Related
I need to generate the result of the log.
I know that:
Then I made my code:
def log(x, base):
log_b = 2
while x != int(round(base ** log_b)):
log_b += 0.01
print(log_b)
return int(round(log_b))
But it works very slowly. Can I use other method?
One other thing you might want to consider is using the Taylor series of the natural logarithm:
Once you've approximated the natural log using a number of terms from this series, it is easy to change base:
EDIT: Here's another useful identity:
Using this, we could write something along the lines of
def ln(x):
n = 1000.0
return n * ((x ** (1/n)) - 1)
Testing it out, we have:
print ln(math.e), math.log(math.e)
print ln(0.5), math.log(0.5)
print ln(100.0), math.log(100.0)
Output:
1.00050016671 1.0
-0.692907009547 -0.69314718056
4.6157902784 4.60517018599
This shows our value compared to the math.log value (separated by a space) and, as you can see, we're pretty accurate. You'll probably start to lose some accuracy as you get very large (e.g. ln(10000) will be about 0.4 greater than it should), but you can always increase n if you need to.
I used recursion:
def myLog(x, b):
if x < b:
return 0
return 1 + myLog(x/b, b)
You can use binary search for that.
You can get more information on binary search on Wikipedia:
Binary search;
Doubling search.
# search for log_base(x) in range [mn, mx] using binary search
def log_in_range(x, base, mn, mx):
if (mn <= mx):
med = (mn + mx) / 2.0
y = base ** med
if abs(y - x) < 0.00001: # or if math.isclose(x, y): https://docs.python.org/3/library/math.html#math.isclose
return med
elif x > y:
return log_in_range(x, base, med, mx)
elif x < y:
return log_in_range(x, base, mn, med)
return 0
# determine range using doubling search, then call log_in_range
def log(x, base):
if base <= 0 or base == 1 or x <= 0:
raise ValueError('math domain error')
elif 0 < base < 1:
return -log(x, 1/base)
elif 1 <= x and 1 < base:
mx = 1
y = base
while y < x:
y *= y
mx *= 2
return log_in_range(x, base, 0, mx)
elif 0 <= x < 1 and 1 < base:
mn = -1
y = 1/base
while y > x:
y = y ** 0.5
mn *= 2
return log_in_range(x, base, mn, 0)
import math
try :
number_and_base = input() ##input the values for number and base
##assigning those values for the variables
number = int(number_and_base.split()[0])
base = int(number_and_base.split()[1])
##exception handling
except ValueError :
print ("Invalid input...!")
##program
else:
n1 = 1 ##taking an initial value to iterate
while(number >= int(round(base**(n1),0))) : ##finding the most similer value to the number given, varying the vlaue of the power
n1 += 0.000001 ##increasing the initial value bit by bit
n2 = n1-0.0001
if abs(number-base**(n2)) < abs(base**(n1)-number) :
n = n2
else :
n = n1
print(math.floor(n)) ##output value
Comparison:-
This is how your log works:-
def your_log(x, base):
log_b = 2
while x != int(round(base ** log_b)):
log_b += 0.01
#print log_b
return int(round(log_b))
print your_log(16, 2)
# %timeit %run your_log.py
# 1000 loops, best of 3: 579 us per loop
This is my proposed improvement:-
def my_log(x, base):
count = -1
while x > 0:
x /= base
count += 1
if x == 0:
return count
print my_log(16, 2)
# %timeit %run my_log.py
# 1000 loops, best of 3: 321 us per loop
which is faster, using the %timeit magic function in iPython to time the execution for comparison.
It will be long process since it goes in a loop. Therefore,
def log(x,base):
result = ln(x)/ln(base)
return result
def ln(x):
val = x
return 99999999*(x**(1/99999999)-1)
log(8,3)
Values are nearly equal but not exact.
I am attempting to create a program to solve a specific system of equations using brute force with python. I solved for one equation and substituted it into the second equation. Variables a through f are given by user input, and I am trying to output values for x and y within a range of -10 to 10 if they exist. Any help is greatly appreciated, Thank You!
My problem is that for each check within the range the output is no solution, whereas I would like it to output no solution only if none of the numbers within the range satisfy the equation. Updated the code to fix the int object not callable error.
To be clear, the code is mostly working and gives the correct answer, but the answer is nested in roughly 100 'no solution' statements
''' Read in first equation, ax + by = c '''
a = int(input())
b = int(input())
c = int(input())
''' Read in second equation, dx + ey = f '''
d = int(input())
e = int(input())
f = int(input())
for x in range(-10, 10, 1):
for y in range(-10, 10, 1):
if a * x + b * y == c and d * x + e * y == f:
print(x, y)
else:
print('No solution')
# a * x + b * y = c
# d * x + e * y = f
To fix the issue of the else statement printing for each iteration of the if statement I created a check and set it to false. I set a condition that set the variable to true if any of the numbers within the range satisfied the condition. Then set an if statement to print 'No solution' if the variable was still false. Thanks to everyone for the help with syntax, but I'm glad I had the opportunity to figure this out on my own.
Here's the updated working code:
''' Read in first equation, ax + by = c '''
a = int(input())
b = int(input())
c = int(input())
''' Read in second equation, dx + ey = f '''
d = int(input())
e = int(input())
f = int(input())
check = False
for x in range(-10, 10, 1):
for y in range(-10, 10, 1):
if a * x + b * y == c and d * x + e * y == f:
check = True
print(x,y)
if check == False:
print('No solution')
# a * x + b * y = c
# d * x + e * y = f
if d * x + b((c - a * x)/b) == f:
Parentheses after a variable is an operator that tries to call the variable as a function. So this line above is trying to call b() as a function. You need to explicitly add the multiply operator instead:
if d * x + b*((c - a * x)/b) == f:
So, there is a lot going on here that can be improved upon.
But with some basic assumptions on best practices I've re-written your code so that it is easy for anyone to understand.
The main things I'd recommend getting use to:
Setting up a "main" in your python code
building functions instead of relying on a procedural design
commenting the goal of your functions, along with something about the input and output
There are plenty of discussion online as to why, but to sum them up it makes it A) more clear what your intentions are and B) provides an easier baseline for troubleshooting your code.
There are still more things that could be discussed about improving the clarity of intention and design but I'll cut myself short at fear of rambling on.
def brute_force_two_equations(first_values, second_values,
max_range=10, min_range=-10):
'''
Brute force way of finding solution in
system of equations
Params:
first_values: a list of three values [a const, a const, and the result]
second_values: a list of three values [a const, a const, and the result]
max_range: the maximum range to test with, default 10
min_range: the minimum range to test with, default -10
'''
for x in range(min_range, max_range + 1):
for y in range(min_range, max_range + 1):
first_result = equation(first_values[0], first_values[1], x, y)
second_result = equation(second_values[0], second_values[1], x, y)
if first_result == first_values[2] and second_result == second_values[2]:
return (x, y)
def equation(const1, const2, x, y):
'''
A simple equation: a * x + b * y = c
Params:
const1: the first constant value, `a` in the above example
const2: the second constant value, `b` in the above example
x: the first variable value
y: the second variable value
'''
return const1 * x + const2 * y
def get_three_integers():
'''
Requests and provides three integers from the user
'''
inputs = []
for i in range(0, 3):
inputs.append(int(input("Please enter a value: ")))
return inputs
if __name__ == "__main__":
print("== First Equation ==")
first_equation_values = get_three_integers()
print("== Second Equation ==")
second_equation_values = get_three_integers()
solution = brute_force_two_equations(first_equation_values,
second_equation_values)
if solution:
print(f"(x, y) {solution}")
else:
print("No Solution found")
You're saying "No solution" without complely looping through all the choices.
Code should be
for x in range(-10, 10, 1):
for y in range(-10, 10, 1):
if a * x + b * y == c and d * x + e * y == f:
print(x, y)
else:
print('No solution')
This is the way I did it and it works:
(I borrowed the check = False idea)
''' Read in first equation, ax + by = c '''
a = int(input())
b = int(input())
c = int(input())
''' Read in second equation, dx + ey = f '''
d = int(input())
e = int(input())
f = int(input())
check = False
for x in range(-10, 11):
for y in range(-10, 11):
if (a * x) + (b * y) != c:
y += 1
continue
if (a * x) + (b * y) == c and (d * x) + (e * y) != f:
y += 1
continue
if (a * x) + (b * y) == c and (d * x) + (e * y) == f:
check = True
print("x = {} , y = {}".format(x,y))
else:
x += 1
continue
if check == False:
print("There is no solution")
I have written a code to find Pythagorean triplets but it is not optimized
it took 5-6 minutes for the algorithm to find answer for big numbers...
my teacher said it should take less than 3 secs...
num = int(input())
def main(n):
for x in range(1, n):
for y in range(1, x):
for z in range(1, y):
if x + y + z == n:
if x * x == y * y + z * z or y * y == x * x + z * z or z * z == x * x + y * y:
a = f'{z} {y} {x}'
print(a)
return
else:
print('Impossible')
for example if you enter 12, you'll get 3,4,5
if you enter 30 , the answer will be 5,12,13
The sum of these three numbers must be equal to the number you entered.
can anyone please help me ?
Note the proof for the parametric representation of primitive pythagorean triples. In the proof, the author states:
We can use this proof to write an optimized algorithm:
def p(num):
a, b, c = 1, 1, 0
n = 0
while c < num:
for m in range(1, n):
a = 2 * m * n
b = n ** 2 - m ** 2
c = n ** 2 + m ** 2
if c >= num:
return "Impossible!"
elif a + b + c == num:
return b, a, c
n = n + 1
print(p(12)) # >>> (3, 4, 5)
print(p(30)) # >>> (5, 12, 13)
print(p(31)) # >>> Impossible!
You're doing a lot of repeated and unnecessary work. You know that A^2 + B^2 = C^2 and you know that C > B > A. It doesn't matter if you want to say C > A > B because any solution you find with that would be satisfied with C > B > A. For instance take 12 and solution 3, 4, 5. It doesn't actually matter if you say that A=3 and B=4 or A=4 and B=3. Knowing this we can adjust the loops of each for loop.
A can go from 1 to num, that's fine. Technically it can go to a bit less since you are adding another value to it that has to be at least 1 as well.
B then can go from A+1 to num since it needs to be greater than it.
So what about C? Well it doesnt' need to go from 1 since that's not possible. In fact we only care about A + B + C = num, so solve for C and you get C = num - A - B. That means you don't need to use a loop to find C since you can just solve for it. Knowing this you can do something like so:
In [142]: def find_triplet(num):
...: for a in range(1, num-1):
...: for b in range(a+1, num):
...: # A^2 + B^2 = C^2
...: # And A+B+C = N
...: c = num - a - b
...: if c > 0:
...: if a*a + b*b == c*c:
...: print(f'{a} {b} {c}')
...: else:
...: break
...:
In [143]: find_triplet(30)
5 12 13
So why check to see if C > 0 and break otherwise? Well, if you know C = num - A - B and you are incrementing B, then once B becomes too large, C is going to continue to get more and more negative. Because of that you can check if C > 0 and if it's not, break out of that inner loop to have A increment and B reset.
The approach you discussed takes O(n^3) complexity.
An efficient solution is to run two loops, where first loop runs from x = 1 to n/3, second loop runs from y = x+1 to n/2. In second loop, we check if (n – x – y) is equal to (x * x + y * y):
def pythagoreanTriplet(n):
# Considering triplets in
# sorted order. The value
# of first element in sorted
# triplet can be at-most n/3.
for x in range(1, int(n / 3) + 1):
# The value of second element
# must be less than equal to n/2
for y in range(x + 1,
int(n / 2) + 1):
z = n - x - y
if (x * x + y * y == z * z):
print(x, " ", y, " ", z)
return
print("Impossible")
# Driver Code
n = int(input())
pythagoreanTriplet(n)
PS: Time complexity = O(n^2)
The tree is as follows:
(1,1)
/ \
(2,1) (1,2)
/ \ / \
(3,1)(2,3) (3,2)(1,3)
and onward
The root is (1,1), all values in the tree are tuples.
Where (x,y) is an element of the tree:
The leftChild will be (x+y,y)
The rightChild will be (x,x+y)
I am building a function that finds the distance from the root (1,1). I cannot build the tree from scratch as it will be too time consuming.
I have found that 1 distance from the tuple being searched for we must subtract the the max with the min. We can work backward.
1 2
(3,2)->(1,2)->(1,1)
(3,2)->(3-2,2) = (1,2)->(1,2-1) = (1,1)
given this is always true:
if x > y:
newtuple = (x-y,y)
distance += 1
else if y > x:
newtuple = (x,y-x)
distance += 1
Yet because possible test cases can go up to even x = 10^50, this is even too slow.
So I have found a formally to find the amount of subtractions of x with y or vice versa to make a x > y change to y < x and vice versa until (x,y) = (1,1).
So X - Y(a certain amount of times, say z) will make x less than y...
X - Y*z = y
find z via algebra... z = (Y-X)/(-Y)
This is my code so far:
from decimal import Decimal
import math
def answer(M,F):
M = int(M)
F = int(F)
i = 0
while True:
if M == 1 and F == 1:
return str(i)
if M > F:
x = math.ceil((F-M)/(-F))
M -= F*x
elif F > M:
x = math.ceil((M-F)/(-M))
F -= M*x
else:
if F == M and F != 1:
return "impossible"
i += x
if M < 1 or F < 1:
return "impossible"
return str(i)
And it's not passing some unknown test cases, yet passes all the test cases I can think of. What test cases could I be failing? Where is my code wrong?
p.s. used Decimal module, just removed from code to make more readible.
Floor division allows for no loss, but maybe -1 in error which I consider for the code below.
def answer(M,F):
M = int(M)
F = int(F)
i = 0
while True:
if M == 1 and F == 1:
return str(i)
if M > F:
x = F-M
x = x//(-F)
if F < M-(F*x):
x += 1
M -= F*x
elif F > M:
x = M-F
x = x//(-M)
if M < F-(M*x):
x += 1
F -= M*x
else:
if F == M and F != 1:
return "impossible"
i += x
if M < 1 or F < 1:
return "impossible"
return str(i)
For a numerical methods class, I need to write a program to evaluate a definite integral with Simpson's composite rule. I already got this far (see below), but my answer is not correct. I am testing the program with f(x)=x, integrated over 0 to 1, for which the outcome should be 0.5. I get 0.78746... etc.
I know there is a Simpson's rule available in Scipy, but I really need to write it myself.
I suspect there is something wrong with the two loops. I tried "for i in range(1, n, 2)" and "for i in range(2, n-1, 2)" before, and this gave me a result of 0.41668333... etc.
I also tried "x += h" and I tried "x += i*h". The first gave me 0.3954, and the second option 7.9218.
# Write a program to evaluate a definite integral using Simpson's rule with
# n subdivisions
from math import *
from pylab import *
def simpson(f, a, b, n):
h=(b-a)/n
k=0.0
x=a
for i in range(1,n/2):
x += 2*h
k += 4*f(x)
for i in range(2,(n/2)-1):
x += 2*h
k += 2*f(x)
return (h/3)*(f(a)+f(b)+k)
def function(x): return x
print simpson(function, 0.0, 1.0, 100)
You probably forget to initialize x before the second loop, also, starting conditions and number of iterations are off. Here is the correct way:
def simpson(f, a, b, n):
h=(b-a)/n
k=0.0
x=a + h
for i in range(1,n/2 + 1):
k += 4*f(x)
x += 2*h
x = a + 2*h
for i in range(1,n/2):
k += 2*f(x)
x += 2*h
return (h/3)*(f(a)+f(b)+k)
Your mistakes are connected with the notion of a loop invariant. Not to get into details too much, it's generally easier to understand and debug cycles which advance at the end of a cycle, not at the beginning, here I moved the x += 2 * h line to the end, which made it easy to verify where the summation starts. In your implementation it would be necessary to assign a weird x = a - h for the first loop only to add 2 * h to it as the first line in the loop.
All you need to do to make this code work is add a variable for a and b in the function bounds() and add a function in f(x) that uses the variable x. You could also implement the function and bounds directly into the simpsonsRule function if desired... Also, these are functions to be implimented into a program, not a program itself.
def simpsonsRule(n):
"""
simpsonsRule: (int) -> float
Parameters:
n: integer representing the number of segments being used to
approximate the integral
Pre conditions:
Function bounds() declared that returns lower and upper bounds of integral.
Function f(x) declared that returns the evaluated equation at point x.
Parameters passed.
Post conditions:
Returns float equal to the approximate integral of f(x) from a to b
using Simpson's rule.
Description:
Returns the approximation of an integral. Works as of python 3.3.2
REQUIRES NO MODULES to be imported, especially not non standard ones.
-Code by TechnicalFox
"""
a,b = bounds()
sum = float()
sum += f(a) #evaluating first point
sum += f(b) #evaluating last point
width=(b-a)/(2*n) #width of segments
oddSum = float()
evenSum = float()
for i in range(1,n): #evaluating all odd values of n (not first and last)
oddSum += f(2*width*i+a)
sum += oddSum * 2
for i in range(1,n+1): #evaluating all even values of n (not first and last)
evenSum += f(width*(-1+2*i)+a)
sum += evenSum * 4
return sum * width/3
def bounds():
"""
Description:
Function that returns both the upper and lower bounds of an integral.
"""
a = #>>>INTEGER REPRESENTING LOWER BOUND OF INTEGRAL<<<
b = #>>>INTEGER REPRESENTING UPPER BOUND OF INTEGRAL<<<
return a,b
def f(x):
"""
Description:
Function that takes an x value and returns the equation being evaluated,
with said x value.
"""
return #>>>EQUATION USING VARIABLE X<<<
You can use this program for calculating definite integrals by using Simpson's 1/3 rule. You can increase your accuracy by increasing the value of the variable panels.
import numpy as np
def integration(integrand,lower,upper,*args):
panels = 100000
limits = [lower, upper]
h = ( limits[1] - limits[0] ) / (2 * panels)
n = (2 * panels) + 1
x = np.linspace(limits[0],limits[1],n)
y = integrand(x,*args)
#Simpson 1/3
I = 0
start = -2
for looper in range(0,panels):
start += 2
counter = 0
for looper in range(start, start+3):
counter += 1
if (counter ==1 or counter == 3):
I += ((h/3) * y[looper])
else:
I += ((h/3) * 4 * y[looper])
return I
For example:
def f(x,a,b):
return a * np.log(x/b)
I = integration(f,3,4,2,5)
print(I)
will integrate 2ln(x/5) within the interval 3 and 4
There is my code (i think that is the most easy method). I done this in jupyter notebook.
The easiest and most accurate code for Simpson method is 1/3.
Explanation
For standard method (a=0, h=4, b=12) and f=100-(x^2)/2
We got:
n= 3.0, y0 = 100.0, y1 = 92.0, y2 = 68.0, y3 = 28.0,
So simpson method = h/3*(y0+4*y1+2*y2+y3) = 842,7 (this is not true).
Using 1/3 rule we got:
h = h/2= 4/2= 2 and then:
n= 3.0, y0 = 100.0, y1 = 98.0, y2 = 92.0, y3 = 82.0, y4 = 68.0, y5 = 50.0, y6 = 28.0,
Now we calculate the integral for each step (n=3 = 3 steps):
Integral of the first step: h/3*(y0+4*y1+y2) = 389.3333333333333
Integral of the second step: h/3*(y2+4*y3+y4) = 325.3333333333333
Integral of the third step: h/3*(y4+4*y5+y6) = 197.33333333333331
Sum all, and we get 912.0 AND THIS IS TRUE
x=0
b=12
h=4
x=float(x)
h=float(h)
b=float(b)
a=float(x)
def fun(x):
return 100-(x**2)/2
h=h/2
l=0 #just numeration
print('n=',(b-x)/(h*2))
n=int((b-a)/h+1)
y = [] #tablica/lista wszystkich y / list of all "y"
yf = []
for i in range(n):
f=fun(x)
print('y%s' %(l),'=',f)
y.append(f)
l+=1
x+=h
print(y,'\n')
n1=int(((b-a)/h)/2)
l=1
for i in range(n1):
nf=(h/3*(y[+0]+4*y[+1]+y[+2]))
y=y[2:] # with every step, we deleting 2 first "y" and we move 2 spaces to the right, i.e. y2+4*y3+y4
print('Całka dla kroku/Integral for a step',l,'=',nf)
yf.append(nf)
l+=1
print('\nWynik całki/Result of the integral =', sum(yf) )
At the beginning I added simple data entry:
d=None
while(True):
print("Enter your own data or enter the word "test" for ready data.\n")
x=input ('Enter the beginning of the interval (a): ')
if x == 'test':
x=0
h=4 #krok (Δx)
b=12 #granica np. 0>12
#w=(20*x)-(x**2) lub (1+x**3)**(1/2)
break
h=input ('Enter the size of the integration step (h): ')
if h == 'test':
x=0
h=4
b=12
break
b=input ('Enter the end of the range (b): ')
if b == 'test':
x=0
h=4
b=12
break
d=input ('Give the function pattern: ')
if d == 'test':
x=0
h=4
b=12
break
elif d != -9999.9:
break
x=float(x)
h=float(h)
b=float(b)
a=float(x)
if d == None or d == 'test':
def fun(x):
return 100-(x**2)/2 #(20*x)-(x**2)
else:
def fun(x):
w = eval(d)
return w
h=h/2
l=0 #just numeration
print('n=',(b-x)/(h*2))
n=int((b-a)/h+1)
y = [] #tablica/lista wszystkich y / list of all "y"
yf = []
for i in range(n):
f=fun(x)
print('y%s' %(l),'=',f)
y.append(f)
l+=1
x+=h
print(y,'\n')
n1=int(((b-a)/h)/2)
l=1
for i in range(n1):
nf=(h/3*(y[+0]+4*y[+1]+y[+2]))
y=y[2:]
print('Całka dla kroku/Integral for a step',l,'=',nf)
yf.append(nf)
l+=1
print('\nWynik całki/Result of the integral =', sum(yf) )
def simps(f, a, b, N): # N must be an odd integer
""" define simpson method, a and b are the lower and upper limits of
the interval, N is number of points, dx is the slice
"""
integ = 0
dx = float((b - a) / N)
for i in range(1,N-1,2):
integ += f((a+(i-1)*dx)) + 4*f((a+i*dx)) + f((a+(i+1)*dx))
integral = dx/3.0 * integ
# if number of points is even, then error araise
if (N % 2) == 0:
raise ValueError("N must be an odd integer.")
return integral
def f(x):
return x**2
integrate = simps(f,0,1,99)
print(integrate)
Example of implementing Simpson's rule for integral sinX with a = 0 and b = pi/4. And use 10 panels for the integration
def simpson(f, a, b, n):
x = np.linspace(a, b, n+1)
w = 2*np.ones(n+1); w[0] = 1.0; w[-1] = 1;
for i in range(len(w)):
if i % 2 == 1:
w[i] = 4
width = x[1] - x[0]
area = 0.333 * width * np.sum( w*f(x))
return area
f = lambda x: np.sin(x)
a = 0.0; b = np.pi/4
areaSim = simpson(f, a, b, 10)
print(areaSim)