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mick001 / option_pricing.cpp
Created Aug 27, 2015
Call option pricing C++ implementation. (Returns are assumed to be normally distributed)
View option_pricing.cpp
// 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
mick001 / Random_forest_example.py
Last active Aug 27, 2015
Example on how to start with scikit-learn and use a Random Forest classifier for a classification task
View Random_forest_example.py
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
mick001 / Arduino_measurement_sketch.ino
Last active Aug 27, 2015
Arduino script for time measurement (to be used with Python and the Arduino class)
View Arduino_measurement_sketch.ino
//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
mick001 / python_measuring_function.py
Last active Aug 27, 2015
Measuring function in Python, to be used with the Arduino script
View python_measuring_function.py
#------------------------------------------------------------------------------
# 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
mick001 / gathering_data.R
Created Aug 28, 2015
Gathering data script (R)
View gathering_data.R
#-------------------------------------------------------------------------------
# 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
mick001 / linear_reg.R
Created Aug 28, 2015
Linear regression in R
View linear_reg.R
#-------------------------------------------------------------------------------
# Linear regression y = a + bx
#-------------------------------------------------------------------------------
# Fit the model
model1 <- lm(wt ~ disp)
# Parameters and information about the model
model1
coef(model1)
@mick001
mick001 / Linear_model.R
Created Aug 28, 2015
Linear model with 4 variables in R
View Linear_model.R
#-------------------------------------------------------------------------------
# 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)
View waveEquation.py
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)
View RC.py
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)
@mick001
mick001 / transportEq.py
Created Aug 28, 2015
Transport equation with decay, a Python implementation. Full article can be found at http://www.firsttimeprogrammer.blogspot.com/2015/07/pdes-time-again-transport-equation.html
View transportEq.py
#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)