tdavchev

for t in range(T):
   exp_feat += gamma**t * (trajectory[t, :] - goal)**2

tdavchev

class QPoptimizer(object):
    def __call__(self, feature_num, learner, expert):
        w = cp.Variable(feature_num)

        obj_func = cp.Minimize(cp.norm(w))
        constraints = [(expert-learner) @ w >= 2]

        prob = cp.Problem(obj_func, constraints)
        prob.solve()

tdavchev

'''
Convert videos to .gif files.

@date: 28.02.2018
@author: Todor Davchev
'''

import argparse
import os

tdavchev

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt


def smooth(scalars, weight): 
    last = scalars[0] 
    smoothed = list() 
    for point in scalars: 
        smoothed_val = last * weight + (1-weight)*point 

tdavchev

import numpy as np

# Taken from https://github.com/openai/baselines/blob/master/baselines/ddpg/noise.py
# based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab
class OrnsteinUhlenbeckActionNoise(object):
    def __init__(self, mu, sigma=0.3, theta=.15, dt=1e-2, x_0=None):
        self.theta = theta
        self.mu = mu
        self.sigma = sigma
        self.dt = dt

tdavchev

sudo rm /usr/lib/i386-linux-gnu/mesa/libGL.so.1

tdavchev

    def get_model_params(self):
        # get trainable params.
        model_names = []
        model_params = []
        model_shapes = []
        with self.g.as_default():
            t_vars = tf.trainable_variables()
            for var in t_vars:
                param_name = var.name
                p = self.sess.run(var)

tdavchev

import pickle
import numpy as np

def social_frame_preprocess():
    '''
    Preprocess the frames from the datasets.
    Convert values to pixel locations from millimeters
    obtain and store all frames data the actually used frames (as some are skipped), 
    the ids of all pedestrians that are present at each of those frames and the sufficient statistics.
    '''

tdavchev

from scipy import misc
import numpy as np
import random
from pylab import *
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.pyplot as plt

im = misc.imread('lena512.bmp')
im = np.asarray(im)

tdavchev

" This must be first, because it changes other options as side effect
set nocompatible

" Use pathogen to easily modify the runtime path to include all
" plugins under the ~/.vim/bundle directory
call pathogen#helptags()
call pathogen#infect()

" Quickly edit/reload the vimrc file
" nmap <silent> <leader>ev :e $MYVIMRC<CR>