Calculating the average of the values ​​of a column in a matrix

I'm trying to have a list of values ​​that corresponds to the average values ​​of each column of a matrix. Here, I show a quick example (my current matrix has a dimension of 1000 * 1000)

If I have a matrix like this:
matrixExample = {{1, 2, 3, 4}, {5, 6, 7, 8}, {9, 9, 9, 9}}
I should get a list like this:
mean values ​​= {5, 5.7, 6.3, 7}.

How can I do that?

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pr.probability – A closed form of average field equations

Suppose that a system at t hour, for example the number of clients one line at a time $ t $ which is noted by $ q (t) $, follows a Markov chain with these dynamics (probabilities)

$$ P (q (t + Delta t) -q (t) = 1) = alpha Delta t $$
$$ P (q (t + Delta t) -q (t) = – 1) = beta hspace {0.1cm} v (q (t)) Delta t $$

for some known constant rates $ alpha $ and $ beta $. or $ v (x) $ is piecewise function

begin {equation}
label {vb}
v (q) = begin {case}
0 & q leq 0 \
frac {q} {q _ {*}} & 0 leq q leq q _ {*} \
1 & q geq q _ {*}
end {cases}
end {equation}

Or $ q _ * $ is a constant.
Using Dyknin's formula, we can see that density, $ E[q(t)]$= the expected number of customers at a time $ t $, can be obtained from this differential equation:
begin {equation}
frac {d} {dt} E[q(t)]= alpha- beta E[v(q(t))]
label {eq: 1}
end {equation}

I was wondering if there was a way to find a closed form above the differential equation, which means finding an expression for $ E[v(q(t))]$ in terms of $ E[q(t)]$.

Some efforts: in the equation above, replace $ E[v(q(t))] = v (E[q(t)]$. Clearly, this is not a good hypothesis because $ v (x) $ is a concave function so according to Jensen's inequality we have
$$ E[v(q(t))] geq v (E[q(t)]) $$

So replace $ E[v(q(t))]$ by $ v (E[q(t)]$ may not give us a good estimate of the exact solution of ODE above. I did simulations for $ E (q (t)) $= the exact solution of the differential equation (not closed) and the solution of the differential equation in closed form where $ E[v(q(t))]$ is replaced by $ v (E[q(t)]$ and this solution is not close to the exact solution.
There are other suggestions like using a quadratic equation in the form of
$$ E[v(q(t))]= a hspace {0.1cm} E[q(t)]^ 2 + b hspace {0.1cm} E[q(t)]+ c $$
then trying to find coefficients $ a $, $ b $ and $ c $but this effort also fails in some cases!
I think any effort should use the nature of the function $ v (x) $ but until now, I do not know how to find it. So please let me know if you have an idea.

How to calculate the average score of the .txt file in Python?

I am struggling with a Python mission. From a txt.file file, the average rating of each mentor must be calculated. The data in the txt.file file looks like this:

first name second name ________ 6.7 3.5 7.7

first name second name ________ 5.1 2.3 8.4

first name second name ________ 7.0 7.0 6.0 9.4

That's the code I did:

import system

def print_grades (input_grades):
grade = input_grades.split ()

grade1 = grade[0]
grade2 = grade[1]
grade3 = grade[2]

average_grade = (grade1 + grade2 + grade3) / 3

def print_mentor (mentor):
mentor_name = mentor.split ("_")

name = mentor name[0]
grades = mentor name [1]

print_grades (input_grades)

& # 39; & # 39; & # 39; Start the program & # 39; & # 39; & # 39;
mentor_grades = open (& # 39; class_grades.txt & # 39;). readlines ()

for mentor in mentor_grades
print_mentor (mentor)

Obviously, this will not give a result like:

The average note for the name secondname is 5.6
The average note for the name secondname is 6.3

Can any one help me with coding so that I can get the output in the format above?

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Dynamic Systems – Calculating the average of higher orders

$ d theta / dt = -1- epsilon z cos theta sin theta $

$ dz / dt = epsilon (r ^ 2-T) $

$ dr / dt = – epsilon rzsin ^ 2 ( theta) $

After averaging, I had:

$ d phi / dt = -1 + O ( epsilon ^ 2) $

$ d zeta / dt = epsilon ( rho ^ 2-T) + O ( epsilon ^ 2) $

$ d rho / dt = – epsilon rho zeta / 2 + O ( epsilon ^ 2) $

Looking for the first order of $ epsilon $,I leave :

$ phi = theta + epsilon f ( theta, z, r) $

$ zeta = z + epsilon g ( theta, z, r) $

$ rho = r + epsilon h ( theta, z, r) $

After differentiating the equations, I $ f = zcos (2 theta) / 4, g = 0, h = rzsine (2 theta) / 4.

Now, I'm looking for the second term,$ O ( epsilon ^ 2) $can I be shown the idea or the steps to follow?

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Name of the plan – NL2GLKVM
Processor cores – 2vCPU (cores)
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IPv4 – 1
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RAM – 1536 MB (DDR3)
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IPv6 – 5
Reduced price – 5.596 Euro / month
TODAY & # 39; HUI

Name of the plan – NL2GWKVM
Processor cores – 2vCPU (cores)
RAM – 2048 MB (DDR3)
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SSD – 40 GB
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A flawless stay

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Where can I find the Google / Facebook average global CPM data?

The reason I asked this question is quite simple:

I found that the CPM of my website had dropped between 25% and 30% of what it was in 2014. It's a very difficult drop and my earnings now reach almost the cost of Hosting my server.

I would like to know if it's a normal situation or just me?

Python – Average Negatives and Positives of a Matrix

Could you please help me orgaanizar this algorithm, I have to find the average of the negative and positive points and the same way the amount of negative and positive points I appreciate your collaboration.

vect = [[float() for i in range(50)] for j in the range (50)]print ("enter the number of rows and columns successively:")
n = float (input ())
m = float (input ())
for i in the range (1, n + 1):
print ("line number", i)
for j in the range (1, m + 1):
vect[i-1][j-1]    = float (raw_input ())

positive = 0
negative = 0
for i in the range (1, n + 1):
for j in the range (1, m + 1):
if vect[i-1][j-1]> 0:
pos = pos + vect[i-1][j-1]
                
                
                
                if not:
neg = neg + vect[i-1][j-1]
        
        
        
        print ("the sum of the positives is", positive)
print ("the sum of negatives is", negative)


for i in the range (len (vector)):
number = input ()
if number> 0:
positive = positive + 1
if not:
if number <0:
negative = negative + 1
print ("number of positives:", positive)
print ("number of negatives:", negatives)

Mining Pools – What is the relationship between the total hash rate (hash power) and the average generation time of blocks?

This graph – hash rate means the total hash rate of all minors?

I think the most important factor for difficulty and block generation time is the hash rate of the largest mining basin not the total hash rate of all minors.

I want to make sure that if there is a mining pool, 30% of the total hash rate is used and this mining pool is the largest pool.

Then, this extraction pool will succeed in finding the target hash value faster than the others (stochastically).

Therefore, I think this mining pool will fix some difficulties in block generation time.

As a result, if the other mining pools do not have a sufficient hash rate, for example 10% of the total hash rate, it will take 3 times longer to find a tragic value.

So I think the most important factor is the hash rate of the largest mining pool

Do I think so?

And that seems to come out of the title, but I have another question.

If fork has arrived, can the mining pool recognize fork has arrived?