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powei-lin · Apr 12, 2024 · bd9e6b4 · bd9e6b4
1 parent adb51aa
commit bd9e6b4
Showing 1 changed file with 35 additions and 37 deletions.
diff --git a/examples/python/try_import.py b/examples/python/try_import.py
@@ -1,5 +1,5 @@
 import tiny_solver
-from tiny_solver import GaussNewtonOptimizer, Problem, LinearSolver, OptimizerOptions, first_derivative_test
+from tiny_solver import GaussNewtonOptimizer, Problem, LinearSolver, OptimizerOptions 
 from tiny_solver.factors import PriorFactor, BetweenFactorSE2, PyFactor
 from tiny_solver.loss_functions import HuberLoss
 import numpy as np
@@ -19,42 +19,40 @@ def main():
     print(f"{tiny_solver.__version__=}")
     print(dir(tiny_solver))
 
-    print(dir(LinearSolver.SparseCholesky))
-    opt_option = OptimizerOptions(linear_solver_type=LinearSolver.SparseQR, max_iteration=12, verbosity_level=1)
-    print(opt_option)
-    loss = HuberLoss(1.0)
-    print(loss)
-    a = np.array([1.0, 2.0])
-    # j = first_derivative_test(f, a)
-    # print(j)
-    a = PyFactor(f)
-    a.call_func()
-    exit()
-    # print(tiny_solver.sum_as_string(1, 2))
-
-    # tiny_solver.mult(np.zeros((1, 2)))
-    # a = tiny_solver.Dual64()
-    # print(a.first_derivative)
-    b = BetweenFactorSE2(1.0, 2.0, 3.0)
-    # print("factor module\n", dir(factors))
-    # b = Costf(1.0, 2.0, 3.0)
-
-    print(type(b))
-    print(dir(b))
-    print(b)
-    problem = Problem()
-    print(dir(problem))
-    problem.num = 200
-    print(problem.num)
-    d = PriorFactor(np.array([1.0, 2.0, 3.0]))
-    problem.add_residual_block(1, [("aa", 1)], d)
-    problem.add_residual_block(1, [("aaa", 1)], b)
-    # c.add_residual_block(1, [("aaa", 1)])
-    # c.add_residual_block(1, [("aa", 1)])
-    # d = tiny_solver.BetweenFactor()
-    # d.ttt()
-    # tiny_solver.te(d)
-    optimizer = GaussNewtonOptimizer()
+    # print(dir(LinearSolver.SparseCholesky))
+    # opt_option = OptimizerOptions(linear_solver_type=LinearSolver.SparseQR, max_iteration=12, verbosity_level=1)
+    # print(opt_option)
+    # loss = HuberLoss(1.0)
+    # print(loss)
+    # a = np.array([1.0, 2.0])
+    # # j = first_derivative_test(f, a)
+    # # print(j)
+    # a = PyFactor(f)
+    # # print(tiny_solver.sum_as_string(1, 2))
+
+    # # tiny_solver.mult(np.zeros((1, 2)))
+    # # a = tiny_solver.Dual64()
+    # # print(a.first_derivative)
+    # b = BetweenFactorSE2(1.0, 2.0, 3.0)
+    # # print("factor module\n", dir(factors))
+    # # b = Costf(1.0, 2.0, 3.0)
+
+    # print(type(b))
+    # print(dir(b))
+    # print(b)
+    # problem = Problem()
+    # print(dir(problem))
+    # problem.num = 200
+    # print(problem.num)
+    # d = PriorFactor(np.array([1.0, 2.0, 3.0]))
+    # problem.add_residual_block(1, [("aa", 1)], d)
+    # problem.add_residual_block(1, [("aaa", 1)], b)
+    # # c.add_residual_block(1, [("aaa", 1)])
+    # # c.add_residual_block(1, [("aa", 1)])
+    # # d = tiny_solver.BetweenFactor()
+    # # d.ttt()
+    # # tiny_solver.te(d)
+    # optimizer = GaussNewtonOptimizer()
     # optimizer.optimize(problem, {"aa": np.array([123, 2, 3, 4], dtype=np.float64)})