Minesh A. Jethva minesh1291

## github-graph-colours-changer.js
var GH = ['#EEEEEE', '#D6E685', '#8CC665', '#44A340', '#1E6823'];
var CO = ['#EF4B4D', '#F89B47', '#FAEA20', '#7DC242', '#5D94CE', '#855CA7'];

var graph = document.getElementsByClassName('js-calendar-graph-svg')[0];
var days = [].slice.call(graph.getElementsByTagName('rect'), 0);

days.forEach(function(rect) {
    switch(rect.getAttribute('fill').toUpperCase()) {
        case GH[0]: rect.setAttribute('fill', CO[2]); break; // yellow
        case GH[1]: rect.setAttribute('fill', CO[Math.floor(Math.random() * 2)]); break; // red || orange

## rf_iris.py
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
df['species'] = pd.Factor(iris.target, iris.target_names)
df.head()

## demo-iris-scienceops.py
from mysql import connector
from sklearn import linear_model
from yhat import Yhat, YhatModel, preprocess


class MySQLIrisClassifier(YhatModel):
    REQUIREMENTS = ['scikit-learn']

    def select_data(self, cursor):
        # Returns the full dataset of features and outputs

## disable_wifi_hotspot.bat
netsh wlan stop hostednetwork
pause

## 0_reuse_code.js
// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

## clean_install.md

      
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                minesh1291
                / clean_install.md
            
            
              Created
              April 18, 2018 14:54
            
              
                Setting up Ubuntu 14.04 for Deep Learning, PySpark, and Climate Science
              
          
    Setting up an Ubuntu 14.04 clean install for Development with PySpark and Deep Learning


Assumes NVIDIA GPU
Prefers Ubuntu native packages over Docker for simplicity

sudo apt-get update && sudo apt-get -y upgrade
0. Install Java


## daemon.py
# From "A simple unix/linux daemon in Python" by Sander Marechal
# See http://stackoverflow.com/a/473702/1422096
#
# Modified to add quit() that allows to run some code before closing the daemon
# See http://stackoverflow.com/a/40423758/1422096
#
# Joseph Ernest, 2016/11/12

import sys, os, time, atexit
from signal import signal, SIGTERM

## classifier_from_little_data_script_2.py
'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

## hmm_1.py
states = ('Rainy', 'Sunny')

observations = ('walk', 'shop', 'clean')

start_probability = {'Rainy': 0.6, 'Sunny': 0.4}

transition_probability = {
   'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},
   'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},
   }

## hmm_2.py
import math

math.exp(model.score(np.array([[0]])))
# 0.30000000000000004
math.exp(model.score(np.array([[1]])))
# 0.36000000000000004
math.exp(model.score(np.array([[2]])))
# 0.3400000000000001
math.exp(model.score(np.array([[2,2,2]])))
# 0.04590400000000001
	var GH = ['#EEEEEE', '#D6E685', '#8CC665', '#44A340', '#1E6823'];
	var CO = ['#EF4B4D', '#F89B47', '#FAEA20', '#7DC242', '#5D94CE', '#855CA7'];

	var graph = document.getElementsByClassName('js-calendar-graph-svg')[0];
	var days = [].slice.call(graph.getElementsByTagName('rect'), 0);

	days.forEach(function(rect) {
	switch(rect.getAttribute('fill').toUpperCase()) {
	case GH[0]: rect.setAttribute('fill', CO[2]); break; // yellow
	case GH[1]: rect.setAttribute('fill', CO[Math.floor(Math.random() * 2)]); break; // red \|\| orange
	from sklearn.datasets import load_iris
	from sklearn.ensemble import RandomForestClassifier
	import pandas as pd
	import numpy as np

	iris = load_iris()
	df = pd.DataFrame(iris.data, columns=iris.feature_names)
	df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
	df['species'] = pd.Factor(iris.target, iris.target_names)
	df.head()
	from mysql import connector
	from sklearn import linear_model
	from yhat import Yhat, YhatModel, preprocess


	class MySQLIrisClassifier(YhatModel):
	REQUIREMENTS = ['scikit-learn']

	def select_data(self, cursor):
	# Returns the full dataset of features and outputs
	// Use Gists to store code you would like to remember later on
	console.log(window); // log the "window" object to the console
	# From "A simple unix/linux daemon in Python" by Sander Marechal
	# See http://stackoverflow.com/a/473702/1422096
	#
	# Modified to add quit() that allows to run some code before closing the daemon
	# See http://stackoverflow.com/a/40423758/1422096
	#
	# Joseph Ernest, 2016/11/12

	import sys, os, time, atexit
	from signal import signal, SIGTERM
	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats
	states = ('Rainy', 'Sunny')

	observations = ('walk', 'shop', 'clean')

	start_probability = {'Rainy': 0.6, 'Sunny': 0.4}

	transition_probability = {
	'Rainy' : {'Rainy': 0.7, 'Sunny': 0.3},
	'Sunny' : {'Rainy': 0.4, 'Sunny': 0.6},
	}
	import math

	math.exp(model.score(np.array([[0]])))
	# 0.30000000000000004
	math.exp(model.score(np.array([[1]])))
	# 0.36000000000000004
	math.exp(model.score(np.array([[2]])))
	# 0.3400000000000001
	math.exp(model.score(np.array([[2,2,2]])))
	# 0.04590400000000001