33 lines
737 B
Python
33 lines
737 B
Python
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import numpy as np
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from logistic import iterate_f
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def fit_r(xs):
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""" Takes a population trajectory and returns the value of r that generated it.
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By far not the most efficient method, but it always finds the optimal value of r with 1/1000
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precision.
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Parameters
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----------
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xs : list of float
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A population trajectory.
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Returns
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-------
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r: float
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The value of r that generated the population trajectory.
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"""
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xs = np.asarray(xs)
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x0 = xs[0]
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it = len(xs) - 1
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def error(r):
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return np.linalg.norm(xs - iterate_f(it, x0, r))
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errors = []
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for r in np.linspace(0, 4, 4001):
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errors.append((r, error(r)))
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return min(errors, key=lambda x: x[1])[0]
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