├── .gitignore ├── LICENSE ├── README.md ├── docs ├── doxygen │ ├── Doxyfile │ └── html │ │ ├── annotated.html │ │ ├── arrowdown.png │ │ ├── arrowright.png │ │ ├── bc_s.png │ │ ├── bdwn.png │ │ ├── classes.html │ │ ├── classintprim_1_1basis_1_1basis__model_1_1BasisModel-members.html │ │ ├── classintprim_1_1basis_1_1basis__model_1_1BasisModel.html │ │ ├── classintprim_1_1basis_1_1basis__model_1_1BasisModel.png │ │ ├── classintprim_1_1basis_1_1gaussian__model_1_1GaussianModel-members.html │ │ ├── classintprim_1_1basis_1_1gaussian__model_1_1GaussianModel.html │ │ ├── classintprim_1_1basis_1_1gaussian__model_1_1GaussianModel.png │ │ ├── classintprim_1_1basis_1_1mixture__model_1_1MixtureModel-members.html │ │ ├── classintprim_1_1basis_1_1mixture__model_1_1MixtureModel.html │ │ ├── classintprim_1_1basis_1_1mixture__model_1_1MixtureModel.png │ │ ├── classintprim_1_1basis_1_1polynomial__model_1_1PolynomialModel-members.html │ │ ├── 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classintprim_1_1filter_1_1linear__system_1_1LinearSystem-members.html │ │ ├── classintprim_1_1filter_1_1linear__system_1_1LinearSystem.html │ │ ├── classintprim_1_1filter_1_1linear__system_1_1LinearSystem.png │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1ekf_1_1ExtendedKalmanFilter-members.html │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1ekf_1_1ExtendedKalmanFilter.html │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1ekf_1_1ExtendedKalmanFilter.png │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1enkf_1_1EnsembleKalmanFilter-members.html │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1enkf_1_1EnsembleKalmanFilter.html │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1enkf_1_1EnsembleKalmanFilter.png │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1nonlinear__system_1_1NonLinearSystem-members.html │ │ ├── classintprim_1_1filter_1_1spatiotemporal_1_1nonlinear__system_1_1NonLinearSystem.html │ │ ├── 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all_7.js │ │ ├── all_8.html │ │ ├── all_8.js │ │ ├── all_9.html │ │ ├── all_9.js │ │ ├── all_a.html │ │ ├── all_a.js │ │ ├── all_b.html │ │ ├── all_b.js │ │ ├── all_c.html │ │ ├── all_c.js │ │ ├── all_d.html │ │ ├── all_d.js │ │ ├── all_e.html │ │ ├── all_e.js │ │ ├── classes_0.html │ │ ├── classes_0.js │ │ ├── classes_1.html │ │ ├── classes_1.js │ │ ├── classes_2.html │ │ ├── classes_2.js │ │ ├── classes_3.html │ │ ├── classes_3.js │ │ ├── classes_4.html │ │ ├── classes_4.js │ │ ├── classes_5.html │ │ ├── classes_5.js │ │ ├── classes_6.html │ │ ├── classes_6.js │ │ ├── classes_7.html │ │ ├── classes_7.js │ │ ├── classes_8.html │ │ ├── classes_8.js │ │ ├── classes_9.html │ │ ├── classes_9.js │ │ ├── close.png │ │ ├── functions_0.html │ │ ├── functions_0.js │ │ ├── functions_1.html │ │ ├── functions_1.js │ │ ├── functions_2.html │ │ ├── functions_2.js │ │ ├── functions_3.html │ │ ├── functions_3.js │ │ ├── functions_4.html │ │ ├── functions_4.js │ │ ├── functions_5.html │ │ ├── functions_5.js │ │ ├── functions_6.html │ │ ├── functions_6.js │ │ ├── functions_7.html │ │ ├── functions_7.js │ │ ├── functions_8.html │ │ ├── functions_8.js │ │ ├── functions_9.html │ │ ├── functions_9.js │ │ ├── functions_a.html │ │ ├── functions_a.js │ │ ├── functions_b.html │ │ ├── functions_b.js │ │ ├── mag_sel.png │ │ ├── nomatches.html │ │ ├── pages_0.html │ │ ├── pages_0.js │ │ ├── search.css │ │ ├── search.js │ │ ├── search_l.png │ │ ├── search_m.png │ │ ├── search_r.png │ │ └── searchdata.js │ │ ├── splitbar.png │ │ ├── sync_off.png │ │ ├── sync_on.png │ │ ├── tab_a.png │ │ ├── tab_b.png │ │ ├── tab_h.png │ │ ├── tab_s.png │ │ └── tabs.css ├── index.html └── notebooks │ ├── 1_introduction.ipynb │ ├── 2_quickstart.ipynb │ ├── 3_indepth_tutorial.ipynb │ ├── 4_mathematical_details.ipynb │ ├── 5_optimizing_interactions.ipynb │ └── media │ ├── acknowledgement_logos.png │ ├── analysis_example.gif │ ├── analysis_screen_cap_720.gif │ ├── bip_overview.png │ ├── box_lq.gif │ ├── catching_lq.gif │ ├── cov_screen_cap_720.gif │ ├── examples_new.png │ ├── geoff_corl20.png │ ├── geoff_icra20_play.png │ ├── geoffrey_corlplay1.png │ ├── handshake_lq.gif │ ├── hugging_lq.gif │ ├── joe_corl17.png │ ├── joe_corl17_play.png │ ├── joe_icra20_play.png │ ├── joe_iros19.png │ ├── joe_rss19.png │ ├── joe_rss19_play.png │ └── walking_lq.gif ├── intprim ├── __init__.py ├── basis │ ├── __init__.py │ ├── basis_model.py │ ├── gaussian_model.py │ ├── mixture_model.py │ ├── polynomial_model.py │ ├── selection.py │ └── sigmoidal_model.py ├── bayesian_interaction_primitives.py ├── constants.py ├── examples │ ├── __init__.py │ ├── minimal.py │ └── tutorial.py ├── filter │ ├── __init__.py │ ├── align │ │ ├── __init__.py │ │ └── dtw.py │ ├── kf.py │ ├── linear_system.py │ └── spatiotemporal │ │ ├── __init__.py │ │ ├── ekf.py │ │ ├── enkf.py │ │ ├── nonlinear_system.py │ │ └── pf.py └── util │ ├── __init__.py │ ├── index.html │ ├── server2.py │ ├── server3.py │ ├── stat_collector.py │ └── visualization.py ├── mainpage.dox └── setup.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | env/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | 49 | # Translations 50 | *.mo 51 | *.pot 52 | 53 | # Django stuff: 54 | *.log 55 | local_settings.py 56 | 57 | # Flask stuff: 58 | instance/ 59 | .webassets-cache 60 | 61 | # Scrapy stuff: 62 | .scrapy 63 | 64 | # Sphinx documentation 65 | docs/_build/ 66 | 67 | # PyBuilder 68 | target/ 69 | 70 | # Jupyter Notebook 71 | .ipynb_checkpoints 72 | 73 | # pyenv 74 | .python-version 75 | 76 | # celery beat schedule file 77 | celerybeat-schedule 78 | 79 | # SageMath parsed files 80 | *.sage.py 81 | 82 | # dotenv 83 | .env 84 | 85 | # virtualenv 86 | .venv 87 | venv/ 88 | ENV/ 89 | 90 | # Spyder project settings 91 | .spyderproject 92 | .spyproject 93 | 94 | # Rope project settings 95 | .ropeproject 96 | 97 | # mkdocs documentation 98 | /site 99 | 100 | # mypy 101 | .mypy_cache/ 102 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2017 Interactive Robotics Lab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /docs/doxygen/html/arrowdown.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ir-lab/intprim/8994b158c3f1306be7f9d6304b370d70e2c257fb/docs/doxygen/html/arrowdown.png -------------------------------------------------------------------------------- /docs/doxygen/html/arrowright.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ir-lab/intprim/8994b158c3f1306be7f9d6304b370d70e2c257fb/docs/doxygen/html/arrowright.png 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-------------------------------------------------------------------------------- 1 | from intprim.bayesian_interaction_primitives import * 2 | import intprim.basis 3 | import intprim.constants 4 | import intprim.examples 5 | import intprim.filter 6 | import intprim.filter.align 7 | import intprim.filter.spatiotemporal 8 | import intprim.util 9 | -------------------------------------------------------------------------------- /intprim/basis/__init__.py: -------------------------------------------------------------------------------- 1 | from intprim.basis.gaussian_model import * 2 | from intprim.basis.mixture_model import * 3 | from intprim.basis.polynomial_model import * 4 | from intprim.basis.selection import * 5 | from intprim.basis.sigmoidal_model import * 6 | -------------------------------------------------------------------------------- /intprim/basis/polynomial_model.py: -------------------------------------------------------------------------------- 1 | ## 2 | # This module defines the PolynomialModel class. 3 | # 4 | # @author Joseph Campbell , Interactive Robotics Lab, Arizona State University 5 | import basis_model 6 | import intprim.constants 7 | import numpy as np 8 | 9 | ## 10 | # The PolynomialModel class implements a basis model consisting of Polynomial basis functions. 11 | # 12 | class PolynomialModel(basis_model.BasisModel): 13 | ## 14 | # Initializer method for PolynomialModel. 15 | # 16 | # @param degree int The number of Sigmoid basis functions which will be uniformly distributed throughout the space. 17 | # @param observed_dof_names array-like, shape (num_observed_dof, ). The names of all observed degrees of freedom. 18 | # @param start_phase float The starting value from which the basis functions are uniformly distributed. The centers are calculated with linspace(start_phase, end_phase, degree). 19 | # @param end_phase float The ending value from which the basis functions are uniformly distributed. The centers are calculated with linspace(start_phase, end_phase, degree). 20 | # 21 | def __init__(self, degree, observed_dof_names, start_phase = 0.0, end_phase = 1.01): 22 | super(PolynomialModel, self).__init__(degree, observed_dof_names) 23 | 24 | self.scale = None 25 | 26 | ## 27 | # Gets the basis function evaluations for the given phase value(s). Essentially a vectorized wrapper to call get_basis_values for each phase value given. 28 | # 29 | # @param x array-like, shape(num_phase_values, ) or float. If array, a list of phase values for which to compute basis values. Otherwise, a single scalar phase value. 30 | # @param degree int. Degree of this basis model. 31 | # 32 | # @return array-like, shape(degree, num_phase_values) or array-like, shape(degree, ) if x is a scalar. The evaluated Polynomial basis functions for the given phase value. 33 | # 34 | def get_basis_functions(self, x, degree = None): 35 | f = lambda x, degree: np.array([(x - (1 - x))**d for d in range(degree)], dtype = intprim.constants.DTYPE) 36 | 37 | return f(x, self._degree) 38 | 39 | ## 40 | # Gets the evaluations for the derivative of the basis functions for the given phase value(s). 41 | # This is necessary for the computation of the Jacobian matrix in the EKF filter. 42 | # Unlike get_basis_functions, this function does not (currently) implement a hash map internally and so re-computes values everytime. 43 | # Since the basis decompositions are simple linear combinations, the partial derivative of the combination with respect to each weight is simply the partial derivative of a single basis function due to the sum rule. 44 | # 45 | # This is the first order partial derivative with respect to x! 46 | # It is used to compute the Jacobian matrix for filtering linear dynamical systems. 47 | # Verified using wolfram alpha: d/dx a*(x-(1-x))^0 + b*(x-(1-x))^1 + c*(x-(1-x))^2 and d/dx a*(2x-1)^n 48 | # 49 | # @param x array-like, shape(num_phase_values, ) or float. If array, a list of phase values for which to compute basis derivative values. Otherwise, a single scalar phase value. 50 | # @param degree int. Degree of this basis model. 51 | # 52 | # @return values array-like, shape(degree, num_phase_values) or array-like, shape(degree, ) if x is a scalar. The evaluated Polynomial basis function derivatives for the given phase value. 53 | def get_basis_function_derivatives(self, x, degree = None): 54 | f = lambda x, degree: np.array([(2*d)*(2*x-1)**(d-1) for d in range(degree)], dtype = intprim.constants.DTYPE) 55 | 56 | return f(x, self._degree) 57 | -------------------------------------------------------------------------------- /intprim/constants.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | DTYPE = np.float64 4 | DEFAULT_NUM_SAMPLES = 100 5 | -------------------------------------------------------------------------------- /intprim/examples/__init__.py: -------------------------------------------------------------------------------- 1 | from intprim.examples.tutorial import * 2 | -------------------------------------------------------------------------------- /intprim/examples/minimal.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | 4 | # Import the library. 5 | import intprim 6 | 7 | # Set a seed for reproducibility 8 | np.random.seed(213413414) 9 | 10 | # Define some parameters used when generating synthetic data. 11 | num_train_trajectories = 100 12 | train_translation_mean = 0.0 13 | train_translation_std = 5.0 14 | train_noise_std = 0.01 15 | train_length_mean = 95 16 | train_length_std = 30 17 | 18 | # Generate some synthetic handwriting trajectories. 19 | training_trajectories = intprim.examples.create_2d_handwriting_data( 20 | num_train_trajectories, 21 | train_translation_mean, 22 | train_translation_std, 23 | train_noise_std, 24 | train_length_mean, 25 | train_length_std) 26 | 27 | # Plot the results. 28 | plt.figure() 29 | for trajectory in training_trajectories: 30 | plt.plot(trajectory[0], trajectory[1]) 31 | plt.show() 32 | 33 | 34 | 35 | # Define the data axis names. 36 | dof_names = np.array(["X (Agent 1)", "Y (Agent 2)"]) 37 | 38 | # Decompose the handwriting trajectories to a basis space with 8 uniformly distributed Gaussian functions and a variance of 0.1. 39 | basis_model = intprim.basis.GaussianModel(8, 0.1, dof_names) 40 | 41 | # Initialize a BIP instance. 42 | primitive = intprim.BayesianInteractionPrimitive(basis_model) 43 | 44 | # Train the model. 45 | for trajectory in training_trajectories: 46 | primitive.add_demonstration(trajectory) 47 | 48 | # Plot the distribution of the trained model. 49 | mean, upper_bound, lower_bound = primitive.get_probability_distribution() 50 | intprim.util.visualization.plot_distribution(dof_names, mean, upper_bound, lower_bound) 51 | 52 | 53 | 54 | # Set an observation noise for the demonstrations. 55 | observation_noise = np.diag([10000.0, train_noise_std ** 2]) 56 | 57 | 58 | 59 | # Compute the phase mean and phase velocities from the demonstrations. 60 | phase_velocity_mean, phase_velocity_var = intprim.examples.get_phase_stats(training_trajectories) 61 | 62 | # Define a filter to use. Here we use an ensemble Kalman filter 63 | filter = intprim.filter.spatiotemporal.EnsembleKalmanFilter( 64 | basis_model = basis_model, 65 | initial_phase_mean = [0.0, phase_velocity_mean], 66 | initial_phase_var = [1e-4, phase_velocity_var], 67 | proc_var = 1e-8, 68 | initial_ensemble = primitive.basis_weights) 69 | 70 | 71 | 72 | 73 | num_test_trajectories = 1 74 | test_translation_mean = 5.0 75 | test_translation_std = 1e-5 76 | test_noise_std = 0.01 77 | test_length_mean = 45 78 | test_length_std = 1e-5 79 | 80 | # Create test trajectories. 81 | test_trajectories = intprim.examples.create_2d_handwriting_data(num_test_trajectories, test_translation_mean, test_translation_std, test_noise_std, test_length_mean, test_length_std) 82 | 83 | # Evaluate the trajectories. 84 | intprim.examples.evaluate_trajectories(primitive, filter, test_trajectories, observation_noise) 85 | -------------------------------------------------------------------------------- /intprim/filter/__init__.py: -------------------------------------------------------------------------------- 1 | from intprim.filter.kf import * 2 | -------------------------------------------------------------------------------- /intprim/filter/align/__init__.py: -------------------------------------------------------------------------------- 1 | import intprim.filter.align.dtw 2 | -------------------------------------------------------------------------------- /intprim/filter/align/dtw.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.spatial.distance 3 | import sklearn.metrics.pairwise 4 | 5 | def compute_cost(x, y, dist_func = "euclidean"): 6 | r, c = len(x), len(y) 7 | 8 | distance = np.zeros((r, c)) 9 | distance[0, 1:] = np.inf 10 | distance[1:, 0] = np.inf 11 | 12 | cost = scipy.spatial.distance.cdist(x, y, "euclidean") 13 | 14 | n_star = np.argmin(cost[:, c - 1]) 15 | 16 | return n_star 17 | 18 | def fastdtw(observed_trajectory, predicted_trajectory): 19 | # If a trajectory DOF is all zeros, then we are considering it to be unobserved and will not use it for phase matching 20 | 21 | min_cost_index = compute_cost(predicted_trajectory, observed_trajectory) 22 | 23 | #phase_value = float(min_cost_index) / float(pro_mp.NUM_SAMPLES) 24 | 25 | #return phase_value 26 | return min_cost_index 27 | -------------------------------------------------------------------------------- /intprim/filter/linear_system.py: -------------------------------------------------------------------------------- 1 | ## 2 | # This module defines a generic LinearSystem class for use in linear spatial filtering. 3 | # 4 | # @author Joseph Campbell , Interactive Robotics Lab, Arizona State University 5 | import numpy as np 6 | 7 | ## 8 | # The LinearSystem class provides general methods which are required by the KF linear filter. 9 | # This includes the generation of transition models, process noise models, and measurement models. 10 | # 11 | class LinearSystem(object): 12 | ## 13 | # Initialization method for LinearSystem. Instantiates internal state matrices for computational efficiency. 14 | # 15 | # @param basis_model The basis model corresponding to this state space. 16 | # 17 | def __init__(self, basis_model): 18 | self.basis_model = basis_model 19 | self.state_dimension = basis_model.block_prototype.shape[0] 20 | self.measurement_dimension = basis_model.num_observed_dof 21 | 22 | self.process_noise = None 23 | self.transition_model = None 24 | self.measurement_model_prototype = np.zeros((self.basis_model.block_prototype.shape[0], self.basis_model.block_prototype.shape[1])) 25 | 26 | ## 27 | # Gets the transition model for spatial filtering. 28 | # Functionally this is just an identity matrix since basis weights are time invariant. 29 | # 30 | # @returns Matrix of dimension B x B containing the transition model. 31 | def get_transition_model(self): 32 | if(self.transition_model is None): 33 | self.transition_model = np.eye(self.state_dimension) 34 | 35 | return self.transition_model 36 | 37 | ## 38 | # Gets the process noise for spatial filtering. 39 | # Functionally this is just a zero matrix since basis weights are time invariant. 40 | # 41 | # @returns Matrix of dimension B x B containing the process noise. 42 | def get_process_noise(self): 43 | if(self.process_noise is None): 44 | self.process_noise = np.zeros((self.state_dimension, self.state_dimension)) 45 | 46 | return self.process_noise 47 | 48 | ## 49 | # Gets the measurement model for spatial filtering. 50 | # This is the Jacobian associated with the partial differential of \f$ \boldsymbol{H}_t = \frac{\partial h(\boldsymbol{s}_t)}{\partial s_t} \f$. 51 | # However, the partial derivative of a linear combination of basis weights and basis functions is simply the basis functions. 52 | # 53 | # @returns Matrix of dimension B x D containing the measurement model. 54 | def get_measurement_model(self, x): 55 | self.basis_model.get_block_diagonal_basis_matrix(x[:1], out_array = self.measurement_model_prototype, start_row = 0, start_col = 0) 56 | 57 | return self.measurement_model_prototype.T 58 | -------------------------------------------------------------------------------- /intprim/filter/spatiotemporal/__init__.py: -------------------------------------------------------------------------------- 1 | from intprim.filter.spatiotemporal.ekf import * 2 | from intprim.filter.spatiotemporal.enkf import * 3 | from intprim.filter.spatiotemporal.pf import * 4 | -------------------------------------------------------------------------------- /intprim/util/__init__.py: -------------------------------------------------------------------------------- 1 | from stat_collector import * 2 | import visualization 3 | -------------------------------------------------------------------------------- /intprim/util/server2.py: -------------------------------------------------------------------------------- 1 | from SimpleHTTPServer import SimpleHTTPRequestHandler 2 | import BaseHTTPServer 3 | import sys 4 | 5 | class CORSRequestHandler (SimpleHTTPRequestHandler): 6 | def end_headers (self): 7 | self.send_header('Access-Control-Allow-Origin', '*') 8 | SimpleHTTPRequestHandler.end_headers(self) 9 | 10 | if __name__ == '__main__': 11 | BaseHTTPServer.test(CORSRequestHandler, BaseHTTPServer.HTTPServer) 12 | -------------------------------------------------------------------------------- /intprim/util/server3.py: -------------------------------------------------------------------------------- 1 | from http.server import HTTPServer, SimpleHTTPRequestHandler, test 2 | 3 | import sys 4 | 5 | class CORSRequestHandler (SimpleHTTPRequestHandler): 6 | def end_headers (self): 7 | self.send_header('Access-Control-Allow-Origin', '*') 8 | SimpleHTTPRequestHandler.end_headers(self) 9 | 10 | if __name__ == '__main__': 11 | test(CORSRequestHandler, HTTPServer, port=int(sys.argv[1]) if len(sys.argv) > 1 else 8000, bind = "127.0.0.1") 12 | -------------------------------------------------------------------------------- /intprim/util/visualization.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | 4 | # Displays the probability that the current trajectory matches the stored trajectores at every instant in time. 5 | def plot_distribution(dof_names, mean, upper_bound, lower_bound): 6 | """Plots a given probability distribution. 7 | """ 8 | figures_per_plot = np.min([4, mean.shape[0]]) 9 | 10 | for index in range(mean.shape[0]): 11 | if(index % figures_per_plot == 0): 12 | fig = plt.figure() 13 | 14 | new_plot = plt.subplot(figures_per_plot, 1, (index % figures_per_plot) + 1) 15 | domain = np.linspace(0, 1, mean.shape[1]) 16 | 17 | new_plot.fill_between(domain, upper_bound[index], lower_bound[index], color = '#ccf5ff') 18 | new_plot.plot(domain, mean[index], color = '#000000') 19 | new_plot.set_title('Trajectory distribution for degree ' + dof_names[index]) 20 | 21 | fig.tight_layout() 22 | 23 | plt.show(block = False) 24 | 25 | def plot_trajectory(dof_names, trajectory, observed_trajectory, mean_trajectory = None): 26 | """Plots a given trajectory. 27 | """ 28 | fig = plt.figure() 29 | 30 | plt.plot(trajectory[0], trajectory[1]) 31 | plt.plot(observed_trajectory[0], observed_trajectory[1]) 32 | if(mean_trajectory is not None): 33 | plt.plot(mean_trajectory[0], mean_trajectory[1]) 34 | 35 | fig.suptitle('Probable trajectory') 36 | 37 | fig = plt.figure() 38 | for index, degree in enumerate(trajectory): 39 | new_plot = plt.subplot(len(trajectory), 1, index + 1) 40 | 41 | domain = np.linspace(0, 1, len(trajectory[index])) 42 | new_plot.plot(domain, trajectory[index], label = "Inferred") 43 | 44 | domain = np.linspace(0, 1, len(observed_trajectory[index])) 45 | new_plot.plot(domain, observed_trajectory[index], label = "Observed") 46 | 47 | if(mean_trajectory is not None): 48 | domain = np.linspace(0, 1, len(mean_trajectory[index])) 49 | new_plot.plot(domain, mean_trajectory[index], label = "Mean") 50 | 51 | new_plot.set_title('Trajectory for degree ' + dof_names[index]) 52 | new_plot.legend() 53 | 54 | plt.show() 55 | 56 | def plot_partial_trajectory(trajectory, partial_observed_trajectory, mean_trajectory = None): 57 | """Plots a trajectory and a partially observed trajectory. 58 | """ 59 | fig = plt.figure() 60 | 61 | plt.plot(partial_observed_trajectory[0], partial_observed_trajectory[1], color = "#6ba3ff", label = "Observed", linewidth = 3.0) 62 | plt.plot(trajectory[0], trajectory[1], "--", color = "#ff6a6a", label = "Inferred", linewidth = 2.0) 63 | if(mean_trajectory is not None): 64 | plt.plot(mean_trajectory[0], mean_trajectory[1], color = "#85d87f", label = "Mean") 65 | 66 | fig.suptitle('Probable trajectory') 67 | plt.legend() 68 | 69 | plt.text(0.01, 0.7, "Observed samples: " + str(partial_observed_trajectory.shape[1]), transform = fig.axes[0].transAxes) 70 | 71 | plt.show() 72 | 73 | def plot_approximation(dof_names, trajectory, approx_trajectory, approx_trajectory_deriv): 74 | """Plots a trajectory and its approximation. 75 | """ 76 | domain = np.linspace(0, 1, len(trajectory[0])) 77 | approx_domain = np.linspace(0, 1, len(approx_trajectory[0])) 78 | 79 | for dof in range(len(trajectory)): 80 | plt.figure() 81 | new_plot = plt.subplot(3, 1, 1) 82 | new_plot.plot(domain, trajectory[dof]) 83 | new_plot.set_title('Original ' + dof_names[dof] + ' Data') 84 | 85 | new_plot = plt.subplot(3, 1, 2) 86 | # The trailing [0] is the dimension of the the state. In this case only plot position. 87 | new_plot.plot(approx_domain, approx_trajectory[dof]) 88 | new_plot.set_title('Approximated ' + dof_names[dof] + ' Data') 89 | 90 | new_plot = plt.subplot(3, 1, 3) 91 | # The trailing [0] is the dimension of the the state. In this case only plot position. 92 | new_plot.plot(approx_domain, approx_trajectory_deriv[dof]) 93 | new_plot.set_title('Approximated ' + dof_names[dof] + ' Derivative') 94 | 95 | plt.show() 96 | 97 | def plot_weights(weight_matrix): 98 | plt.figure() 99 | plt.imshow(weight_matrix, cmap = "gray", interpolation = "none") 100 | plt.colorbar() 101 | plt.show() 102 | -------------------------------------------------------------------------------- /mainpage.dox: -------------------------------------------------------------------------------- 1 | /*! 2 | @mainpage IntPrim 3 | 4 | This is the API and code documentation for the IntPrim Python library. 5 | While Doxygen is an unusual choice for documenting Python code, it has been chosen to maintain consistency with intprim_framework_ros which utilizes both Python and C++. 6 | 7 | While the classes have not yet been organized into a package, they can be individually browsed under the Classes tab. 8 | */ 9 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup, find_packages 2 | from setuptools.extension import Extension 3 | import numpy as np 4 | import sys 5 | 6 | setup( 7 | name='intprim', 8 | version='2.0', 9 | description='Interaction Primitives library from the Interactive Robotics Lab at Arizona State University', 10 | url='https://github.com/ir-lab/intprim', 11 | author='Joseph Campbell', 12 | author_email='jacampb1@asu.edu', 13 | license='MIT', 14 | packages=find_packages() 15 | ) 16 | --------------------------------------------------------------------------------