bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

kezzbracey

<!--To center an image-->
<p align="center">
![alt]()
</p>

<!--To right align an image-->
<p align="right">
![alt]()
</p>

tartakynov

import numpy as np
import pylab as pl
from numpy import fft
    
def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x
    x_notrend = x - p[0] * t        # detrended x

mrecos

require(caret)

#load some data
data(USArrests)

### Prepare Data (postive observations)
# add a column to be the strata.  In this case it is states, it can be sites, or other locations
# the original data has 50 rows, so this adds a state label to 10 consecutive observations
USArrests$state <- c(rep(c("PA","MD","DE","NY","NJ"), each = 5))
# this replaces the existing rownames (states) with a simple numerical index

mathebox

import math

def rgb_to_hsv(r, g, b):
    r = float(r)
    g = float(g)
    b = float(b)
    high = max(r, g, b)
    low = min(r, g, b)
    h, s, v = high, high, high

ololobus

Apache Spark installation + ipython/jupyter notebook integration guide for macOS

Tested with Apache Spark 2.1.0, Python 2.7.13 and Java 1.8.0_112

For older versions of Spark and ipython, please, see also previous version of text.

Install Java Development Kit

swayson

minmax_scaler <- function(x, a, b) {
    "
    x: data. numeric vector of values to be scaled
    a: desired minimum after scaling takes place
    b: desired maximum after scaling takes place
    
    e.g. f(c(1,2,3,4), 1, 17)
    [1]  1.000000  6.333333 11.666667 17.000000
    "
    (((b - a)*(x - min(x))) / (max(x) - min(x))) + a

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs:

tuxdna

#!/usr/bin/env python
# coding=utf-8
import numpy as np

"""
1: Procedure Policy_Iteration(S,A,P,R)
2:           Inputs
3:                     S is the set of all states
4:                     A is the set of all actions
5:                     P is state transition function specifying P(s'|s,a)

sash13

# -*- coding: UTF-8 -*-
import sys
from wit import Wit
from pyowm import OWM
from telegram import InlineKeyboardButton, InlineKeyboardMarkup
from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackQueryHandler
import logging

DEFAULT_MAX_STEPS = 5
# Enable logging