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