Polynomial regression: Difference between revisions
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>>> x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
>>> x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] |
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>>> y = [1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321] |
>>> y = [1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321] |
Revision as of 06:41, 5 February 2008
Polynomial regression
You are encouraged to solve this task according to the task description, using any language you may know.
You are encouraged to solve this task according to the task description, using any language you may know.
Find an approximating polynom of known degree for a given data.
Example
For input data:
x = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; y = {1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321};
The approximatting polynom is:
2 3 x + 2 x + 1
Here, polynom's coefficients are (3, 2, 1).
This task is intended as a subtask for Measure relative performance of sorting algorithms implementations.
Python
Interpreter: Python
>>> x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] >>> y = [1, 6, 17, 34, 57, 86, 121, 162, 209, 262, 321] >>> coeffs = numpy.polyfit(x,y,deg=2) >>> coeffs array([ 3., 2., 1.])
Substitute back received coefficients.
>>> yf = numpy.polyval(numpy.poly1d(coeffs), x) >>> yf array([ 1., 6., 17., 34., 57., 86., 121., 162., 209., 262., 321.])
Find max absolute error.
>>> '%.1g' % max(y-yf) '1e-013'
Example
For input arrays `x' and `y':
>>> x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] >>> y = [2.7, 2.8, 31.4, 38.1, 58.0, 76.2, 100.5, 130.0, 149.3, 180.0]
>>> p = numpy.poly1d(numpy.polyfit(x, y, deg=2), variable='N') >>> print p 2 1.085 N + 10.36 N - 0.6164
Thus we confirm once more that for already sorted sequences the considered quick sort implementation has quadratic dependence on sequence length (see Example section for Python language on Query Performance page).