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def scatter_hist(x, y, xlabel="", ylabel="", title="", left=0.1, width=0.65,
                 bottom=0.1, height=0.65, figsize=(8,8,),
                 ortho=False,
                 xbinwidth=None,
                 ybinwidth=None,
                 binwidth=5):
    # scatter hist from [1] with small tweaks to work with different
    # distributions on X and Y:
    # [1]: https://matplotlib.org/examples/pylab_examples/scatter_hist.html 
    

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#!/usr/bin/env python3
import torch

def model_equals(model1, model2):
    for p1, p2 in zip(model1.values(), model2.values()):
        if p1.data.ne(p2.data).sum() > 0:
            return False
    return True

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#!/usr/bin/env python
"""
https://twitter.com/ThomasCabaret84/status/1103324141493600256i
"""
import torch

def run(b, n=100, in_a_row=3):
    """
        Expérience du lancer de pièce.
        Une pièce parfaitement équilibrée est lancée `n` fois.

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#!/bin/bash

set -e

source activate tensorflow

# See what problems, models, and hyperparameter sets are available.
# You can easily swap between them (and add new ones).
#t2t-trainer --registry_help

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#!/bin/sh

# stop execution on error
set -e

# installation of Oracle Java JDK.
sudo apt-get -y update
sudo apt-get -y install python-software-properties
sudo add-apt-repository -y ppa:webupd8team/java
sudo apt-get -y update