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

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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}

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//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)

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#------------------------------------------------------------------------------
# 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:

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

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

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

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

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#-------------------------------------------------------------------------------
#  Another linear model using more information y = a*x1 + b*x2 + c*x3 + d*x4
#-------------------------------------------------------------------------------

# Plot data
plot(disp,wt,type='p',xlab='Disp',ylab='Wt',main='Linear regression')

# Add a legend
legend("topleft",c("Observ.","Predicted"), col=c("black","red"), lwd=3)

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import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.animation as animation

plt.style.use('dark_background')

fig = plt.figure()
fig.set_dpi(100)
ax1 = fig.add_subplot(1,1,1)

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import numpy as np
import matplotlib.pyplot as plt

plt.style.use('ggplot')

c = 100 * 10**(-6)
v = 5
r = 2000

t = np.linspace(0,1,1000)

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#Transport equation with decay implementation

import numpy as np
from numpy import pi
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()
fig.set_dpi(100)
ax1 = fig.add_subplot(1,1,1)