for example, I have 1 computer with a discount, the price of this computer is $ 450 with a 10% discount, I want to know the real price of it,
I want to learn this both over 10% and as 10% money.
Computer 10% off Price = 450$
Computer $10 off Price = 490$
$net_total = 450;
$discount_value = 10; < percent or amount
$gross_price = ?;
Well, let's solve the equations:
Computer 10% off Price = 450$
Computer $10 off Price = 490$
Which can be written as (let x be the initial price of the computer)
x - x * 10 / 100 = 450 # initial price - x without 10 % from x - x * 10% / 100%
x - 10 = 490 # just 10$ off
Or
0.9 * x = 450
x = 500
Finally
x = 450 / 0.9 = 500
x = 500
So from both equations we have that the initial computer's price is 500$
Edit: in general case,
if $discount_value stands for per cent (i.e. $discount_value = 10 means 10% discount) then
$gross_price = $net_total * 100.0 / (100.0 - $discount_value)
if $discount_value stands for money (i.e. $discount_value = 10 means 10$ discount), then
$gross_price = $net_total + $discount_value
The renewal function for Weibull distribution m(t) with t = 10 is given as below.
I want to find the value of m(t). I wrote the following r code to compute m(t)
last_term = NULL
gamma_k = NULL
n = 50
for(k in 1:n){
gamma_k[k] = gamma(2*k + 1)/factorial(k)
}
for(j in 1: (n-1)){
prev = gamma_k[n-j]
last_term[j] = gamma(2*j + 1)/factorial(j)*prev
}
final_term = NULL
find_value = function(n){
for(i in 2:n){
final_term[i] = gamma_k[i] - sum(last_term[1:(i-1)])
}
return(final_term)
}
all_k = find_value(n)
af_sum = NULL
m_t = function(t){
for(k in 1:n){
af_sum[k] = (-1)^(k-1) * all_k[k] * t^(2*k)/gamma(2*k + 1)
}
return(sum(na.omit(af_sum)))
}
m_t(20)
The output is m(t) = 2.670408e+93. Does my iteratvie procedure correct? Thanks.
I don't think it will work. First, lets move Γ(2k+1) from denominator of m(t) into Ak. Thus, Ak will behave roughly as 1/k!.
In the nominator of the m(t) terms there is t2k, so roughly speaking you're computing sum with terms
100k/k!
From Stirling formula
k! ~ kk, making terms
(100/k)k
so yes, they will start to decrease and converge to something but after 100th term
Anyway, here is the code, you could try to improve it, but it breaks at k~70
N <- 20
A <- rep(0, N)
# compute A_k/gamma(2k+1) terms
ps <- 0.0 # previous sum
A[1] = 1.0
for(k in 2:N) {
ps <- ps + A[k-1]*gamma(2*(k-1) + 1)/factorial(k-1)
A[k] <- 1.0/factorial(k) - ps/gamma(2*k+1)
}
print(A)
t <- 10.0
t2 <- t*t
r <- 0.0
for(k in 1:N){
r <- r + (-t2)^k*A[k]
}
print(-r)
UPDATE
Ok, I calculated Ak as in your question, got the same answer. I want to estimate terms Ak/Γ(2k+1) from m(t), I believe it will be pretty much dominated by 1/k! term. To do that I made another array k!*Ak/Γ(2k+1), and it should be close to one.
Code
N <- 20
A <- rep(0.0, N)
psum <- function( pA, k ) {
ps <- 0.0
if (k >= 2) {
jmax <- k - 1
for(j in 1:jmax) {
ps <- ps + (gamma(2*j+1)/factorial(j))*pA[k-j]
}
}
ps
}
# compute A_k/gamma(2k+1) terms
A[1] = gamma(3)
for(k in 2:N) {
A[k] <- gamma(2*k+1)/factorial(k) - psum(A, k)
}
print(A)
B <- rep(0.0, N)
for(k in 1:N) {
B[k] <- (A[k]/gamma(2*k+1))*factorial(k)
}
print(B)
shows that
I got the same Ak values as you did.
Bk is indeed very close to 1
It means that term Ak/Γ(2k+1) could be replaced by 1/k! to get quick estimate of what we might get (with replacement)
m(t) ~= - Sum(k=1, k=Infinity) (-1)k (t2)k / k! = 1 - Sum(k=0, k=Infinity) (-t2)k / k!
This is actually well-known sum and it is equal to exp() with negative argument (well, you have to add term for k=0)
m(t) ~= 1 - exp(-t2)
Conclusions
Approximate value is positive. Probably will stay positive after all, Ak/Γ(2k+1) is a bit different from 1/k!.
We're talking about 1 - exp(-100), which is 1-3.72*10-44! And we're trying to compute it precisely summing and subtracting values on the order of 10100 or even higher. Even with MPFR I don't think this is possible.
Another approach is needed
OK, so I ended up going down a pretty different road on this. I have implemented a simple discretization of the integral equation which defines the renewal function:
m(t) = F(t) + integrate (m(t - s)*f(s), s, 0, t)
The integral is approximated with the rectangle rule. Approximating the integral for different values of t gives a system of linear equations. I wrote a function to generate the equations and extract a matrix of coefficients from it. After looking at some examples, I guessed a rule to define the coefficients directly and used that to generate solutions for some examples. In particular I tried shape = 2, t = 10, as in OP's example, with step = 0.1 (so 101 equations).
I found that the result agrees pretty well with an approximate result which I found in a paper (Baxter et al., cited in the code). Since the renewal function is the expected number of events, for large t it is approximately equal to t/mu where mu is the mean time between events; this is a handy way to know if we're anywhere in the neighborhood.
I was working with Maxima (http://maxima.sourceforge.net), which is not efficient for numerical stuff, but which makes it very easy to experiment with different aspects. At this point it would be straightforward to port the final, numerical stuff to another language such as Python.
Thanks to OP for suggesting the problem, and S. Pappadeux for insightful discussions. Here is the plot I got comparing the discretized approximation (red) with the approximation for large t (blue). Trying some examples with different step sizes, I saw that the values tend to increase a little as step size gets smaller, so I think the red line is probably a little low, and the blue line might be more nearly correct.
Here is my Maxima code:
/* discretize weibull renewal function and formulate system of linear equations
* copyright 2020 by Robert Dodier
* I release this work under terms of the GNU General Public License
*
* This is a program for Maxima, a computer algebra system.
* http://maxima.sourceforge.net/
*/
"Definition of the renewal function m(t):" $
renewal_eq: m(t) = F(t) + 'integrate (m(t - s)*f(s), s, 0, t);
"Approximate integral equation with rectangle rule:" $
discretize_renewal (delta_t, k) :=
if equal(k, 0)
then m(0) = F(0)
else m(k*delta_t) = F(k*delta_t)
+ m(k*delta_t)*f(0)*(delta_t / 2)
+ sum (m((k - j)*delta_t)*f(j*delta_t)*delta_t, j, 1, k - 1)
+ m(0)*f(k*delta_t)*(delta_t / 2);
make_eqs (n, delta_t) :=
makelist (discretize_renewal (delta_t, k), k, 0, n);
make_vars (n, delta_t) :=
makelist (m(k*delta_t), k, 0, n);
"Discretized integral equation and variables for n = 4, delta_t = 1/2:" $
make_eqs (4, 1/2);
make_vars (4, 1/2);
make_eqs_vars (n, delta_t) :=
[make_eqs (n, delta_t), make_vars (n, delta_t)];
load (distrib);
subst_pdf_cdf (shape, scale, e) :=
subst ([f = lambda ([x], pdf_weibull (x, shape, scale)), F = lambda ([x], cdf_weibull (x, shape, scale))], e);
matrix_from (eqs, vars) :=
(augcoefmatrix (eqs, vars),
[submatrix (%%, length(%%) + 1), - col (%%, length(%%) + 1)]);
"Subsitute Weibull pdf and cdf for shape = 2 into discretized equation:" $
apply (matrix_from, make_eqs_vars (4, 1/2));
subst_pdf_cdf (2, 1, %);
"Just the right-hand side matrix:" $
rhs_matrix_from (eqs, vars) :=
(map (rhs, eqs),
augcoefmatrix (%%, vars),
[submatrix (%%, length(%%) + 1), col (%%, length(%%) + 1)]);
"Generate the right-hand side matrix, instead of extracting it from equations:" $
generate_rhs_matrix (n, delta_t) :=
[delta_t * genmatrix (lambda ([i, j], if i = 1 and j = 1 then 0
elseif j > i then 0
elseif j = i then f(0)/2
elseif j = 1 then f(delta_t*(i - 1))/2
else f(delta_t*(i - j))), n + 1, n + 1),
transpose (makelist (F(k*delta_t), k, 0, n))];
"Generate numerical right-hand side matrix, skipping over formulas:" $
generate_rhs_matrix_numerical (shape, scale, n, delta_t) :=
block ([f, F, numer: true], local (f, F),
f: lambda ([x], pdf_weibull (x, shape, scale)),
F: lambda ([x], cdf_weibull (x, shape, scale)),
[genmatrix (lambda ([i, j], delta_t * if i = 1 and j = 1 then 0
elseif j > i then 0
elseif j = i then f(0)/2
elseif j = 1 then f(delta_t*(i - 1))/2
else f(delta_t*(i - j))), n + 1, n + 1),
transpose (makelist (F(k*delta_t), k, 0, n))]);
"Solve approximate integral equation (shape = 3, t = 1) via LU decomposition:" $
fpprintprec: 4 $
n: 20 $
t: 1;
[AA, bb]: generate_rhs_matrix_numerical (3, 1, n, t/n);
xx_by_lu: linsolve_by_lu (ident(n + 1) - AA, bb, floatfield);
"Iterative solution of approximate integral equation (shape = 3, t = 1):" $
xx: bb;
for i thru 10 do xx: AA . xx + bb;
xx - (AA.xx + bb);
xx_iterative: xx;
"Should find iterative and LU give same result:" $
xx_diff: xx_iterative - xx_by_lu[1];
sqrt (transpose(xx_diff) . xx_diff);
"Try shape = 2, t = 10:" $
n: 100 $
t: 10 $
[AA, bb]: generate_rhs_matrix_numerical (2, 1, n, t/n);
xx_by_lu: linsolve_by_lu (ident(n + 1) - AA, bb, floatfield);
"Baxter, et al., Eq. 3 (for large values of t) compared to discretization:" $
/* L.A. Baxter, E.M. Scheuer, D.J. McConalogue, W.R. Blischke.
* "On the Tabulation of the Renewal Function,"
* Econometrics, vol. 24, no. 2 (May 1982).
* H(t) is their notation for the renewal function.
*/
H(t) := t/mu + sigma^2/(2*mu^2) - 1/2;
tx_points: makelist ([float (k/n*t), xx_by_lu[1][k, 1]], k, 1, n);
plot2d ([H(u), [discrete, tx_points]], [u, 0, t]), mu = mean_weibull(2, 1), sigma = std_weibull(2, 1);
I need to write some code that can calculate a variable which shows the preference of a consumer to buy a component for his laptop. The preference changes by the tax (T) and the importance of prices on people's purchases (PriceI). I need to include both T and PriceI to find the person's willingness (W) for purchasing a laptop. Tax changes in a slider ranging from 50 Cent to $6 . I want to keep the variable W in a range from 1 to 2, where 1 is when the tax is on its default, minimum values which is 50 cent.
So There are 2 variables that have influence on W:
50<T<600
0.6 < PriceI < 9
Since I want 1<W<2, I thought it should work if I first normalize all the data by dividing them by their max, then in order to find a fraction to be between 1 and 2, I made the numerator to be less than 4 and the denominator to be less than 2, hoping to have the result between 1 to 2 :
to setup-WCalculator
ask consumers [
set PP ((PriceI / 9) * 2)
set TT ((T / 600) * 4)
set W TT / PP
]
end
However, Netlogo makes both PP and TT zero while they should be a small value like 0.15! Does the logic for finding W make sense?
Thanks,
Normalization is normally done with a formula such as
TT = (T - Tmin) / (Tmax - Tmin)
or here
TT = (T - 50) / (600 - 50)
That gives a normalized value between 0 and 1 as T ranges between 50 and 600. If you want TTT to range between 1 and x, where x is > 1, then you can set
TTT = 1.0 + TT * (x - 1.0)
So
TTT = 1.0 + TT * (4.0 - 1.0) = 1.0 + TT * 3.0
will give you a value between 1 and 4.
My math is pretty weak, and I'm having confusion over the differences. I'm trying to find out the midi formula, to output frequency when I have midi value
MidiNumber = 69+12* Log2(440/Frequency)
So I derived
Frequency = (-69 + 5280 Log2 + MidiNumber)/(12 Log2)
If I plugin things this works correctly
440 = (-69 + 5280 Log2 + 69)/(12 Log2)
If I do this though things do not work correctly
(-69 + Log[2, 5280.] + 69)/Log[2, 12.]
This is the output I get in my programming, I don't know exactly the difference between the two equations. Maybe it's 12*Log2, but is that 12*Log2[1] or, ...? No idea.
Part of your confusion seems to be treating Log2(n) as Log2 * n. Log2 is actually a function, the inverse of which is 2^x.
So your derivation should go something as follows:
MidiNumber = 69 + 12 * Log2(440 / Frequency)
MidiNumber - 69 = 12 * Log2(440 / Frequency)
(MidiNumber - 69) / 12 = Log2(440 / Frequency)
2^((MidiNumber - 69) / 12) = 440 / Frequency
Frequency = 440 / 2^((MidiNumber - 69) / 12)
I have 1 result and which i will receive in Bank account, Based on that account i have to Put a balance to user account.
How can you find the Handling cost from total tried 491.50 / 0.95 = 517.36 which is wrong ? It should be 500.00 (to my expectation)
User balance requires 500.00
When 500.00 selected he gets 5% discount
There is a handling cost for this
ex:
1) Discount: 500.00 - 5% = 475.00
2) Handling cost: (475.00 x 0.034) + 0.35 = 16.50
3) Total: 475.00 + 16.50 = 491.50
So problem is from 491.50, i have to find atleast handling cost to get promised Balance.
Any solution ? Cant figure it out myself...
In short cut:
a) i put 491.50 -> b) my formula will suggest me apply balance 500.00 (which is the main goal)
So, your maths can be represented as:
((0.95 * initialCost * 0.034) + 0.35) + (0.95 * initialCost ) = finalCost
which reduces to
(0.9823 * initialCost) + 0.35 = finalCost
It follows that
initialCost = (finalCost - 0.35) / 0.9823
(final_price - 0.35) / 1.034 / 0.95
For 491.50, this yields 500.
You might try to combine the last 2 divisions to be divide by 1.034 * 0.95 = 0.9823, but you will have to guard against rounding errors due to using floating point arithmetic.