def grade_Q9(): x = np.random.rand(100, 100) y = prj01.Q9(8).compile(golden.Builder())(x=x) x2 = prj01.Q3(x, x) x4 = prj01.Q3(x2, x2) x8 = prj01.Q3(x4, x4) return np.allclose(y, x8)
def grade_Q3(): a = np.array([[i for i in range(10)]]) b = a.transpose() c = prj01.Q3(a, b) d = prj01.Q3(b, a) if not hasattr(c, "shape") or not hasattr(d, "shape"): return False if c.shape != (1, 1) or d.shape != (10, 10): return False return c[0, 0] == 285 and all( [d[i, j] == i * j for i in range(10) for j in range(10)])
def grade_Q8(): A = np.random.rand(100, 100) x = np.random.rand(100, 1) b = np.random.rand(100, 1) y = prj01.Q8(A, b).compile(golden.Builder())(x=x) return np.allclose(y, prj01.Q2(prj01.Q3(A, x), b))
def grade_Q7(): a = np.random.rand(100, 50) b = np.random.rand(100, 10) c = np.random.rand(10, 50) d = prj01.Q7().compile(golden.Builder())(a=a, b=b, c=c) return np.allclose(d, prj01.Q2(a, prj01.Q3(b, c)))
def grade_Q5(): A = -np.random.rand(100, 100) + np.diag([100] * 100) b = np.random.rand(100, 1) x = prj01.Q5(A, b) Ax = prj01.Q3(A, x) return np.allclose(Ax, b)