Disabled external gits

cs457-gc/assignment_1_3/test/test3.py

@@ -0,0 +1,67 @@
+# import pytest
+import time
+import pytest
+import json
+import sys
+import igl
+import numpy as np
+sys.path.append('../')
+sys.path.append('../src')
+from src.energies import *
+from src.linesearch import *
+from src.bfgs import *
+eps = 1E-6
+
+with open('test_data3.json', 'r') as infile:
+    homework_datas = json.load(infile)
+
+@pytest.mark.timeout(1)
+@pytest.mark.parametrize("data", homework_datas[0])
+def test_armijo_rule(data):
+    Fx = data[0]
+    Fy = data[1]
+    p =  np.array(data[2], dtype=float)
+    grad =  np.array(data[3], dtype=float)
+    c = data[4]
+    alpha = data[5]
+    armijo_rule_ground_truth = data[6]
+    armijo_rule_student = int(evaluate_armijo_rule(Fx, Fy, p, grad, c, alpha))
+    assert armijo_rule_ground_truth == armijo_rule_student
+
+def func(x):
+    return np.linalg.norm(x) ** 2
+
+@pytest.mark.timeout(1)
+@pytest.mark.parametrize("data", homework_datas[1])
+def test_backtracking_line_search(data):
+    p =  np.array(data[0], dtype=float)
+    grad = np.array(data[1], dtype=float)
+    x = np.array(data[2], dtype=float)
+    theta = data[3]
+    beta = data[4]
+    c = data[5]
+    backtracking_line_search_ground_truth = data[6]
+    backtracking_line_search_student = backtracking_line_search(p, grad, x, theta, beta, c, func)
+
+    assert np.linalg.norm(backtracking_line_search_ground_truth - backtracking_line_search_student) < eps
+
+@pytest.mark.timeout(1)
+@pytest.mark.parametrize("data", homework_datas[2])
+def test_compute_approximate_hessian_matrix(data):
+    sk = np.array(data[0], dtype=float)
+    yk = np.array(data[1], dtype=float)
+    Bk = np.array(data[2], dtype=float)
+
+    newBk_student = compute_approximate_hessian_matrix(sk, yk, Bk)
+    newBk_ground_truth = np.array(data[3], dtype=float)
+    assert np.linalg.norm(newBk_student - newBk_ground_truth) < eps
+
+@pytest.mark.timeout(1)
+@pytest.mark.parametrize("data", homework_datas[3])
+def test_compute_inverse_approximate_hessian_matrix(data):
+    sk = np.array(data[0], dtype=float)
+    yk = np.array(data[1], dtype=float)
+    invBk = np.array(data[2], dtype=float)
+    inv_newBk_student = compute_inverse_approximate_hessian_matrix(sk, yk, invBk)
+    inv_newBk_ground_truth = np.array(data[3], dtype=float)
+    assert np.linalg.norm(inv_newBk_student - inv_newBk_ground_truth) < eps