testing_project/logistic.py

@@ -1 +1,11 @@
 # Your code goes here
+
+def f(x, r):
+    return r * x * (1-x) 
+
+def iterate_f(it, x, r):
+    result = [x]
+    for i in range(it):
+        x = f(x, r)
+        result.append(x)
+    return result

testing_project/test_logistic.py

@@ -1,15 +1,11 @@
 from numpy.testing import assert_allclose
-
+import pytest
-from logistic import f
+from logistic import f, iterate_f
 
 
-def test_f_corner_cases():
+@pytest.mark.parametrize('x,r,expected', [(0, 1.1, 0),(1, 3.7, 0)])
+def test_f_corner_cases(x, r, expected):
     # Test cases are (x, r, expected)
-    cases = [
-        (0, 1.1, 0),
-        (1, 3.7, 0),
-    ]
-    for x, r, expected in cases:
     result = f(x, r)
     assert_allclose(result, expected)
 
@@ -19,6 +15,15 @@ def test_f_corner_cases():
 # x=0.2, r=3.4 => f(x, r)=0.544
 # x=0.5, r=2   => f(x, r)=0.5
 
+def test_f_normal_cases():
+    cases = [
+        (.1, 2.2, .198),
+        (.2, 3.4, .544),
+        (.5, 2, .5),
+    ]
+    for x, r, expected in cases:
+        result = f(x, r)
+        assert_allclose(result, expected)
 
 # Hands on 2:
 # parametrize the above test using @pytest.mark.parametrize
@@ -29,3 +34,11 @@ def test_f_corner_cases():
 # x=0.1, r=2.2, it=1 => iterate_f(it, x, r)=[0.1, 0.198]
 # x=0.2, r=3.4, it=4 => iterate_f(it, x, r)=[0.2, 0.544, 0.843418, 0.449019, 0.841163]
 # x=0.5, r=2, it=3 => iterate_f(it, x, r)=[0.5, 0.5, 0.5]
+@pytest.mark.parametrize('x,r,it,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(x, r, it, expected):
+    result = iterate_f(it, x, r)
+    assert_allclose(result, expected, rtol=1e-06)

testing_project/test_logistic_fit.py

@@ -0,0 +1,9 @@
+from numpy.testing import assert_allclose
+import pytest
+from logistic_fit import fit_r
+from logistic import iterate_f
+
+def test_logistic_fit(x0=0.3, r=3.421, it=23):
+    xs = iterate_f(it, x0, r)
+    r_fitted = fit_r(xs)
+    assert_allclose(r, r_fitted)