def remove_transfered_files(self):
for remote_filepath in self.transfered:
try:
self.client.remove(remote_filepath)
except Exception:
error("Couldn't remove zip file.")
self.destroy()
def inferior_IQ(data: [float]) -> None:
"""Writes a percentage of people with inferior IQ."""
if 0 < data[2] < 200:
result = 0
for i in range(0, data[2] * 100):
result += 1 / (data[1] * sqrt(2 * pi)) * \
exp(-0.5 * pow(((i / 100) - data[0]) / data[1], 2))
print('{:.01f}% of people have an IQ inferior to {:d}'.format(
result, data[2]))
else:
error('IQ must be integer between 0 and 200.\n')
def class_divider(tmpdata):
class1 = []
class2 = []
for i in tmpdata:
if i[-1] == 1:
class1.append(i[:-1])
elif i[-1] == -1:
class2.append(i[:-1])
else:
print(i)
error('Bad data')
exit(0)
return class1, class2
def between_IQ(data: [float]) -> None:
"""Writes a percentage of people with IQ between IQ1 and IQ2."""
if 0 < data[2] < data[3] < 200:
result = 0
i = data[2]
while (i < data[3]):
result += 1 / (data[1] * sqrt(2 * pi)) * \
exp(-0.5 * pow((i - data[0]) / data[1], 2))
i += 0.01
print('{:.01f}% of people have an IQ between {:d} and {:d}'.format(
result, data[2], data[3]))
else:
error('IQ must be integers between 0 and 200, and IQ1 < IQ2.\n')
def connect(self):
try:
self.client = paramiko.SSHClient()
self.client.load_system_host_keys()
self.client.set_missing_host_key_policy(
paramiko.AutoAddPolicy())
self.client.connect(
self.url,
username=self.username,
password=self.password
)
except:
error("SSH client couldn't connect.")
self.destroy()
def connect(self):
try:
self.connection = paramiko.SSHClient()
self.connection.load_system_host_keys()
self.connection.set_missing_host_key_policy(
paramiko.AutoAddPolicy())
self.connection.connect(
self.url,
username=self.username,
password=self.password
)
self.client = self.connection.open_sftp()
except:
error("SFTP client couldn't connect.")
def _load_keys(self):
"""Loads the api keys into the queue."""
cur_path = os.path.dirname(__file__)
file_name = os.path.join(cur_path, "api_keys.json")
try:
f = open(file_name, 'r')
key_json = json.load(f)
except FileNotFoundError as e:
error(e)
error("Must Create a json file with api keys")
exit(-1)
except json.decoder.JSONDecodeError:
error("Error: Json file is not properly formatted!")
exit(-1)
list(map(self._api_keys.put, key_json["keys"]))
# See COPYING and license/LICENSE.steambridge for license information
import os
import subprocess
from utilities import execute, error, Options
options = Options()
options.load()
config = options.configure()
build = config['steambridge']['build']
prefix = config['steambridge']['prefix']
if not os.path.isfile(build['bridge_lib']):
error("Install", "Winelib bridge library isn't compiled (run make)!")
if not os.path.isfile(build['proxy_dll']):
error("Install", "Win32 proxy library isn't found")
try:
# os.makedirs raise an exception(!) if the directory already exists
# Thanks Python!
execute('mkdir -p {}'.format(prefix['shared']))
execute('mkdir -p {}'.format(prefix['bin']))
execute('mkdir -p {}'.format(prefix['pysteambridge']))
execute('mkdir -p {}'.format(prefix['winedllpath']))
execute('mkdir -p {}'.format(prefix['documentation']))
execute('mkdir -p {}'.format(prefix['licenses']))
# shutils.copy and friends seem to work kinda weird and not Unixy, so just use the system cp
def __download_file(self, url, save_as):
try:
urllib.request.urlretrieve(url, save_as)
except Exception:
error("Couldn't download file.")
return save_as
def __extract_archive(zipfile, output_dir):
# print('Extracting...')
try:
shutil.unpack_archive(zipfile, output_dir)
except Exception:
error("Couldn't extract archive.")
# See COPYING and license/LICENSE.steambridge for license information
import os
import subprocess
from utilities import execute, error, Options
options = Options()
options.load()
config = options.configure()
build = config['steambridge']['build']
prefix = config['steambridge']['prefix']
if not os.path.isfile(build['bridge_lib']):
error("Install", "Winelib bridge library isn't compiled (run make)!")
if not os.path.isfile(build['proxy_dll']):
error("Install", "Win32 proxy library isn't found")
try:
# os.makedirs raise an exception(!) if the directory already exists
# Thanks Python!
execute('mkdir -p {}'.format(prefix['shared']))
execute('mkdir -p {}'.format(prefix['bin']))
execute('mkdir -p {}'.format(prefix['pysteambridge']))
execute('mkdir -p {}'.format(prefix['winedllpath']))
execute('mkdir -p {}'.format(prefix['documentation']))
execute('mkdir -p {}'.format(prefix['licenses']))
# shutils.copy and friends seem to work kinda weird and not Unixy, so just use the system cp
test1 = open('inputs/test01.txt')
test1Data = np.loadtxt(test1, delimiter=',')
test1Data = util.normalizeData(test1Data)
errors = []
NUM_INPUTS = len(test1Data) - offset
for i in range(1750, NUM_INPUTS):
current = test1Data[i]
features = util.createFeatureRow(test1Data, i, offset, current)
td = np.array(features)
predX = neigh.predict(td)
predY = neighY.predict(td)
actual = test1Data[i + offset]
prediction = [predX[0], predY[0]]
actuals.append(actual)
predictions.append(prediction)
errors.append(util.error([actual], [prediction]))
if onlyTrainingAndCV == False:
# util.plotLines(actuals, predictions, 'Actual position', 'Predicted position')
# util.plotGraph(actuals, predictions, 'Actual position', 'Predicted position')
util.plotData(actuals, 'Actual position')
util.plotData(predictions, 'Predicted position')
util.plotLine(errors, 'Error graph')
print len(actuals), len(predictions)
print np.sum(errors)
else:
util.plotLine(cvScoresX, 'CV label x')
util.plotLine(cvScoresY, 'CV label y')
# util.plotLines(cvScoresX, cvScoresY, 'CV label X', 'CV label y')
def __init__(self, github_token):
try:
self.github = Github(github_token)
except:
error("Couldn't authenticate with GitHub.")
#starting the simulation
for _ in range(25):
#generate the pointset
P = generate((2, 14), 10.)
#store the initial guess
PI = P
#update the pointset
P = update(P, 1.)
#compute the EDM
D = EDM(P)
#add the different errors
D0 = add_error(D, error(0., shape))
D1 = add_error(D, error(.5, shape))
D2 = add_error(D, error(1.5, shape))
D3 = add_error(D, error(2., shape))
D4 = add_error(D, error(2.5, shape))
sys.stdout.write("-")
sys.stdout.flush()
#reconstruct the pointsets
W0P0 = rec(PI, D0, W0, 0.0001)
W0P1 = rec(PI, D1, W0, 0.0001)
W0P2 = rec(PI, D2, W0, 0.0001)
W0P3 = rec(PI, D3, W0, 0.0001)
W0P4 = rec(PI, D4, W0, 0.0001)
#define the anchors position, which is fixed and known
A = np.array([[0., 0., 10., 10.], [0., 10., 0., 10.]], dtype=np.float64)
#generate the initial point configuration
TAG = generate(shape, side)
#Initzialize original and reconstructed pointset
OR[:, 0:4] = A
OR[:, 4:] = TAG
PR[:, 0:4] = A
PR[:, 4:] = TAG
#compute the EDM and add error
D = EDM(OR) #
E = error(2., (14, 14))
D = add_error(D, E)
#define the mask. Only distances between sensors are missing
W = np.ones((14, 14), dtype=np.float64)
W[4:, 4:] = np.zeros((10, 10), dtype=np.float64)
my_dpi = 96 #constant for figure size
#init the progress bar
toolbar_width = n_step
sys.stdout.write("[%s]" % (" " * toolbar_width))
sys.stdout.flush()
sys.stdout.write("\b" * (toolbar_width + 1)) # return to start of line, after
#start the simulatin
for i in range(n_step):
f = open('inputs/test0' + str(i) + '.txt');
testdata =np.loadtxt(f, delimiter = ',');
errors=[]
tempdiff=[]
for i in range(len(testdata)-numberOfPredictions,len(testdata)):
temp = util.getClosestRowFromTraining(testdata[i], trainingData)
# prediction
x = (F * x) + u
P = F * P * F.transpose()
# measurement update
Z = matrix([temp])
y = Z.transpose() - (H * x)
S = H * P * H.transpose() + R
K = P * H.transpose() * S.inverse()
x = x + (K * y)
P = (I - (K * H)) * P
actuals.append(testdata[i])
predictions.append([x.value[0],x.value[1]])
errors.append(util.error([testdata[i]], [prediction]))
tempdiff.append(util.error([temp], [prediction]))
#util.plotData(actuals, 'Actual position')
#util.plotData(predictions, 'Predicted position')
util.plotLine(errors, 'Error graph')
#util.plotLine(tempdiff, 'tempdiff graph')
util.plotGraph(actuals, predictions, 'Actual position', 'Predicted position')
