# # Normalise data to zero mean and unit variance. # scaler = StandardScaler() # normalized_hinselmann_data = scaler.fit_transform(hinselmann_data) # normalized_green_data = scaler.fit_transform(green_data) # normalized_schiller_data = scaler.fit_transform(schiller_data) # Define all possible {source, target} pairs from list of modalities. modalities = ['h', 'g', 's'] tasks = permutations(modalities, r=2) # Loop through each task. for (source, target) in tasks: # Define file path for results. dataset_str = 'colposcopy_{}_{}'.format(source, target) dp_gp_lvm_results_file = RESULTS_FILE_NAME.format(model='dp_gp_lvm', dataset=dataset_str) # Visualise results if they exist. if isfile(dp_gp_lvm_results_file): results = np.load(dp_gp_lvm_results_file) ground_truth = results['y_test_unobserved'][:, :-1] predicted_mean_assessments = results['predicted_mean'][:, :-1] plot.imshow(ground_truth, interpolation='nearest', aspect='auto', extent=(0, ground_truth.shape[1], ground_truth.shape[0], 0), origin='upper') plot.colorbar()
from src.utils.constants import ResultKeys, RESULTS_FILE_NAME, PLOTS_PATH import src.visualisation.plotters as vis import matplotlib.pyplot as plot import numpy as np from os.path import isfile if __name__ == '__main__': # Define booleans. TODO: Allow them to be set from command line. save_plots = False show_plots = True dataset_str = 'cmu_walking_normal_swapped' bgplvm_results_file = RESULTS_FILE_NAME.format(model='bgplvm', dataset=dataset_str) # 93 dims per view. mrd_results_file = RESULTS_FILE_NAME.format(model='mrd', dataset=dataset_str) # 3 dims per view so keep 3d points together. mrd_fully_independent_results_file = RESULTS_FILE_NAME.format( model='mrd_fully_independent', dataset=dataset_str) # Keep 3d points together so mask of 3. gpdp_results_file = RESULTS_FILE_NAME.format(model='dp_gp_lvm', dataset=dataset_str) # Keep skeletons together so mask of 93. gpdp_mask_results_file = RESULTS_FILE_NAME.format( model='dp_gp_lvm_mask_93', dataset=dataset_str)
normal_swapped_motions[::4]) # subsample frames. num_samples, num_output_dimensions = y_train.shape # Print info. print('\nCMU Walking 35 with Normal and Swapped Legs Motion:') print(' Total number of observations (N): {}'.format(num_samples)) print(' Total number of output dimensions (D): {}'.format( num_output_dimensions)) print(' Total number of inducing points (M): {}'.format( num_inducing_points)) print(' Total number of latent dimensions (Q): {}'.format( num_latent_dimensions)) # Define file path for results. dataset_str = 'cmu_walking_normal_swapped' bgplvm_results_file = RESULTS_FILE_NAME.format(model='bgplvm', dataset=dataset_str) mrd_results_file = RESULTS_FILE_NAME.format( model='mrd', dataset=dataset_str) # 93 dims per view. # 3 dims per view so keep 3d points together. mrd_fully_independent_results_file = RESULTS_FILE_NAME.format( model='mrd_fully_independent', dataset=dataset_str) gpdp_results_file = RESULTS_FILE_NAME.format( model='dp_gp_lvm', dataset=dataset_str) # Keep 3d points together. gpdp_mask_results_file = RESULTS_FILE_NAME.format( model='dp_gp_lvm_mask_93', dataset=dataset_str) # Define instance of necessary model. if not isfile(gpdp_results_file): # Reset default graph before building new model graph. This speeds up script. tf.reset_default_graph() np.random.seed(1) # Random seed.
:return: """ file_path = skin_cancer_mnist_path + 'hmnist_8_8_L.csv' observed_data, diagnosis_labels = read_hmnist_csv(file_path) assert observed_data.shape[ 1] == 64, 'Number of pixels does not match expected number of 64.' return observed_data, diagnosis_labels if __name__ == '__main__': # Define file path for results. bgplvm_results_file = RESULTS_FILE_NAME.format(model='bgplvm', dataset='skin_cancer_mnist') mrd_results_file = RESULTS_FILE_NAME.format(model='mrd', dataset='skin_cancer_mnist') mrd_fully_independent_results_file = RESULTS_FILE_NAME.format( model='mrd_fully_independent', dataset='skin_cancer_mnist') gpdp_results_file = RESULTS_FILE_NAME.format(model='dp_gp_lvm', dataset='skin_cancer_mnist') gpdp_mask_results_file = RESULTS_FILE_NAME.format( model='dp_gp_lvm_mask_64', dataset='skin_cancer_mnist') # Choose what model we are looking at. results_file = bgplvm_results_file if isfile(results_file): # Read results. results = np.load(results_file)