IlievskiV

import tensorflow as tf

def make_dataset():
	sentences = ['The cat sat on the mat', 'The quick brown fox jumped over the lazy dog']
	dataset = tf.data.Dataset.from_tensor_slices(sentences) #
	dataset = dataset.shuffle(buffer_size=1000)
	dataset = dataset.map(lambda sentence: text_to_sequence(sentence), num_parallel_calls=4)
	dataset = dataset.batch(batch_size=32)
	dataset = dataset.prefetch(1)

IlievskiV

import tensorflow as tf

class Feeder:
	def __init__(self):
		self.sess = tf.Session() # session to run operations
		self.coordinator = tf.train.Coordinator() # create coordinator for threads

		self.placeholder = tf.placeholder(tf.int32, shape=(None, None), name='sentence')
		self.queue = tf.FIFOQueue(5, tf.int32, name='input_queue') # hold 5 elements
		self.enqueue_op = queue.enqueue(placeholder) # push op

IlievskiV

license: gpl-3.0
height: 800
border: yes

IlievskiV

IlievskiV

import numpy as np
np.random.seed(1234)

def random_walk(N):
    """
    Simulates a discrete random walk
    :param int N : the number of steps to take
    """
    # event space: set of possible increments
    increments = np.array([1, -1])

IlievskiV

import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure(figsize=(21, 10))
ax = plt.axes(xlim=(0, N), ylim=(np.min(X) - 0.5, np.max(X) + 0.5)) 
line, = ax.plot([], [], lw=2, color='#0492C2')
ax.set_xticks(np.arange(0, N+1, 50))
ax.set_yticks(np.arange(np.min(X) - 0.5, np.max(X) + 0.5, 0.2))
ax.set_title('2D Random Walk', fontsize=22)
ax.set_xlabel('Steps', fontsize=18)

IlievskiV

num_simulations = 10000 # num of simulation
N = 100000 # number od steps in each simulation
dt = 1./N # the time step
X_norm = [0] * num_simulations # the normalized random variable

# run the simulations
for i in range(num_simulations):
    X, _ = random_walk(N)
    X_norm[i] = X[N - 1] * np.sqrt(dt)
    

IlievskiV

import numpy as np
np.random.seed(1234)

def brownian_motion(N, T, h):
    """
    Simulates a Brownian motion
    :param int N : the number of discrete steps
    :param int T: the number of continuous time steps
    :param float h: the variance of the increments
    """   

IlievskiV

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

fig = plt.figure(figsize=(21, 10))
ax = plt.axes(xlim=(0, 1)) 
line, = ax.step([], [], where='mid', color='#0492C2')
    
# formatting options
ax.set_xticks(np.linspace(0,1,11))
ax.set_xlabel('Time', fontsize=30)

IlievskiV

from scipy import stats

num_simulations = 10000  # how many times to repeat
Ns, Ts, hs = 20000, 10.0, 1.0 # discrete steps, continuous steps, veriance
dts = 1.0 * T/N  # total number of time steps

u = 2.  # the difference in time points
t = int(np.floor((np.random.uniform(low=u+0.01, high=1. * T - u)/T) * N)) # random starting point

# initialize the means