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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

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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()

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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

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netsh wlan stop hostednetwork
pause

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

minesh1291

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

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# 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 

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'''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

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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},
   }

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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