initial commit with 2024 materials

testing_project/solution/logistic_fit.py

@@ -0,0 +1,32 @@
+import numpy as np
+
+from logistic import iterate_f
+
+
+def fit_r(xs):
+    """ Takes a population trajectory and returns the value of r that generated it.
+
+    By far not the most efficient method, but it always finds the optimal value of r with 1/1000
+    precision.
+
+    Parameters
+    ----------
+    xs : list of float
+        A population trajectory.
+
+    Returns
+    -------
+    r: float
+        The value of r that generated the population trajectory.
+    """
+    xs = np.asarray(xs)
+    x0 = xs[0]
+    it = len(xs) - 1
+
+    def error(r):
+        return np.linalg.norm(xs - iterate_f(it, x0, r))
+
+    errors = []
+    for r in np.linspace(0, 4, 4001):
+        errors.append((r, error(r)))
+    return min(errors, key=lambda x: x[1])[0]