petrosDemetrakopoulos

def create_model():
    model = Sequential()
    model.add(ConvLSTM2D(filters=64, kernel_size=(7, 7),
                    input_shape=(18,344,315,1),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(BatchNormalization())
    model.add(ConvLSTM2D(filters=64, kernel_size=(5, 5),
                    padding='same',activation=LeakyReLU(alpha=0.01), return_sequences=True))
    model.add(BatchNormalization())
    model.add(ConvLSTM2D(filters=64, kernel_size=(3, 3),

petrosDemetrakopoulos

def create_dataset_from_raw(directory_path, resize_to):
    resize_width = resize_to[0]
    resize_height = resize_to[1]
    batch_names = [directory_path + name for name in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, name))]
    dataset = np.zeros(shape=(len(batch_names),36,resize_height,resize_width)) # (samples, filters, rows = height, cols = width)

    for batch_idx,batch in enumerate(batch_names):
        files = [x for x in os.listdir(batch) if x != '.DS_Store']
        files.sort()
        crn_batch = np.zeros(shape=(36, resize_height, resize_width)) 

petrosDemetrakopoulos

from dataclasses import dataclass
from typing import List
import datetime

@dataclass
class Order:
    date: datetime.datetime
    price: float = 0.0
 
@dataclass

petrosDemetrakopoulos

from dataclasses import dataclass

@dataclass
class User:
    email: str
    name: str = "First name"
    surname: str = "Last name"
    
    def fullName(self):
        return self.name + " " + self.surname

petrosDemetrakopoulos

from dataclasses import dataclass

@dataclass
class User:
    email: str
    name: str = "First name"
    surname: str = "Last name"
    

petrosDemetrakopoulos

from dataclasses import dataclass

@dataclass
class User:
    name: str
    surname: str
    email: str

user_instance = User('Petros', 'Demetrakopoulos', '[email protected]')
print(user_instance)

petrosDemetrakopoulos

print(monte_carlo(['Ah','Ad'], ['Ac','2d','9s']))

petrosDemetrakopoulos

def monte_carlo(hand, table, players=2, samples=10000):
    dist = [0,0,0]

    for i in range(samples):
        outcome = simulate(hand, table, players)
        dist[outcome] += 1
    return list(map(lambda x: x/samples, dist))

petrosDemetrakopoulos

def simulate(hand, table, players):
    hands = []
    deck = random.sample(cards,len(cards)) #shuffle the deck
    hand = hand[:]
    table = table[:]

    full = table + hand
    deck = list(filter(lambda x: x not in full, deck))

    #deal cards to players

petrosDemetrakopoulos

from phevaluator import evaluate_cards
import random

suits = ['d','s','c','h']
ranks = ['A','2','3','4','5','6','7','8','9','T','J','Q','K']
cards = []
for r in ranks:
    for s in suits:
        cards.append(r+s)