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import numpy 
import pylab
from pylab import *
from matplotlib.widgets import Slider, Button, RadioButtons

# default 2d transformation matrix values
x1_def, x2_def, y1_def, y2_def = 1,0,0,1

ax = subplot(111)
subplots_adjust(bottom=0.25)

yrevar

# -*- coding: utf-8 -*-
import numpy as np
import pylab

"""
Imagine you have two random variables X_1 and X_2 and both of them are normally distributed, but with different parameters. In this example, let's say distribution of X_1 is g1(x) and for X2 it is g2(x). 

P(X_1) ~ N(µ1,Σ1), P(X_2) ~ N(µ2, Σ2). Where Σ1 and Σ2 are variances of X_1 and X_2 consecutively which are in this example s1^2 and s2^2.

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# Target OS: Linux/Mac
# Prerequisites: Make sure you have installed and setup gist (https://github.com/defunkt/gist), and modified bashrc path as per your own setup which in my case was $HOME/.bash_profile.  
gist -u $(cat $HOME/.bashrc_gisturl) -c -p -f "my_last_bashrc" -d "`date`" $HOME/.bash_profile > $HOME/.bashrc_gisturl

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export PATH=$PATH:/$HOME/bin
if [ -z ${DISPLAY} ]
then
  export DISPLAY=:0.0
fi

# Terminal history settings
export HISTCONTROL=ignoredups:erasedups  # no duplicate entries
export HISTSIZE=1000000                   # big big history
export HISTFILESIZE=1000000               # big big history

yrevar

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {

yrevar

name: "CaffeNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }
}
layer {
  name: "conv1"
  type: "Convolution"

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class VideoHelper(object):
    
    def __init__(self, video_file, frame_start=0, frame_end=np.float('inf'), do_loop=True, preload_frames=1000):
        
        self.preloaded = False
        self.preload_frames = preload_frames
        self._init_capture(video_file, frame_start, frame_end)
        self.do_loop = do_loop
        self.paused = False        
    

yrevar

import numpy as np
from search import *

## Flip-flop
class MaximizeAlternations(Problem):
    def __init__(self, initial):
        """initial state is a numpy array with each element representing a bit 0 or 1, 
        goal state is same size as initial state with all bits turned 1"""
        assert sum([n != 0 and n != 1 for n in initial]) == 0, "initial state must contain binary values."
        self.initial = initial