So, I'm trying to find the value of cos(x), where x=1.2. I feel the script I have written should be fine, however, the value I get out isn't correct. That is; cos(1.2)=0.6988057880877979, for 25 terms, when I should get out: cos(1.2)=0.36235775.
I have created a similar program for calculating sin(1.2) which works fine.
Calculating sin(1.2):
import math as m
x=1.2
k=1
N=25
s=x
sign=1.0
while k<N:
sign=-sign
k=k+2
term=sign*x**k/m.factorial(k)
s=s+term
print('sin(%g) = %g (approximation with %d terms)' % (x,s,N))
Now trying to calculate cos(1.2):
import math as m
x=1.2
k=1
N=25
s=x
sign=1.0
while k<N:
sign=-sign
k=k+1
term=sign*x**k/m.factorial(k)
s=s+term
print(s)
You shouldn't be setting your initial sum to 1.2, and your representation of the expansion
is a bit off - we need to account for the even-ness of the function, so increment k by 2. Also, without modifying your program structure, you'd have to set the initial variables so they are correctly put to their starting values at the beginning of the first loop. Re-ordering your loop control flow a bit, we have
import math as m
x=1.2
k=0
N=25
s=0
sign=1.0
while k<N:
term=sign*x**(k)/m.factorial(k)
s=s+term
k += 2
sign = -sign
print(s)
Gives
0.3623577544766735
I think you're using the wrong series for the cosine, the correct formula would be (I highlighted the important differences with ^):
sum_over_n [(-1)**n * x ** (2 * n) / (math.factorial(2 * n))]
# ^^^^ ^^^^
that means to add n-terms you have something like:
def cosine_by_series(x, terms):
cos = 0
for n in range(terms):
cos += ((-1)**n) * (x ** (2*n)) / (math.factorial(2 * n))
return cos
# or simply:
# return sum(((-1)**n) * (x ** (2*n)) / (math.factorial(2 * n)) for n in range(terms)
which gives:
>>> cosine_by_series(1.2, 30)
0.3623577544766735
Related
In excel, there is this formula:
=ROUNDDOWN(D28-D33-D34, -2) and I got the results ==> 166400. But the result without the Rounddown formulas is ==> 166468.50.
My question is how to get the same in python. When I do round (n, -2) I get ==> 166500.
Any help would be much appreciated! Thank you.
This is an implementation of the ROUNDDOWN(x,n) function in Python
def rounddown(x,n):
return int(x// 10**n * 10**n)
print(rounddown(166468.50,1)) #166460
print(rounddown(166468.50,2)) #166400
print(rounddown(166468.50,3)) #166000
[Update]
A new version of the function rounddown, that can handle both positive and negative values of n. (simulate the ROUNDDOWN(x,n) found in Excel)
def rounddown(x,n):
sign=1 if n>0 else 0 # getting the sign of n.
n=abs(n) # getting the absolute value of n.
p= 10**n # calculating p the nth power of 10.
result= (x // p) * p + sign * p # calculating result.
return int( result )
# sample result
print(rounddown(166468.50,1)) #166470
print(rounddown(166468.50,2)) #166500
print(rounddown(166468.50,3)) #167000
print(rounddown(166468.50,0)) #166468
print(rounddown(166468.50,-1)) #166460
print(rounddown(166468.50,-2)) #166400
print(rounddown(166468.50,-3)) #166000
I'm new to sympy and I'm trying to use it to get the values of higher order Greeks of options (basically higher order derivatives). My goal is to do a Taylor series expansion. The function in question is the first derivative.
f(x) = N(d1)
N(d1) is the P(X <= d1) of a standard normal distribution. d1 in turn is another function of x (x in this case is the price of the stock to anybody who's interested).
d1 = (np.log(x/100) + (0.01 + 0.5*0.11**2)*0.5)/(0.11*np.sqrt(0.5))
As you can see, d1 is a function of only x. This is what I have tried so far.
import sympy as sp
from math import pi
from sympy.stats import Normal,P
x = sp.symbols('x')
u = (sp.log(x/100) + (0.01 + 0.5*0.11**2)*0.5)/(0.11*np.sqrt(0.5))
N = Normal('N',0,1)
f = sp.simplify(P(N <= u))
print(f.evalf(subs={x:100})) # This should be 0.5155
f1 = sp.simplify(sp.diff(f,x))
f1.evalf(subs={x:100}) # This should also return a float value
The last line of code however returns an expression, not a float value as I expected like in the case with f. I feel like I'm making a very simple mistake but I can't find out why. I'd appreciate any help.
Thanks.
If you define x with positive=True (which is implied by the log in the definition of u assuming u is real which is implied by the definition of f) it looks like you get almost the expected result (also using f1.subs({x:100}) in the version without the positive x assumption shows the trouble is with unevaluated polar_lift(0) terms):
import sympy as sp
from sympy.stats import Normal, P
x = sp.symbols('x', positive=True)
u = (sp.log(x/100) + (0.01 + 0.5*0.11**2)*0.5)/(0.11*sp.sqrt(0.5)) # changed np to sp
N = Normal('N',0,1)
f = sp.simplify(P(N <= u))
print(f.evalf(subs={x:100})) # 0.541087287864516
f1 = sp.simplify(sp.diff(f,x))
print(f1.evalf(subs={x:100})) # 0.0510177033783834
In a python program, the following function is called about 20,000 times from another function that is called about 1000 times from yet another function that executes 30 times. Thus the total number of times this particular function is called is about 600,000,000. In python it takes more than two hours (perhaps much longer; I aborted the program without waiting for it to finish), while essentially the same task coded in Java takes less than 5 minutes. If I change the 20,000 above to 400 (keeping everything else in the rest of the program untouched), the total time drops to about 4 minutes (this means this particular function is the culprit). What can I do to speed up the Python version, or is it just not possible? No lists are manipulated inside this function (there are lists elsewhere in the whole program, but in those places I tried to use numpy arrays as far as possible). I understand that replacing python lists with numpy arrays speeds things up, but there are cases in my program (not in this particular function) where I must build a list iteratively, using append; and those must-have lists are lists of objects (not floats or ints), so numpy would be of little help even if I converted those lists of objects to numpy arrays.
def compute_something(arr):
'''
arr is received as a numpy array of ints and floats (I think python upcasts them to all floats,
doesn’t it?).
Inside this function, elements of arr are accessed using indexing (arr[0], arr[1], etc.), because
each element of the array has its own unique use. It’s not that I need the array as a whole (as in
arr**2 or sum(arr)).
The arr elements are used in several simple arithmetic operations involving nothing costlier than
+, -, *, /, and numpy.log(). There is no other loop inside this function; there are a few if’s though.
Inside this function, use is made of constants imported from other modules (I doubt the
importing, as in AnotherModule.x is expensive).
'''
for x in numpy.arange(float1, float2, float3):
do stuff
return a, b, c # Return a tuple of three floats
Edit:
Thanks for all the comments. Here’s the inside of the function (I made the variable names short for convenience). The ndarray array arr has only 3 elements in it. Can you please suggest any improvement?
def compute_something(arr):
a = Mod.b * arr[1] * arr[2] + Mod.c
max = 0.0
for c in np.arange(a, arr[1] * arr[2] * (Mod.d – Mod.e), Mod.f):
i = c / arr[2]
m1 = Mod.A * np.log( (i / (arr[1] *Mod.d)) + (Mod.d/Mod.e))
m2 = -Mod.B * np.log(1.0 - (i/ (arr[1] *Mod.d)) - (Mod.d /
Mod.e))
V = arr[0] * (Mod.E - Mod.r * i / arr[1] - Mod.r * Mod.d -
m1 – m2)
p = c * V /1000.0
if p > max:
max = p
vmp = V
pen = Mod.COEFF1 * (Mod.COEFF2 - max) if max < Mod.CONST else 0.0
wo = Mod.COEFF3 * arr[1] * arr[0] + Mod.COEFF4 * abs(Mod.R5 - vmp) +
Mod.COEFF6 * arr[2]
w = wo + pen
return vmp, max, w
Python supports profiling of code. (module cProfile). Also there is option to use line_profiler to find most expensive part of code tool here.
So you do not need to guessing which part of code is most expensive.
In this code which you presten the problem is in usage for loop which generates many conversion between types of objects. If you use numpy you can vectorize your calculation.
I try to rewrite your code to vectorize your operation. You do not provide information what is Mod object, but I have hope it will work.
def compute_something(arr):
a = Mod.b * arr[1] * arr[2] + Mod.c
# start calculation on vectors instead of for lop
c_arr = np.arange(a, arr[1] * arr[2] * (Mod.d – Mod.e), Mod.f)
i_arr = c_arr/arr[2]
m1_arr = Mod.A * np.log( (i_arr / (arr[1] *Mod.d)) + (Mod.d/Mod.e))
m2_arr = -Mod.B * np.log(1.0 - (i_arr/ (arr[1] *Mod.d)) - (Mod.d /
Mod.e))
V_arr = arr[0] * (Mod.E - Mod.r * i_arr / arr[1] - Mod.r * Mod.d -
m1_arr – m2_arr)
p = c_arr * V_arr / 1000.0
max_val = p.max() # change name to avoid conflict with builtin function
max_ind = np.nonzero(p == max_val)[0][0]
vmp = V_arr[max_ind]
pen = Mod.COEFF1 * (Mod.COEFF2 - max_val) if max_val < Mod.CONST else 0.0
wo = Mod.COEFF3 * arr[1] * arr[0] + Mod.COEFF4 * abs(Mod.R5 - vmp) +
Mod.COEFF6 * arr[2]
w = wo + pen
return vmp, max_val, w
I would suggest to use range as it is approximately 2 times faster:
def python():
for i in range(100000):
pass
def numpy():
for i in np.arange(100000):
pass
from timeit import timeit
print(timeit(python, number=1000))
print(timeit(numpy, number=1000))
Output:
5.59282787179696
10.027646953771665
I wrote the following code, after printing about 10 outputs it gets error
return 1/sqrt(Om*(1+z)**3+omg0*(1+z)**6+(1-Om-omg0))
ValueError: math domain error
the three first functions are correct and work well in other programs please do not be sensitive about their structures, I think the problem is in for-loop and choosing some values which cannot satisfy the condition of sqrt. Could I add some lines to tell Python avoid numbers which lead to math domain error? If yes, How should I do that? I mean passing the step which leads to negative value under sqrt?
Cov is a 31*31 mtrix, xx[n] is a list of 31 numbers.
def ant(z,Om,w):
return 1/sqrt(Om*(1+z)**3+w*(1+z)**6+(1-Om-w))
def dl(n,Om,w,M):
q=quad(ant,0,xx[n],args=(Om,w))[0]
h=5*log10((1+xx[n])*q)
fn=(yy[n]-M-h)
return fn
def c(Om,w,M):
f_list = []
for i in range(31): # the value '2' reflects matrix size
f_list.append(dl(i,Om,w,M))
A=[f_list]
B=[[f] for f in f_list]
C=np.dot(A,Cov)
D=np.dot(C,B)
F=np.linalg.det(D)*0.000001
return F
N=100
for i in range (1,N):
R3=np.random.uniform(0,1)
Omn[i]=Omo[i-1]+0.05*np.random.normal()
wn[i]=wo[i-1]+0.05*np.random.normal()
Mn[i]=Mo[i-1]+0.1*np.random.normal()
L=exp(-0.5*(c(Omn[i],wn[i],Mn[i])-c(Omo[i-1],wo[i-1],Mo[i-1])))
if L>R3:
wo[i]=wn[i]
else:
wo[i]=wo[i-1]
print(wo[i])
The output is:
0.12059556415714912
0.16292726528216397
0.16644447885609648
0.1067588804671105
0.0321446951572128
0.0321446951572128
0.013169965429457382
Traceback (most recent call last):
......
return 1/sqrt(Om*(1+z)**3+omg0*(1+z)**6+(1-Om-omg0))
ValueError: math domain error
Here are some options:
Replace sqrt(...) with (...)**0.5. This will produce complex numbers, which may or may not be acceptable. For example (-1)**0.5 produces i which in Python will appear as 1j (ignoring floating point errors).
Catch errors and move on. Since you probably want to catch the errors higher up, I recommend translating the ValueError from sqrt into a custom error:
class SqrtError(ValueError):
pass
def ant(z, Om, w):
val = Om * (1 + z) ** 3 + w * (1 + z) ** 6 + (1 - Om - w)
try:
sq = sqrt(val)
except ValueError:
raise SqrtError
return 1 / sq
Then it sounds like you want to keep trying new random numbers until you get one that works, which could be done like so:
for i in range(1, N):
R3 = np.random.uniform(0, 1)
while True:
Omn[i] = Omo[i - 1] + 0.05 * np.random.normal()
wn[i] = wo[i - 1] + 0.05 * np.random.normal()
Mn[i] = Mo[i - 1] + 0.1 * np.random.normal()
try:
L = exp(-0.5 * (c(Omn[i], wn[i], Mn[i]) - c(Omo[i - 1], wo[i - 1], Mo[i - 1])))
except SqrtError:
continue
else:
break
if L > R3:
wo[i] = wn[i]
else:
wo[i] = wo[i - 1]
Your code is trying to find the square root of a negative number.
This is not allowed with real-valued numbers (for obvious mathematical reasons), so your best bet is to use a try/except block:
def ant(z,Om,w):
try:
return 1/sqrt(Om*(1+z)**3+omg0*(1+z)**6+(1-Om-omg0))
except ValueError:
return None
I understand there is a erf (Wikipedia) function in python. But in this assignment we are specifically asked to write the error function as if it was not already implemented in python while using a while loop.
erf (x) is simplified already as : (2/ (sqrt(pi)) (x - x^3/3 + x^5/10 - x^7/42...)
Terms in the series must be added until the absolute total is less than 10^-20.
First of all - SO is not the place when people code for you, here people help you to solve particular problem not the whole task
Any way:
It's not too hard to implement wikipedia algorithm:
import math
def erf(x):
n = 1
res = 0
res1 = (2 / math.sqrt(math.pi)) * x
diff = 1
s = x
while diff > math.pow(10, -20):
dividend = math.pow((-1), n) * math.pow(x, 2 * n + 1)
divider = math.factorial(n) * (2 * n + 1)
s += dividend / divider
res = ((2 / math.sqrt(math.pi)) * s)
diff = abs(res1 - res)
res1 = res
n += 1
return res
print(erf(1))
Please read the source code carefully and post all questions that you don't understand.
Also you may check python sources and see how erf is implemented