SmartyPy

Simple repository featuring duplicated Matlab/Octave and Python Machine Learning utilities. Made to pair with the Coursera Machine Learning introduction course taught by Andrew Ng. https://www.coursera.org/specializations/machine-learning?utm_source=gg&utm_medium=sem&campaignid=655866957&adgroupid=34113811915&device=c&keyword=coursera%20machine%20learning&matchtype=p&network=g&devicemodel=&adpostion=1t1&creativeid=119391632947&hide_mobile_promo&gclid=CjwKEAjwmMS-BRCm5dn51JLbp1wSJACc61tF1ufsQo0cWmXtoBUISADwzRp9PQf8vZAxP1N3APWpTRoC7-vw_wcB

Intent and Audience

This is predomenantly intended to be a learning exercise. Not at upstaging existing and sophisticated machine learning libraries

Notation Convention

Summarized from: https://share.coursera.org/wiki/index.php/ML:Linear_Regression_with_Multiple_Variables

n:      Number of features (excluding x0 feature)
m:      Number of examples/samples
x:      Feature column vector dataset (m x 1)
X:      Feature or Design Matrix (m x n+1)
Xn:     Normalized Feature Matrix (m x n+1)
y:      Target/Solution vector (m x 1)
J:      Cost of a sample (single value)
theta:  Linear Regression Coefficient Vector (n+1 x 1) ==> theta0 + theta1*x1 + theta2*x2 ... + thetan*xn
h:      Hypothesis of form: h(X) = X @ theta
                            h(x) = theta.T @ x ==> [ --- theta --- ] @ [x]

Visual

        ┌                  ┐
        | 1   x1^1    xn^1 |
X ==>   | 1   x1^2    xn^2 |
        |         ...      |
        | 1   x1^m    xn^m |
        └                  ┘

Setup

Python

Anaconda's Python distribution is recomended for getting started. Easiest way to get started is to install and create a new Python 3.5 environment from the included (not minimal) py-environment.yml file. http://conda.pydata.org/docs/using/envs.html

$ conda env create -f py-environment.yml

Otherwise, if you don't have Anaconda, a working Python 3.5+ environment (yes, you need 3.5+) and a few ancilary modules is all you need.

Clone And Run

Contact me for direct repo access, or Fork and create Pull Requests as needed. Any contributions and updates are welcome, this repo is definitely still in the early development stages.

git clone git@github.com:ZachDischner/smartypy.git

Examples

In smartypy top level directory: ipython

import pandas as pd
import numpy as np
import smartypy

## Read dataset
df = pd.read_csv("test/data/ex1data2.txt",names=["area","rooms","price"])

## Form X and y arrays
y=df.price.values
X=np.ones((len(y),3))
X[:,1]=df.area.values
X[:,2]=df.rooms.values

## Normalize X 
Xn,mu,sigma=smartypy.linearRegression.normalize_features(X)

## Gradient Descent
theta, Jhistory = smartypy.linearRegression.gradient_descent(Xn,y,[0,0,0],0.01)

## Somewhat helpful plotting utilities

You can also feel free to work directly in the src directory for easier relative imports.

Todo

Never ending list. Top among them:

Testing cases with pytest
Logistic Regression Functions
numba.njit optimizations

hanjackcyw / smartypy