Jan 05

ভলিউম ম্যানেজার ঃ ইনফ্রাস্ট্রাকচার জব এর জন্য

By Sayed in ব্লগ । Blog

আমরা CCNA/CCNP/MCSE সম্পর্কে জানি। হয়ত ভলিউম ম্যানেজার সম্পর্কে জানি না।

Linux Logical Volume Manager (LVM)
https://www.udemy.com/linux-logical-volume-manager-lvm/

Solaris Volume Manager Administration
https://education.oracle.com/solaris-volume-manager-administration/courP_504

Veritas Volume Manager 6.1
http://www.krnetworkcloud.org/vvm.html

Solaris Volume Manager Administration Training & Certification Courses

https://www.koenig-solutions.com/oracle-solaris-volume-manager-administration-training-certification-course.aspx#tab2

Veritas Volume Manager Administration 6.0 for RHEL Training & Certification Courses
https://www.koenig-solutions.com/rhel-veritas-manager-vxvm-admin-6-training-course.aspx#tab2

HP-UX Logical Volume Manager
https://www.qa.com/training-courses/technical-it-training/hp/hp-hardware/hp-ux–hp-integrity/system-administrator/hp-ux-logical-volume-manager

Jan 05

Basic Matrix Operations:

def matrix_multiplication ( m, n ):

# Creates a list containing 5 lists, each of 8 items, all set to 0

o_row, o_col = len(m), len(n[0]);

output_matrix = [[0 for x in range(o_row)] for y in range(o_col)]

one_output_cell = 0

rowCount = 0

colCount = 0

temp = 0

for aRow in m:

colCount = 0

for aCol in range(len(n[0])):

for anItem in aRow:

temp += anItem * n[colCount][rowCount]

colCount += 1

output_matrix[rowCount][colCount] = temp

temp = 0

rowCount += 1

"""

for row in range(o_row):

out_col = 0

for col in range(o_col):

one_output_cell += m[row][col] * n[col][row]

output_matrix[row][out_col] = one_output_cell

one_output_cell = 0

out_col += 1

"""

return(output_matrix)

# 3 * 2

m = [

[1, 2],

[1,2],

[1,2]

]

# 2 * 3

n = [

[1, 2, 3],

[1, 2, 3 ]

]

m[2][1]*n[1][2]

print(n[0][0])

output_matrix = matrix_multiplication (m, n)

print(output_matrix)

Jan 05

Basic Numpy Operations

import numpy as np

a = np.arange(15).reshape(3, 5)

print(a)

print(a.shape)

print(a.ndim)

print(a.dtype.name)

print(a.itemsize)

print(a.size)

print(type(a))

b = np.array([6, 7, 8])

print(b)

type(b)

Jan 05

Implement Gradient Descend:

By Sayed in Math and Statistics for Data Science, and Engineering, ব্লগ । Blog

"

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function usinggradient descent, one takes steps proportional to the negative of the gradient (or approximategradient) of the function at the current point.

Gradient descent – Wikipedia

https://en.wikipedia.org/wiki/Gradient_descent

"

Gradient Descend

# From calculation, it is expected that the local minimum occurs at x=9/4

"""

cur_x = 6 # The algorithm starts at x=6

gamma = 0.01 # step size multiplier

precision = 0.00001

previous_step_size = 1

max_iters = 10000 # maximum number of iterations

iters = 0 #iteration counter

df = lambda x: 4 * x**3 – 9 * x**2

while previous_step_size > precision and iters < max_iters:

prev_x = cur_x

cur_x -= gamma * df(prev_x)

previous_step_size = abs(cur_x – prev_x)

iters+=1

print("The local minimum occurs at", cur_x)

#The output for the above will be: (‘The local minimum occurs at’, 2.2499646074278457)

"""

#—-

print(‘my part’)

co_ef = 6

iter = 0

max_iter = 1000

gamma = 0.001

step = 1

precision = 0.0000001

df = lambda x: 4 * x * x * x – 9 * x * x

while (iter <= max_iter) or (step >= precision ) :

prev_co_ef = co_ef

co_ef -= gamma * df (prev_co_ef)

step = abs (prev_co_ef – co_ef)

print(co_ef)

Sayed Ahmed
sayedum

Linkedin: https://ca.linkedin.com/in/sayedjustetc

Blog: http://sitestree.com, http://bangla.salearningschool.com

Dec 30

International News: Stock Market melty, Part II: Theme of the Week

By Sayed in ব্লগ । Blog

International News: Stock Market melty, Part II: Theme of the Week: Bloomberg
There Is Logic Behind the Stock Market – Shira Ovide

Ugly Markets Might Contaminate the U.S. Economy – Mohamed A. El-Erian

Trump’s ‘Tremendous Buy’ Stock Market Isn’t a Bargain – Stephen Gandel

The Market Swoon Isn’t All About Trump – Justin Fox

Trump’s Favorite Barometer Is Warning Him – Nir Kaissar

Trump Has Likely Done Lasting Damage to the Fed – Tim Duy

Investors Are More Wary of Trump Than of Wall Street – Conor Sen

Leveraged-Loan Lifers Battle the Negative Tide – Brian Chappatta

Dec 23

Market melty: Theme of the Week

By Sayed in ব্লগ । Blog

From Bloomberg Opinion on Stock Market News

Junk Bonds Endure an Awful Day But Live to Tell About It – Brian Chappatta

Mnuchin Forgot to Check His Figures on Stock Volatility – Nir Kaissar

Fed’s No-Win: Balancing Growth and Market Fragility – Mohamed A. El-Erian

A Financial Canary in a Coal Mine – John Authers

The Fed’s Not Ready to Retire Forward Guidance – Daniel Moss

When the Dollar Talks, the Fed Should Listen – Robert Burgess

Is the Market Overreacting or Is the Fed Underreacting? – Karl W. Smith

Is Quantitative Tightening Really So Very Frightening? – John Authers

There’s No Pleasing the Stock Market If You’re the Fed – Robert Burgess

The Fed’s Risky Plan to Boost Unemployment – Narayana Kocherlakota

Stocks Face Obstacle No Matter What the Fed Does – Stephen Gandel

Markets Join Trump in Pleading for the Fed to Stop – Brian Chappatta

The Fed Needs a Little Push From the Data for a Pause – Tim Duy

This is the Theme of the Week edition of Bloomberg Opinion Today, Sunday’s roundup of our biggest commentary topic this week. New subscribers to the newsletter can sign up here.

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Dec 22

Exciting new post today: 12/22/2018

By sayedjustetc in Academic

Vacations

Vacations are the best thing in this life.

Dec 21

Different types of Distance for ML Algorithms

By Sayed in ব্লগ । Blog

Manhattan, Cosine, Ln Form
# http://dataaspirant.com/2015/04/11/five-most-popular-similarity-measures-implementation-in-python/

# Manhattan
def manhattan(x, y):
return sum( abs(a-b) for a, b in zip(x,y))

d = manhattan([5,10], [30,20])
print(d)

#—— Cosine Distance
import math
def square_rooted(x):
return math.sqrt(sum([a * a for a in x]))

def cosine_distance(x, y):
numerator = sum ( a * b for a, b in zip(x, y ) )
denominator = square_rooted(x) * square_rooted(y)
return numerator/denominator
print ( cosine_distance([3, 45, 7, 2], [2, 54, 13, 15]) )

#—————-
#Ln Form Distance

def ln_form_distance(x,y, dim):
return pow(sum([ pow(abs(a-b),dim) for a,b in zip(x,y)]), 1/dim)

print (ln_form_distance([3, 45, 7, 2], [2, 54, 13, 15], 3))

Oct 26