silgon

#! /usr/bin/python
"""
Simple example for IRL based on apprenticeship learning
"""
from __future__ import print_function
import numpy as np


def linearMDP(S=5, goal=3):
    """

silgon

This is an excellent tool when you're compiling latex files. Nevertheless, there's a bug when you insert a tilde as a nbsp. Something that needs to be modified in ''/usr/bin/texi2dvi'', and that's this part of code ''catcode_special=true'' to''catcode_special=false''. After that, a nice example for your latex file is:

  texi2dvi --pdf --clean --verbose --batch file.tex 

silgon

"""
Basic Least Squares Implementation with pseudo inverse
"""
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)

t = np.linspace(0, 3, 20)  # horizontal axis
x = np.array([3, 0, 4])  # a,b,c  of f(t)=a+b*t+c*t^2
f = lambda t, x: x[0]+x[1]*t+x[2]*t**2  # f(t)

silgon

# curvefit with non linear least squares (curve_fit function)
import numpy as np
from scipy.optimize import curve_fit

def func(x, a, b, c):
    return a*np.sin(x[0])+b*np.cos(x[1])+c

limits = [0, 2*np.pi, 0, 2*np.pi]  # [x1_min, x1_max, x2_min, x2_max]
side_x = np.linspace(limits[0], limits[1], 100)
side_y = np.linspace(limits[2], limits[3], 100)

silgon

"""
this is an extended version of http://hmmlearn.github.io/hmmlearn/auto_examples/plot_hmm_sampling.html#example-plot-hmm-sampling-py
sampling as in the example, then fitting data to another model (with also 4 variables) and then doing some scoring with
the same model and also with a mixture of gaussians
"""
import numpy as np
import matplotlib.pyplot as plt

from hmmlearn import hmm

silgon

Installation of IPOPT on LINUX

NOTE: this page is for a from-source install (useful if you want to contribute to CasADi). If you just want to evaluate CasADi quickly, use a binary install.

• To get started quickly, start off by grabbing as many dependencies as possible from repositories.

• Ubuntu: sudo apt-get install gcc g++ gfortran git cmake liblapack-dev pkg-config --install-recommends
• Fedora: sudo yum install gcc gcc-c++ gcc-gfortran git cmake lapack-devel

• To compile the Python interface, you also need SWIG and a decent Python installation:

• Ubuntu: sudo apt-get install swig ipython python-dev python-numpy python-scipy python-matplotlib --install-recommends
• Fedora: sudo yum install swig ipython python-devel numpy scipy python-matplotlib

silgon

// compile with
// g++ -std=c++11 test_log_default.cpp -DBOOST_LOG_DYN_LINK -lboost_log -lboost_thread -lpthread -lboost_system
#include <iostream>
#include <boost/log/expressions.hpp>
#include <boost/log/sources/severity_channel_logger.hpp>
#include <boost/log/sources/record_ostream.hpp>
#include <boost/log/utility/setup/console.hpp>
#include <boost/log/utility/setup/common_attributes.hpp>
#include <boost/log/utility/setup/file.hpp>
#include <boost/log/sinks.hpp>

silgon

Eigen::ArrayXXb findPeaks(Eigen::ArrayXXd &M, unsigned int &footprint_size){
    Eigen::ArrayXXb result = Eigen::ArrayXXb::Zero(M.rows(), M.cols());
    bool tmp_max;
    bool quit_for=false;
    for(int i = footprint_size; i<M.rows()-footprint_size; ++i){
        for(int j = footprint_size; j<M.cols()-footprint_size; ++j){
            tmp_max=true;
            for(int ii=i-footprint_size; ii<i+footprint_size;++ii){
                for(int jj=j-footprint_size; jj<j+footprint_size;++jj){
                    if(ii==i && jj==j)

silgon

#include <ros/ros.h>
#include <nav_msgs/OccupancyGrid.h>
#include <geometry_msgs/PoseStamped.h>
#include <tf/tf.h>
#include <tf/transform_listener.h>
#include <iterator>
#include <vector>

// some definitions of functions
#define MAP_INDEX(map, i, j) ((i) + (j) * map.size_x)

silgon

import JSON
###################
### Write data ####
###################
# dictionary to write
dict1 = Dict("param1" => 1, "param2" => 2,
            "dict" => Dict("d1"=>1.,"d2"=>1.,"d3"=>1.))
            
# pass data as a json string (how it shall be displayed in a file)
stringdata = JSON.json(dict1)