testing_project/logistic.py

@@ -1 +1,14 @@
 # Your code goes here
+import numpy as np
+
+def f(x, r):
+    return r * x * (1 - x)
+
+def iterate_f(x, r, n_iterations):
+    trajectory = np.zeros(n_iterations+1)
+    trajectory[0] = x
+    for i in range(n_iterations):
+        trajectory[i+1] = f(x, r)
+        x = trajectory[i+1]
+    return trajectory
+

testing_project/logistic_fit.py

@@ -24,7 +24,7 @@ def fit_r(xs):
     it = len(xs) - 1
 
     def error(r):
-        return np.linalg.norm(xs - iterate_f(it, x0, r))
+        return np.linalg.norm(xs - iterate_f(x0, r, it))
 
     errors = []
     for r in np.linspace(0, 4, 4001):

testing_project/test_logistic.py

@@ -1,6 +1,7 @@
+import pytest
 from numpy.testing import assert_allclose
 
-from logistic import f
+from logistic import f, iterate_f
 
 
 def test_f_corner_cases():
@@ -24,6 +25,30 @@ def test_f_corner_cases():
 # parametrize the above test using @pytest.mark.parametrize
 
 
+@pytest.mark.parametrize(
+    'x, r, expected',
+    [
+        (0.1, 2.2 ,0.198),
+        (0.2, 3.4 ,0.544),
+        (0.5, 2   ,0.5)
+    ]
+)
+def test_f_generic_cases(x, r, expected):
+    result = f(x, r)
+    assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize(
+    'x, r, n_iterations, expected',
+    [
+        (0.1, 2.2 , 1, [0.1, 0.198]),
+        (0.2, 3.4 , 4, [0.2, 0.544, 0.843418, 0.449019, 0.841163]),
+        (0.5, 2, 3, [0.5, 0.5, 0.5, 0.5])
+    ]
+)
+def test_iterate_f_generic(x, r, n_iterations, expected):
+    result = iterate_f(x, r, n_iterations)
+    assert_allclose(result, expected, rtol=1e-6)
 # Hands on 3
 # Implement a function iterate_f that runs f for it iterations. Write tests for the following cases:
 # x=0.1, r=2.2, it=1 => iterate_f(it, x, r)=[0.1, 0.198]

testing_project/test_logistic_fit.py

@@ -0,0 +1,22 @@
+from numpy.testing import assert_allclose
+import numpy as np
+
+from logistic import iterate_f
+from logistic_fit import fit_r
+
+def test_fit_r():
+    r = 3.421
+    trajectory = iterate_f(0.3, r, 23)
+    fit_result_r = fit_r(trajectory)
+    assert_allclose(r, fit_result_r)
+
+
+def test_fit_r_randomized():
+    random_state = np.random.RandomState(42)
+    x0 = 0.3
+    for _ in range(100):
+        r = random_state.rand()
+        trajectory = iterate_f(x0, r, 23)
+        fit_result_r = fit_r(trajectory)
+        assert_allclose(r, fit_result_r, atol=0.1)
+