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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)
# -*- 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.
@yrevar
yrevar / bashrc backup gist
Created March 17, 2016 19:51
Backup your bashrc on gist with a very simple command. It preserves the gist url and updates the same file you backed up on gist before.
# 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 > $HOME/.bashrc_gisturl
export PATH=$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
name: "CaffeNet"
layer {
name: "data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
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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@yrevar
yrevar / ocv_video_helper.py
Created April 23, 2017 17:46
OpenCV VideoCapture Helper
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
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