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 2 years ago
How to make a function that finds the derivative using python? Is there a builtin one?
 2 years ago
How to make a function that finds the derivative using python? Is there a builtin one?

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Chris2332
 2 years ago
Best ResponseYou've already chosen the best response.0Just create a recursive function that multiplies the factor by the exponential and then decrements the exponent with each call.

Rohangrr
 2 years ago
Best ResponseYou've already chosen the best response.1Binding variables as if you want to find the area of a circle use this >>>> pi = 3.14159 >>>> radius = 11.2 >>>> area = pi( radius ** 2) >>>> area = 394.081

Chris2332
 2 years ago
Best ResponseYou've already chosen the best response.0Huh? Did I miss something?

ParthKohli
 2 years ago
Best ResponseYou've already chosen the best response.0@chris2332: That doesn't work for other functions... only polynomials.

Chris2332
 2 years ago
Best ResponseYou've already chosen the best response.0Something like that? def derivative(f): ....""" ....Computes the numerical derivative of a function. ....""" ....def df(x, h=0.1e5): ........return ( f(x+h/2)  f(xh/2) )/h ....return df

Chris2332
 2 years ago
Best ResponseYou've already chosen the best response.0Then what derivative do you want?

Rohangrr
 2 years ago
Best ResponseYou've already chosen the best response.1The mathematical definition of the derivative of a function f(x) at point x is to take a limit as "h" goes to zero of the following expression: ( f(x+h)  f(x) ) / h An example is given below: the red curve is the function f(x) and the green curve is the tangent line at x (with same slope as the derivative). The blue curve is a line going through x whose slope equals that of the above formula with a nonzero value of h. A better way of numerically computing the derivative for the same value of "h" is by taking a symmetrical interval around x as follows: ( f(x + h/2)  f(x  h/2) ) / h This is illustrated in the next picture. As you can see, the two straight lines have much more similar slopes  hence, the value computed for the derivative is going to be more accurate. The corresponding python code is as follows: def derivative(f): ....""" ....Computes the numerical derivative of a function. ....""" ....def df(x, h=0.1e5): ........return ( f(x+h/2)  f(xh/2) )/h ....return df And we use it as follows: # sample function def g(x): return x*x*x # first derivative dg = derivative(g) # second derivative d2g = derivative(dg) # == derivative(derivative(g)) # printing the value computed at a given point: print dg(3) print dg(3, 0.001) print dg(3, 1e10) # smaller h is not always more precise source : http://aroberge.blogspot.in/2005/04/computingderivativesusingpython.html

Chris2332
 2 years ago
Best ResponseYou've already chosen the best response.0Exactly... that's where I got the snippet from!

Rohangrr
 2 years ago
Best ResponseYou've already chosen the best response.1@ParthKohli where u r??

estudier
 2 years ago
Best ResponseYou've already chosen the best response.1from sympy import init_printing, symbols, ln, diff init_printing() x,y = symbols('x y') f = x**2 / y + 2 * x  ln(y) diff(f,x) > 2*x/y +2

estudier
 2 years ago
Best ResponseYou've already chosen the best response.1I am assuming u do not want to reinvent the wheel, so just use sympy....

TuringTest
 2 years ago
Best ResponseYou've already chosen the best response.0The next Pset in 6.00x has one problem in which to find the derivative of polynomials written as lists. I would show you the program I wrote but I don't want to give out the answer before the due date.

ParthKohli
 2 years ago
Best ResponseYou've already chosen the best response.0Heh, I'm not following that course.

TuringTest
 2 years ago
Best ResponseYou've already chosen the best response.0then I will pm my code to you, but don't give it to anyone taking the course

ParthKohli
 2 years ago
Best ResponseYou've already chosen the best response.0Yup! I discovered this Python group so thought I'd ask...

estudier
 2 years ago
Best ResponseYou've already chosen the best response.1numPy/sciPy are good too....
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