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import numpy as np
import matplotlib.pyplot as plt

def basic_linear_reg(x, y):
    # linear reg on 2 axes
    length = len(x)
    sum_x = sum(x)
    sum_y = sum(y)
    sum_xsq = sum(map(lambda a: a * a, x))
    sum_xy = sum(map(lambda a, b: a * b, x, y))

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Shortcut	Meaning
\a	an ASCII bell character (07)
\d	the date in “Weekday Month Date” format (e.g., “Tue May 26”)
\D{format}	the format is passed to strftime(3) and the result is inserted into the prompt string; an empty format results in a locale-specific time representation. The braces are requir

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"""This tutorial introduces Contractive auto-encoders (cA) using Theano.

 They are based on auto-encoders as the ones used in Bengio et
 al. 2007.  An autoencoder takes an input x and first maps it to a
 hidden representation y = f_{\theta}(x) = s(Wx+b), parameterized by
 \theta={W,b}. The resulting latent representation y is then mapped
 back to a "reconstructed" vector z \in [0,1]^d in input space z =
 g_{\theta'}(y) = s(W'y + b').  The weight matrix W' can optionally be
 constrained such that W' = W^T, in which case the autoencoder is said
 to have tied weights. The network is trained such that to minimize

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# Code adapted from TensorFlow source example:
# https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/learn/python/learn/datasets/mnist.py

class DataSet:
    """Base data set class
    """

    def __init__(self, shuffle=True, labeled=True, **data_dict):
        assert '_data' in data_dict
        if labeled:

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# Your init script
#
# Atom will evaluate this file each time a new window is opened. It is run
# after packages are loaded/activated and after the previous editor state
# has been restored.
#
# An example hack to log to the console when each text editor is saved.
#
# atom.workspace.observeTextEditors (editor) ->
#   editor.onDidSave ->

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def jaccard_metric(x, y):
    """
    x: scipy.sparse CSR matrix shape (1, n)
    y: scipy.sparse CSR matrix shape (1, n)
    returns: jaccard similarity
    """
    return x.minimum(y).sum()/x.maximum(y).sum()

def l2_metric(x,y):
    """

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from spinup.utils.run_utils import ExperimentGrid
from spinup import ddpg

import gym
import tensorflow as tf

def run_experiment(args):
    def env_fn():
        import envs  # registers custom envs to gym env registry
        return gym.make(args.env_name)

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// code courtesy of Toby team

chrome.storage.local.get("state", o => (
((f, t) => {
let e = document.createElement("a");
e.setAttribute("href", `data:text/plain;charset=utf-8,${encodeURIComponent(t)}`);
e.setAttribute("download", f);
e.click();
})(`TobyBackup${Date.now()}.json`, o.state)
));