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# Using airquality dataset
data <- airquality
data[4:10,3] <- rep(NA,7)
data[1:5,4] <- NA

# Removing categorical variables
data <- airquality[-c(5,6)]
summary(data)

#-------------------------------------------------------------------------------

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# Set a seed
set.seed(500)

library(MASS)
data <- Boston

# Check that no data is missing
apply(data,2,function(x) sum(is.na(x)))

# Train-test random splitting for linear model

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# The following code takes as input a string of text, and then it outputs the barplot of the
# frequencies of occurrence of letters in the string.
import pylab as pl
import numpy as np

string1 = """ Example string """

alphabet = ["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z",".",",",";","-","_","+"]

# The following functon takes a list and a string of characters, it calculates how often a certain character appears

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import numpy as np
import matplotlib.pyplot as plt
from copulalib.copulalib import Copula
plt.style.use('ggplot')

def generateData():
    global x,y
    x = np.random.normal(size=250)
    y = 2.5*x + np.random.normal(size=250)

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# Load the raw training data and replace missing values with NA
training.data.raw <- read.csv('train.csv',header=T,na.strings=c(""))

# Output the number of missing values for each column
sapply(training.data.raw,function(x) sum(is.na(x)))

# Quick check for how many different values for each feature
sapply(training.data.raw, function(x) length(unique(x)))

# A visual way to check for missing data

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################################################################################
# Loading data

rm( list=ls() )

# load libs
require(neuralnet)
require(nnet)

# Load data and set names

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#Load library mass and set seed
library(MASS)
set.seed(100)

# We are going to use 3 random variables
m <- 3

# Number of samples to be drawn
n <- 2000

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# Copula class

import numpy as np
import matplotlib.pyplot as plt
from copulalib.copulalib import Copula
from scipy.stats import norm
plt.style.use('ggplot')

class copulaClass(object):

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# Imports

import os
import sys
import time
import psutil
import logging
import pyautogui as pgui
from datetime import datetime

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# A simple Markov chain model for the weather in Python

import numpy as np
import random as rm
import time

# Let's define the statespace
states = ["Sunny","Cloudy"]

# Possible sequences of events