def main():
#モデルデータを取得する
import test_data
data = test_data.get_data()
history = {"URL0" : [100, 101, 102, 103, 104, 105, 106, 107, 108],
"URL2" : [100, 101, 102, 103, 104, 104, 104, 104, 104],
"URL3" : [100, 101, 102, 103, 104, 106, 108, 110, 112]}
#GPを計算する
i = 0
for d in data:
sim = mc_sim(data = d, history = history)
#GPを出力する
x = sim.get_gp()
print "TEST CASE ", i, ": ", x
i += 1
def test_data_3():
# NGC3516
import test_data
x, y, e = test_data.get_data()
return x, y
break
grid[y * height + padding:(y + 1) * height,
x * width + padding:(x + 1) * width, :] = de_norm_img[k]
k = k + 1
return grid
def save_samples(gen_sample, save_path, epoch, step, i=0, color='rgb'):
gen_sample = de_norm(gen_sample)
img_name = 'epoch-{:06d}-step-{:06d}-num-{:02d}.jpg'.format(epoch, step, i)
if color == 'rgb':
plt.imsave(os.path.join(save_path, img_name), gen_sample)
else:
cv.imwrite(os.path.join(save_path, img_name), gen_sample)
return
if __name__ == '__main__':
import test_data
cifar_data = test_data.get_data(batch_size=36, data_name='cifar10')
for imgs, labels in cifar_data:
de_norm_img_ = make_gird(imgs, denorm=True, padding=0)
print(np.max(de_norm_img_))
print(np.min(de_norm_img_))
print(de_norm_img_.dtype)
plt.imshow(de_norm_img_)
plt.axis('off')
plt.show()
break
ax1.set_xlabel('# Epochs', fontsize=12)
plt.show()
############
# 定数定義 #
############
# windowを設定
_WINDOW_LEN = 10
##############
# データ取得 #
##############
df = get_data()
# df.plot()
# plt.show()
##############
# データ加工 #
##############
def make_data_for_lstm(in_data):
_lstm_in = []
_data = pd.DataFrame({'noisy wave': in_data['noisy wave']})
for i in range(len(_data) - _WINDOW_LEN):
temp = _data[i:(i + _WINDOW_LEN)].copy()
_lstm_in.append(temp)
_lstm_in = [np.array(_lstm_input) for _lstm_input in _lstm_in]
return wave, flux
# Example usage
def print_results(wave, flux, converted_wave, converted_flux):
for k in range(4):
print(wave[k], " ", converted_wave[k])
for k in range(4):
print(flux[k], " ", converted_flux[k])
import test_data
if __name__ == "__main__":
# real emission line from UV spectrum of NGC3516
x, y, e = test_data.get_data()
# test conversions from SpectrumData objects.
sp_data = SpectrumData()
sp_data.set_x(x, unit="Angstrom")
sp_data.set_y(y, unit="erg.s-1.cm**-2.Angstrom-1") # flam
converter = UnitsConverter(u.micron, u.Jy)
converter.convertSpectrumData(sp_data)
print(sp_data.x.unit, " ", sp_data.y.unit)
for k in range(4):
print(sp_data.x.data[k], " ", sp_data.y.data[k])
# test conversions from Quantity objects.
wave = test_data.get_data()[0] * u.Unit('angstrom')
raise e
return wave, flux
# Example usage
def print_results(wave, flux, converted_wave, converted_flux):
for k in range(4):
print(wave[k], " ", converted_wave[k])
for k in range(4):
print(flux[k], " ", converted_flux[k])
import test_data
if __name__ == "__main__":
# real emission line from UV spectrum of NGC3516
x,y,e = test_data.get_data()
# test conversions from SpectrumData objects.
sp_data = SpectrumData()
sp_data.set_x(x, unit="Angstrom")
sp_data.set_y(y, unit="erg.s-1.cm**-2.Angstrom-1") # flam
converter = UnitsConverter(u.micron, u.Jy)
converter.convertSpectrumData(sp_data)
print(sp_data.x.unit, " ", sp_data.y.unit)
for k in range(4):
print(sp_data.x.data[k], " ", sp_data.y.data[k])
import test_data
from KNN import KNN
import numpy as np
X_train, X_test, Y_train, Y_test = test_data.get_data()
knn = KNN(k=3)
knn.fit(X_train, Y_train)
y_hat = knn.predict(X_test)
performance = knn.evaluate(Y_test)
if __name__ == '__main__':
print('Length of Y_test: {}'.format(len(Y_test)))
print('Length of Y_hat: {}'.format(len(y_hat)))
print('Y_hat output: {}'.format(y_hat))
print('Model accuracy: {}%'.format(performance))
def test_data_3():
# NGC3516
import test_data
x,y,e = test_data.get_data()
return x,y
