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log_returns = np.log(ticker_df['Open']).diff()[1:]
n = log_returns.shape[0]
test_statistic = n**(1/2) * max(abs(calculate_cusum(log_returns)))
lrv_estimator = estimate_lrv(log_returns)
if test_statistic > quantile * lrv_estimator**(1/2):
print('The null hypothesis can be rejected.')
else:
print('The null hypothesis cannot be rejected.')
@Rubenia-Borge
Rubenia-Borge / astar.py
Created March 25, 2020 23:02 — forked from jamiees2/astar.py
A* Algorithm implementation in python.
# Enter your code here. Read input from STDIN. Print output to STDOUT
class Node:
def __init__(self,value,point):
self.value = value
self.point = point
self.parent = None
self.H = 0
self.G = 0
def move_cost(self,other):
return 0 if self.value == '.' else 1
@Rubenia-Borge
Rubenia-Borge / product_style
Created February 18, 2019 02:54 — forked from atomjar/product_style
Stylesheet for Vue Mastery's Intro to Vue course
body {
font-family: tahoma;
color:#282828;
margin: 0px;
}
.nav-bar {
background: linear-gradient(-90deg, #84CF6A, #16C0B0);
height: 60px;
margin-bottom: 15px;
<LinearLayout
android:orientation="vertical"
android:layout_width="wrap_content"
android:layout_height="wrap_content">
<TextView
android:text="Guest List"
android:layout_width="wrap_content"
android:layout_height="wrap_content" />
@Rubenia-Borge
Rubenia-Borge / sample
Created February 4, 2018 19:30 — forked from anonymous/sample
<Text View
android:text="Hapy Birthday"
android:layout_width="150dp"
android:layout_height="150"
android:background="@android:color/darker_groy"
>
@Rubenia-Borge
Rubenia-Borge / ml-recs.md
Created December 19, 2017 03:13 — forked from bsletten/ml-recs.md
Machine Learning Path Recommendations

This is an incomplete, ever-changing curated list of content to assist people into the worlds of Data Science and Machine Learning. If you have a recommendation for something to add, please let me know. If something isn't here, it doesn't mean I don't recommend it, I just may not have had a chance to review it yet or not.

I will generally list things in order of easier to more formal/challenging content.

It may feel like there is an overwhelming amount of stuff for you to learn (because there is). But, there is a guided path that will get you there in time. You need to focus on Linear Algebra, Calculus, Statistics and probably Python (or R). Your best bet is to get a Safari Books Online account (https://www.safaribooksonline.com) which you may already have access to through school or work. If not, it is a reasonable way to get access to a tremendous number of books and videos.

I'm not saying you will get what you need out of everything here, but I have read/watched at least some of all of the following an