| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <style> | |
| canvas { | |
| border: 1px dotted black; | |
| } | |
| </style> | |
| </head> | |
| <body> |
| function dist(x, y) { | |
| return Math.sqrt(Math.pow(y[0]-x[0], 2)+Math.pow(y[1]-x[1], 2)); | |
| } | |
| function ln(x) { | |
| return Math.log(x); | |
| } | |
| function abs(x) { | |
| return Math.abs(x); | |
| } | |
| function disth(p, q) { |
Correct include_dirs in setup.py to be whichever folder contains all of the ROS libraries. For example, [your_include_dir]/ros/ros.h should be the main ros library for C++.
Run python2 setup.py build_ext --inplace to build the program
Run python2 to enter a python environment, and type import node to start it off.
You will probably see an error message that looks something like ImportError: /file/path/to/node.so: undefined symbol: [some mangled symbol name].
| import numpy as np | |
| # Single point cloud case | |
| point_cloud = np.random.rand(6,3) # 6 3D points | |
| flattened = point_cloud.reshape(point_cloud.shape[0],-1) # One point in R^18 | |
| unflattened = flattened.reshape(-1,3) # 6 3D points | |
| # Check that they're identical | |
| print point_cloud |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| mean = np.array([2, 2]) | |
| cov = 0.5 * np.array([[1, 0.9], [0.9, 1]]) | |
| x, y = np.random.multivariate_normal(mean, cov, 75).T | |
| from sklearn.decomposition import PCA | |
| pca = PCA(n_components=2) |
| import numpy as np | |
| from pyclustering.cluster.kmeans import kmeans, kmeans_visualizer | |
| from pyclustering.cluster.kmedians import kmedians | |
| from pyclustering.cluster.kmedoids import kmedoids | |
| from pyclustering.cluster import cluster_visualizer | |
| sample = np.array([ | |
| [0, 0], |
| # Experimenting with alphashapes to generate 2D worlds with randomly sized and shaped obstacles. | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib.patches import Polygon | |
| from descartes import PolygonPatch | |
| import alphashape | |
| n_points = 200 | |
| alpha = 25. |
| # Use proprietary solvers | |
| build --config gurobi | |
| build --config mosek | |
| build --config snopt |
| # Built off of commit hash 8ddd6672b1 | |
| import numpy as np | |
| from pydrake.all import ContinuousAlgebraicRiccatiEquation | |
| A = np.fromstring("0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 1.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.0000000000 |