def visualize_predictions(self, X, M, title=None):
"""Visualize the predicted probabilities for each of the missing pixels."""
P = self.posterior_predictive_means(X, M)
imgs = np.where(M, X, P)
obs = np.where(M, X, 0.3)
pylab.figure('Observations')
pylab.matshow(util.arrange(obs.reshape((-1, IMAGE_DIM, IMAGE_DIM))),
fignum=False, cmap='gray')
pylab.title('Observations')
pylab.figure('Model predictions')
pylab.matshow(util.arrange(imgs.reshape((-1, IMAGE_DIM, IMAGE_DIM))),
fignum=False, cmap='gray')
if title is None:
title = 'Model predictions'
pylab.title(title)
pylab.draw()
def visualize_components(self, title=None):
"""Visualize the learned components. Each of the images shows the Bernoulli parameters
(probability of the pixel being 1) for one of the mixture components."""
pylab.figure('Mixture components')
pylab.matshow(util.arrange(self.params.theta.reshape((-1, IMAGE_DIM, IMAGE_DIM))),
fignum=False, cmap='gray')
if title is None:
title = 'Mixture components'
pylab.title(title)
pylab.draw()
diffcoef_img_path = os.path.join(results_path, 'diffcoef')
##########################################################################
#dpi設定
fine = 300
pylab.figure(figsize=(10, 4), dpi=fine)
#W0時点の乾燥時間dtを定義
dt = 1 * np.exp(-9223372036854775808)
# 重量測定をcsvファイルから読み込み
rawdata_df = pd.read_csv(rawdata_path)
#csvファイルから読み込んだデータを編集(dataframe : data_df)
util.arrange(rawdata_df, dt)
data_df, groups, days, weight = util.arrange(rawdata_df, dt)
data_df = data_df.dropna()
#legendを設定
legends_dict = util.make_legends(group_data)
def watercontent(data_df, days, color, legends_dict):
watercontent_df = pd.DataFrame(index=days, columns=groups)
for group in groups:
df = pd.DataFrame(index=days, columns=['ratio'])
for i in days:
# 乾燥日数ごとに平均の含水率算出
