Warped Coordinate System

I was trying to find a coordinate system to find the correspondence between the coordinates of points in an original image of a rectangle and the coordinates of points in a warped image of a rectangle. I believe I found a coordinate system based on the boundaries of the warped rectangle but I still have to check if it is accurate. Here is an image of the coordinate system I found:

Aligning Images

I recently wrote a program to align images using OpenCV in Python. I drew a rectangle with ten dots on it and took pictures of it at different angles. I used the locations where each of the corresponding dots moved to find a homography between the straight picture and each angled picture. I then warped the angled pictures using this homography to align them.

Here is a link to download the folder with the images and Python code in it as well as an image of the output. Make sure to download the whole folder and run the python code from within that folder.

 

 

 

 

 

Habitable Exoplanets

I developed a machine learning Python program that uses a Support Vector Machine classifier from the sklearn module to predict if an exoplanet is in the habitable zone of its star system (the zone where liquid water can exist). The features of the planet required by the classifier to make the prediction are the orbital period of planet in days, number of planets in the system, and number of stars in the system.

I used the astroquery module to obtain the data from the Open Exoplanet Catalog

Link to download Python code

Here are some pictures of the program:

Predicting Intel Revenue

I wrote a machine learning Python program using the sklearn module that predicts the revenue for Intel Corporation. I got the data for the training set from a website called Quandl that has a wide variety of financial data. I used Ridge regression with a 3rd degree polynomial for my machine learning algorithm.

Link to download Python code

Here is a picture of the output of my program:

Classifying Hydrocarbons

I developed a machine learning Python program using the sklearn module that classifies a simple single-chain hydrocarbon  into categories based on the type of bonds it has given the number of carbons and hydrogens (alkanes have single bonds, alkenes have double bonds and alkynes have triple bonds).

Although there are easy ways to tell what type of bonds a simple single-chain hydrocarbon has given the number of carbons and hydrogens, I wanted to use a training set and a Linear SVM(Linear Support Vector Machine) to classify them in order to introduce myself to machine learning.

Link to download Python code

Here is a picture of the output of my program: