vvanirudh

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
import math

plt.rcParams["figure.figsize"] = (5,2)
fig, ax = plt.subplots()

pdf = lambda x: 0.7 * norm.pdf(x, 0.5, 0.1) + 0.3 * norm.pdf(x, 1, 0.1)

vvanirudh

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm

plt.rcParams["figure.figsize"] = (5,2)
fig, ax = plt.subplots()

pdf = lambda x: 0.7 * norm.pdf(x, 0.5, 0.1) + 0.3 * norm.pdf(x, 1, 0.1)

dx = 1e-2

vvanirudh

Automatically push an updated file whenever it is changed

Linux

1. Make sure inotify-tools is installed (https://github.com/rvoicilas/inotify-tools)
2. Configure git as usual
3. Clone the git repository of interest from github and, if necessary, add file you want to monitor
4. Allow username/password to be cached so you aren't asked everytime

git config credential.helper store

vvanirudh

#!/usr/bin/env python

from __future__ import print_function
import numpy as np
import pybullet as p
from pybullet_tools.utils import (connect, disable_real_time,
                                  set_default_camera, wait_for_user,
                                  disconnect, load_pybullet, TABLE_URDF, add_data_path,
                                  create_box, FLOOR_URDF, set_point, load_model,
                                  HideOutput, set_base_values, get_unit_vector, WorldSaver,

vvanirudh

import numpy as np
from sklearn.utils import shuffle
import random
import argparse
import matplotlib.pyplot as plt
import time

parser = argparse.ArgumentParser()
parser.add_argument('--ng', action='store_true')
parser.add_argument('--seed', type=int, default=10)

vvanirudh

# From https://www.johndcook.com/blog/standard_deviation/
# and https://github.com/modestyachts/ARS

class RunningStat(object):

    def __init__(self, shape=None):
        self._n = 0
        self._M = np.zeros(shape, dtype = np.float64)
        self._S = np.zeros(shape,  dtype = np.float64)
        self._M2 = np.zeros(shape,  dtype = np.float64)

vvanirudh

"""
Copyright (c) 2017, Gavin Weiguang Ding
All rights reserved.

Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this
    list of conditions and the following disclaimer.

vvanirudh

### 1. Clone your fork:

    git clone git@github.com:YOUR-USERNAME/YOUR-FORKED-REPO.git

### 2. Add remote from original repository in your forked repository: 

    cd into/cloned/fork-repo
    git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
    git fetch upstream

vvanirudh

import numpy as np

def sliding_mean(data_array, window=5):  
    data_array = np.array(data_array)  
    new_list = []  
    for i in range(len(data_array)):  
        indices = range(max(i - window + 1, 0),  
                        min(i + window + 1, len(data_array)))  
        avg = 0  
        for j in indices:  

vvanirudh

from sklearn.base import BaseEstimator
from sklearn.exceptions import NotFittedError
import numpy as np


class IRFF(BaseEstimator):
    '''
    Random fourier features using the improved embedding
    https://www.cs.cmu.edu/~schneide/DougalRandomFeatures_UAI2015.pdf
    '''