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Vincent LefoulonVayel

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Last active Jul 27, 2018
View coin_model.py
 import random # A stochastic model with a single input `p` and a single output `side` # `p` is the probability of throwing tail # `side` is either 'head' or 'tail' def coin_model(p, seed=None): # The seed allows us to control randomness and ensure reproducibility random.seed(seed) # Mathematically, `side_output` is a discrete random variable
Last active Aug 10, 2018
View ks_test.py
 import scipy.stats import numpy as np def ks_test(real_series, simulated_series): # `real_series` and `simulated_series` are lists of series, i.e. lists of lists. # All series have the same size but we don't need to have as many real series as # simulated series. real_series, simulated_series = map(np.asarray, (real_series, simulated_series)) n_steps = len(real_series[0])
Last active Aug 15, 2018
ks_test_data
View data.csv
0.0 0.253 0.490 0.695 0.855 0.013 0.133 0.217 0.713 1.04 0.044 0.284 0.587 0.855 0.739 0.238 0.361 0.400 0.598 0.829 -0.098 0.054 0.428 0.791 0.836 -0.042 0.310 0.474 0.633 0.799
Created Aug 21, 2018
View asset.py
 import random import json Y0 = 100 noise = lambda: random.gauss(0, 0.01) def run(steps): y = [Y0] * steps for t in range(1, steps): y[t] = y[t-1] * (1 + noise())
Last active Aug 24, 2018
View ks_test_distribution.py
 import numpy as np import matplotlib.pyplot as plt from scipy.stats import kstwobign d_min, d_max = kstwobign.ppf(0.01), kstwobign.ppf(0.99) d_vals = np.linspace(d_min, d_max, num=100) cdf = kstwobign.cdf(d_vals) fig, ax = plt.subplots() ax.plot(d_vals, cdf, '-')
Created Nov 7, 2019
keybase.md
View gist:d4ce2ce6753e4788c4b0306b350fef60
 ### Keybase proof I hereby claim: * I am vayel on github. * I am vayel (https://keybase.io/vayel) on keybase. * I have a public key ASBu4piKL-qabomWg9YNFVEaJls_4hnebIMSwdm-oaTkAAo To claim this, I am signing this object:
You can’t perform that action at this time.