find a solution to Kepler's equation (iOS) (swift) - ios

I am writing codes to find a solution (E) to Kepler's equation:
E - e*sin(E) = M
and all angles are expressed in radians,
M = 3.52821,
e = 0.016714
and theses are the steps:
First guess, put E = Eo = M
Find the value of O = E - e*sin(E) - M
If |O| <= 0.000006, go to step 6
If |O| > 0.000006, proceed with step 4
Find delta_E = O/(1-e*sin(E))
Take new value E1 = E - delta_E, go to step 2
The present value of E is the solution, correct within 0.000006 of the true value
However, I don't know how to write codes (swift) of those steps, please help me.
Thanks to #NSNoob
I finally figure the solution out!!!
let M = 3.52821
var e = 0.016714
var E = M
var O = E - (e * sin(E)) - M
while (abs(O) > 0.000006) {
var Delta_E = O / (1-(e*cos(E)))
E = E - Delta_E
O = E - (e * sin(E)) - M
}
print(E)

First of all some corrections in your question which you apparently forgot to mention and I had to look for here.
E0 = M
E = E1 on next iteration if solution not found
Regarding technical terms, E here is called Eccentric Anomaly and M is called mean Anomaly. Where as eps is precision diameter. Also, E=e according to the shared article
Also in swift, we use camel case naming convention for variables and constants but here I have tried to use your names so that you could understand the code.
Now back to business, Following methods will do it for you using recursion:
func solveKeplersEquationForParamas(M:Double)->Void{
let E:Double = M
let eps:Double = 0.000006
let AbsoluteValueOfO:Double = getAbsoluteValueOfO(E, M: M,eps: eps)
print(NSString(format:"Answer is:%f", AbsoluteValueOfO))
}
func getAbsoluteValueOfO(E:Double,M:Double,eps:Double) -> Double {
var SinOFE: Double = Double(sin(E))
SinOFE = E*SinOFE
var E1 = E;
let O = E - SinOFE - M
var AbsoluteValueOfO = fabs(O)
if AbsoluteValueOfO > eps {
print("Solution not found. go to step 4");
let denom = 1-E*sin(E)
let absDenom = fabs(denom)
if absDenom>0{
let deltaE = O/denom
E1 = E-deltaE
AbsoluteValueOfO = getAbsoluteValueOfO(E1, M: M, eps: eps)
}
else{
print("Denom became 0, Can't divide by zero Denom is:%f",denom);
return AbsoluteValueOfO
}
}
else if AbsoluteValueOfO < eps || AbsoluteValueOfO == eps{
print("Solution found. Returning the value");
print(NSString(format:"Value of E is:%f", E1))
}
return AbsoluteValueOfO
}
Running this in playground like:
solveKeplersEquationForParamas(3.094763)
Playground output:
NOTE: This is the Swift solution to the steps you have mentioned. Responsibility for any error in steps lies on you.

Related

Precision and recall missunderstanding

In pycocotools in cocoeval.py sctipt there is COCOeval class and in this class there is accumulate function for calculating Precision and Recall. Does anyone know what is this npig variable? Is this negative-positive or?
Because I saw this formula for recall: Recall = (True Positive)/(True Positive + False Negative)
Can I just use this precision and recall variable inside dictionary self.eval to get precision and recall of my model which I'm testing, and plot a precision-recall curve?
And the variable scores is this F1 score?
Because I'm not very well understand this T,R,K,A,M what is happening with this.
How can I print precision and recall in terminal?
def accumulate(self, p = None):
'''
Accumulate per image evaluation results and store the result in self.eval
:param p: input params for evaluation
:return: None
'''
print('Accumulating evaluation results...')
tic = time.time()
if not self.evalImgs:
print('Please run evaluate() first')
# allows input customized parameters
if p is None:
p = self.params
p.catIds = p.catIds if p.useCats == 1 else [-1]
T = len(p.iouThrs)
R = len(p.recThrs)
K = len(p.catIds) if p.useCats else 1
A = len(p.areaRng)
M = len(p.maxDets)
precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories
recall = -np.ones((T,K,A,M))
scores = -np.ones((T,R,K,A,M))
# create dictionary for future indexing
_pe = self._paramsEval
catIds = _pe.catIds if _pe.useCats else [-1]
setK = set(catIds)
setA = set(map(tuple, _pe.areaRng))
setM = set(_pe.maxDets)
setI = set(_pe.imgIds)
# get inds to evaluate
k_list = [n for n, k in enumerate(p.catIds) if k in setK]
m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA]
i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
I0 = len(_pe.imgIds)
A0 = len(_pe.areaRng)
# retrieve E at each category, area range, and max number of detections
for k, k0 in enumerate(k_list):
Nk = k0*A0*I0
for a, a0 in enumerate(a_list):
Na = a0*I0
for m, maxDet in enumerate(m_list):
E = [self.evalImgs[Nk + Na + i] for i in i_list]
E = [e for e in E if not e is None]
if len(E) == 0:
continue
dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E])
# different sorting method generates slightly different results.
# mergesort is used to be consistent as Matlab implementation.
inds = np.argsort(-dtScores, kind='mergesort')
dtScoresSorted = dtScores[inds]
dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds]
dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds]
gtIg = np.concatenate([e['gtIgnore'] for e in E])
npig = np.count_nonzero(gtIg==0 )
if npig == 0:
continue
tps = np.logical_and( dtm, np.logical_not(dtIg) )
fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) )
tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
tp = np.array(tp)
fp = np.array(fp)
nd = len(tp)
rc = tp / npig
pr = tp / (fp+tp+np.spacing(1))
q = np.zeros((R,))
ss = np.zeros((R,))
if nd:
recall[t,k,a,m] = rc[-1]
else:
recall[t,k,a,m] = 0
# numpy is slow without cython optimization for accessing elements
# use python array gets significant speed improvement
pr = pr.tolist(); q = q.tolist()
for i in range(nd-1, 0, -1):
if pr[i] > pr[i-1]:
pr[i-1] = pr[i]
inds = np.searchsorted(rc, p.recThrs, side='left')
try:
for ri, pi in enumerate(inds):
q[ri] = pr[pi]
ss[ri] = dtScoresSorted[pi]
except:
pass
precision[t,:,k,a,m] = np.array(q)
scores[t,:,k,a,m] = np.array(ss)
self.eval = {
'params': p,
'counts': [T, R, K, A, M],
'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'precision': precision,
'recall': recall,
'scores': scores,
}
toc = time.time()
print('DONE (t={:0.2f}s).'.format( toc-tic))

Why does "array[index]" return "nil"?

this problem seems very simple but I cannot find a solution for it, actually I don't even know what is wrong!!!
So basically I have this Lua code:
io.write("\nPlease provide the message to be decyphered: ")
message = io.read()
seq = #message
ffib = {}
a = 0
b = 1
c = a + b
fib = 0
while c < (seq - 10) do
fib = fib + 1
ffib[fib] = c
a = b
b = c
c = a + b
end
decyphered = ""
for i = 1,seq do
decyphered = table.concat{decyphered, message:sub(ffib[i],ffib[i])}
end
io.write("\nDecyphered message: ", decyphered, "\n\n")
and trying to access ffib[fib] returns nil. So trying to message:sub(ffib[i]... later throws an error.
When I try accessing ffib's values manually, ffib[1] for example, it works alright, it's only when trying to access it with an iterator that it screws up.
Somewhere else in my code I have this:
io.write("\nPlease provide the message to be cyphered: ")
message = io.read()
cyphered = ""
seq = #message
ffib = {}
a = 0
b = 1
c = a + b
for fib = 1,seq do
ffib[fib] = c
a = b
b = c
c = a + b
end
which is basically the same thing but instead of using a while loop, it uses a for loop, and it works just fine!
Please help me solve this I am going insane.
Alright, I figured it out!
io.write("\nPlease provide the message to be decyphered: ")
message = io.read()
seq = #message
ffib = {}
a = 0
b = 1
c = a + b
fib = 0
while c < (seq - 10) do
fib = fib + 1
ffib[fib] = c
a = b
b = c
c = a + b
end
decyphered = ""
for i = 1,seq do <--------------
decyphered = table.concat{decyphered, message:sub(ffib[i],ffib[i])}
end
io.write("\nDecyphered message: ", decyphered, "\n\n")
I was using the wrong variable in the for loop, so it was looping through the entire message length instead of the fibonacci array length, the "nil" values were indexes out of bounds!
To correct this, I simply changed seq for #ffib in that For Loop, marked by an arrow.
Thanks everyone who tried to help me anyway!
this part doesn't make much sense I think
while c < (seq - 10) do
Why the minus 10? ffib will have less entries than seq while in the loop after that you expect a value in ffib from 1 to seq
And even if you change it to
while c < seq do
Then there still won't be enough for messages larger than length 2.
If anything, you might want to do
while c < (seq + 10) do
But even there you will run into an issue when the message is a certain length.
I'm also not familiar with that algorithm, but it looks pretty weird to me and I wonder what it actually establishes

How to look for anagrams in 1 or 2 dictionaries?

This is our code now:
#anagram is a word formed by rearranging the letters of a different word
text = open("words.txt")
counter = 0
d = {}
e = {}
for word in text:
w = word
a = list(word)
s = sorted(a)
counter += 1
d[counter] = s
e[counter] = s
print(d)
print(e)
We want to ask python to show us the words which have the same values/letters. So for example: AAB is the same as BAA.
Our file exists from:
aba
aab
acaba
ackba
abaca
casaba
Does anyone know how to program this?

Ruby/Rails while loop not breaking correctly?

I am working on a client's site, and I'm writing an amortization schedule calculator in in ruby on rails. For longer loan term calculations, it doesn't seem to be breaking when the balance reaches 0
Here is my code:
def calculate_amortization_results
p = params[:price].to_i
i = params[:rate].to_d
l = params[:term].to_i
j = i/(12*100)
n = l * 12
m = p * (j / (1 - (1 + j) ** (-1 * n)))
#loanAmount = p
#rateAmount = i
#monthlyAmount = m
#amort = []
#interestAmount = 0
while p > 0
line = Hash.new
h = p*j
c = m-h
p = p-c
line["interest"] = h
line["principal"] = c
if p <= 0
line["balance"] = 0
else
line["balance"] = p
end
line["payment"] = h+c
#amort.push(line)
#interestAmount += h
end
end
And here is the view:
- #amort.each_with_index do |a, i|
%li
.m
= i+1
.i
= number_to_currency(a["interest"], :unit => "$")
.p
= number_to_currency(a["principal"], :unit => "$")
.pp
= number_to_currency(a["payment"], :unit => "$")
.b
= number_to_currency(a["balance"], :unit => "$")
What I am seeing is, in place of $0.00 in the final payment balance, it shows "-$-inf", iterates one more loop, then displays $0.00, but shows "-$-inf" for interest. It should loop until p gets to 0, then stop and set the balance as 0, but it isn't. Any idea what I've done wrong?
The calculator is here. It seems to work fine for shorter terms, like 5 years, but longer terms cause the above error.
Edit:
Changing the while loop to n.times do
and then changing the balance view to
= number_to_currency(a["balance"], :unit => "$", :negative_format => "$0.00")
Is a workaround, but i'd like to know why the while loop wouldn't work correctly
in Ruby the default for numerical values is Fixnum ... e.g.:
> 15 / 4
=> 3
You will see weird rounding errors if you try to use Fixnum values and divide them.
To make sure that you use Floats, at least one of the numbers in the calculation needs to be a Float
> 15.0 / 4
=> 3.75
> 15 / 4.0
=> 3.75
You do two comparisons against 0 , which should be OK if you make sure that p is a Float.
As the other answer suggests, you should use "decimal" type in your database to represent currency.
Please try if this will work:
def calculate_amortization_results
p = params[:price].to_f # instead of to_i
i = params[:rate].to_f # <-- what is to_d ? use to_f
l = params[:term].to_i
j = i/(12*100.0) # instead of 100
n = l * 12
m = p * (j / (1 - (1 + j) ** (-1 * n))) # division by zero if i==0 ==> j==0
#loanAmount = p
#rateAmount = i
#monthlyAmount = m
#amort = []
#interestAmount = 0.0 # instead of 0
while p > 0
line = Hash.new
h = p*j
c = m-h
p = p-c
line["interest"] = h
line["principal"] = c
if p <= 0
line["balance"] = 0
else
line["balance"] = p
end
line["payment"] = h+c
#amort.push(line)
#interestAmount += h
end
end
If you see "inf" in your output, you are doing a division by zero somewhere.. better check the logic of your calculation, and guard against division by zero.
according to Wikipedia the formula is:
http://en.wikipedia.org/wiki/Amortization_calculator
to improve rounding errors, it's probably better to re-structure the formula like this:
m = (p * j) / (1 - (1 + j) ** (-1 * n) # these are two divisions! x**-1 == 1/x
which is equal to:
m = (p * j) + (p * j) / ((1 + j) ** n) - 1.0)
which is equal to: (use this one)
q = p * j # this is much larger than 1 , so fewer rounding errors when dividing it by something
m = q + q / ((1 + j) ** n) - 1.0) # only one division
I think it has something to do with the floating point operations precision. It has already been discussed here: Ruby number precision with simple arithmetic and it would be better to use decimal format for financial purposes.
The answer could be computing the numbers in the loop, but with precomputed number of iterations and from the scratch.

How can I do mod without a mod operator?

This scripting language doesn't have a % or Mod(). I do have a Fix() that chops off the decimal part of a number. I only need positive results, so don't get too robust.
Will
// mod = a % b
c = Fix(a / b)
mod = a - b * c
do? I'm assuming you can at least divide here. All bets are off on negative numbers.
a mod n = a - (n * Fix(a/n))
For posterity, BrightScript now has a modulo operator, it looks like this:
c = a mod b
If someone arrives later, here are some more actual algorithms (with errors...read carefully)
https://eprint.iacr.org/2014/755.pdf
There are actually two main kind of reduction formulae: Barett and Montgomery. The paper from eprint repeat both in different versions (algorithms 1-3) and give an "improved" version in algorithm 4.
Overview
I give now an overview of the 4. algorithm:
1.) Compute "A*B" and Store the whole product in "C" that C and the modulus $p$ is the input for that algorithm.
2.) Compute the bit-length of $p$, say: the function "Width(p)" returns exactly that value.
3.) Split the input $C$ into N "blocks" of size "Width(p)" and store each in G. Start in G[0] = lsb(p) and end in G[N-1] = msb(p). (The description is really faulty of the paper)
4.) Start the while loop:
Set N=N-1 (to reach the last element)
precompute $b:=2^{Width(p)} \bmod p$
while N>0 do:
T = G[N]
for(i=0; i<Width(p); i++) do: //Note: that counter doesn't matter, it limits the loop)
T = T << 1 //leftshift by 1 bit
while is_set( bit( T, Width(p) ) ) do // (N+1)-th bit of T is 1
unset( bit( T, Width(p) ) ) // unset the (N+1)-th bit of T (==0)
T += b
endwhile
endfor
G[N-1] += T
while is_set( bit( G[N-1], Width(p) ) ) do
unset( bit( G[N-1], Width(p) ) )
G[N-1] += b
endwhile
N -= 1
endwhile
That does alot. Not we only need to recursivly reduce G[0]:
while G[0] > p do
G[0] -= p
endwhile
return G[0]// = C mod p
The other three algorithms are well defined, but this lacks some information or present it really wrong. But it works for any size ;)
What language is it?
A basic algorithm might be:
hold the modulo in a variable (modulo);
hold the target number in a variable (target);
initialize modulus variable;
while (target > 0) {
if (target > modulo) {
target -= modulo;
}
else if(target < modulo) {
modulus = target;
break;
}
}
This may not work for you performance-wise, but:
while (num >= mod_limit)
num = num - mod_limit
In javascript:
function modulo(num1, num2) {
if (num2 === 0 || isNaN(num1) || isNaN(num2)) {
return NaN;
}
if (num1 === 0) {
return 0;
}
var remainderIsPositive = num1 >= 0;
num1 = Math.abs(num1);
num2 = Math.abs(num2);
while (num1 >= num2) {
num1 -= num2
}
return remainderIsPositive ? num1 : 0 - num1;
}

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