thomasnield

apt-get update -y && apt-get upgrade -y
apt-get install -y nginx
echo "Hello World from " $HOSTNAME  | sudo tee -a /var/www/html/index.html

thomasnield

^+c:: 
Clipboard := random_Chars(10)
return 

^+v:: Send, %Clipboard%

;-------------------------------------------------------------------------------
random_Chars(Count) { ; returns count random characters
;-------------------------------------------------------------------------------

thomasnield

^+c:: 
Clipboard := random_Chars(10)
return 

^+v:: Send, %Clipboard%

;-------------------------------------------------------------------------------
random_Chars(Count) { ; returns count random characters
;-------------------------------------------------------------------------------
    static Char_List := "ABCDEFGHIJKLMNOPQRSTUVW"

thomasnield

from moviepy.editor import *
clip = VideoFileClip("C:/Users/thoma/OneDrive/Workspace/Projects/Yawman/media/prototype_2_demo.mp4") \
    .subclip(8*60+7,8*60+30)\
    .without_audio()

final_clip = concatenate_videoclips([clip])
clip.write_videofile("C:/Users/thoma/OneDrive/Workspace/Projects/Yawman/media/turn.mp4")

thomasnield

from sympy import *

m, b, i, n = symbols('m b i n')
x, y = symbols('x y', cls=Function)

sum_of_squares = Sum((m*x(i) + b - y(i)) ** 2, (i, 0, n))

d_m = diff(sum_of_squares, m) 
d_b = diff(sum_of_squares, b)
print(d_m)

thomasnield

from sympy import *

# Declare weight, bias, layer outputs, and input variables
# W1 and W2 are hidden and output layer weights 
# B1 and B2 are hidden and output layer biases 
# A1 and A2 are activated layer outputs
# Z1 and Z2 are inactivated layer outputs
# X and Y are the training data 

W1, W2, B1, B2, A1, A2, Z1, Z2, X, Y = \

thomasnield

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split

all_data = pd.read_csv("https://tinyurl.com/y2qmhfsr")

# Learning rate controls how slowly we approach a solution
# Make it too small, it will take too long to run.
# Make it too big, it will likely overshoot and miss the solution.
L = 0.05

thomasnield

from sympy import *

a, b, c, d, e, f = symbols('a b c d e f')
x1, x2, x3, x4 = symbols('x:4')
y1, y2, y3, y4 = symbols('y:4')
z1, z2, z3, z4 = symbols('z:4')

"""
input = Matrix([
    [x1, y1],

thomasnield

import pandas as pd
from sympy import *

# Import points from CSV
points = list(pd.read_csv("https://bit.ly/2KF29Bd").itertuples())

m, b, i, n = symbols('m b i n')
x, y = symbols('x y', cls=Function)

sum_of_squares = Sum((m*x(i) + b - y(i)) ** 2, (i, 0, n))

thomasnield

from sympy import *
import pandas as pd

m, b, i, n = symbols('m b i n')
x, y = symbols('x y', cls=Function)

joint_likelihood = Sum(log((1.0 / (1.0 + exp(-(b + m * x(i)))))**y(i) * (1.0 - (1.0 / (1.0 + exp(-(b + m * x(i))))))**(1-y(i))), (i, 0, n))

points = list(pd.read_csv("https://tinyurl.com/y2cocoo7").itertuples())