From Bloomberg: China Inc. Is Battling a Crisis of Confidence – Anjani Trivedi Home Truths Are Holding Back China’s Consumers – Nisha Gopalan The U.S.-China Cold War Will Worsen Before It Improves – Tyler Cowen How the U.S. Could Lose a Tech Cold War – Andrew Browne China Wants to Dominate the Internet – Emily de La Bruyere and Nathan Picarsic China’s Plan to End the U.S. Trade Surplus Is a Red Herring – Noah Smith China’s Offer Isn’t Just Bad, It May Be Illegal – James Bacchus Take China’s Grain-Buying With a Pinch of Wheat – David Fickling China’s Slowdown Forces the Rest of Asia to Rethink – Dan Moss Europe Must Face Its China Problem Too – Leonid Bershidsky Why Big Brother Doesn’t Really Bother Most Chinese – Adam Minter Beware a China Junk-Bond Rally Beijing Hasn’t Blessed – Shuli Ren

To Chrome

"
import webbrowser
webbrowser.register(‘chrome’, None, webbrowser.GenericBrowser(‘C:\Program Files (x86)\Google\Chrome\Application\chrome.exe’))
c.NotebookApp.browser = ‘chrome’

"

Ref: https://support.anaconda.com/customer/en/portal/articles/2925919-change-default-browser-in-jupyter-notebook

মেশিন লারনিং ঃ Implement: Multivariate Regression: Python

শত ভাগ সঠিক নাউ হতে পারে।

Theory reference: https://www.cmpe.boun.edu.tr/~ethem/i2ml/slides/v1-1/i2ml-chap5-v1-1.pdf . This is an approximate solution to start with. Understand the theory and then adjust/fix/improve

import numpy as np

import random

print(‘Iterations: rows: Please enter the number of samples for each variable/dimension’)

n_number_of_samples_rows = int(input())

print(‘Columns: Dimensions: Please enter  number of variables’)

m_number_of_variables_cols = int(input())

# initialize the input data variables

print(m_number_of_variables_cols, n_number_of_samples_rows)

X = np.zeros((n_number_of_samples_rows, m_number_of_variables_cols))  

#Y_actually_R = np.zeros((n_number_of_samples_rows, m_number_of_variables_cols))

Y_actually_R = np.zeros((n_number_of_samples_rows * 1)) #m_number_of_variables_cols

#generate random data

for n in range (n_number_of_samples_rows):

   for m in range(m_number_of_variables_cols):

       X[n,m] = random.random()

   Y_actually_R[n] = random.random()

print(“X”)        

print(X)

print(“Y in row format”)

print(Y_actually_R)

print(“Y in column/vector format”)

Y_actually_R = np.transpose(Y_actually_R) #transpose

print(Y_actually_R)

#convert to matrix

X = np.matrix(X)

Y_actually_R = np.matrix(Y_actually_R)

print(‘————-matrix-print—X and then Y’)

print(X)

print(‘Y matrix’)

print(Y_actually_R)

#THE EQUATION: steps to calculate W matrix:  w = (((X.Transpose) * X).invert) * X.Transpose * r

#transpose X

X_transpose = np.transpose(X)

print(‘X_transpose’)

print(X_transpose)

#(X.Transpose) * X)   of [w = (((X.Transpose) * X).invert) * X.Transpose * r]

w_parameters =  np.dot(X_transpose, X) #does np.multiply work? probably need shapoing/reshaping

print(‘first dot’)

print(w_parameters)

#(X.Transpose) * X).invert

w_parameters = np.linalg.inv(w_parameters)

print(‘inverted’)

print(w_parameters)

#(((X.Transpose) * X).invert) * X.Transpose

w_parameters = np.dot(w_parameters, X_transpose)

print(‘2nd dot’)

print(w_parameters)

#(((X.Transpose) * X).invert) * X.Transpose * r

#Y_actually_R = np.transpose(Y_actually_R)

w_parameters = np.dot(w_parameters, np.transpose(Y_actually_R)) #np.dot( np.transpose(w_parameters), np.transpose(Y_actually_R))

#two times transpose – redundant. actually, we could avoid both transpose of Y upto this point  

print(‘w_matrix’)

print(w_parameters)

w_matrix = w_parameters  

#sum of ( rt – w0 – w1x1 – w2x2 ….. wd * xd )   rt = Y_Actually_R[t 1….N][variable_1….d]

#d eqv to m_number_of_variables_cols — i used m for that

#E(w 0 ,w 1 ,…,w d |X )

#Should it be a matrix or just one total sum? I assume one total sum

error_matrix = np.zeros(m_number_of_variables_cols)

error_sum = 0

#calculate error

Y_actually_R = np.transpose(Y_actually_R) #np.array(Y_actually_R)

for n in range(n_number_of_samples_rows):

   sum = 0

   for m in range(m_number_of_variables_cols):

       sum = Y_actually_R[m]

       #2nd part ie  w1x1 w2x2 wdxd of the equation: sum of ( rt – w0 – w1x1 – w2x2 ….. wd * xd )

       wpart = w_matrix[0]        

       for ii in range(1,m_number_of_variables_cols): # d = m_number_of_variables_cols, sum of w1x1, w2x2 to wdxd  

           wpart += w_matrix[ii] * X[n,m] #+ w_matrix[2][m] * X[n][m]

       #sum = sum – wpart

       sum = pow( (sum – wpart), 2)

   error_matrix[m] = 0.5 * pow (  (sum – wpart), 2)

   error_sum += sum #error_matrix[m] #pow (  (sum – wpart), 2)

error_sum = 0.5 * error_sum      

print(‘error matrix if supposed to be = number of variables’)  

print(error_matrix)        

print(‘Error if supposed to be one number i.e. sum of all errors’)

print(error_sum)

Course on Cluster Manager: Cluster Server Manager: Veritas : Solaris, and Similar
CCNA/CCNP/RHCE/MCSE are popular topics. However, for infrastructure jobs, cluster manager skills for sure will help.

List of cluster management software
https://en.wikipedia.org/wiki/List_of_cluster_management_software

Veritas Cluster Server 6.0 for Windows: Administration
https://www.globalknowledge.com/en-AE/Courses/Veritas/Storage/HA0435

Symantec Cluster Server
https://www.symantec.com/en/ca/products-solutions/training/product-training/detail.jsp?pkid=cluster_server

VERITAS CLUSTER SERVER 6.0 Administration Training & Certification Courses
https://www.koenig-solutions.com/veritas-cluster-server-6-administration-training-course.aspx

VERITAS CLUSTER SERVER 6.0/6.1
https://www.radicaltechnologies.co.in/high-availability/veritas-cluster-server-5-1-training-in-pune/

Veritas Cluster Server 6.x for Unix: Advanced Administration
https://www.learnquest.com/course-detail-v3.aspx?cnum=ha0414-e1xc

Veritas Cluster Manager
http://www.forscheredu.com/veritas-cluster-manager/

Microsoft Cluster Service Alternatives
https://www.itprotoday.com/compute-engines/microsoft-cluster-service-alternatives

Cluster Management Topics
http://haifux.org/lectures/168/linux-ha-clusters.html

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

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)

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)

#