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