R2(col_names, data, "Single")
#combined_r2 = model.get_R2(col_names, data, "Multi")
#bivar_r2 = model.get_R2(col_names, data, "BiVar")
#print(bivar_r2)
def get_R2(col_names, data_training, data_testing, num_vars):
total_cols = len(col_names)
y_testing = data_testing[:, (total_cols - 1)]
y_training = data_training[:, (total_cols - 1)]
all_training = data_training[: , 0:total_cols - 1]
all_testing = data_testing[: , 0:total_cols - 1]
y_mean = (y_testing.sum()/len(y_testing))