diff --git a/EIVPackage/EIVData/cubic.py b/EIVPackage/EIVData/cubic.py index 710ea870d532d26d7a86bec1c05b71cfa844158b..dce6ea452fba08280d457e995295d5fe87d46d6d 100644 --- a/EIVPackage/EIVData/cubic.py +++ b/EIVPackage/EIVData/cubic.py @@ -5,12 +5,12 @@ from torch.utils.data import TensorDataset from EIVGeneral.manipulate_tensors import add_noise, normalize_tensor,\ unnormalize_tensor -total_number_of_datapoints = 500 -input_range = [-4,4] +total_number_of_datapoints = 1000 +input_range = [-1,1] slope = 1.0 intercept = 0.0 -x_noise_strength = 0.2 -y_noise_strength = 3 +x_noise_strength = 0.1 +y_noise_strength = 0.1 func = lambda true_x: slope * true_x**3 + intercept def load_data(seed=0, splitting_part=0.8, normalize=True, diff --git a/Experiments/configurations/eiv_cubic.json b/Experiments/configurations/eiv_cubic.json index 2a3ac2c6696175d083c2b6b7bcab90b614842f7c..0e9f9e88edb30a99bd73a2814a92c4ca504218fd 100644 --- a/Experiments/configurations/eiv_cubic.json +++ b/Experiments/configurations/eiv_cubic.json @@ -16,7 +16,7 @@ "init_std_y_list": [0.5], "gamma": 0.5, "hidden_layers": [128, 128, 128, 128], - "fixed_std_x": 0.20, + "fixed_std_x": 0.10, "seed_range": [0,10], "gpu_number": 1 } diff --git a/Experiments/plot_prediction.py b/Experiments/plot_prediction.py index 9ba7fd80c14251c9b22a4de7653f5afb63c9bc27..c0e1817fc8d20a865f9f236a5da0b3e5ecb95725 100644 --- a/Experiments/plot_prediction.py +++ b/Experiments/plot_prediction.py @@ -216,10 +216,13 @@ def compute_predictions_and_uncertainties(data, x_range, eiv, number_of_draws, return plotting_dictionary -data_list = ['sine'] # short datanames -list_x_range = [torch.linspace(-0.3,0.9, 50)] -list_color = [('red','blue')] -list_number_of_draws = [((100,5), 100)] +data_list = ['linear','quadratic','cubic','sine'] # short datanames +list_x_range = [torch.linspace(-1.0,1.0, 50), + torch.linspace(-1.0,1.0, 50), + torch.linspace(-1.0,1.0, 50), + torch.linspace(-0.2,0.8, 50)] +list_color = [('red','blue')] * len(data_list) +list_number_of_draws = [((100,5), 100)] * len(data_list) for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list, list_x_range, list_color, list_number_of_draws)): eiv_plotting_dictionary = compute_predictions_and_uncertainties( @@ -234,7 +237,7 @@ for i, (data, x_range, color, number_of_draws) in enumerate(zip(data_list, number_of_draws=number_of_draws[1]) input_dim = eiv_plotting_dictionary['input_dim'] if input_dim == 1: - plt.figure(i) + plt.figure(i+1) plt.clf() x_values, y_values = eiv_plotting_dictionary['range_points'] plt.plot(x_values.flatten(), y_values.flatten(),'-', color='k')