def OptimAlgo(self):
wind_tbl = pd.merge(self.data['wind'],
self.wind_dir,
how='left',
on='wind_dir').set_index(self.data['wind'].index)
cannon = np.array(self.data['balloon'].iloc[0, :])
cannon_0 = np.append(cannon, 0)
enemy = np.array(self.data['cannon'].iloc[0, :])
direc_init = (enemy - cannon) / la.norm(enemy - cannon)
print('cannon, enemy:', cannon, enemy)
sb.optim(100, 45, direc_init, cannon, enemy, wind_tbl)
def GridSearch(self):
self.OptimOutput.setPlainText('각도 계산 중...')
wind_tbl = pd.merge(self.data['wind'],
self.wind_dir,
how='left',
on='wind_dir').set_index(self.data['wind'].index)
cannon = np.array(self.data['cannon'].iloc[0, :])
cannon_0 = np.append(cannon, 0)
enemy = np.array(self.data['enemy'].iloc[0, :])
print('cannon, enemy:', cannon, enemy)
ran = 5000
under = la.norm(enemy - cannon)
print('밑변:', under)
if ran <= under:
QMessageBox.about(
self, "오류",
"포와 적의 거리가 너무 멀어서({}) 현재 사거리로는 목표지점에 착탄 불가합니다.".format(under))
else:
theta_init = np.arccos(under / ran)
print('theta_init:', theta_init)
direc_init = (enemy - cannon) / la.norm(enemy - cannon)
print('direc_init:', direc_init)
theta_grid = np.linspace(0.01, np.pi / 2 - 0.01, 300)
scores = np.array([
sb.optim(100, atheta, direc_init, cannon_0, enemy, wind_tbl,
ran) for atheta in theta_grid
])
theta_best = theta_grid[scores.argmin()]
opt_eval = (sb.shoot_for_optim(
100, cannon_0,
sb.ang2coord(cannon[:1], theta_best, direc_init, 5000),
wind_tbl, 5000))
self.OptimOutput.setPlainText(
'\n 최적화 해의 값을 가지고 100발 쐈을 때의 탄착군 중심: {}'.format(opt_eval))
self.ax.plot(theta_grid, scores)
self.canvas.draw()
def f(x):
return sb.optim(100, x[0], np.array([x[1], x[2]]), cannon,
enemy, wind_tbl)
def f(x):
return (sb.optim(100, x[0], np.array([x[1], x[2]]), cannon_0,
enemy, wind_tbl, ran))