Create logistic function and tests for generic cases

logistic.py

@@ -1 +1,9 @@
-# Your code goes here
+def f(x, r):
+    return r * x * (1-x)
+
+def iterate_f(it, x, r):
+    l = [x]
+    for i in range(it):
+        x = f(x, r)
+        l.append(x)
+    return l 

test_logistic.py

@@ -1,16 +1,72 @@
+import pytest
+import numpy as np
 from numpy.testing import assert_allclose
 
-from logistic import f
+from logistic import f, iterate_f
+from logistic_fit import fit_r
 
-# Add here your test for the logistic map
+cases = [
+        (0.1, 2.2, 0.198),
+        (0.2, 3.4, 0.544),
+        (0.5, 2.0, 0.5)
+    ]
+@pytest.mark.parametrize('x, r, expected', cases)
+def test_generic_cases(x, r, expected):
+    result = f(x, r)
+    assert_allclose(result, expected) 
 
 
-def test_f_corner_cases():
-    # Test cases are (x, r, expected)
-    cases = [
+cases = [
         (0, 1.1, 0),
         (1, 3.7, 0),
     ]
-    for x, r, expected in cases:
-        result = f(x, r)
-        assert_allclose(result, expected)
+@pytest.mark.parametrize('x, r, expected', cases)
+def test_f_corner_cases(x, r, expected):
+    result = f(x, r)
+    assert_allclose(result, expected)
+
+
+cases = [
+        (1, 0.1, 2.2, [0.1, 0.198]),
+        (4, 0.2, 3.4, [0.2, 0.544, 0.843418, 0.449019, 0.841163]),
+        (3, 0.5, 2, [0.5, 0.5, 0.5, 0.5])
+    ]
+@pytest.mark.parametrize('it, x, r, expected', cases)
+def test_iterate_f(it, x, r, expected):
+    result = iterate_f(it, x, r)
+    assert_allclose(result, expected, atol=0.000001)
+
+
+cases = [
+        (23, 0.3, 3.421),
+        (50, 0.6, 2.57),
+        (40, 0.8, 0),
+        (37, 0.1, 2.56)
+    ]
+@pytest.mark.parametrize('it, x0, r', cases)
+def test_fit_r(it, x0, r):
+    xs = iterate_f(it, x0, r)
+    result = fit_r(xs)
+    assert np.isclose(result, r, atol=0.0001)
+
+SEED = np.random.randint(0, 2**31)
+
+@pytest.fixture
+def random_state():
+    print(f"Seed: ", SEED)
+    random_state = np.random.RandomState(SEED)
+    return random_state
+
+
+def test_iterate_f_convergence(random_state):
+    r=1.5
+    it=35
+    expected = 1/3
+
+    for _ in range(100):
+        x0 = random_state.uniform(0.0001, 0.99999)
+
+        result = iterate_f(it, x0, r)
+
+        assert np.isclose(result[-1], expected, atol=0.0001)
+