mick001

from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import train_test_split
import pandas as pd
import os

# Loading data
data = pd.read_csv('data.csv')

# Encoding categorical features into numerical
buying_map = {'vhigh':4,'high':3,'med':2,'low':1}

mick001

//Initialize global variables
int ledRed = 8;                //Red LED pin
int ledGreen = 9;              //Green LED pin
int sensorCurrent = 10;        //Sensor pin
int sensorRed = 0;             //Red laser light sensor
int sensorGreen = 0;           //Green laser light sensor
int measurements = 0;          //Number of measurements done
int numberOfMeasurements = 1;  //Measurements to be done (must be the same as nSamples in the Python script)
float t1;                      //Initial time (time at the start of the measurement)
float t2;                      //End time (time at the end of the measurement)

mick001

#------------------------------------------------------------------------------
# Measuring function. Makes nSamples measurements through an Arduino object
# Notes:
# The program loops until it collects the nSamples readings
#
# This function accepts an object of the Arduino class

def measure(nSamples,arduino,save=False):

    try:

mick001

import numpy as np
import time

start = ['R','Su']
#Start probability
p_start = [0.2,0.8]

#t1 = [['R|R','Su|R'],['R|Su','Su|Su']]
#Transition probability
t1 = ['R','Su']

mick001

"""
MONTE CARLO PLAIN VANILLA OPTION PRICING

This script is used to estimate the price of a plain vanilla
option using the Monte Carlo method and assuming normally
distributed returns using a parametric normal distribution
approach.

Call option quotations are available at:
http://www.google.com/finance/option_chain?q=NASDAQ%3AAAPL&ei=fNHBVaicDsbtsAHa7K-QDQ

mick001

"""
MONTE CARLO PLAIN VANILLA OPTION PRICING

This script is used to estimate the price of a plain vanilla
option using the Monte Carlo method and assuming that returns
can be simulated using an estimated probability density (KDE estimate)

Call option quotations are available at:
http://www.google.com/finance/option_chain?q=NASDAQ%3AAAPL&ei=fNHBVaicDsbtsAHa7K-QDQ

mick001

// Call option Monte Carlo evaluation

#include <iostream>
#include <random>
#include <math.h>
#include <chrono>

using namespace std;

/*  double stoc_walk: returns simulated price after periods

mick001

#-------------------------------------------------------------------------------
#                          Gathering the data
#-------------------------------------------------------------------------------

# Loading the entire mtcars dataset
data(mtcars)

# Subsetting the dataset for our use
dat <- subset(mtcars,select=c(mpg,disp,hp,drat,wt))
dat

mick001

#-------------------------------------------------------------------------------
#               Linear regression  y = a + bx  
#-------------------------------------------------------------------------------

# Fit the model
model1 <- lm(wt ~ disp)

# Parameters and information about the model
model1
coef(model1)

mick001

#-------------------------------------------------------------------------------
#                          Parabola y = a + bx + cx^2
#-------------------------------------------------------------------------------

model2 <- lm(wt ~ disp+I(disp^2))
summary(model2)
coef(model2)

# Predicted vs original
predicted <- fitted(model2)