def run_steepest_func_2():
csv_table = CSVFormatter()
# x_0 * x_1 + (1-x_0)^2 + (2-x_1)^2+ (3-x_2)^2+ (4-x_3)^2+ (5-x_4)^2+ (6-x_5)^2+ (7-x_6)^2+ (8-x_7)^2+ (9-x_8)^2+ (10-x_9)^2
## 10 Dimension
test_func = lambda x: x[0] * x[1] + sum([(ele - x[ele - 1])**2
for ele in range(1, 11)])
fprime = lambda x: [x[1] + -2 * (1 - x[0]),
x[0] + -2 * (2 - x[1])] + \
[-2*(ele - x[ele - 1]) for ele in range(3, 11)]
csv_table.add_row(STEEP_HEADER)
x0_param = [[random.gauss(-10, 10) for _ in range(10)]
for _ in range(NUM_RUNS)]
eps = 1e-14
for i in range(10):
gc_iterations_param_dist = [10] * NUM_RUNS
epsilon_abs_param_dist = [eps] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
alternate_method, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list, (1, 1), 1)
print(stats)
csv_table.add_row([
epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1])
])
eps *= 10
csv_table.export_table("steepest_func2.csv")
print("Finished steepest decent function 2")
csv_table.clear_table()
def run_steepest_func_1():
csv_table = CSVFormatter()
## FIRST FUNCTION
## Rosenblock
test_func = lambda x: (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2 + 1
fprime = lambda x: (2 *
(200 * x[0]**3 - 200 * x[0] * x[1] + x[0] - 1), 200 *
(x[1] - x[0]**2))
csv_table.add_row(STEEP_HEADER)
# Func 1 epsilon variation
x0_param = [(random.gauss(-10, 10), random.gauss(-10, 10))
for _ in range(NUM_RUNS)]
eps = 1e-14
for i in range(10):
gc_iterations_param_dist = [10] * NUM_RUNS
epsilon_abs_param_dist = [eps] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
alternate_method, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list, (1, 1), 1)
print(stats)
csv_table.add_row([
epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1])
])
eps *= 10
csv_table.export_table("steepest_func1.csv")
print("Finished steepest decent function 1")
csv_table.clear_table()
def run_cg_func_2():
csv_table = CSVFormatter()
test_func = lambda x: x[0] * x[1] + sum([(ele - x[ele - 1])**2
for ele in range(1, 11)])
fprime = lambda x: [x[1] + -2 * (1 - x[0]),
x[0] + -2 * (2 - x[1])] + \
[-2*(ele - x[ele - 1]) for ele in range(3, 11)]
x0_param = [[random.gauss(-10, 10) for _ in range(10)]
for _ in range(NUM_RUNS)]
epsilon_abs_param_dist = [1.14e-14] * NUM_RUNS
csv_table.add_row(CG_HEADER)
for i in range(NUM_VARIATIONS):
gc_iterations_param_dist = [random.randint(2, NUM_VARIATIONS)
] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
conjugate_gradients, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], gc_iterations_param_dist,
epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list,
(0, 2, 3, 4, 5, 6, 7, 8, 9, 10),
1)
print(stats)
csv_table.add_row([
gc_iterations_param_dist[i], epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1]),
CELL_FORMAT_STR.format(stats[2][0], stats[2][1])
])
csv_table.export_table("function2_gc_freq_variation.csv")
csv_table.clear_table()
print("Finished function 2 GC restart variation")
csv_table.add_row(CG_HEADER)
# Func 1 epsilon variation
eps = 1e-14
for i in range(10):
gc_iterations_param_dist = [10] * NUM_RUNS
epsilon_abs_param_dist = [eps] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
conjugate_gradients, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], gc_iterations_param_dist,
epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list,
(0, 2, 3, 4, 5, 6, 7, 8, 9, 10),
1)
print(stats)
csv_table.add_row([
gc_iterations_param_dist[i], epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1]),
CELL_FORMAT_STR.format(stats[2][0], stats[2][1])
])
eps *= 10
csv_table.export_table("function2_epsilon_variation.csv")
print("Finished function 2 epsilon variation")
csv_table.clear_table()
def run_cg_func_1():
csv_table = CSVFormatter()
## FIRST FUNCTION
## Rosenblock
test_func = lambda x: (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2 + 1
fprime = lambda x: (2 *
(200 * x[0]**3 - 200 * x[0] * x[1] + x[0] - 1), 200 *
(x[1] - x[0]**2))
# Vary GC restart frequency (2->50 drawn from uniform distribution
# Func 1 conj variation
epsilon_abs_param_dist = [1.14e-14] * NUM_RUNS
x0_param = [(random.gauss(-10, 10), random.gauss(-10, 10))
for _ in range(NUM_RUNS)]
csv_table.add_row(CG_HEADER)
for i in range(NUM_VARIATIONS):
gc_iterations_param_dist = [random.randint(2, NUM_VARIATIONS)
] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
conjugate_gradients, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], gc_iterations_param_dist,
epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list, (1, 1), 1)
print(stats)
csv_table.add_row([
gc_iterations_param_dist[i], epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1]),
CELL_FORMAT_STR.format(stats[2][0], stats[2][1])
])
csv_table.export_table("function1_gc_freq_variation.csv")
csv_table.clear_table()
print("Finished function 1 GC restart variation")
csv_table.add_row(CG_HEADER)
# Func 1 epsilon variation
eps = 1e-14
for i in range(10):
gc_iterations_param_dist = [10] * NUM_RUNS
epsilon_abs_param_dist = [eps] * NUM_RUNS
run_report_list = TestHarness.test_optimizer(
conjugate_gradients, test_func, x0_param,
[fprime for _ in range(NUM_RUNS)], gc_iterations_param_dist,
epsilon_abs_param_dist)
stats = StatsGenerator.generate_stats(run_report_list, (1, 1), 1)
print(stats)
csv_table.add_row([
gc_iterations_param_dist[i], epsilon_abs_param_dist[i],
CELL_FORMAT_STR.format(stats[0][0], stats[0][1]),
CELL_FORMAT_STR.format(stats[1][0], stats[1][1]),
CELL_FORMAT_STR.format(stats[2][0], stats[2][1])
])
eps *= 10
csv_table.export_table("function1_epsilon_variation.csv")
print("Finished function 1 epsilon variation")
csv_table.clear_table()
