merylldindin

def define_clusters(lst, fil, neighbors):
    
    # lst -> ordered list of indexes
    # fil -> mapped dictionnary of filtration values with indexes
    # neighbors -> number of closest elements to consider per query

    unf = UnionFind()

    for idx in lst:
        grp, srt = [], np.where(lst == idx)[0][0]

merylldindin

from sklearn.metrics import confusion_matrix

def kappa_score(y_true, y_pred):

    cfm = confusion_matrix(y_true, y_pred)
    n_c = len(np.unique(y_true))
    s_0 = np.sum(cfm, axis=0)
    s_1 = np.sum(cfm, axis=1)
    exp = np.outer(s_0, s_1).astype(np.double) / np.sum(s_0) 
    mat = np.ones([n_c, n_c], dtype=np.int)

merylldindin

# Load the dataset
x,y = load_iris(return_X_y=True)
# Split it in training and validation
x_t, x_v, y_t, y_v = train_test_split(x, y, test_size=0.2, shuffle=True, random_state=42)
# Build a representation of the problem
arg = {'threads': cpu_count(), 'weights': False}
prb = Prototype(x_t, x_v, y_t, y_v, 'ETS', 'classification', 'acc', **arg)
# Instantiate and run a one-shot learning based on the given configuration
exp = Experiment()
exp.single(prb, random_state=42)

merylldindin

{
      'computeFFT': True, 'mainFrequency': True,
      'computePeriodogram': True, 'computeSpectrogram': True,
      'frequencyBands': True,
      'coefficientsAR': True,
      'crossingOver': True, 'computePolarity': True,
      'computeWavelet': True,
      'computeChaos': True, 'computeFractals': True,
      'signalDecomposition': True,
      'computeStatistics': True,  

merylldindin

# Initialize San Francisco mapping
map_center_coordinates = (37.7749, -122.4194)
m = folium.Map(location=map_center_coordinates, tiles="Stamen Terrain", zoom_start=12)
# Load the processed graph of San Francisco
trj = Trajectory('../datasets/sanfrancisco.jb')
obj = np.random.choice(list(trj.G.keys()), 6)
# Build five random objectives given one starting point
for i in range(5):
    pth = trj.shortest_path(obj[0], obj[i+1])
    pts = np.asarray([np.asarray(e.split(':')).astype('float')[::-1] for e in pth])

merylldindin

files:
  "/etc/httpd/conf.d/wsgi_custom.conf":
    mode: "000644"
    owner: root
    group: root
    content: |
      WSGIApplicationGroup %{GLOBAL}

  "/etc/httpd/conf.d/proxy-pass.conf":
    mode: "000644"

merylldindin

# Initialize the Flask application
application = Flask(__name__)
app_sockets = Sockets(application)

# Websocket usage example
@app_sockets.route('/stream')
def stream(websocket):
    while not websocket.closed:
        msg = websocket.receive()
        if msg is None: continue

merylldindin

import json, base64, websocket

class Base64Encoder(json.JSONEncoder):
    
    def default(self, o):
        
        if isinstance(o, bytes): return base64.b64encode(o).decode()
        return json.JSONEncoder.default(self, o)
    
class Emitter:

merylldindin

from application import wss

if __name__ == '__main__':

    from gevent import pywsgi
    from geventwebsocket.handler import WebSocketHandler
    # Development server to run locally on port 5000
    app = pywsgi.WSGIServer(('', 5000), wss, handler_class=WebSocketHandler)
    app.serve_forever()

merylldindin

FROM python:3.7-slim

RUN mkdir -p /app/storage
VOLUME /app
WORKDIR /app
COPY ./requirements.txt /app/requirements.txt
RUN pip install gunicorn
RUN pip install -r requirements.txt
COPY . /app