approximation methods

Question

I attached image:

So in this image there is a diagram of the function, which is defined on the given points. For example on points x=1..N.

Another diagram, which was drawn as a semitransparent curve, That is what I want to get from the original diagram, i.e. I want to approximate the original function so that it becomes smooth.

Are there any methods for doing that?

I heard about least squares method, which can be used to approximate a function by straight line or by parabolic function. But I do not need to approximate by parabolic function. I probably need to approximate it by trigonometric function. So are there any methods for doing that? And one idea, is it possible to use the Least squares method for this problem, if we can deduce it for trigonometric functions?

One more question! If I use the discrete Fourier transform and think about the function as a sum of waves, so may be noise has special features by which we can define it and then we can set to zero the corresponding frequency and then perform inverse Fourier transform. So if you think that it is possible, then what can you suggest in order to identify the frequency of noise?

Answer 1

You could use a digital filter like a FIR filter. The simplest FIR filter is just a running average. For more sophisticated treatment look a something like a FFT.

Answer 2

This is called curve fitting. The best way to do this is to find a numeric library that can do it for you. Here is a page showing how to do this using scipy. The picture on that page shows what the code does:

Now it's only 4 lines of code, but the author doesn't explain it at all. I'll try to explain briefly here.

First you have to decide what form you want the answer to be. In this example the author wants a curve of the form

f(x) = p₀ cos (2π/p₁ x + p₂) + p₃ x

You might instead want the sum of several curves. That's OK; the formula is an input to the solver.

The goal of the example, then, is to find the constants p₀ through p₃ to complete the formula. scipy can find this array of four constants. All you need is an error function that scipy can use to see how close its guesses are to the actual sampled data points.

fitfunc = lambda p, x: p[0]*cos(2*pi/p[1]*x+p[2]) + p[3]*x # Target function
errfunc = lambda p: fitfunc(p, Tx) - tX # Distance to the target function

errfunc takes just one parameter: an array of length 4. It plugs those constants into the formula and calculates an array of values on the candidate curve, then subtracts the array of sampled data points tX. The result is an array of error values; presumably scipy will take the sum of the squares of these values.

Then just put some initial guesses in and scipy.optimize.leastsq crunches the numbers, trying to find a set of parameters p where the error is minimized.

p0 = [-15., 0.8, 0., -1.] # Initial guess for the parameters
p1, success = optimize.leastsq(errfunc, p0[:])

The result p1 is an array containing the four constants. success is 1, 2, 3, or 4 if ths solver actually found a solution. (If the errfunc is sufficiently crazy, the solver can fail.)

Answer 3

This looks like a polynomial approximation. You can play with polynoms in Excel ("Add Trendline" to a chart, select Polynomial, then increase the order to the level of approximation that you need). It shouldn't be too hard to find an algorithm/code for that. Excel can show the equation that it came up with for the approximation, too.

Answer 4

Use loess in R (free).

E.g. here the loess function approximates a noisy sine curve.

As you can see you can tweak the smoothness of your curve with span

Here's some sample R code from here:

Step-by-Step Procedure

Let's take a sine curve, add some "noise" to it, and then see how the loess "span" parameter affects the look of the smoothed curve.

1. Create a sine curve and add some noise:

   period <- 120 x <- 1:120 y <- sin(2*pi*x/period) + runif(length(x),-1,1)

2. Plot the points on this noisy sine curve:

   plot(x,y, main="Sine Curve + 'Uniform' Noise") mtext("showing loess smoothing (local regression smoothing)")

3. Apply loess smoothing using the default span value of 0.75:

   y.loess <- loess(y ~ x, span=0.75, data.frame(x=x, y=y))

4. Compute loess smoothed values for all points along the curve:

   y.predict <- predict(y.loess, data.frame(x=x))

5. Plot the loess smoothed curve along with the points that were already plotted:

   lines(x,y.predict)

Answer 5

Unfortunately many solutions here presented don't solve the problem and/or they are plain wrong. There are many approaches and they are specifically built to solve conditions and requirements you must be aware of !

a) Approximation theory: If you have a very sharp defined function without errors (given by either definition or data) and you want to trace it exactly as possible, you are using polynominal or rational approximation by Chebyshev or Legendre polynoms, meaning that you approach the function by a polynom or, if periodical, by Fourier series.

b) Interpolation: If you have a function where some points (but not the whole curve!) are given and you need a function to get through this points, you can use several methods:

Newton-Gregory, Newton with divided differences, Lagrange, Hermite, Spline

c) Curve fitting: You have a function with given points and you want to draw a curve with a given (!) function which approximates the curve as closely as possible. There are linear and nonlinear algorithms for this case.

Your drawing implicates:

• It is not remotely like a mathematical function.
• It is not sharply defined by data or function
• You need to fit the curve, not some points.

What do you want and need is

d) Smoothing: Given a curve or datapoints with noise or rapidly changing elements, you only want to see the slow changes over time.

You can do that with LOESS as Jacob suggested (but I find that overkill, especially because choosing a reasonable span needs some experience). For your problem, I simply recommend the running average as suggested by Jim C.

http://en.wikipedia.org/wiki/Running_average

Sorry, cdonner and Orendorff, your proposals are well-minded, but completely wrong because you are using the right tools for the wrong solution.

These guys used a sixth polynominal to fit climate data and embarassed themselves completely.

http://scienceblogs.com/deltoid/2009/01/the_australians_war_on_science_32.php

http://network.nationalpost.com/np/blogs/fullcomment/archive/2008/10/20/lorne-gunter-thirty-years-of-warmer-temperatures-go-poof.aspx